id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
9,162 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import math
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autogr... | null |
9,163 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import math
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autogr... | null |
9,164 | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import distributed
from torch.nn import init
from torch.nn.parameter import Parameter
from torch.autograd import Function
from micronet.base_module.op import *
def reshape_to_activation(input):
return input.reshape(1, -1, 1, 1... | null |
9,165 | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import distributed
from torch.nn import init
from torch.nn.parameter import Parameter
from torch.autograd import Function
from micronet.base_module.op import *
def reshape_to_activation(input):
return input.reshape(1, -1, 1, 1... | null |
9,166 | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import distributed
from torch.nn import init
from torch.nn.parameter import Parameter
from torch.autograd import Function
from micronet.base_module.op import *
def reshape_to_activation(input):
def reshape_to_weight(input):
def re... | null |
9,167 | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import distributed
from torch.nn import init
from torch.nn.parameter import Parameter
from torch.autograd import Function
from micronet.base_module.op import *
def reshape_to_activation(input):
return input.reshape(1, -1, 1, 1... | null |
9,168 | import copy
import sys
import os
import argparse
import numpy as np
import torch
import torch.nn as nn
from models import nin_gc, nin
import quantize
def bn_fuse_module(module):
for name, child in module.named_children():
if isinstance(child, quantize.QuantBNFuseConv2d):
bn_fused_conv = bn_fuse(... | null |
9,169 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import math
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autogr... | null |
9,170 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import math
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autogr... | null |
9,171 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import math
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autogr... | null |
9,172 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import math
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autogr... | null |
9,173 | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
def __init__(self, a_bits):
def prepare(model, inplace=False, a_bits=8, w_bits=8, quant_inference=False):
if not inplace:
model = copy.deepcopy(model)
layer_counter = [0]
add_quant... | null |
9,177 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import math
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autogr... | null |
9,178 | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
def __init__(self, A=2):
for name, child in module.named_children():
if isinstance(child, nn.Conv2d):
layer_counter[0] += 1
if layer_counter[0] > 1 and layer_counter... | null |
9,179 | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
def __init__(self, A=2):
for name, child in module.named_children():
if isinstance(child, nn.Conv2d):
layer_counter[0] += 1
if layer_counter[0] > 1 and layer_counter... | null |
9,180 | import copy
import sys
import os
import argparse
import numpy as np
import torch
import torch.nn as nn
from models import nin_gc, nin
import quantize
def bn_fuse_module(module):
for name, child in module.named_children():
if isinstance(child, nn.Conv2d):
conv_name_temp = name
conv_ch... | null |
9,181 | import torch.nn as nn
The provided code snippet includes necessary dependencies for implementing the `channel_shuffle` function. Write a Python function `def channel_shuffle(x, groups)` to solve the following problem:
shuffle channels of a 4-D Tensor
Here is the function:
def channel_shuffle(x, groups):
"""shuff... | shuffle channels of a 4-D Tensor |
9,182 | import torch
import torch.nn as nn
from micronet.base_module.op import *
class BasicBlock(nn.Module):
"""Basic Block for resnet 18 and resnet 34"""
# BasicBlock and BottleNeck block
# have different output size
# we use class attribute expansion
# to distinct
expansion = 1
def __init__(self,... | return a ResNet 18 object |
9,183 | import torch
import torch.nn as nn
from micronet.base_module.op import *
class BasicBlock(nn.Module):
"""Basic Block for resnet 18 and resnet 34"""
# BasicBlock and BottleNeck block
# have different output size
# we use class attribute expansion
# to distinct
expansion = 1
def __init__(self,... | return a ResNet 34 object |
9,184 | import torch
import torch.nn as nn
from micronet.base_module.op import *
class BottleNeck(nn.Module):
"""Residual block for resnet over 50 layers"""
expansion = 4
def __init__(self, in_channels, out_channels, stride=1):
super().__init__()
self.residual_function = nn.Sequential(
