id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
26,392 | import importlib
import argparse
import gc
import math
import os
import sys
import random
import time
import json
from multiprocessing import Value
from tqdm import tqdm
import torch
from accelerate.utils import set_seed
from diffusers import DDPMScheduler
from library import model_util
import library.train_util as tra... | null |
26,393 | import os, sys
import argparse
import torch
from torch import load, save
from safetensors import safe_open
from safetensors.torch import save_file
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"lora_model",
help="The model you want to pack embeddings into it.",
... | null |
26,394 | import os, sys
import argparse
import torch
from torch import load, save
from safetensors import safe_open
from safetensors.torch import save_file
def load_state_dict(file_path):
is_safetensors = file_path.rsplit(".", 1)[-1] == "safetensors"
if is_safetensors:
state_dict = {}
with safe_open(fil... | null |
26,395 | import os, sys
import argparse
from lycoris.utils import merge
from lycoris.kohya.model_utils import (
load_models_from_stable_diffusion_checkpoint,
save_stable_diffusion_checkpoint,
load_file,
)
from lycoris.kohya.sdxl_model_util import (
load_models_from_sdxl_checkpoint,
save_stable_diffusion_chec... | null |
26,396 | import os, sys
import argparse
from lycoris.utils import extract_diff
from lycoris.kohya.model_utils import load_models_from_stable_diffusion_checkpoint
from lycoris.kohya.sdxl_model_util import load_models_from_sdxl_checkpoint
import torch
from safetensors.torch import save_file
def get_args():
parser = argparse.... | null |
26,397 | import os
import argparse
import warnings
from typing import List
from collections import defaultdict
import torch
from torch import load, save
from safetensors import safe_open
from safetensors.torch import save_file
def load_state_dict(file_path):
is_safetensors = file_path.rsplit(".", 1)[-1] == "safetensors"
... | null |
26,398 | import os
import argparse
import warnings
from typing import List
from collections import defaultdict
import torch
from torch import load, save
from safetensors import safe_open
from safetensors.torch import save_file
def save_state_dict(state, output_path):
if output_path.endswith(".safetensors"):
save_fi... | null |
26,399 | import os
import argparse
import warnings
from typing import List
from collections import defaultdict
import torch
from torch import load, save
from safetensors import safe_open
from safetensors.torch import save_file
def pack_bundle(lora, emb_dict, verbose=False):
for emb, emb_sd in emb_dict.items():
for ... | null |
26,400 | import os
import argparse
import warnings
from typing import List
from collections import defaultdict
import torch
from torch import load, save
from safetensors import safe_open
from safetensors.torch import save_file
def print_emb_information(emb_dict):
for emb, emb_sd in emb_dict.items():
print(emb)
... | null |
26,401 | import os
import argparse
import warnings
from typing import List
from collections import defaultdict
import torch
from torch import load, save
from safetensors import safe_open
from safetensors.torch import save_file
The provided code snippet includes necessary dependencies for implementing the `gather_files_from_lis... | Gather files from given paths based on specific extensions. Args: paths (List[str]): A list of paths which can be files or directories. extensions (List[str]): A list of file extensions to filter by. recursive (bool): If True, search for files recursively in directories. Returns: List[str]: A list of file paths that ma... |
26,402 | import os
import argparse
import warnings
from typing import List
from collections import defaultdict
import torch
from torch import load, save
from safetensors import safe_open
from safetensors.torch import save_file
def extract_step(file_path):
filename = os.path.splitext(os.path.basename(file_path))[0]
step ... | Associate LoRA model files with embedding files based on their step count. This function takes in lists of LoRA file paths and embedding file paths, extracts their step counts, and associates them based on matching steps. If a file's step count cannot be determined, it uses the key 'none'. Args: lora_files (List[str]):... |
26,403 | import os
import argparse
import warnings
from typing import List
from collections import defaultdict
import torch
from torch import load, save
from safetensors import safe_open
from safetensors.torch import save_file
def extract_step(file_path):
