id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
9,265 | import os
from tqdm import tqdm
import numpy as np
import tiktoken
from datasets import load_dataset
enc = tiktoken.get_encoding("gpt2")
def process(example):
ids = enc.encode_ordinary(example['text']) # encode_ordinary ignores any special tokens
ids.append(enc.eot_token) # add the end of text token, e... | null |
9,266 | import os
import pickle
import requests
import numpy as np
stoi = { ch:i for i,ch in enumerate(chars) }
def encode(s):
return [stoi[c] for c in s] # encoder: take a string, output a list of integers | null |
9,267 | import os
import pickle
import requests
import numpy as np
itos = { i:ch for i,ch in enumerate(chars) }
def decode(l):
return ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string | null |
9,268 | import os
import re
from typing import List, Optional, Tuple, Union
import click
import dnnlib
import numpy as np
import PIL.Image
import torch
from tqdm import tqdm
import mrcfile
import legacy
from camera_utils import LookAtPoseSampler, FOV_to_intrinsics
from torch_utils import misc
from training.triplane import TriP... | Parse a comma separated list of numbers or ranges and return a list of ints. Example: '1,2,5-10' returns [1, 2, 5, 6, 7] |
9,269 | import os
import re
from typing import List, Optional, Tuple, Union
import click
import dnnlib
import numpy as np
import PIL.Image
import torch
from tqdm import tqdm
import mrcfile
import legacy
from camera_utils import LookAtPoseSampler, FOV_to_intrinsics
from torch_utils import misc
from training.triplane import TriP... | Parse a floating point 2-vector of syntax 'a,b'. Example: '0,1' returns (0,1) |
9,270 | import os
import re
from typing import List, Optional, Tuple, Union
import click
import dnnlib
import numpy as np
import PIL.Image
import torch
from tqdm import tqdm
import mrcfile
import legacy
from camera_utils import LookAtPoseSampler, FOV_to_intrinsics
from torch_utils import misc
from training.triplane import TriP... | null |
9,271 | import os
import re
from typing import List, Optional, Tuple, Union
import click
import dnnlib
import numpy as np
import PIL.Image
import torch
from tqdm import tqdm
import mrcfile
import legacy
from camera_utils import LookAtPoseSampler, FOV_to_intrinsics
from torch_utils import misc
from training.triplane import TriP... | Generate images using pretrained network pickle. Examples: \b # Generate an image using pre-trained FFHQ model. python gen_samples.py --outdir=output --trunc=0.7 --seeds=0-5 --shapes=True\\ --network=ffhq-rebalanced-128.pkl |
9,272 | import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
fr... | null |
9,273 | import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
fr... | Convert the seconds to human readable string with days, hours, minutes and seconds. |
9,274 | import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
fr... | Convert the seconds to human readable string with days, hours, minutes and seconds. |
9,275 | import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
fr... | Ask the user the question until the user inputs a valid answer. |
9,276 | import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
fr... | Calculate the product of the tuple elements. |
9,277 | import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
fr... | Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes. |
9,278 | import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
fr... | null |
9,279 | import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
fr... | Finds the python class with the given name and constructs it with the given arguments. |
9,280 | import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
fr... | Get the directory path of the module containing the given object name. |
9,281 | import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
fr... | Return the fully-qualified name of a top-level function. |
9,282 | import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
fr... | List all files recursively in a given directory while ignoring given file and directory names. Returns list of tuples containing both absolute and relative paths. |
9,283 | import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
fr... | Takes in a list of tuples of (src, dst) paths and copies files. Will create all necessary directories. |
9,284 | import ctypes
import fnmatch
import importlib
import inspect
import numpy as np
import os
import shutil
import sys
import types
import io
import pickle
import re
import requests
import html
import hashlib
import glob
import tempfile
import urllib
import urllib.request
import uuid
from distutils.util import strtobool
fr... | Download the given URL and return a binary-mode file object to access the data. |
9,285 | import os
import click
import re
import json
import tempfile
import torch
import dnnlib
from training import training_loop
from metrics import metric_main
from torch_utils import training_stats
from torch_utils import custom_ops
def subprocess_fn(rank, c, temp_dir):
dnnlib.util.Logger(file_name=os.path.join(c.run_d... | null |
9,286 | import os
import click
import re
import json
import tempfile
import torch
import dnnlib
from training import training_loop
from metrics import metric_main
from torch_utils import training_stats
from torch_utils import custom_ops
def init_dataset_kwargs(data):
try:
dataset_kwargs = dnnlib.EasyDict(class_nam... | null |
9,287 | import os
import click
import re
import json
import tempfile
import torch
import dnnlib
from training import training_loop
from metrics import metric_main
from torch_utils import training_stats
from torch_utils import custom_ops
def parse_comma_separated_list(s):
if isinstance(s, list):
return s
if s i... | null |
9,288 | import click
import pickle
import re
import copy
import numpy as np
import torch
import dnnlib
from torch_utils import misc
def load_network_pkl(f, force_fp16=False):
data = _LegacyUnpickler(f).load()