n... | return a ResNet 50 object |
9,185 | import torch
import torch.nn as nn
from micronet.base_module.op import *
class BottleNeck(nn.Module):
"""Residual block for resnet over 50 layers"""
expansion = 4
def __init__(self, in_channels, out_channels, stride=1):
super().__init__()
self.residual_function = nn.Sequential(
n... | return a ResNet 101 object |
9,186 | import torch
import torch.nn as nn
from micronet.base_module.op import *
class BottleNeck(nn.Module):
"""Residual block for resnet over 50 layers"""
expansion = 4
def __init__(self, in_channels, out_channels, stride=1):
super().__init__()
self.residual_function = nn.Sequential(
n... | return a ResNet 152 object |
9,187 | import argparse
import os
import sys
import tempfile
import threading
import webbrowser
import time
import gradio as gr
import librosa.display
import numpy as np
import os
import torch
import torchaudio
import traceback
from utils.formatter import format_audio_list
from utils.cfg import TTSMODEL_DIR
from TTS.demos.xtts... | null |
9,188 | import argparse
import os
import sys
import tempfile
import threading
import webbrowser
import time
import gradio as gr
import librosa.display
import numpy as np
import os
import torch
import torchaudio
import traceback
from utils.formatter import format_audio_list
from utils.cfg import TTSMODEL_DIR
from TTS.demos.xtts... | null |
9,189 | import argparse
import os
import sys
import tempfile
import threading
import webbrowser
import time
import gradio as gr
import librosa.display
import numpy as np
import os
import torch
import torchaudio
import traceback
from utils.formatter import format_audio_list
from utils.cfg import TTSMODEL_DIR
from TTS.demos.xtts... | null |
9,190 | import argparse
import os
import sys
import tempfile
import threading
import webbrowser
import time
import gradio as gr
import librosa.display
import numpy as np
import os
import torch
import torchaudio
import traceback
from utils.formatter import format_audio_list
from utils.cfg import TTSMODEL_DIR
from TTS.demos.xtts... | null |
9,191 | import argparse
import os
import sys
import tempfile
import threading
import webbrowser
import time
import gradio as gr
import librosa.display
import numpy as np
import os
import torch
import torchaudio
import traceback
from utils.formatter import format_audio_list
from utils.cfg import TTSMODEL_DIR
from TTS.demos.xtts... | null |
9,192 | import argparse
import os
import sys
import tempfile
import threading
import webbrowser
import time
import gradio as gr
import librosa.display
import numpy as np
import os
import torch
import torchaudio
import traceback
from utils.formatter import format_audio_list
from utils.cfg import TTSMODEL_DIR
from TTS.demos.xtts... | null |
9,193 | import datetime
import logging
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
from gevent.pywsgi import WSGIServer, WSGIHandler
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone... | null |
9,194 | import datetime
import logging
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
from gevent.pywsgi import WSGIServer, WSGIHandler
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone... | null |
9,195 | import datetime
import logging
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
from gevent.pywsgi import WSGIServer, WSGIHandler
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone... | null |
9,196 | import datetime
import logging
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
from gevent.pywsgi import WSGIServer, WSGIHandler
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone... | null |
9,197 | import datetime
import logging
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
from gevent.pywsgi import WSGIServer, WSGIHandler
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone... | null |
9,198 | import datetime
import logging
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
from gevent.pywsgi import WSGIServer, WSGIHandler
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone... | null |
9,199 | import datetime
import logging
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
from gevent.pywsgi import WSGIServer, WSGIHandler
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone... | audio:原始声音wav,作为音色克隆源 voice:已有的声音名字，如果存在 voice则先使用，否则使用audio text:文字一行 language：语言代码 Returns: |
9,200 | import datetime
import logging
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
from gevent.pywsgi import WSGIServer, WSGIHandler
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone... | null |
9,201 | import datetime
import logging
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
from gevent.pywsgi import WSGIServer, WSGIHandler
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone... | null |
9,202 | import datetime
import logging
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
from gevent.pywsgi import WSGIServer, WSGIHandler
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone... | null |