def convert_lora_name(network_path, dst_dir, to_bundle):
name, step ... | null |
26,404 | import os
import sys
import math
import argparse
import warnings
from typing import List, Dict
from collections import defaultdict
import torch
from safetensors.torch import load_file
from hcpdiff.ckpt_manager import auto_manager
The provided code snippet includes necessary dependencies for implementing the `gather_fi... | Gather files from given paths based on specific extensions. Args: paths (List[str]): A list of paths which can be files or directories. extensions (List[str]): A list of file extensions to filter by. recursive (bool): If True, search for files recursively in directories. Returns: List[str]: A list of file paths that ma... |
26,405 | import os
import sys
import math
import argparse
import warnings
from typing import List, Dict
from collections import defaultdict
import torch
from safetensors.torch import load_file
from hcpdiff.ckpt_manager import auto_manager
The provided code snippet includes necessary dependencies for implementing the `get_unet_... | Get unet and text encoder pairs from a list of files. Args: files (List[str]): A list of candidate file paths. Returns: Dict[str, Dict[str, str]]: A dictionary where keys are file names and values are dictionaries containing paths to unet and text encoder files. Raises: ValueError: If muliple unet or text encoder files... |
26,406 | import os
import sys
import math
import argparse
import warnings
from typing import List, Dict
from collections import defaultdict
import torch
from safetensors.torch import load_file
from hcpdiff.ckpt_manager import auto_manager
def save_and_print_path(sd, path):
try:
# Old HCP
ckpt_manager = auto... | null |
26,407 | import os
import sys
import math
import argparse
import warnings
from typing import List, Dict
from collections import defaultdict
import torch
from safetensors.torch import load_file
from hcpdiff.ckpt_manager import auto_manager
def get_network_types(sd_unet, sd_te):
network_types = []
for network_type in ["l... | null |
26,408 | import os, sys
import argparse
from lycoris.kohya.model_utils import load_file, load_models_from_stable_diffusion_checkpoint
from lycoris.kohya import create_hypernetwork
import torch
import torch.nn as nn
from torchvision.transforms.functional import resize, to_tensor
from PIL import Image
from safetensors.torch impor... | null |
26,409 | from typing import List, Dict, Any, Callable, Type, TypeVar, Union
from .dataexchange import Response
from .datatypes import DataType, Integer, Real
T = TypeVar("T")
The provided code snippet includes necessary dependencies for implementing the `number_square_function_body` function. Write a Python function `def numbe... | Body for integer/float square |
26,410 | from typing import List, Dict, Any, Callable, Type, TypeVar, Union
from .dataexchange import Response
from .datatypes import DataType, Integer, Real
The provided code snippet includes necessary dependencies for implementing the `value_count_function_body` function. Write a Python function `def value_count_function_bod... | Note: count(*) counts every row (not supported in learndb) count(column) should only count non-null columns |
26,411 | from typing import List, Dict, Any, Callable, Type, TypeVar, Union
from .dataexchange import Response
from .datatypes import DataType, Integer, Real
_SCALAR_FUNCTION_REGISTRY = {
"square": integer_square_function,
"square_float": float_square_function,
}
The provided code snippet includes necessary dependencie... | Return list of all scalar function names |
26,412 | from typing import List, Dict, Any, Callable, Type, TypeVar, Union
from .dataexchange import Response
from .datatypes import DataType, Integer, Real
_AGGREGATE_FUNCTION_REGISTRY = {"count": count_function}
The provided code snippet includes necessary dependencies for implementing the `get_aggregate_functions_names` fu... | Return list of all aggregate function names |
26,413 | from typing import List, Dict, Any, Callable, Type, TypeVar, Union
from .dataexchange import Response
from .datatypes import DataType, Integer, Real
def resolve_function_name(name: str) -> FunctionDefinition:
"""
Resolve function name, i.e. lookup name in registry.
In the future this could be extended to su... | null |
26,414 | from typing import List, Dict, Any, Callable, Type, TypeVar, Union
from .dataexchange import Response
from .datatypes import DataType, Integer, Real
def resolve_function_name(name: str) -> FunctionDefinition:
"""
Resolve function name, i.e. lookup name in registry.