# Legacy TensorFlow pickle => convert.
if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net... | Convert legacy network pickle into the native PyTorch format. The tool is able to load the main network configurations exported using the TensorFlow version of StyleGAN2 or StyleGAN2-ADA. It does not support e.g. StyleGAN2-ADA comparison methods, StyleGAN2 configs A-D, or StyleGAN1 networks. Example: \b python legacy.p... |
9,289 | import copy
import numpy as np
import torch
from . import metric_utils
def slerp(a, b, t):
a = a / a.norm(dim=-1, keepdim=True)
b = b / b.norm(dim=-1, keepdim=True)
d = (a * b).sum(dim=-1, keepdim=True)
p = t * torch.acos(d)
c = b - d * a
c = c / c.norm(dim=-1, keepdim=True)
d = a * torch.c... | null |
9,290 | import os
import time
import json
import torch
import dnnlib
from . import metric_utils
from . import frechet_inception_distance
from . import kernel_inception_distance
from . import precision_recall
from . import perceptual_path_length
from . import inception_score
from . import equivariance
_metric_dict = dict()
def... | null |
9,291 | import os
import time
import json
import torch
import dnnlib
from . import metric_utils
from . import frechet_inception_distance
from . import kernel_inception_distance
from . import precision_recall
from . import perceptual_path_length
from . import inception_score
from . import equivariance
def fid50k_full(opts):
... | null |
9,292 | import os
import time
import json
import torch
import dnnlib
from . import metric_utils
from . import frechet_inception_distance
from . import kernel_inception_distance
from . import precision_recall
from . import perceptual_path_length
from . import inception_score
from . import equivariance
def kid50k_full(opts):
... | null |
9,293 | import os
import time
import json
import torch
import dnnlib
from . import metric_utils
from . import frechet_inception_distance
from . import kernel_inception_distance
from . import precision_recall
from . import perceptual_path_length
from . import inception_score
from . import equivariance
def pr50k3_full(opts):
... | null |
9,294 | import os
import time
import json
import torch
import dnnlib
from . import metric_utils
from . import frechet_inception_distance
from . import kernel_inception_distance
from . import precision_recall
from . import perceptual_path_length
from . import inception_score
from . import equivariance
def ppl2_wend(opts):
... | null |
9,295 | import os
import time
import json
import torch
import dnnlib
from . import metric_utils
from . import frechet_inception_distance
from . import kernel_inception_distance
from . import precision_recall
from . import perceptual_path_length
from . import inception_score
from . import equivariance
def eqt50k_int(opts):
... | null |
9,296 | import os
import time
import json
import torch
import dnnlib
from . import metric_utils
from . import frechet_inception_distance
from . import kernel_inception_distance
from . import precision_recall
from . import perceptual_path_length
from . import inception_score
from . import equivariance
def eqt50k_frac(opts):
... | null |
9,297 | import os
import time
import json
import torch
import dnnlib
from . import metric_utils
from . import frechet_inception_distance
from . import kernel_inception_distance
from . import precision_recall
from . import perceptual_path_length
from . import inception_score
from . import equivariance
def eqr50k(opts):
opt... | null |
9,298 | import os
import time
import json
import torch
import dnnlib
from . import metric_utils
from . import frechet_inception_distance
from . import kernel_inception_distance