9,203 | import datetime
import logging
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
from gevent.pywsgi import WSGIServer, WSGIHandler
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone... | null |
9,204 | import datetime
import logging
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
from gevent.pywsgi import WSGIServer, WSGIHandler
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone... | null |
9,205 | import argparse
import os
import sys
import tempfile
import gradio as gr
import librosa.display
import numpy as np
import os
import torch
import torchaudio
import traceback
from TTS.demos.xtts_ft_demo.utils.formatter import format_audio_list
from TTS.demos.xtts_ft_demo.utils.gpt_train import train_gpt
from TTS.demos.xt... | null |
9,206 | import argparse
import os
import sys
import tempfile
import gradio as gr
import librosa.display
import numpy as np
import os
import torch
import torchaudio
import traceback
from TTS.demos.xtts_ft_demo.utils.formatter import format_audio_list
from TTS.demos.xtts_ft_demo.utils.gpt_train import train_gpt
from TTS.demos.xt... | null |
9,207 | import argparse
import os
import sys
import tempfile
import gradio as gr
import librosa.display
import numpy as np
import os
import torch
import torchaudio
import traceback
from TTS.demos.xtts_ft_demo.utils.formatter import format_audio_list
from TTS.demos.xtts_ft_demo.utils.gpt_train import train_gpt
from TTS.demos.xt... | null |
9,208 | import datetime
import logging
import queue
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone
from clone import cfg
from clone.cfg... | null |
9,209 | import datetime
import logging
import queue
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone
from clone import cfg
from clone.cfg... | null |
9,210 | import datetime
import logging
import queue
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone
from clone import cfg
from clone.cfg... | null |
9,211 | import datetime
import logging
import queue
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone
from clone import cfg
from clone.cfg... | null |
9,212 | import datetime
import logging
import queue
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone
from clone import cfg
from clone.cfg... | null |
9,213 | import datetime
import logging
import queue
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone
from clone import cfg
from clone.cfg... | audio:原始声音wav,作为音色克隆源 voice:已有的声音名字，如果存在 voice则先使用，否则使用audio text:文字一行 language：语言代码 Returns: |
9,214 | import datetime
import logging
import queue
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone
from clone import cfg
from clone.cfg... | null |
9,215 | import datetime
import logging
import queue
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone
from clone import cfg
from clone.cfg... | null |
9,216 | import datetime
import logging
import queue
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone
from clone import cfg
from clone.cfg... | null |
9,217 | import datetime
import logging
import queue
import re
import threading
import time
import sys
from flask import Flask, request, render_template, jsonify, send_file, send_from_directory
import os
import glob
import hashlib
from logging.handlers import RotatingFileHandler
import clone
from clone import cfg
from clone.cfg... | null |
9,218 | import hashlib
import json
import os
import re
import shutil
import tempfile
import threading
import time
import webbrowser
import aiohttp
import requests
import torch
import torchaudio
from pydub import AudioSegment
import clone
from clone import cfg
from clone.cfg import langlist
from TTS.api import TTS
from TTS.tts.... | null |
9,219 | import hashlib
import json
import os
import re
import shutil
import tempfile
import threading
import time
import webbrowser
import aiohttp
import requests
import torch
import torchaudio
from pydub import AudioSegment
import clone
from clone import cfg
from clone.cfg import langlist
from TTS.api import TTS
from TTS.tts.... | null |
9,220 | import hashlib
import json
import os
import re
import shutil
import tempfile
import threading
import time
import webbrowser
import aiohttp
import requests
import torch
import torchaudio
from pydub import AudioSegment
import clone
from clone import cfg
from clone.cfg import langlist
from TTS.api import TTS
from TTS.tts.... | null |
9,221 | import hashlib
import json
import os
import re
import shutil
import tempfile
import threading
import time
import webbrowser
import aiohttp
import requests
import torch
import torchaudio
from pydub import AudioSegment
import clone
from clone import cfg
from clone.cfg import langlist
from TTS.api import TTS
from TTS.tts.... | null |
9,222 | import hashlib
import json
import os
import re
import shutil
import tempfile
import threading
import time
import webbrowser
import aiohttp
import requests
import torch
import torchaudio
from pydub import AudioSegment
import clone
from clone import cfg
from clone.cfg import langlist