In the future this could be extended to su... | null |
26,415 | from enum import Enum
from typing import Type
from .constants import CELL_KEY_SIZE_SIZE, CELL_DATA_SIZE_SIZE, INTEGER_SIZE
from .datatypes import DataType, Null, Integer, Text, Blob, Real
from .dataexchange import Response
from .schema import SimpleSchema
from .record_utils import SimpleRecord
class SerialType(Enum):
... | Serialize an entire record and return the bytes corresponding to a cell. For now, serialize each value and concatenate the resulting bytes. If this is not performant, consider using struct.pack See docs/file-format.txt for complete details; the following are the key details of a node: - (low address) header, cell point... |
26,416 | from enum import Enum
from typing import Type
from .constants import CELL_KEY_SIZE_SIZE, CELL_DATA_SIZE_SIZE, INTEGER_SIZE
from .datatypes import DataType, Null, Integer, Text, Blob, Real
from .dataexchange import Response
from .schema import SimpleSchema
from .record_utils import SimpleRecord
class SerialType(Enum):
... | deserialize cell corresponding to schema :param cell: :param schema: :return: Response[Record] |
26,417 | from enum import Enum
from typing import Type
from .constants import CELL_KEY_SIZE_SIZE, CELL_DATA_SIZE_SIZE, INTEGER_SIZE
from .datatypes import DataType, Null, Integer, Text, Blob, Real
from .dataexchange import Response
from .schema import SimpleSchema
from .record_utils import SimpleRecord
def get_cell_key_in_page(... | :param cell: :return: |
26,418 | from enum import Enum
from typing import Type
from .constants import CELL_KEY_SIZE_SIZE, CELL_DATA_SIZE_SIZE, INTEGER_SIZE
from .datatypes import DataType, Null, Integer, Text, Blob, Real
from .dataexchange import Response
from .schema import SimpleSchema
from .record_utils import SimpleRecord
CELL_KEY_SIZE_SIZE = WOR... | null |
26,419 | from enum import Enum, auto
from typing import Type
from .datatypes import DataType, Integer, Real, Blob, Text
from .lang_parser.symbols import SymbolicDataType
class DataType:
"""
This is a datatype of a value in the database.
This provides an interface to provide serde of implemented type
and detail... | Convert symbols.DataType to datatypes.DataType |
26,420 | from __future__ import annotations
from copy import copy
from typing import List, Optional, Union
from .datatypes import DataType, Integer, Text, Blob, Real
from .dataexchange import Response
from .lang_parser.symbols import TableName, SymbolicDataType, ColumnName
class SimpleSchema(AbstractSchema):
"""
Represe... | convert a schema to canonical ddl parser rule: create_stmnt -> "create" "table" table_name "(" column_def_list ")" e.g. ddl create table catalog ( pkey int primary key type text, name text, tbl_name text, rootpage integer, sql text ) :return: |
26,421 | from __future__ import annotations
from copy import copy
from typing import List, Optional, Union
from .datatypes import DataType, Integer, Text, Blob, Real
from .dataexchange import Response
from .lang_parser.symbols import TableName, SymbolicDataType, ColumnName
class Column:
"""
Represents a column in a sche... | Generate schema from a create stmnt. There is a very thin layer of translation between the stmnt and the schema object. But I want to distinguish the (create) stmnt from the schema. Note if the operation is successful, a valid schema was read. :param create_stmnt: :return: |
26,422 | from __future__ import annotations
from copy import copy
from typing import List, Optional, Union
from .datatypes import DataType, Integer, Text, Blob, Real
from .dataexchange import Response
from .lang_parser.symbols import TableName, SymbolicDataType, ColumnName
class Column:
"""
Represents a column in a sche... | Generate an unvalidated schema with argument `columns`. This is used for output schema, which doesn't have primary key. NOTE: `unvalidated` means we don't run any validations, e.g. generated schema must have a primary key. TODO: apply any validations that do hold, e.g. column name uniqueness? |
26,423 | from __future__ import annotations
from copy import copy
from typing import List, Optional, Union
from .datatypes import DataType, Integer, Text, Blob, Real
from .dataexchange import Response
from .lang_parser.symbols import TableName, SymbolicDataType, ColumnName
class GroupedSchema(AbstractSchema):
"""
Repres... | Generate a grouped schema from a non-grouped schema. How will this handle both simple, and multi-schema |
26,424 | from __future__ import annotations
from typing import Any, List, Optional, Union, Tuple
from .dataexchange import Response
from .lang_parser.symbols import ColumnName, ColumnNameList, ValueList, Literal
from .schema import SimpleSchema, ScopedSchema, GroupedSchema
class SimpleRecord(AbstractRecord):
