id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
21,000 | from typing import Union, Optional
import torch
import torch.nn as nn
import numpy as np
import inspect
from bert4torch.snippets import take_along_dim, torch_div, sequence_padding, create_position_ids_start_at_padding
from bert4torch.snippets import log_info, log_warn, log_warn_once
from bert4torch.tokenizers import To... | null |
21,001 | from bert4torch.models.transformer import Decoder
from bert4torch.snippets import delete_arguments
from bert4torch.layers import MultiHeadAttentionLayer, BertLayer, BlockIdentity
import math
import torch
from torch import nn
import copy
The provided code snippet includes necessary dependencies for implementing the `ap... | 执行alibi相对位置编码，单独拎出来主要是falcon是在+之后再执行attention_scale的 |
21,002 | import torch
from torch import nn
from bert4torch.layers import LayerNorm
from bert4torch.snippets import log_warn, load_state_dict_into_meta_model, find_tied_parameters, JsonConfig
from bert4torch.snippets import get_parameter_device, load_checkpoint, save_checkpoint, copytree
import warnings
from typing import Union,... | 添加torch4keras的BaseModel, 可以使用.compile, .fit等Trainer的功能 |
21,003 | import torch
from torch import nn
from bert4torch.layers import LayerNorm
from bert4torch.snippets import log_warn, load_state_dict_into_meta_model, find_tied_parameters, JsonConfig
from bert4torch.snippets import get_parameter_device, load_checkpoint, save_checkpoint, copytree
import warnings
from typing import Union,... | 添加下三角的Attention Mask（语言模型用） |
21,004 | import torch
from torch import nn
from bert4torch.layers import LayerNorm
from bert4torch.snippets import log_warn, load_state_dict_into_meta_model, find_tied_parameters, JsonConfig
from bert4torch.snippets import get_parameter_device, load_checkpoint, save_checkpoint, copytree
import warnings
from typing import Union,... | 添加UniLM的Attention Mask（Seq2Seq模型用） |
21,005 | import torch
import torch.nn.functional as F
import numpy as np
import random
from multiprocessing import Process, Queue
import os
from os import path, listdir
import argparse
import json
import subprocess
import sys
from typing import List, Dict
import itertools
from warnings import warn
from datetime import datetime
... | null |
21,006 | import torch
import torch.nn.functional as F
import numpy as np
import random
from multiprocessing import Process, Queue
import os
from os import path, listdir
import argparse
import json
import subprocess
import sys
from typing import List, Dict
import itertools
from warnings import warn
from datetime import datetime
... | null |
21,007 | import torch
import torch.nn.functional as F
import numpy as np
import random
from multiprocessing import Process, Queue
import os
from os import path, listdir
import argparse
import json
import subprocess
import sys
from typing import List, Dict
import itertools
from warnings import warn
from datetime import datetime
... | null |
21,008 | import torch
import torch.nn.functional as F
import numpy as np
import random
from multiprocessing import Process, Queue
import os
from os import path, listdir
import argparse
import json
import subprocess
import sys
from typing import List, Dict
import itertools
from warnings import warn
from datetime import datetime
... | null |
21,009 | import torch
import torch.nn.functional as F
import numpy as np
import random
from multiprocessing import Process, Queue
import os
from os import path, listdir
import argparse
import json
import subprocess
import sys
from typing import List, Dict
import itertools
from warnings import warn
from datetime import datetime
... | null |
21,010 | import torch
import torch.nn.functional as F
import numpy as np
import random
from multiprocessing import Process, Queue
import os
from os import path, listdir
import argparse
import json
import subprocess
import sys
from typing import List, Dict
import itertools
from warnings import warn
from datetime import datetime
... | Create a dict for each setting of variable values (product across lists) |
21,011 | import torch
import torch.nn.functional as F
import numpy as np
import random
from multiprocessing import Process, Queue
import os
from os import path, listdir
import argparse
import json
import subprocess
import sys
from typing import List, Dict
import itertools
from warnings import warn
from datetime import datetime
... | null |
21,012 | import torch
import torch.cuda
import torch.optim
import torch.nn.functional as F
import svox2
import json
import imageio
import os
from os import path
import shutil
import gc
import numpy as np