from TTS.api import TTS
from TTS.tts.... | null |
9,223 | import hashlib
import json
import os
import re
import shutil
import tempfile
import threading
import time
import webbrowser
import aiohttp
import requests
import torch
import torchaudio
from pydub import AudioSegment
import clone
from clone import cfg
from clone.cfg import langlist
from TTS.api import TTS
from TTS.tts.... | null |
9,224 | import locale
import os
import queue
import re
import sys
import threading
import torch
from dotenv import load_dotenv
os.environ['TTS_HOME'] = ROOT_DIR
if os.path.exists(os.path.join(ROOT_DIR, "tts/tts_models--multilingual--multi-dataset--xtts_v2/model.pth")):
if not os.path.exists(VOICE_DIR):
os.makedirs(VOICE_DI... | null |
9,225 | import torch
import os
rootdir=os.getcwd()
os.environ['TTS_HOME']=rootdir
from TTS.api import TTS
from dotenv import load_dotenv
def updatecache():
# 禁止更新，避免无代理时报错
file=os.path.join(rootdir,'tts_cache/cache')
if file:
import json,time
j=json.load(open(file,'r',encoding='utf-8'))
for... | null |
9,226 | import os
import gc
import torchaudio
import pandas
from faster_whisper import WhisperModel
from glob import glob
from tqdm import tqdm
import torch
import torchaudio
from TTS.tts.layers.xtts.tokenizer import multilingual_cleaners
import os
from .cfg import FASTERMODEL_DIR,ZH_PROMPT
audio_types = (".wav", ".mp3", ".fla... | null |
9,227 | from setuptools import find_packages
import subprocess
from difflib import get_close_matches
from glob import glob
import os
import platform
from distutils.core import setup, Extension
from pathlib import Path
def find_pkg_dirs(package):
close_matches = get_close_matches(package.lower(),
... | null |
9,228 | from setuptools import find_packages
import subprocess
from difflib import get_close_matches
from glob import glob
import os
import platform
from distutils.core import setup, Extension
from pathlib import Path
def pkgconfig(package, kw):
flag_map = {'-I': 'include_dirs', '-L': 'library_dirs', '-l': 'libraries'}
... | null |
9,229 | from argparse import ArgumentParser
from typing import List
import time
import numpy as np
from tqdm import tqdm
import torch as ch
from torch.cuda.amp import GradScaler, autocast
from torch.nn import CrossEntropyLoss, Conv2d, BatchNorm2d
from torch.optim import SGD, lr_scheduler
import torchvision
from fastargs import... | null |
9,230 | from argparse import ArgumentParser
from typing import List
import time
import numpy as np
from tqdm import tqdm
import torch as ch
from torch.cuda.amp import GradScaler, autocast
from torch.nn import CrossEntropyLoss, Conv2d, BatchNorm2d
from torch.optim import SGD, lr_scheduler
import torchvision
from fastargs import... | null |
9,231 | from argparse import ArgumentParser
from typing import List
import time
import numpy as np
from tqdm import tqdm
import torch as ch
from torch.cuda.amp import GradScaler, autocast
from torch.nn import CrossEntropyLoss, Conv2d, BatchNorm2d
from torch.optim import SGD, lr_scheduler
import torchvision
from fastargs import... | null |
9,232 | from argparse import ArgumentParser
from typing import List
import time
import numpy as np
from tqdm import tqdm
import torch as ch
from torch.cuda.amp import GradScaler, autocast
from torch.nn import CrossEntropyLoss, Conv2d, BatchNorm2d
from torch.optim import SGD, lr_scheduler
import torchvision
from fastargs import... | null |
9,233 | from tqdm import tqdm
import time
import numpy as np
import pickle as pkl
import torch as ch
from torch.utils.data import TensorDataset, DataLoader
from ffcv.fields import NDArrayField, FloatField
from ffcv.fields.basics import FloatDecoder
from ffcv.fields.decoders import NDArrayDecoder
from ffcv.loader import Loader,... | null |
9,234 | from functools import partial
from typing import Callable, List, Mapping
from os import SEEK_END, path
import numpy as np
from time import sleep
import ctypes
from multiprocessing import (shared_memory, cpu_count, Queue, Process, Value)
from tqdm import tqdm
from tqdm.contrib.concurrent import thread_map
from .utils im... | null |
9,235 | from functools import partial
from typing import Callable, List, Mapping
from os import SEEK_END, path
import numpy as np
from time import sleep
import ctypes
from multiprocessing import (shared_memory, cpu_count, Queue, Process, Value)
from tqdm import tqdm
from tqdm.contrib.concurrent import thread_map
from .utils im... | null |
9,236 | from functools import partial
from typing import Callable, List, Mapping
from os import SEEK_END, path
import numpy as np
from time import sleep
import ctypes
from multiprocessing import (shared_memory, cpu_count, Queue, Process, Value)
from tqdm import tqdm
from tqdm.contrib.concurrent import thread_map
from .utils im... | null |
9,237 | import numpy as np
from numba import types
from numba.extending import intrinsic
from threading import Lock
def chunks(lst, n):
for i in range(0, len(lst), n):
yield lst[i:i + n] | null |