"""
Represe... | TODO: remove if unused join records and return a multi-record left_, right_empty are used to handle left, right outer joined records :return: |
26,425 | from __future__ import annotations
from typing import Any, List, Optional, Union, Tuple
from .dataexchange import Response
from .lang_parser.symbols import ColumnName, ColumnNameList, ValueList, Literal
from .schema import SimpleSchema, ScopedSchema, GroupedSchema
class SimpleRecord(AbstractRecord):
"""
Represe... | given a `schema` return a record with the given schema and all fields set to null :param schema: :return: |
26,426 | from __future__ import annotations
from typing import Any, List, Optional, Union, Tuple
from .dataexchange import Response
from .lang_parser.symbols import ColumnName, ColumnNameList, ValueList, Literal
from .schema import SimpleSchema, ScopedSchema, GroupedSchema
class SimpleRecord(AbstractRecord):
"""
Represe... | Needed for creating final output recordset; Uses raw values, i.e. unboxed values # TODO: refactor to remove `column_names` which can be derived from schema, like: [col.name for col schema.columns] |
26,427 | from __future__ import annotations
from typing import Any, List, Optional, Union, Tuple
from .dataexchange import Response
from .lang_parser.symbols import ColumnName, ColumnNameList, ValueList, Literal
from .schema import SimpleSchema, ScopedSchema, GroupedSchema
def create_record(
column_name_list: ColumnNameList... | Create a catalog record. NOTE: This must produce a type identical output to parser :param pkey: :param table_name: :param root_page_num: :param sql_text: :param catalog_schema: :return: |
26,428 | import re
The provided code snippet includes necessary dependencies for implementing the `camel_to_snake` function. Write a Python function `def camel_to_snake(name: str) -> str` to solve the following problem:
change casing abcdXyz -> abcd_xyz
Here is the function:
def camel_to_snake(name: str) -> str:
"""
... | change casing abcdXyz -> abcd_xyz |
26,429 | import re
The provided code snippet includes necessary dependencies for implementing the `pascal_to_snake` function. Write a Python function `def pascal_to_snake(name) -> str` to solve the following problem:
convert case HelloWorld -> hello_world :return:
Here is the function:
def pascal_to_snake(name) -> str:
"... | convert case HelloWorld -> hello_world :return: |
26,430 | import sys
import struct
from abc import ABCMeta
from typing import Any, Type
from .constants import INTEGER_SIZE, REAL_SIZE
class DataType:
"""
This is a datatype of a value in the database.
This provides an interface to provide serde of implemented type
and details of underlying encoding.
Note: Th... | Return True, if term is valid for given datatype |
26,431 | from __future__ import annotations
import os
import os.path
import sys
import logging
from typing import List
from .constants import DB_FILE, USAGE, EXIT_SUCCESS
from .lang_parser.sqlhandler import SqlFrontEnd
from .lang_parser.symbols import Program
from .dataexchange import Response, MetaCommandResult
from .pipe impo... | null |
26,432 | from __future__ import annotations
import os
import os.path
import sys
import logging
from typing import List
from .constants import DB_FILE, USAGE, EXIT_SUCCESS
from .lang_parser.sqlhandler import SqlFrontEnd
from .lang_parser.symbols import Program
from .dataexchange import Response, MetaCommandResult
from .pipe impo... | parse args and starts :return: |
26,433 | import os
import cv2
import torch
import numpy as np
from math import factorial
from pyquaternion import Quaternion
import mmcv
from mmdet.datasets import DATASETS
from mmdet3d.datasets import Custom3DDataset
from openlanev2.dataset import Collection
from openlanev2.evaluation import evaluate as openlanev2_evaluate
fro... | null |
26,434 | import os
import cv2
import torch
import numpy as np
from math import factorial
from pyquaternion import Quaternion
import mmcv
from mmdet.datasets import DATASETS
from mmdet3d.datasets import Custom3DDataset
from openlanev2.dataset import Collection
from openlanev2.evaluation import evaluate as openlanev2_evaluate
fro... | null |
26,435 | import os
import cv2
import torch
import numpy as np
from math import factorial
from pyquaternion import Quaternion
import mmcv
from mmdet.datasets import DATASETS
from mmdet3d.datasets import Custom3DDataset
from openlanev2.dataset import Collection
from openlanev2.evaluation import evaluate as openlanev2_evaluate
fro... | null |
26,436 | from cmath import pi
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, cons... | 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. |
26,437 | import copy
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner import BaseModule