import math
import argparse
import cv2
from util.dataset import datasets
from util.util import Timing, get_expon_lr_func, gen... | null |
21,013 | import torch
import torch.cuda
import torch.optim
import torch.nn.functional as F
import svox2
import json
import imageio
import os
from os import path
import shutil
import gc
import numpy as np
import math
import argparse
import cv2
from util.dataset import datasets
from util.util import Timing, get_expon_lr_func, gen... | null |
21,014 | import os
import os.path as osp
from typing import NamedTuple, List
import argparse
import random
class Dir(NamedTuple):
name: str
valid_exts: List[str]
dirs, dir_idx = list_filter_dirs(args.root_dir)
def list_filter_dirs(base):
all_dirs = [x for x in os.listdir(base) if osp.isdir(osp.join(base, x))]
i... | null |
21,015 | import os
import os.path as osp
import click
from typing import NamedTuple, List
import argparse
class Dir(NamedTuple):
name: str
valid_exts: List[str]
dirs, dir_idx = list_filter_dirs(args.root_dir)
def list_filter_dirs(base):
all_dirs = [x for x in os.listdir(base) if osp.isdir(osp.join(base, x))]
im... | null |
21,016 | import sys
import os
from os import path
import warnings
import numpy as np
import math
from argparse import ArgumentParser
from nerfvis import Scene
from scipy.spatial.transform import Rotation
def align_umeyama(model, data, known_scale=False, yaw_only=False):
"""Implementation of the paper: S. Umeyama, Least-Squ... | Align translation + rotation :param t_a: camera translations to align (N, 3) :param q_a: camera rotations to align (xyz axis-angle, xyzw quaternion, or rotation matrix) (N, {3, 4, 9}) :param t_ref: reference camera translations (N, 3) :param use_first_k: int, if set, uses only first k number of cameras to align :param ... |
21,017 | import sys
import os
from os import path
import warnings
import numpy as np
import math
from argparse import ArgumentParser
from nerfvis import Scene
from scipy.spatial.transform import Rotation
The provided code snippet includes necessary dependencies for implementing the `get_image_size` function. Write a Python fu... | Get image size without loading it |
21,018 | import sys
import os
from os import path
import warnings
import numpy as np
import math
from argparse import ArgumentParser
from nerfvis import Scene
from scipy.spatial.transform import Rotation
def sort_key(x):
if len(x) > 2 and x[1] == "_":
return x[2:]
return x | null |
21,019 | import os
import os.path as osp
import numpy as np
import struct
import collections
import argparse
import shutil
def qvec2rotmat(qvec):
return np.array(
[
[
1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2,
2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
2... | null |
21,020 | import os
import os.path as osp
import numpy as np
import struct
import collections
import argparse
import shutil
Camera = collections.namedtuple("Camera", ["id", "model", "width", "height", "params"])
Point3D = collections.namedtuple(
"Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"]
)
class Im... | null |
21,021 | import os
import collections
import numpy as np
import struct
import argparse
def read_cameras_text(path):
"""
see: src/base/reconstruction.cc
void Reconstruction::WriteCamerasText(const std::string& path)
void Reconstruction::ReadCamerasText(const std::string& path)
"""
cameras = {}
... | null |
21,022 | import os
import collections
import numpy as np
import struct
import argparse
def write_cameras_text(cameras, path):
def write_cameras_binary(cameras, path_to_model_file):
def write_images_text(images, path):
def write_images_binary(images, path_to_model_file):
def write_points3D_text(points3D, path):
def write_points3... | null |
21,023 | import os
import collections
import numpy as np
import struct
import argparse
def rotmat2qvec(R):
Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat
K = np.array([
[Rxx - Ryy - Rzz, 0, 0, 0],
[Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0],
[Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0],
[Ryz ... | null |
21,024 | import os
import shutil
from glob import glob
import json
import numpy as np
from PIL import Image
import argparse
The provided code snippet includes necessary dependencies for implementing the `convert` function. Write a Python function `def convert(data_dir : str, out_data_dir : str)` to solve the following problem:... | Convert Instant-NGP (modified NeRF) data to NSVF :param data_dir: the dataset dir (NeRF-NGP format) to convert :param out_data_dir: output dataset directory NSVF |
21,025 | import cv2
import moviepy