9,238 | import numpy as np
from numba import types
from numba.extending import intrinsic
from threading import Lock
def is_power_of_2(n):
return (n & (n-1) == 0) and n != 0 | null |
9,239 | import numpy as np
from numba import types
from numba.extending import intrinsic
from threading import Lock
def align_to_page(ptr, page_size):
# If we are not aligned with the start of a page:
if ptr % page_size != 0:
ptr = ptr + page_size - ptr % page_size
return ptr | null |
9,240 | import numpy as np
from numba import types
from numba.extending import intrinsic
from threading import Lock
def decode_null_terminated_string(bytes: np.ndarray):
return bytes.tobytes().decode('ascii').split('\x00')[0] | null |
9,241 | import numpy as np
from numba import types
from numba.extending import intrinsic
from threading import Lock
def cast_int_to_byte_ptr(typingctx, src):
# check for accepted types
if isinstance(src, types.Integer):
# create the expected type signature
result_type = types.CPointer(types.uint8)
... | null |
9,242 | import numpy as np
from numba import types
from numba.extending import intrinsic
from threading import Lock
s_print_lock = Lock()
The provided code snippet includes necessary dependencies for implementing the `s_print` function. Write a Python function `def s_print(*a, **b)` to solve the following problem:
Thread safe... | Thread safe print function |
9,243 | import random
from typing import Sequence, TYPE_CHECKING
from numba import njit
import numpy as np
from torch.utils.data import DistributedSampler
from .base import TraversalOrder
def generate_order_inner(seed, page_to_samples_array, page_sizes,
result, buffer_size=6):
num_pages = len(page... | null |
9,244 | from typing import List
import numpy as np
from .fields.base import Field
from .fields import (
FloatField, IntField, RGBImageField,
BytesField, NDArrayField, JSONField,
TorchTensorField
)
TYPE_ID_HANDLER = {
255 : None,
0 : FloatField,
1 : IntField,
2 : RGBImageField,
3 : BytesF... | null |
9,245 | from typing import List
import numpy as np
from .fields.base import Field
from .fields import (
FloatField, IntField, RGBImageField,
BytesField, NDArrayField, JSONField,
TorchTensorField
)
class Field(ABC):
"""
Abstract Base Class for implementing fields (e.g., images, integers).
Each dataset ... | null |
9,246 | from itertools import product
from time import time
from collections import defaultdict
from contextlib import redirect_stderr
import pathlib
import numpy as np
from tqdm import tqdm
from .benchmark import Benchmark
ALL_SUITES = {}
def benchmark(arg_values={}):
args_list = product(*arg_values.values())
runs = [... | null |
9,247 | from abc import ABCMeta, abstractmethod
from dataclasses import replace
from typing import Optional, Callable, TYPE_CHECKING, Tuple, Type
import cv2
import numpy as np
from numba.typed import Dict
from PIL.Image import Image
from .base import Field, ARG_TYPE
from ..pipeline.operation import Operation
from ..pipeline.st... | null |
9,248 | from abc import ABCMeta, abstractmethod
from dataclasses import replace
from typing import Optional, Callable, TYPE_CHECKING, Tuple, Type
import cv2
import numpy as np
from numba.typed import Dict
from PIL.Image import Image
from .base import Field, ARG_TYPE
from ..pipeline.operation import Operation
from ..pipeline.st... | null |
9,249 | from abc import ABCMeta, abstractmethod
from dataclasses import replace
from typing import Optional, Callable, TYPE_CHECKING, Tuple, Type
import cv2
import numpy as np
from numba.typed import Dict
from PIL.Image import Image
from .base import Field, ARG_TYPE
from ..pipeline.operation import Operation
from ..pipeline.st... | null |
9,250 | from abc import ABCMeta, abstractmethod
from dataclasses import replace
from typing import Optional, Callable, TYPE_CHECKING, Tuple, Type
import cv2
import numpy as np
from numba.typed import Dict
from PIL.Image import Image
from .base import Field, ARG_TYPE
from ..pipeline.operation import Operation
from ..pipeline.st... | null |
9,251 | from collections import defaultdict
from dataclasses import dataclass
from typing import Mapping
from queue import Queue
import numpy as np
from .page_reader import PageReader
class Schedule:
# Number of slots needed
num_slots: int
# Which slot to use for each page
page_to_slot: Mapping[int, int]
# ... | null |
9,252 | from collections import defaultdict
from threading import Thread, Event
from queue import Queue, Full
from contextlib import nullcontext
from typing import Sequence, TYPE_CHECKING
import torch as ch
from ..traversal_order.quasi_random import QuasiRandom
from ..utils import chunks
from ..pipeline.compiler import Compile... | Util function to select the relevent subpart of a buffer for a given batch_slot and batch size |
9,253 | from typing import Optional, Sequence, Tuple, Union
from dataclasses import dataclass
import numpy as np
import torch as ch
class AllocationQuery:
shape: Tuple[int, ...]