from mmdet3d.models import NECKS
The provided code snippet includes necessary dependencies for implementing the `get_campos` function. Write a Python function `def get_campos(reference_points, ... | 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 |
26,438 | import copy
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner import BaseModule
from mmdet3d.models import NECKS
The provided code snippet includes necessary dependencies for implementing the `construct_plane_grid` function. Write a Python function `def construct_plane_gri... | Returns: plane: H, W, 3 |
26,439 | 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 mmdet.utils import get_root_logger
from mmdet.mode... | null |
26,440 | 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 mmdet.utils import get_root_logger
from mmdet.mode... | null |
26,441 | 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 |
26,442 | 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
def build_act_lay... | null |
26,443 | 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
def _is_power_of_... | null |
26,444 | import os
import glob
import torch
from torch.utils.cpp_extension import CUDA_HOME
from torch.utils.cpp_extension import CppExtension
from torch.utils.cpp_extension import CUDAExtension
from setuptools import find_packages
from setuptools import setup
def get_extensions():
this_dir = os.path.dirname(os.path.abspat... | null |
26,445 | 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 |
26,446 |
def format_metric(metric):
for key, val in metric.items():
print(f'{key} - {val["score"]}')
for k, v in val.items():
if 'score' not in k:
print(f' {k} - {v}') | null |
26,447 | import cv2
import numpy as np
from .utils import THICKNESS, COLOR_DEFAULT, COLOR_DICT, interp_arc
def _draw_traffic_element(image, traffic_element):
top_left = (
int(traffic_element['points'][0][0]),
int(traffic_element['points'][0][1]),
)
bottom_right = (
int(traffic_element['points... | null |
26,448 | import numpy as np
def assign_attribute(annotation):
topology_lcte = np.array(annotation['topology_lcte'], dtype=bool)
for i in range(len(annotation['lane_centerline'])):
annotation['lane_centerline'][i]['attributes'] = \
set([ts['attribute'] for j, ts in enumerate(annotation['traffic_eleme... | null |
26,449 | import numpy as np
def assign_topology(annotation):
topology_lcte = np.array(annotation['topology_lcte'], dtype=bool)
annotation['topology'] = []
for i in range(topology_lcte.shape[0]):
for j in range(topology_lcte.shape[1]):
if topology_lcte[i][j]:
annotation['topology'... | null |
26,450 | import cv2
import numpy as np
from .utils import THICKNESS, COLOR_DEFAULT, COLOR_DICT, interp_arc
BEV_SCALE = 10
BEV_RANGE = [-50, 50, -25, 25]
def _draw_lane_centerline(image, lane_centerline, with_attribute):
def _draw_vertex(image, lane_centerline):
def draw_annotation_bev(annotation, with_attribute):
image = n... | null |
26,451 | import numpy as np
from scipy.interpolate import interp1d
from ortools.graph import pywrapgraph
The provided code snippet includes necessary dependencies for implementing the `resample_laneline_in_x` function. Write a Python function `def resample_laneline_in_x(input_lane, steps, out_vis=False)` to solve the following... | Interpolate y, z values at each anchor grid, including those beyond the range of input lnae x range :param input_lane: N x 2 or N x 3 ndarray, one row for a point (x, y, z-optional). It requires y values of input lane in ascending order :param steps: a vector of steps :param out_vis: whether to output visibility indica... |
26,452 | import numpy as np
from scipy.interpolate import interp1d
from ortools.graph import pywrapgraph
The provided code snippet includes necessary dependencies for implementing the `SolveMinCostFlow` function. Write a Python function `def SolveMinCostFlow(adj_mat, cost_mat)` to solve the following problem:
Solving an Assign... | Solving an Assignment Problem with MinCostFlow" :param adj_mat: adjacency matrix with binary values indicating possible matchings between two sets :param cost_mat: cost matrix recording the matching cost of every possible pair of items from two sets :return: |
26,453 | import numpy as np
from tqdm import tqdm
from .f_score import f1
from .distance import pairwise, chamfer_distance, frechet_distance, iou_distance
from ..io import io
from ..preprocessing import check_results
from ..utils import TRAFFIC_ELEMENT_ATTRIBUTE
THRESHOLDS_FRECHET = [1.0, 2.0, 3.0]
THRESHOLDS_IOU = [0.75]
def _... | r""" Evaluate the road structure cognition task. Parameters ---------- ground_truth : str / dict Dict of ground truth of path to pickle file storing the dict. predictions : str / dict Dict of predictions of path to pickle file storing the dict. Returns ------- dict A dict containing all defined metrics. Notes ----- One... |