import moviepy.editor
import numpy
import argparse
import os
import random
import shutil
import sys
import tempfile
import torch
import torchvision
import glob
import numpy as np
from tqdm import tqdm
from warnings import warn
def compute_poses(vid_root, args, overwrite=False):
vid_name =... | null |
21,026 | import cv2
import moviepy
import moviepy.editor
import numpy
import argparse
import os
import random
import shutil
import sys
import tempfile
import torch
import torchvision
import glob
import numpy as np
from tqdm import tqdm
from warnings import warn
def generate_masks(vid_root, args, overwrite=False):
print('com... | null |
21,027 | import numpy as np
import os
import imageio
def ptstocam(pts, c2w):
tt = np.matmul(c2w[:3, :3].T, (pts - c2w[:3, 3])[..., np.newaxis])[..., 0]
return tt | null |
21,028 | import numpy as np
import os
import imageio
def normalize(x):
return x / np.linalg.norm(x)
def viewmatrix(z, up, pos):
vec2 = normalize(z)
vec1_avg = up
vec0 = normalize(np.cross(vec1_avg, vec2))
vec1 = normalize(np.cross(vec2, vec0))
m = np.stack([vec0, vec1, vec2, pos], 1)
return m
def re... | null |
21,029 | import numpy as np
import os
import imageio
def _load_data(basedir, factor=None, width=None, height=None, load_imgs=True):
def normalize(x):
def poses_avg(poses):
def render_path_spiral(c2w, up, rads, focal, zrate, rots, N):
def recenter_poses(poses):
def spherify_poses(poses, bds):
def load_llff_data(
basedir,
... | null |
21,030 | import torch
import torch.cuda
import torch.nn.functional as F
from typing import Optional, Union, List
from dataclasses import dataclass
import numpy as np
import cv2
from scipy.spatial.transform import Rotation
from scipy.interpolate import CubicSpline
from matplotlib import pyplot as plt
from warnings import warn
T... | Continuous learning rate decay function. Adapted from JaxNeRF The returned rate is lr_init when step=0 and lr_final when step=max_steps, and is log-linearly interpolated elsewhere (equivalent to exponential decay). If lr_delay_steps>0 then the learning rate will be scaled by some smooth function of lr_delay_mult, such ... |
21,031 | import torch
import torch.cuda
import torch.nn.functional as F
from typing import Optional, Union, List
from dataclasses import dataclass
import numpy as np
import cv2
from scipy.spatial.transform import Rotation
from scipy.interpolate import CubicSpline
from matplotlib import pyplot as plt
from warnings import warn
T... | Save an image to disk. Image should have values in [0,1]. |
21,032 | import torch
import torch.cuda
import torch.nn.functional as F
from typing import Optional, Union, List
from dataclasses import dataclass
import numpy as np
import cv2
from scipy.spatial.transform import Rotation
from scipy.interpolate import CubicSpline
from matplotlib import pyplot as plt
from warnings import warn
T... | Convert ray direction vectors into equirectangular pixel coordinates. Inverse of equirect2xyz. Taken from Vickie Ye |
21,033 | import torch
import torch.cuda
import torch.nn.functional as F
from typing import Optional, Union, List
from dataclasses import dataclass
import numpy as np
import cv2
from scipy.spatial.transform import Rotation
from scipy.interpolate import CubicSpline
from matplotlib import pyplot as plt
from warnings import warn
cl... | null |
21,034 | import torch
import torch.cuda
import torch.nn.functional as F
from typing import Optional, Union, List
from dataclasses import dataclass
import numpy as np
import cv2
from scipy.spatial.transform import Rotation
from scipy.interpolate import CubicSpline
from matplotlib import pyplot as plt
from warnings import warn
T... | Computes SSIM from two images. This function was modeled after tf.image.ssim, and should produce comparable output. Args: img0: torch.tensor. An image of size [..., width, height, num_channels]. img1: torch.tensor. An image of size [..., width, height, num_channels]. max_val: float > 0. The maximum magnitude that `img0... |
21,035 | import torch
import torch.cuda
import torch.nn.functional as F
from typing import Optional, Union, List
from dataclasses import dataclass
import numpy as np
import cv2
from scipy.spatial.transform import Rotation
from scipy.interpolate import CubicSpline
from matplotlib import pyplot as plt
from warnings import warn
cl... | Generate perspective camera rays. Principal point is at center. Args: w: int image width h: int image heigth focal: float real focal length camtoworlds: jnp.ndarray [B, 4, 4] c2w homogeneous poses equirect: if true, generates spherical rays instead of pinhole Returns: rays: Rays a namedtuple(origins [B, 3], directions ... |