dtype: Union[np.dtype, ch.dtype]
device: Optional[ch.device] = None
def allocate_query(memory_allocation: AllocationQuery, batch_size: in... | null |
9,254 | import ctypes
from numba import njit
import numpy as np
import platform
from ctypes import CDLL, c_int64, c_uint8, c_uint64, POINTER, c_void_p, c_uint32, c_bool, cdll
import ffcv._libffcv
read_c.argtypes = [c_uint32, c_void_p, c_uint64, c_uint64]
def read(fileno:int, destination:np.ndarray, offset:int):
return rea... | null |
9,255 | import ctypes
from numba import njit
import numpy as np
import platform
from ctypes import CDLL, c_int64, c_uint8, c_uint64, POINTER, c_void_p, c_uint32, c_bool, cdll
import ffcv._libffcv
ctypes_resize = lib.resize
ctypes_resize.argtypes = 11 * [c_int64]
def resize_crop(source, start_row, end_row, start_col, end_col, ... | null |
9,256 | import ctypes
from numba import njit
import numpy as np
import platform
from ctypes import CDLL, c_int64, c_uint8, c_uint64, POINTER, c_void_p, c_uint32, c_bool, cdll
import ffcv._libffcv
ctypes_imdecode = lib.imdecode
ctypes_imdecode.argtypes = [
c_void_p, c_uint64, c_uint32, c_uint32, c_void_p, c_uint32, c_uint32... | null |
9,257 | import ctypes
from numba import njit
import numpy as np
import platform
from ctypes import CDLL, c_int64, c_uint8, c_uint64, POINTER, c_void_p, c_uint32, c_bool, cdll
import ffcv._libffcv
ctypes_memcopy = lib.my_memcpy
ctypes_memcopy.argtypes = [c_void_p, c_void_p, c_uint64]
def memcpy(source: np.ndarray, dest: np.nda... | null |
9,258 | from collections.abc import Sequence
from typing import Tuple
import numpy as np
import torch as ch
from numpy import dtype
from numpy.random import rand
from dataclasses import replace
from typing import Callable, Optional, Tuple
from ..pipeline.allocation_query import AllocationQuery
from ..pipeline.operation import ... | null |
9,259 | import ctypes
from numba import njit
import numpy as np
from ...libffcv import ctypes_resize
ctypes_resize = lib.resize
ctypes_resize.argtypes = 11 * [c_int64]
def resize_crop(source, start_row, end_row, start_col, end_col, destination):
ctypes_resize(0,
source.ctypes.data,
sou... | null |
9,260 | import ctypes
from numba import njit
import numpy as np
from ...libffcv import ctypes_resize
def get_random_crop(height, width, scale, ratio):
area = height * width
log_ratio = np.log(ratio)
for _ in range(10):
target_area = area * np.random.uniform(scale[0], scale[1])
aspect_ratio = np.exp... | null |
9,261 | import ctypes
from numba import njit
import numpy as np
from ...libffcv import ctypes_resize
def get_center_crop(height, width, ratio):
s = min(height, width)
c = int(ratio * s)
delta_h = (height - c) // 2
delta_w = (width - c) // 2
return delta_h, delta_w, c, c | null |
9,262 | import os
import time
import math
import pickle
from contextlib import nullcontext
import numpy as np
import torch
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed import init_process_group, destroy_process_group
from model import GPTConfig, GPT
eval_iters = 200
torch.manual_seed(1337... | null |
9,263 | import os
import time
import math
import pickle
from contextlib import nullcontext
import numpy as np
import torch
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed import init_process_group, destroy_process_group
from model import GPTConfig, GPT
learning_rate = 6e-4
warmup_iters = 200... | null |
9,264 | import os
from contextlib import nullcontext
import numpy as np
import time
import torch
from model import GPTConfig, GPT
batch_size = 12
block_size = 1024
device = 'cuda'
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
def get_ba... | null |
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