26,454 | import numpy as np
from iso3166 import countries
from functools import reduce
The provided code snippet includes necessary dependencies for implementing the `check_results` function. Write a Python function `def check_results(results : dict) -> None` to solve the following problem:
r""" Check format of results. Parame... | r""" Check format of results. Parameters ---------- results : dcit Dict storing predicted results. |
26,455 | import numpy as np
from tqdm import tqdm
from ..io import io
io = IO()
The provided code snippet includes necessary dependencies for implementing the `collect` function. Write a Python function `def collect(root_path : str, data_dict : dict, collection : str, point_interval : int = 1) -> None` to solve the following ... | r""" Load meta data of data in data_dict, and store in a .pkl with split as file name. Parameters ---------- root_path : str data_dict : dict A dict contains ids of data to be preprocessed. collection : str Name of the collection. point_interval : int Interval for subsampling points of lane centerlines, not subsampling... |
26,456 | from __future__ import division
import argparse
import copy
import os
import time
import warnings
from os import path as osp
import mmcv
import torch
import torch.distributed as dist
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmdet import __version__ as mmdet_version
from ... | null |
26,457 | import argparse
from os import path as osp
from tools.data_converter import indoor_converter as indoor
from tools.data_converter import kitti_converter as kitti
from tools.data_converter import lyft_converter as lyft_converter
from tools.data_converter import nuscenes_converter as nuscenes_converter
from tools.data_con... | Prepare data related to Kitti 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. out_dir (str): Output directory of the groundtruth da... |
26,458 | import argparse
from os import path as osp
from tools.data_converter import indoor_converter as indoor
from tools.data_converter import kitti_converter as kitti
from tools.data_converter import lyft_converter as lyft_converter
from tools.data_converter import nuscenes_converter as nuscenes_converter
from tools.data_con... | 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... |
26,459 | import argparse
from os import path as osp
from tools.data_converter import indoor_converter as indoor
from tools.data_converter import kitti_converter as kitti
from tools.data_converter import lyft_converter as lyft_converter
from tools.data_converter import nuscenes_converter as nuscenes_converter
from tools.data_con... | Prepare data related to Lyft dataset. Related data consists of '.pkl' files recording basic infos. Although the ground truth database and 2D annotations are not used in Lyft, it can also be generated like nuScenes. Args: root_path (str): Path of dataset root. info_prefix (str): The prefix of info filenames. version (st... |
26,460 | import argparse
from os import path as osp
from tools.data_converter import indoor_converter as indoor
from tools.data_converter import kitti_converter as kitti
from tools.data_converter import lyft_converter as lyft_converter
from tools.data_converter import nuscenes_converter as nuscenes_converter
from tools.data_con... | Prepare the info file for scannet dataset. Args: root_path (str): Path of dataset root. info_prefix (str): The prefix of info filenames. out_dir (str): Output directory of the generated info file. workers (int): Number of threads to be used. |
26,461 | import argparse
from os import path as osp
from tools.data_converter import indoor_converter as indoor
from tools.data_converter import kitti_converter as kitti
from tools.data_converter import lyft_converter as lyft_converter
from tools.data_converter import nuscenes_converter as nuscenes_converter
from tools.data_con... | Prepare the info file for s3dis dataset. Args: root_path (str): Path of dataset root. info_prefix (str): The prefix of info filenames. out_dir (str): Output directory of the generated info file. workers (int): Number of threads to be used. |
26,462 | import argparse
from os import path as osp
from tools.data_converter import indoor_converter as indoor
from tools.data_converter import kitti_converter as kitti
from tools.data_converter import lyft_converter as lyft_converter
from tools.data_converter import nuscenes_converter as nuscenes_converter
from tools.data_con... | Prepare the info file for sunrgbd dataset. Args: root_path (str): Path of dataset root. info_prefix (str): The prefix of info filenames. out_dir (str): Output directory of the generated info file. workers (int): Number of threads to be used. |