21,036 | import torch
import torch.cuda
import torch.nn.functional as F
from typing import Optional, Union, List
from dataclasses import dataclass
import numpy as np
import cv2
from scipy.spatial.transform import Rotation
from scipy.interpolate import CubicSpline
from matplotlib import pyplot as plt
from warnings import warn
T... | Get a similarity transform to normalize dataset from c2w (OpenCV convention) cameras :param c2w: (N, 4) :return T (4,4) , scale (float) |
21,037 | import torch
import torch.cuda
import torch.nn.functional as F
from typing import Optional, Union, List
from dataclasses import dataclass
import numpy as np
import cv2
from scipy.spatial.transform import Rotation
from scipy.interpolate import CubicSpline
from matplotlib import pyplot as plt
from warnings import warn
T... | For generating a novel trajectory close to known trajectory :param poses: torch.Tensor (B, 4, 4) :param n_inter: int, number of views to interpolate in total :param noise_std: float, default 0 |
21,038 | import torch
import torch.cuda
import torch.nn.functional as F
from typing import Optional, Union, List
from dataclasses import dataclass
import numpy as np
import cv2
from scipy.spatial.transform import Rotation
from scipy.interpolate import CubicSpline
from matplotlib import pyplot as plt
from warnings import warn
de... | Generate spherical rendering poses, from NeRF. Forgive the code horror :return: r (3,), t (3,) |
21,039 | from scipy.spatial.transform import Rotation
import struct
import json
import glob
import copy
import numpy as np
import os
import torch
import torch.nn.functional as F
from collections import deque
from tqdm import tqdm
import imageio
import cv2
from .util import Rays, Intrin
from .dataset_base import DatasetBase
from... | null |
21,040 | from scipy.spatial.transform import Rotation
import struct
import json
import glob
import copy
import numpy as np
import os
import torch
import torch.nn.functional as F
from collections import deque
from tqdm import tqdm
import imageio
import cv2
from .util import Rays, Intrin
from .dataset_base import DatasetBase
from... | null |
21,041 | from scipy.spatial.transform import Rotation
import struct
import json
import glob
import copy
import numpy as np
import os
import torch
import torch.nn.functional as F
from collections import deque
from tqdm import tqdm
import imageio
import cv2
from .util import Rays, Intrin
from .dataset_base import DatasetBase
from... | null |
21,042 | from scipy.spatial.transform import Rotation
import struct
import json
import glob
import copy
import numpy as np
import os
import torch
import torch.nn.functional as F
from collections import deque
from tqdm import tqdm
import imageio
import cv2
from .util import Rays, Intrin
from .dataset_base import DatasetBase
from... | null |
21,043 | from scipy.spatial.transform import Rotation
import struct
import json
import glob
import copy
import numpy as np
import os
import torch
import torch.nn.functional as F
from collections import deque
from tqdm import tqdm
import imageio
import cv2
from .util import Rays, Intrin
from .dataset_base import DatasetBase
from... | null |
21,044 | import torch
import argparse
from util.dataset import datasets
import json
datasets = {
'nerf': NeRFDataset,
'llff': LLFFDataset,
'nsvf': NSVFDataset,
'co3d': CO3DDataset,
'auto': auto_dataset
}
def define_common_args(parser : argparse.ArgumentParser):
parser.add_argument('data_dir', type=str)... | null |
21,045 | import torch
import argparse
from util.dataset import datasets
import json
The provided code snippet includes necessary dependencies for implementing the `build_data_options` function. Write a Python function `def build_data_options(args)` to solve the following problem:
Arguments to pass as kwargs to the dataset cons... | Arguments to pass as kwargs to the dataset constructor |
21,046 | import torch
import argparse
from util.dataset import datasets
import json
The provided code snippet includes necessary dependencies for implementing the `maybe_merge_config_file` function. Write a Python function `def maybe_merge_config_file(args, allow_invalid=False)` to solve the following problem:
Load json config... | Load json config file if specified and merge the arguments |
21,047 | import torch
import argparse
from util.dataset import datasets
import json
The provided code snippet includes necessary dependencies for implementing the `setup_render_opts` function. Write a Python function `def setup_render_opts(opt, args)` to solve the following problem:
Pass render arguments to the SparseGrid rend... | Pass render arguments to the SparseGrid renderer options |
21,048 | from .nerf_dataset import NeRFDataset
from .llff_dataset import LLFFDataset
from .nsvf_dataset import NSVFDataset
from .co3d_dataset import CO3DDataset
from os import path
class NeRFDataset(DatasetBase):
def __init__(
self,
root,
split,
epoch_size : Optional[int] = ... | null |
21,049 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
def inthroot(x : int, n : int):
if x <= 0:
return None
lo, hi = 1, x
while lo <= hi:
mi = lo + (hi - lo) // 2
p = mi **... | null |
21,050 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
def _get_c_extension():
from warnings import warn
try:
import svox2.csrc as _C
if not hasattr(_C, "sample_grid"):
_C = ... | null |
21,051 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
def _unexpand_bits(v):
v &= 0x49249249
v = (v | (v >> 2)) & 0xc30c30c3
v = (v | (v >> 4)) & 0xf00f00f
v = (v | (v >> 8)) & 0xff0000ff
v ... | null |
21,052 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
def is_pow2(x : int):
def morton_code_3(x, y, z):
def gen_morton(D, device='cpu', dtype=torch.long):
assert is_pow2(D), "Morton code requires power of ... | null |
21,053 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
SH_C0 = 0.28209479177387814
SH_C1 = 0.4886025119029199
SH_C2 = [
1.0925484305920792,
-1.0925484305920792,
0.31539156525252005,
-1.0925484305... | Evaluate spherical harmonics bases at unit directions, without taking linear combination. At each point, the final result may the be obtained through simple multiplication. :param basis_dim: int SH basis dim. Currently, 1-25 square numbers supported :param dirs: torch.Tensor (..., 3) unit directions :return: torch.Tens... |
21,054 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
class CubemapCoord:
ax : torch.Tensor
ori : torch.Tensor
u : torch.Tensor
v : torch.Tensor
def query_in(self, cubemap : torch.Tensor):
... | Convert a direction on a sphere (not necessarily normalized) :param xyz: direction (not necessarily normalized) :param face_reso: int, resolution of cubemap face :param eac: bool, if true (default) then uses equi-angular cubemaps (EAC) instead of standard cubemap; see https://blog.google/products/google-ar-vr/bringing-... |
21,055 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
class CubemapCoord:
ax : torch.Tensor
ori : torch.Tensor
u : torch.Tensor
v : torch.Tensor
def query_in(self, cubemap : torch.Tensor):
... | Compute the points on the cubemap for bilinear sampling given a cubemap coordinate from dir_to_cubemap_coord; to be used with cubemap_sample. :param idx: CubemapCoord, cube map coordinate from dir_to_cubemap_coord :param face_reso: int, resolution of cubemap face :param mode: str, interpolation mode; one of nearest, li... |
21,056 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
class CubemapBilerpQuery:
i00: CubemapCoord
i01: CubemapCoord
i10: CubemapCoord
i11: CubemapCoord
du: torch.Tensor
dv: torch.Tensor
... | Perform bilinear sampling on a cubemap given a query from cubemap_build_query :param cubemap: torch.Tensor float (6, face_reso, face_reso, C) or (B, 6, face_reso, face_reso, C) :param idx4: CubemapBilerpQuery from cubemap_build_query where each tensor has batch size B :return: (B, C) |
21,057 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
def memlog(device='cuda'):
# Memory debugging
print(torch.cuda.memory_summary(device))
import gc
for obj in gc.get_objects():
try:
... | null |
21,058 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
The provided code snippet includes necessary dependencies for implementing the `spher2cart` function. Write a Python function `def spher2cart(theta : torch... | Convert spherical coordinates into Cartesian coordinates on unit sphere. |
21,059 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
The provided code snippet includes necessary dependencies for implementing the `eval_sg_at_dirs` function. Write a Python function `def eval_sg_at_dirs(sg_... | Evaluate spherical Gaussian functions at unit directions using learnable SG basis, without taking linear combination Works with torch. ... Can be 0 or more batch dimensions. N is the number of SG basis we use. :math:`Output = \sigma_{i}{exp ^ {\lambda_i * (\dot(\mu_i, \dirs) - 1)}` :param sg_lambda: The sharpness of th... |
21,060 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
def init_weights(m):
if type(m) == nn.Linear:
nn.init.xavier_uniform_(m.weight)
m.bias.data.fill_(0.0) | null |
21,061 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