from . import precision_recall
from . import perceptual_path_length
from . import inception_score
from . import equivariance
def fid50k(opts):
opt... | null |
9,299 | import os
import time
import json
import torch
import dnnlib
from . import metric_utils
from . import frechet_inception_distance
from . import kernel_inception_distance
from . import precision_recall
from . import perceptual_path_length
from . import inception_score
from . import equivariance
def kid50k(opts):
opt... | null |
9,300 | import os
import time
import json
import torch
import dnnlib
from . import metric_utils
from . import frechet_inception_distance
from . import kernel_inception_distance
from . import precision_recall
from . import perceptual_path_length
from . import inception_score
from . import equivariance
def pr50k3(opts):
opt... | null |
9,301 | import os
import time
import json
import torch
import dnnlib
from . import metric_utils
from . import frechet_inception_distance
from . import kernel_inception_distance
from . import precision_recall
from . import perceptual_path_length
from . import inception_score
from . import equivariance
def is50k(opts):
opts... | null |
9,302 | import os
import click
import json
import tempfile
import copy
import torch
import dnnlib
import legacy
from metrics import metric_main
from metrics import metric_utils
from torch_utils import training_stats
from torch_utils import custom_ops
from torch_utils import misc
from torch_utils.ops import conv2d_gradfix
def ... | null |
9,303 | import os
import click
import json
import tempfile
import copy
import torch
import dnnlib
import legacy
from metrics import metric_main
from metrics import metric_utils
from torch_utils import training_stats
from torch_utils import custom_ops
from torch_utils import misc
from torch_utils.ops import conv2d_gradfix
def s... | Calculate quality metrics for previous training run or pretrained network pickle. Examples: \b # Previous training run: look up options automatically, save result to JSONL file. python calc_metrics.py --metrics=eqt50k_int,eqr50k \\ --network=~/training-runs/00000-stylegan3-r-mydataset/network-snapshot-000000.pkl \b # P... |
9,304 | import os
import re
from typing import List, Optional, Tuple, Union
import click
import dnnlib
import imageio
import numpy as np
import scipy.interpolate
import torch
from tqdm import tqdm
import mrcfile
import legacy
from camera_utils import LookAtPoseSampler
from torch_utils import misc
The provided code snippet inc... | Parse a comma separated list of numbers or ranges and return a list of ints. Example: '1,2,5-10' returns [1, 2, 5, 6, 7] |
9,305 | import os
import re
from typing import List, Optional, Tuple, Union
import click
import dnnlib
import imageio
import numpy as np
import scipy.interpolate
import torch
from tqdm import tqdm
import mrcfile
import legacy
from camera_utils import LookAtPoseSampler
from torch_utils import misc
The provided code snippet inc... | Parse a 'M,N' or 'MxN' integer tuple. Example: '4x2' returns (4,2) '0,1' returns (0,1) |
9,306 | import os
import re
from typing import List, Optional, Tuple, Union
import click
import dnnlib
import imageio
import numpy as np
import scipy.interpolate
import torch
from tqdm import tqdm
import mrcfile
import legacy
from camera_utils import LookAtPoseSampler
from torch_utils import misc
def gen_interp_video(G, mp4: s... | Render a latent vector interpolation video. Examples: \b # Render a 4x2 grid of interpolations for seeds 0 through 31. python gen_video.py --output=lerp.mp4 --trunc=1 --seeds=0-31 --grid=4x2 \\ --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl Anima... |