26,463 | import argparse
from os import path as osp
from tools.data_converter import indoor_converter as indoor
from tools.data_converter import kitti_converter as kitti
from tools.data_converter import lyft_converter as lyft_converter
from tools.data_converter import nuscenes_converter as nuscenes_converter
from tools.data_con... | Prepare the info file for waymo dataset. Args: root_path (str): Path of dataset root. info_prefix (str): The prefix of info filenames. out_dir (str): Output directory of the generated info file. workers (int): Number of threads to be used. max_sweeps (int, optional): Number of input consecutive frames. Default: 5. Here... |
26,464 | import argparse
import torch
from mmcv import Config, DictAction
from mmdet3d.models import build_model
def parse_args():
parser = argparse.ArgumentParser(description='Train a detector')
parser.add_argument('config', help='train config file path')
parser.add_argument(
'--shape',
type=int,
... | null |
26,465 | import argparse
import json
from collections import defaultdict
import numpy as np
import seaborn as sns
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 |
26,466 | import argparse
import json
from collections import defaultdict
import numpy as np
import seaborn as sns
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 |
26,467 | import argparse
import json
from collections import defaultdict
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
def add_plot_parser(subparsers):
parser_plt = subparsers.add_parser(
'plot_curve', help='parser for plotting curves')
parser_plt.add_argument(
'json_logs'... | null |
26,468 | import argparse
import json
from collections import defaultdict
import numpy as np
import seaborn as sns
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 |
26,469 | import argparse
import time
import torch
from mmcv import Config
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint, wrap_fp16_model
from mmdet3d.datasets import build_dataloader, build_dataset
from mmdet3d.models import build_detector
from tools.misc.fuse_conv_bn import fuse_module
def p... | null |
26,470 | import argparse
import tempfile
import torch
from mmcv import Config
from mmcv.runner import load_state_dict
from mmdet3d.models import build_detector
def parse_args():
parser = argparse.ArgumentParser(
description='MMDet3D upgrade model version(before v0.6.0) of VoteNet')
parser.add_argument('checkpoi... | null |
26,471 | import argparse
import tempfile
import torch
from mmcv import Config
from mmcv.runner import load_state_dict
from mmdet3d.models import build_detector
The provided code snippet includes necessary dependencies for implementing the `parse_config` function. Write a Python function `def parse_config(config_strings)` to so... | Parse config from strings. Args: config_strings (string): strings of model config. Returns: Config: model config |
26,472 | import argparse
import tempfile
import torch
from mmcv import Config
from mmcv.runner import load_state_dict
from mmdet3d.models import build_detector
def parse_args():
parser = argparse.ArgumentParser(
description='MMDet3D upgrade model version(before v0.6.0) of H3DNet')
parser.add_argument('checkpoin... | null |
26,473 | import argparse
import tempfile
import torch
from mmcv import Config
from mmcv.runner import load_state_dict
from mmdet3d.models import build_detector
The provided code snippet includes necessary dependencies for implementing the `parse_config` function. Write a Python function `def parse_config(config_strings)` to so... | Parse config from strings. Args: config_strings (string): strings of model config. Returns: Config: model config |
26,475 | import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output checkpoint filename')
args = parse... | null |
26,476 | import argparse
import subprocess
import torch
def process_checkpoint(in_file, out_file):
checkpoint = torch.load(in_file, map_location='cpu')
# remove optimizer for smaller file size
if 'optimizer' in checkpoint:
del checkpoint['optimizer']
# if it is necessary to remove some sensitive data in... | null |
26,477 | import argparse
import base64
from os import path as osp
import mmcv
import numpy as np
from nuimages import NuImages
from nuimages.utils.utils import mask_decode, name_to_index_mapping
def parse_args():
parser = argparse.ArgumentParser(description='Data converter arg parser')
parser.add_argument(
'--d... | null |
26,478 | import argparse
import base64
from os import path as osp
import mmcv
import numpy as np
from nuimages import NuImages
from nuimages.utils.utils import mask_decode, name_to_index_mapping
nus_categories = ('car', 'truck', 'trailer', 'bus', 'construction_vehicle',
'bicycle', 'motorcycle', 'pedestrian', '... | null |