The provided code snippet includes necessary dependencies for implementing the `cross_broadcast` function. Write a Python function `def cross_broadcast(x :... | Cross broadcasting for 2 tensors :param x: torch.Tensor :param y: torch.Tensor, should have the same ndim as x :return: tuple of cross-broadcasted tensors x, y. Any dimension where the size of x or y is 1 is expanded to the maximum size in that dimension among the 2. Formally, say the shape of x is (a1, ... an) and of ... |
21,062 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
The provided code snippet includes necessary dependencies for implementing the `posenc` function. Write a Python function `def posenc( x: torch.Tensor,... | Positional encoding function. Adapted from jaxNeRF (https://github.com/google-research/google-research/tree/master/jaxnerf). With support for mip-NeFF IPE (by passing cov_diag != 0, keeping enable_ipe=True). And BARF-nerfies frequency attenuation (setting cutoff) Cat x with a positional encoding of x with scales 2^[min... |
21,063 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
def net_to_dict(out_dict : dict,
prefix : str,
model : nn.Module):
for child in model.named_children():
layer_n... | null |
21,064 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
def net_from_dict(in_dict,
prefix : str,
model : nn.Module):
for child in model.named_children():
layer_nam... | null |
21,065 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
The provided code snippet includes necessary dependencies for implementing the `convert_to_ndc` function. Write a Python function `def convert_to_ndc(origi... | Convert a set of rays to NDC coordinates. |
21,066 | from functools import partial
import torch
from torch import nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass
import math
The provided code snippet includes necessary dependencies for implementing the `xyz2equirect` function. Write a Python function `def xyz2equirect(bearings,... | Convert ray direction vectors into equirectangular pixel coordinates. Inverse of equirect2xyz. Taken from Vickie Ye |
21,067 |
def setup(app):
import sphinx.search as search
import zh
search.languages["zh_CN"] = zh.SearchChinese | null |
21,068 | import os
import subprocess
import platform
base_link = "http://python.iswbm.com/en/latest/"
def get_file_info(filename):
with open(filename, 'r', encoding="utf-8") as file:
first_line = file.readline().replace("#", "").strip()
return first_line.split(' ', 1)
def make_line(chapter, file):
page_name... | null |
21,069 | import os
import subprocess
import platform
index_path = os.path.join(pwd, "README.md")
readme_header = '''
<p align="center">
<img src='https://img.shields.io/badge/language-Python-blue.svg' alt="Build Status">
<img src='https://img.shields.io/badge/framwork-Sphin... | 生成 readme.md 索引文件，包含所有文件目录 |
21,070 | import os
import subprocess
import platform
The provided code snippet includes necessary dependencies for implementing the `convert_md5_to_rst` function. Write a Python function `def convert_md5_to_rst(file)` to solve the following problem:
转换格式：md5转换成rst
Here is the function:
def convert_md5_to_rst(file):
'''
... | 转换格式：md5转换成rst |
21,071 | import os
import subprocess
import platform
blog_path = os.path.join(pwd, "source")
The provided code snippet includes necessary dependencies for implementing the `get_all_dir` function. Write a Python function `def get_all_dir()` to solve the following problem:
获取所有的目录
Here is the function:
def get_all_dir():
'... | 获取所有的目录 |
21,072 | import os
import subprocess
import platform
blog_path = os.path.join(pwd, "source")
The provided code snippet includes necessary dependencies for implementing the `init_index_info` function. Write a Python function `def init_index_info()` to solve the following problem:
初始化索引
Here is the function:
def init_index_inf... | 初始化索引 |
21,073 | import os
import re
import linecache
from glob import glob
source_dir = os.path.join(pwd, "source")
def get_all_chapter():
all_chapters_path = []
os.chdir(source_dir)
for dir_name in glob("c*"):
if dir_name == "chapters" or dir_name == "conf.py":
continue
all_chapters_path.appen... | null |
21,074 | import os
import re
import linecache
from glob import glob
pwd = os.getcwd()
def get_chapter_name(file):
return linecache.getline(file, 2).strip()
def generate_mapping(all_chapters_path):
mapping = dict.fromkeys([os.path.basename(chapter_path) for chapter_path in all_chapters_path])
for key in mapping.keys... | null |
21,075 | import os
import re
import linecache
from glob import glob