9,307 | import numpy as np
import torch
from torch_utils import misc
from torch_utils import persistence
from torch_utils.ops import conv2d_resample
from torch_utils.ops import upfirdn2d
from torch_utils.ops import bias_act
from torch_utils.ops import fma
def normalize_2nd_moment(x, dim=1, eps=1e-8):
return x * (x.square(... | null |
9,308 | import numpy as np
import torch
from torch_utils import misc
from torch_utils import persistence
from torch_utils.ops import conv2d_resample
from torch_utils.ops import upfirdn2d
from torch_utils.ops import bias_act
from torch_utils.ops import fma
def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, fl... | null |
9,309 | import torch
def sample_cross_section(G, ws, resolution=256, w=1.2):
axis=0
A, B = torch.meshgrid(torch.linspace(w/2, -w/2, resolution, device=ws.device), torch.linspace(-w/2, w/2, resolution, device=ws.device), indexing='ij')
A, B = A.reshape(-1, 1), B.reshape(-1, 1)
C = torch.zeros_like(A)
coordi... | null |
9,310 | import numpy as np
import torch
from torch_utils import persistence
from torch_utils.ops import upfirdn2d
from training.networks_stylegan2 import DiscriminatorBlock, MappingNetwork, DiscriminatorEpilogue
def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
r"""Pad, upsample, filter... | null |
9,311 | import numpy as np
import scipy.signal
import scipy.optimize
import torch
from torch_utils import misc
from torch_utils import persistence
from torch_utils.ops import conv2d_gradfix
from torch_utils.ops import filtered_lrelu
from torch_utils.ops import bias_act
def modulated_conv2d(
x, # Input ten... | null |
9,312 | import math
import torch
import torch.nn as nn
from training.volumetric_rendering.ray_marcher import MipRayMarcher2
from training.volumetric_rendering import math_utils
The provided code snippet includes necessary dependencies for implementing the `generate_planes` function. Write a Python function `def generate_plane... | Defines planes by the three vectors that form the "axes" of the plane. Should work with arbitrary number of planes and planes of arbitrary orientation. |
9,313 | import math
import torch
import torch.nn as nn
from training.volumetric_rendering.ray_marcher import MipRayMarcher2
from training.volumetric_rendering import math_utils
def project_onto_planes(planes, coordinates):
def sample_from_planes(plane_axes, plane_features, coordinates, mode='bilinear', padding_mode='zeros', b... | null |
9,314 | import math
import torch
import torch.nn as nn
from training.volumetric_rendering.ray_marcher import MipRayMarcher2
from training.volumetric_rendering import math_utils
The provided code snippet includes necessary dependencies for implementing the `sample_from_3dgrid` function. Write a Python function `def sample_from... | Expects coordinates in shape (batch_size, num_points_per_batch, 3) Expects grid in shape (1, channels, H, W, D) (Also works if grid has batch size) Returns sampled features of shape (batch_size, num_points_per_batch, feature_channels) |
9,315 | import torch
The provided code snippet includes necessary dependencies for implementing the `transform_vectors` function. Write a Python function `def transform_vectors(matrix: torch.Tensor, vectors4: torch.Tensor) -> torch.Tensor` to solve the following problem:
Left-multiplies MxM @ NxM. Returns NxM.
Here is the fu... | Left-multiplies MxM @ NxM. Returns NxM. |
9,316 | import torch
The provided code snippet includes necessary dependencies for implementing the `torch_dot` function. Write a Python function `def torch_dot(x: torch.Tensor, y: torch.Tensor)` to solve the following problem:
Dot product of two tensors.