26,479 | import pickle
from os import path as osp
import mmcv
import numpy as np
from mmcv import track_iter_progress
from mmcv.ops import roi_align
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 |
26,480 | from collections import OrderedDict
from concurrent import futures as futures
from os import path as osp
from pathlib import Path
import mmcv
import numpy as np
from PIL import Image
from skimage import io
def get_kitti_info_path(idx,
prefix,
info_type='image_2',
... | null |
26,481 | from collections import OrderedDict
from concurrent import futures as futures
from os import path as osp
from pathlib import Path
import mmcv
import numpy as np
from PIL import Image
from skimage import io
def get_kitti_info_path(idx,
prefix,
info_type='image_2',
... | null |
26,482 | from collections import OrderedDict
from concurrent import futures as futures
from os import path as osp
from pathlib import Path
import mmcv
import numpy as np
from PIL import Image
from skimage import io
def get_image_index_str(img_idx, use_prefix_id=False):
if use_prefix_id:
return '{:07d}'.format(img_id... | null |
26,483 | from collections import OrderedDict
from pathlib import Path
import mmcv
import numpy as np
from nuscenes.utils.geometry_utils import view_points
from mmdet3d.core.bbox import box_np_ops, points_cam2img
from .kitti_data_utils import WaymoInfoGatherer, get_kitti_image_info
from .nuscenes_converter import post_process_co... | convert kitti info v1 to v2 if possible. Args: info (dict): Info of the input kitti data. - image (dict): image info - calib (dict): calibration info - point_cloud (dict): point cloud info |
26,484 | from concurrent import futures as futures
from os import path as osp
import mmcv
import numpy as np
from scipy import io as sio
The provided code snippet includes necessary dependencies for implementing the `random_sampling` function. Write a Python function `def random_sampling(points, num_points, replace=None)` to s... | Random sampling. Sampling point cloud to a certain number of points. Args: points (ndarray): Point cloud. num_points (int): The number of samples. replace (bool): Whether the sample is with or without replacement. Returns: points (ndarray): Point cloud after sampling. |
26,485 | import argparse
import os
import numpy as np
def fix_lyft(root_folder='./data/lyft', version='v1.01'):
# refer to https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/discussion/110000 # noqa
lidar_path = 'lidar/host-a011_lidar1_1233090652702363606.bin'
root_folder = os.path.join(root_fold... | null |
26,486 | import argparse
import warnings
from os import path as osp
from pathlib import Path
import mmcv
import numpy as np
from mmcv import Config, DictAction, mkdir_or_exist
from mmdet3d.core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
... | null |
26,487 | import argparse
import warnings
from os import path as osp
from pathlib import Path
import mmcv
import numpy as np
from mmcv import Config, DictAction, mkdir_or_exist
from mmdet3d.core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
... | Build data config for loading visualization data. |
26,488 | import argparse
import warnings
from os import path as osp
from pathlib import Path
import mmcv
import numpy as np
from mmcv import Config, DictAction, mkdir_or_exist
from mmdet3d.core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
... | Visualize 3D point cloud and 3D bboxes. |
26,489 | import argparse
import warnings
from os import path as osp
from pathlib import Path
import mmcv
import numpy as np
from mmcv import Config, DictAction, mkdir_or_exist
from mmdet3d.core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
... | Visualize 3D point cloud and segmentation mask. |
26,490 | import argparse
import warnings
from os import path as osp
from pathlib import Path
import mmcv
import numpy as np
from mmcv import Config, DictAction, mkdir_or_exist
from mmdet3d.core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
... | Visualize 3D bboxes on 2D image by projection. |
26,491 | import argparse
import mmcv
from mmcv import Config
from mmdet3d.datasets import build_dataset
def parse_args():
parser = argparse.ArgumentParser(
description='MMDet3D visualize the results')
parser.add_argument('config', help='test config file path')
parser.add_argument('--result', help='results f... | null |
26,492 | import argparse
import torch
from mmcv.runner import save_checkpoint
from torch import nn as nn
from mmdet3d.apis import init_model
def fuse_conv_bn(conv, bn):
"""During inference, the functionary of batch norm layers is turned off but
only the mean and var alone channels are used, which exposes the chance to
... | null |
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