source_dir = os.path.join(pwd, "source")
def get_title(file):
first_line = linecache.getline(file, 1)
if first_line.startswith("#"):
return first_line.strip()
def get_toc_info(all_chapters_path):
toc = {}
for dir_name in all_chapters_pa... | null |
21,076 | import os
import re
import linecache
from glob import glob
def print_md_toc(toc_info, mapping):
for chapter in sorted(toc_info.items(), key=lambda item: item[0]):
posts = chapter[1]
chapter_name = mapping[chapter[0]]
print(f"- **{chapter_name}**")
for post in sorted(posts.items(), k... | null |
21,077 | import json
import os
import argparse
import deepspeed
import deepspeed.comm as dist
import numpy as np
import sentencepiece as spm
import torch
from models.configuration_baichuan import BaiChuanConfig
from models.modeling_baichuan import BaiChuanForCausalLM
def get_argument_parser():
parser = argparse.ArgumentPar... | null |
21,078 | import json
import os
import argparse
import deepspeed
import deepspeed.comm as dist
import numpy as np
import sentencepiece as spm
import torch
from models.configuration_baichuan import BaiChuanConfig
from models.modeling_baichuan import BaiChuanForCausalLM
args = arg_parser.parse_args()
class DataEngine():
def __... | null |
21,079 | import json
import os
import argparse
import deepspeed
import deepspeed.comm as dist
import numpy as np
import sentencepiece as spm
import torch
from models.configuration_baichuan import BaiChuanConfig
from models.modeling_baichuan import BaiChuanForCausalLM
args = arg_parser.parse_args()
deepspeed.init_distributed()
... | null |
21,080 | import json
import os
import argparse
import deepspeed
import deepspeed.comm as dist
import numpy as np
import sentencepiece as spm
import torch
from models.configuration_baichuan import BaiChuanConfig
from models.modeling_baichuan import BaiChuanForCausalLM
args = arg_parser.parse_args()
def train(data_engine, model_... | null |
21,081 | import argparse
import json
import os
from tqdm import tqdm
import numpy as np
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
PreTrainedModel,
PreTrainedTokenizerBase,
)
def parse_argument():
parser = argparse.ArgumentParser()
pars... | null |
21,082 | import argparse
import os
import torch
import numpy as np
import pandas as pd
from categories import subcategories, categories
from transformers import AutoTokenizer,AutoModelForCausalLM
import time
choices = ["A", "B", "C", "D"]
def format_example(df, idx, include_answer=True):
prompt = df.iloc[idx, 0]
k = df.... | null |
21,083 | import math
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
import torch.utils.checkpoint
from transformers import PreTrainedModel, add_start_docstrings
from transformers.activations import ACT2FN
from transformers.model... | Make causal mask used for bi-directional self-attention. |
21,084 | import math
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
import torch.utils.checkpoint
from transformers import PreTrainedModel, add_start_docstrings
from transformers.activations import ACT2FN
from transformers.model... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
21,085 | import math
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
import torch.utils.checkpoint
from transformers import PreTrainedModel, add_start_docstrings
from transformers.activations import ACT2FN
from transformers.model... | null |
21,086 | import os
from typing import Dict, List, Tuple
from setuptools import find_packages, setup
def _setup_packages() -> List:
return find_packages(
"src", include=["sparseml", "sparseml.*"], exclude=["*.__pycache__.*"]
) | null |
21,087 | import os
from typing import Dict, List, Tuple
from setuptools import find_packages, setup
def _setup_package_dir() -> Dict:
return {"": "src"} | null |
21,088 | import os
from typing import Dict, List, Tuple
from setuptools import find_packages, setup
_deps = [
"setuptools<=59.5.0",
"pyyaml>=5.0.0",
"numpy>=1.0.0",
"matplotlib>=3.0.0",
"merge-args>=0.1.0",
"onnx>=1.5.0,<1.15.0",
"pandas>=0.25.0",
"packaging>=20.0",
"psutil>=5.0.0",
"pyda... | null |
21,089 | import os
from typing import Dict, List, Tuple
from setuptools import find_packages, setup
_deepsparse_deps = [
f"{'deepsparse' if is_release else 'deepsparse-nightly'}~={version_nm_deps}"
]
_deepsparse_ent_deps = [f"deepsparse-ent~={version_nm_deps}"]
_onnxruntime_deps = ["onnxruntime>=1.0.0"]
_clip_deps = ["open_... | null |
21,090 | import os
from typing import Dict, List, Tuple
from setuptools import find_packages, setup
def _setup_entry_points() -> Dict:
entry_points = {
"console_scripts": [
# export
"sparseml.export=sparseml.export.export:main",