Here is the function:
def torch_dot(x: torch.Tensor, y: torch.Tensor)... | Dot product of two tensors. |
9,317 | import torch
The provided code snippet includes necessary dependencies for implementing the `get_ray_limits_box` function. Write a Python function `def get_ray_limits_box(rays_o: torch.Tensor, rays_d: torch.Tensor, box_side_length)` to solve the following problem:
Author: Petr Kellnhofer Intersects rays with the [-1, ... | Author: Petr Kellnhofer Intersects rays with the [-1, 1] NDC volume. Returns min and max distance of entry. Returns -1 for no intersection. https://www.scratchapixel.com/lessons/3d-basic-rendering/minimal-ray-tracer-rendering-simple-shapes/ray-box-intersection |
9,318 | import torch
The provided code snippet includes necessary dependencies for implementing the `linspace` function. Write a Python function `def linspace(start: torch.Tensor, stop: torch.Tensor, num: int)` to solve the following problem:
Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from st... | Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive. Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch. |
9,319 | import numpy as np
import scipy.signal
import torch
from torch_utils import persistence
from torch_utils import misc
from torch_utils.ops import upfirdn2d
from torch_utils.ops import grid_sample_gradfix
from torch_utils.ops import conv2d_gradfix
def matrix(*rows, device=None):
assert all(len(row) == len(rows[0]) fo... | null |
9,320 | import numpy as np
import scipy.signal
import torch
from torch_utils import persistence
from torch_utils import misc
from torch_utils.ops import upfirdn2d
from torch_utils.ops import grid_sample_gradfix
from torch_utils.ops import conv2d_gradfix
def matrix(*rows, device=None):
assert all(len(row) == len(rows[0]) fo... | null |
9,321 | import numpy as np
import scipy.signal
import torch
from torch_utils import persistence
from torch_utils import misc
from torch_utils.ops import upfirdn2d
from torch_utils.ops import grid_sample_gradfix
from torch_utils.ops import conv2d_gradfix
def matrix(*rows, device=None):
assert all(len(row) == len(rows[0]) fo... | null |
9,322 | import numpy as np
import scipy.signal
import torch
from torch_utils import persistence
from torch_utils import misc
from torch_utils.ops import upfirdn2d
from torch_utils.ops import grid_sample_gradfix
from torch_utils.ops import conv2d_gradfix
def translate2d(tx, ty, **kwargs):
return matrix(
[1, 0, tx],
... | null |
9,323 | import numpy as np
import scipy.signal
import torch
from torch_utils import persistence
from torch_utils import misc
from torch_utils.ops import upfirdn2d
from torch_utils.ops import grid_sample_gradfix
from torch_utils.ops import conv2d_gradfix
def scale2d(sx, sy, **kwargs):
return matrix(
[sx, 0, 0],
... | null |
9,324 | import numpy as np
import scipy.signal
import torch
from torch_utils import persistence
from torch_utils import misc
from torch_utils.ops import upfirdn2d
from torch_utils.ops import grid_sample_gradfix
from torch_utils.ops import conv2d_gradfix
def rotate2d(theta, **kwargs):
def rotate2d_inv(theta, **kwargs):
ret... | null |
9,325 | import glob
import os
import re
import dnnlib
import imgui
import numpy as np
from gui_utils import imgui_utils
from . import renderer
def _locate_results(pattern):
return pattern | null |
9,326 | import sys
import copy
import traceback
import numpy as np
import torch
import torch.fft
import torch.nn
import matplotlib.cm
import dnnlib
from torch_utils.ops import upfirdn2d
import legacy
from camera_utils import LookAtPoseSampler
def _construct_affine_bandlimit_filter(mat, a=3, amax=16, aflt=64, up=4, cutoff_in=1,... | null |
9,327 | import time
import plyfile
import glob
import logging
import numpy as np
import os
import random
import torch
import torch.utils.data
import trimesh
import skimage.measure
import argparse
import mrcfile
from tqdm import tqdm
def convert_sdf_samples_to_ply(
numpy_3d_sdf_tensor,