# sparsification
"sparseml.framework=sp... | null |
21,091 | import os
from typing import Dict, List, Tuple
from setuptools import find_packages, setup
def _setup_long_description() -> Tuple[str, str]:
return open("README.md", "r", encoding="utf-8").read(), "text/markdown" | null |
21,092 | from datetime import date
version_base = "1.7.0"
is_release = False
is_dev = False
dev_number = None
def _generate_version():
if is_release:
return version_base
elif is_dev:
return f"{version_base}.dev{dev_number}"
else:
return f"{version_base}.{date.today().strftime('%Y%m%d')}" | null |
21,093 | import logging
import os
import shutil
from pathlib import Path
from typing import Any, List, Optional, Union
import numpy
import click
import sparseml.core.session as session_manager
from sparseml.export.helpers import (
AVAILABLE_DEPLOYMENT_TARGETS,
ONNX_MODEL_NAME,
create_deployment_folder,
create_ex... | Export a PyTorch model that is either: - located in source_path (and will be loaded) - passed directly to the function to target_path. The deployment files will be located at target_path/deployment_directory_name The exporting logic consists of the following steps: 1. Create the model (if required) and the data loader ... |
21,094 | import logging
import os
import shutil
from pathlib import Path
from typing import Any, List, Optional, Union
import numpy
import click
import sparseml.core.session as session_manager
from sparseml.export.helpers import (
AVAILABLE_DEPLOYMENT_TARGETS,
ONNX_MODEL_NAME,
create_deployment_folder,
create_ex... | null |
21,095 | import logging
import os
import shutil
from pathlib import Path
from typing import Any, List, Optional, Union
import numpy
import click
import sparseml.core.session as session_manager
from sparseml.export.helpers import (
AVAILABLE_DEPLOYMENT_TARGETS,
ONNX_MODEL_NAME,
create_deployment_folder,
create_ex... | null |
21,096 | import os
from pathlib import Path
from typing import Union
import onnx
import torch
from sparseml.exporters import ExportTargets
from sparseml.exporters.onnx_to_deepsparse import ONNXToDeepsparse
from sparseml.pytorch.opset import TORCH_DEFAULT_ONNX_OPSET
from sparseml.pytorch.torch_to_onnx_exporter import TorchToONNX... | Exports the torch model to the deployment target :param model: The torch model to export :param sample_data: The sample data to use for the export :param target_path: The path to export the model to :param onnx_model_name: The name to save the exported ONNX model as :param deployment_target: The deployment target to ex... |
21,097 | import glob
import logging
import os.path
from collections import OrderedDict
from pathlib import Path
from typing import Callable, List, Optional, Union
import numpy
from sparseml.export.export_data import InputsNames, LabelNames, OutputsNames
from sparseml.export.helpers import ONNX_MODEL_NAME, onnx_data_files
from s... | Validates the structure of the targe_path by checking if the expected files, that should exist as a result of the export, are present. :param target_path: The directory where the exported files are stored. :param deployment_directory_name: The name of the deployment directory. :param onnx_model_name: The name of the ON... |
21,098 | import glob
import logging
import os.path
from collections import OrderedDict
from pathlib import Path
from typing import Callable, List, Optional, Union
import numpy
from sparseml.export.export_data import InputsNames, LabelNames, OutputsNames
from sparseml.export.helpers import ONNX_MODEL_NAME, onnx_data_files
from s... | Validates the correctness of the exported ONNX model by running it on a set of sample inputs and comparing the resulting outputs using a validation function. :param target_path: The directory where the sample inputs and outputs are stored. :param directory: The directory where the ONNX model is stored. :param onnx_mode... |
21,099 | from typing import Optional
from sparseml.pytorch import recipe_template
The provided code snippet includes necessary dependencies for implementing the `create_recipe` function. Write a Python function `def create_recipe( model: Optional["Module"] = None, # noqa: F821 pruning: str = "true", quant: bool = ... | Convenience function to create a recipe based on supplied args and kwargs :param model: an instantiated PyTorch Module, or the local path to a torch.jit loadable *.pt file, if supplied then the recipe is built according to this architecture :param pruning: optional pruning algorithm to use in the recipe, can be any of ... |
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