voxel_grid_origin,
voxel_size,... | null |
9,328 | import functools
from typing import Optional
import dnnlib
import numpy as np
import PIL.Image
import PIL.ImageFont
import scipy.ndimage
from . import gl_utils
def get_array(string, *, dropshadow_radius: int=None, **kwargs):
if dropshadow_radius is not None:
offset_x = int(np.ceil(dropshadow_radius*2/3))
... | null |
9,329 | import contextlib
import imgui
def set_default_style(color_scheme='dark', spacing=9, indent=23, scrollbar=27):
s = imgui.get_style()
s.window_padding = [spacing, spacing]
s.item_spacing = [spacing, spacing]
s.item_inner_spacing = [spacing, spacing]
s.columns_min_spacing = spaci... | null |
9,330 | import contextlib
import imgui
def scoped_by_object_id(method):
def decorator(self, *args, **kwargs):
imgui.push_id(str(id(self)))
res = method(self, *args, **kwargs)
imgui.pop_id()
return res
return decorator | null |
9,331 | import contextlib
import imgui
def grayed_out(cond=True):
if cond:
s = imgui.get_style()
text = s.colors[imgui.COLOR_TEXT_DISABLED]
grab = s.colors[imgui.COLOR_SCROLLBAR_GRAB]
back = s.colors[imgui.COLOR_MENUBAR_BACKGROUND]
imgui.push_style_color(imgui.COLOR_TEXT, *text)
... | null |
9,332 | import contextlib
import imgui
def button(label, width=0, enabled=True):
def popup_button(label, width=0, enabled=True):
if button(label, width, enabled):
imgui.open_popup(label)
opened = imgui.begin_popup(label)
return opened | null |
9,333 | import contextlib
import imgui
def item_width(width=None):
if width is not None:
imgui.push_item_width(width)
yield
imgui.pop_item_width()
else:
yield
def input_text(label, value, buffer_length, flags, width=None, help_text=''):
old_value = value
color = list(imgui.get_s... | null |
9,334 | import contextlib
import imgui
def button(label, width=0, enabled=True):
with grayed_out(not enabled):
clicked = imgui.button(label, width=width)
clicked = clicked and enabled
return clicked
def drag_previous_control(enabled=True):
dragging = False
dx = 0
dy = 0
if imgui.begin_drag_d... | null |
9,335 | import contextlib
import imgui
def drag_previous_control(enabled=True):
dragging = False
dx = 0
dy = 0
if imgui.begin_drag_drop_source(imgui.DRAG_DROP_SOURCE_NO_PREVIEW_TOOLTIP):
if enabled:
dragging = True
dx, dy = imgui.get_mouse_drag_delta()
imgui.reset_mou... | null |
9,336 | import os
import functools
import contextlib
import numpy as np
import OpenGL.GL as gl
import OpenGL.GL.ARB.texture_float
import dnnlib
def init_egl():
assert os.environ['PYOPENGL_PLATFORM'] == 'egl' # Must be set before importing OpenGL.
import OpenGL.EGL as egl
import ctypes
# Initialize EGL.
di... | null |
9,337 | import os
import functools
import contextlib
import numpy as np
import OpenGL.GL as gl
import OpenGL.GL.ARB.texture_float
import dnnlib
def get_texture_format(dtype, channels):
return _texture_formats[(np.dtype(dtype).name, int(channels))]
def prepare_texture_data(image):
image = np.asarray(image)
if image.... | null |
9,338 | import os
import functools
import contextlib
import numpy as np
import OpenGL.GL as gl
import OpenGL.GL.ARB.texture_float
import dnnlib
def get_texture_format(dtype, channels):
return _texture_formats[(np.dtype(dtype).name, int(channels))]
def read_pixels(width, height, *, pos=0, dtype='uint8', channels=3):
po... | null |
9,339 | import os
import functools
import contextlib
import numpy as np
import OpenGL.GL as gl
import OpenGL.GL.ARB.texture_float
import dnnlib
def draw_shape(vertices, *, mode=gl.GL_TRIANGLE_FAN, pos=0, size=1, color=1, alpha=1):
assert vertices.ndim == 2 and vertices.shape[1] == 2
pos = np.broadcast_to(np.asarray(pos... | null |
9,340 | import os
import functools
import contextlib
import numpy as np
import OpenGL.GL as gl
import OpenGL.GL.ARB.texture_float
import dnnlib
def draw_shape(vertices, *, mode=gl.GL_TRIANGLE_FAN, pos=0, size=1, color=1, alpha=1):
def _setup_circle(hole):
def draw_circle(*, center=0, radius=100, hole=0, color=1, alpha=1):
... | null |
9,341 | import re
import numpy as np
import torch
import dnnlib
from . import misc
_num_moments = 3
_counter_dtype = torch.float64
_sync_device = None
_sync_called = False
_counters = dict()
_cumulative = dict()
The provided code snippet includes necessary de... | r"""Synchronize the global cumulative counters across devices and processes. Called internally by `Collector.update()`. |
9,342 | import sys
import pickle
import io
import inspect
import copy
import uuid
import types
import dnnlib
_import_hooks = []
The provided code snippet includes necessary dependencies for implementing the `import_hook` function. Write a Python function `def import_hook(hook)` to solve the following problem:
r"""Regist... | r"""Register an import hook that is called whenever a persistent object is being unpickled. A typical use case is to patch the pickled source code to avoid errors and inconsistencies when the API of some imported module has changed. The hook should have the following signature: hook(meta) -> modified meta `meta` is an ... |
9,343 | import torch
def _should_use_custom_op():
class _GridSample2dForward(torch.autograd.Function):
def forward(ctx, input, grid):
def backward(ctx, grad_output):
def grid_sample(input, grid):
if _should_use_custom_op():
return _GridSample2dForward.apply(input, grid)
return torch.nn.functional.gri... | null |
9,344 | import os
import numpy as np
import torch
from .. import custom_ops
from .. import misc
from . import conv2d_gradfix
The provided code snippet includes necessary dependencies for implementing the `setup_filter` function. Write a Python function `def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filt... | r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`. Args: f: Torch tensor, numpy array, or python list of the shape `[filter_height, filter_width]` (non-separable), `[filter_taps]` (separable), `[]` (impulse), or `None` (identity). device: Result device (default: cpu). normalize: Normalize the filter so ... |
9,345 | import contextlib
import torch
weight_gradients_disabled = False
def no_weight_gradients(disable=True):
global weight_gradients_disabled
old = weight_gradients_disabled
if disable:
weight_gradients_disabled = True
yield
weight_gradients_disabled = old | null |
9,346 | import torch
def _unbroadcast(x, shape):
extra_dims = x.ndim - len(shape)
assert extra_dims >= 0
dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)]
if len(dim):
x = x.sum(dim=dim, keepdim=True)
if extra_dims:
x = x.reshape(-1, *x.sh... | null |
9,347 | import re
import contextlib
import numpy as np
import torch
import warnings
import dnnlib
def profiled_function(fn):
def decorator(*args, **kwargs):
with torch.autograd.profiler.record_function(fn.__name__):
return fn(*args, **kwargs)
decorator.__name__ = fn.__name__
return decorator | null |
9,348 | import re
import contextlib
import numpy as np
import torch
import warnings
import dnnlib
def ddp_sync(module, sync):
assert isinstance(module, torch.nn.Module)
if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
yield
else:
with module.no_sync():
yield | null |
9,349 | import functools
import gzip
import io
import json
import os
import pickle
import re
import sys
import tarfile
import zipfile
from pathlib import Path
from typing import Callable, Optional, Tuple, Union
import click
import numpy as np
import PIL.Image
from tqdm import tqdm
The provided code snippet includes necessary ... | Parse a 'M,N' or 'MxN' integer tuple. Example: '4x2' returns (4,2) '0,1' returns (0,1) |
9,350 | import functools
import gzip
import io
import json
import os
import pickle
import re
import sys
import tarfile
import zipfile
from pathlib import Path
from typing import Callable, Optional, Tuple, Union
import click
import numpy as np
import PIL.Image
from tqdm import tqdm
def error(msg):
print('Error: ' + msg)
... | Convert an image dataset into a dataset archive usable with StyleGAN2 ADA PyTorch. The input dataset format is guessed from the --source argument: \b --source *_lmdb/ Load LSUN dataset --source cifar-10-python.tar.gz Load CIFAR-10 dataset --source train-images-idx3-ubyte.gz Load MNIST dataset --source path/ Recursively... |
9,351 | import json
import numpy as np
import os
from tqdm import tqdm
import argparse
def list_recursive(folderpath):
return [os.path.join(folderpath, filename) for filename in os.listdir(folderpath)] | null |
9,352 | import json
import numpy as np
from PIL import Image, ImageOps
import os
from tqdm import tqdm
import argparse
def gen_pose(rot_mat):
rot_mat = np.array(rot_mat).copy()
forward = rot_mat[:, 2]
translation = forward * -2.7
pose = np.array([
[rot_mat[0, 0], rot_mat[0, 1], rot_mat[0, 2], translati... | null |
9,353 | import json
import numpy as np
from PIL import Image, ImageOps
import os
from tqdm import tqdm
import argparse
def flip_yaw(pose_matrix):
flipped = pose_matrix.copy()
flipped[0, 1] *= -1
flipped[0, 2] *= -1
flipped[1, 0] *= -1
flipped[2, 0] *= -1
flipped[0, 3] *= -1
return flipped | null |
9,355 | import os
import sys
import requests
import html
import hashlib
import PIL.Image
import PIL.ImageFile
import numpy as np
import scipy.ndimage
import threading
import queue
import time
import json
import uuid
import glob
import argparse
import itertools
import shutil
from collections import OrderedDict, defaultdict
impo... | null |
9,356 | import multiprocessing
import os
import re
import sys
import requests
import html
import hashlib
import PIL.Image
import PIL.ImageFile
import numpy as np
import scipy.ndimage
import threading
import queue
import time
import json
import uuid
import glob
import argparse
import itertools
import shutil
from collections imp... | null |
9,357 | import json
import numpy as np
from PIL import Image, ImageOps
import os
from tqdm import tqdm
import argparse
import torch
import sys
from camera_utils import create_cam2world_matrix
def fix_intrinsics(intrinsics):
intrinsics = np.array(intrinsics).copy()
assert intrinsics.shape == (3, 3), intrinsics
intr... | null |
9,358 | import json
import numpy as np
from PIL import Image, ImageOps
import os
from tqdm import tqdm
import argparse
import torch
import sys
from camera_utils import create_cam2world_matrix
def fix_pose(pose):
COR = np.array([0, 0, 0.175])
pose = np.array(pose).copy()
location = pose[:3, 3]
direction = (loca... | null |
9,359 | import json
import numpy as np
from PIL import Image, ImageOps
import os
from tqdm import tqdm
import argparse
import torch
import sys
from camera_utils import create_cam2world_matrix
def fix_pose_orig(pose):
pose = np.array(pose).copy()
location = pose[:3, 3]
radius = np.linalg.norm(location)
pose[:3,... | null |
9,360 | import json
import numpy as np
from PIL import Image, ImageOps
import os
from tqdm import tqdm
import argparse
import torch
import sys
from camera_utils import create_cam2world_matrix
def create_cam2world_matrix(forward_vector, origin):
def fix_pose_simplify(pose):
cam_location = torch.tensor(pose).clone()[:3, 3]... | null |
9,361 | import json
import numpy as np
from PIL import Image, ImageOps
import os
from tqdm import tqdm
import argparse
import torch
import sys
from camera_utils import create_cam2world_matrix
def flip_yaw(pose_matrix):
flipped = pose_matrix.copy()
flipped[0, 1] *= -1
flipped[0, 2] *= -1
flipped[1, 0] *= -1
... | null |
9,362 | from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
model = c... | null |
9,363 | from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
def crea... | null |
9,364 | from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.canny import CannyDetector
from cldm.model import create_model, load_state_dict
from cld... | null |
9,365 | from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.hed import HEDdetector, nms
from cldm.model import create_model, load_state_dict
from cl... | null |
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