id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
7,449 | import requests
import re
import streamlit as st
from dataclasses import dataclass
from enum import auto, Enum
from PIL.Image import Image
from PIL import ImageDraw
from streamlit.delta_generator import DeltaGenerator
The provided code snippet includes necessary dependencies for implementing the `postprocess_image` fu... | Processes the given text to identify and draw bounding boxes on the provided image. This function searches for patterns in the text that represent coordinates for bounding boxes and draws rectangles on the image at these coordinates. Each box is drawn in a different color for distinction. Args: text (str): The text con... |
7,450 | import requests
import re
import streamlit as st
from dataclasses import dataclass
from enum import auto, Enum
from PIL.Image import Image
from PIL import ImageDraw
from streamlit.delta_generator import DeltaGenerator
The provided code snippet includes necessary dependencies for implementing the `translate_baidu` func... | Translates text using Baidu's translation service. (if you are not use English) This function sends a request to the Baidu translation API to translate the provided text from the source language to the target language. Args: translate_text (str): The text to be translated. source_lan (str): The source language code (e.... |
7,451 | import streamlit as st
import base64
import re
from PIL import Image
from io import BytesIO
from streamlit.delta_generator import DeltaGenerator
from client import get_client
from utils import images_are_same
from conversation import Conversation, Role, postprocess_image, postprocess_text
class Conversation:
"""
... | null |
7,452 | from io import BytesIO
import base64
import streamlit as st
import re
from streamlit.delta_generator import DeltaGenerator
from client import get_client
from conversation import postprocess_text, Conversation, Role, postprocess_image
from PIL import Image
from utils import images_are_same
class Conversation:
"""
... | null |
7,453 | from __future__ import annotations
from threading import Thread
import streamlit as st
import torch
import warnings
import os
from typing import Any, Protocol
from collections.abc import Iterable
from huggingface_hub.inference._text_generation import TextGenerationStreamResponse, Token
from transformers import AutoToke... | null |
7,454 | from __future__ import annotations
from threading import Thread
import streamlit as st
import torch
import warnings
import os
from typing import Any, Protocol
from collections.abc import Iterable
from huggingface_hub.inference._text_generation import TextGenerationStreamResponse, Token
from transformers import AutoToke... | Process the input history to extract the query and the history pairs. Args: History(list[Conversation]): A list of Conversation objects representing all conversations. Returns: query(str): The current user input string. history_pairs(list[(str,str)]): A list of (user, assistant) pairs. last_user_image(Image): The last ... |
7,456 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTrainCogVLMModel
from utils.utils import llama2_text_processor, llam... | null |
7,457 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTrainCogVLMModel
from utils.utils import llama2_text_processor, llam... | null |
7,458 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTrainCogVLMModel
from utils.utils import llama2_text_processor, llam... | null |
7,459 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTrainCogVLMModel
from utils.utils import llama2_text_processor, llam... | Forward step. |
7,460 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTrainCogVLMModel
from utils.utils import llama2_text_processor, llam... | null |
7,461 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTestCogVLMModel
from utils.utils import llama2_text_processor, llama... | null |
7,462 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTestCogVLMModel
from utils.utils import llama2_text_processor, llama... | null |
7,463 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTestCogVLMModel
from utils.utils import llama2_text_processor, llama... | Forward step. |
7,464 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTestCogVLMModel
from utils.utils import llama2_text_processor, llama... | null |
7,465 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTrainCogAgentModel
from utils.utils import llama2_text_processor, ll... | null |
7,466 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTrainCogAgentModel
from utils.utils import llama2_text_processor, ll... | null |
7,467 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTrainCogAgentModel
from utils.utils import llama2_text_processor, ll... | null |
7,468 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTrainCogAgentModel
from utils.utils import llama2_text_processor, ll... | Forward step. |
7,469 | import os
import torch
import argparse
from functools import partial
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from utils.models import FineTuneTrainCogAgentModel
from utils.utils import llama2_text_processor, ll... | null |
7,470 | import os
import torch
import argparse
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from collections import defaultdict
from functools import partial
from utils.models import FineTuneTestCogAgentModel
from utils.uti... | null |
7,471 | import os
import torch
import argparse
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from collections import defaultdict
from functools import partial
from utils.models import FineTuneTestCogAgentModel
from utils.uti... | null |
7,472 | import os
import torch
import argparse
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from collections import defaultdict
from functools import partial
from utils.models import FineTuneTestCogAgentModel
from utils.uti... | Forward step. |
7,473 | import os
import torch
import argparse
import sys
from sat import mpu, get_args, get_tokenizer
from sat.training.deepspeed_training import training_main
from sat.helpers import print_rank0
from collections import defaultdict
from functools import partial
from utils.models import FineTuneTestCogAgentModel
from utils.uti... | null |
7,474 | import os
import shutil
os.makedirs("archive_split", exist_ok=True)
os.makedirs("archive_split/train", exist_ok=True)
os.makedirs("archive_split/valid", exist_ok=True)
os.makedirs("archive_split/test", exist_ok=True)
import random
print("building train")
print("building valid")
print("building test")
print("done")
def... | null |
7,475 | from sat.model.official.llama_model import LLaMAModel
import json
import torch
from functools import partial
from sat.model.base_model import BaseMixin
import torch.nn as nn
import numpy as np
from sat.resources.urls import MODEL_URLS
from .eva_clip_L_hf import Eva2LargeEncoder
from .mixin import LlamaVisionExpertFCMix... | null |
7,476 | from math import pi
import torch
from torch import nn
from einops import rearrange, repeat
import logging
import torch.nn as nn
import os
from dataclasses import dataclass
from typing import Optional, Tuple, Union
from functools import partial
import numpy as np
import torch
import torch.nn.functional as F
from torch i... | null |
7,477 | from math import pi
import torch
from torch import nn
from einops import rearrange, repeat
import logging
import torch.nn as nn
import os
from dataclasses import dataclass
from typing import Optional, Tuple, Union
from functools import partial
import numpy as np
import torch
import torch.nn.functional as F
from torch i... | null |
7,478 | from math import pi
import torch
from torch import nn
from einops import rearrange, repeat
import logging
import torch.nn as nn
import os
from dataclasses import dataclass
from typing import Optional, Tuple, Union
from functools import partial
import numpy as np
import torch
import torch.nn.functional as F
from torch i... | null |
7,479 | from sat.model.official.llama_model import LLaMAModel
import json
import torch
from sat.model.base_model import BaseMixin
import torch.nn as nn
from .mixin import LlamaVisionExpertFCMixin, LlamaVisionExpertAttnMixin
from sat.resources.urls import MODEL_URLS
from .eva_clip_model import EVA2CLIPModel
import argparse
from... | null |
7,480 | from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
import torch
class BlipImageEvalProcessor:
def __init__(self, image_size=384, mean=None, std=None):
super().__init__()
if mean is None:
mean = (0.48145466, 0.4578275, 0.40821073)
if... | null |
7,481 | from transformers import LlamaTokenizer
import re
import numpy as np
import torch
from functools import partial
def base_history_to_prompt(self, query, history):
prompt = '<EOI>' + query
return prompt | null |
7,482 | from transformers import LlamaTokenizer
import re
import numpy as np
import torch
from functools import partial
def chat_history_to_prompt(self, query, history):
prompt = "<EOI> [INST] "
for i, (old_query, response) in enumerate(history):
prompt += old_query + " [/INST] " + response + " [INST] "
pr... | null |
7,483 | from transformers import LlamaTokenizer
import re
import numpy as np
import torch
from functools import partial
def vqa_history_to_prompt(self, query, history):
# Only support single round chat in vqa mode
prompt = "<EOI>Question: "
# for i, (old_query, response) in enumerate(history):
# prompt += ... | null |
7,484 | from transformers import LlamaTokenizer
import re
import numpy as np
import torch
from functools import partial
def chat_old_history_to_prompt(self, query, history):
prompt = "<EOI>Question: "
for i, (old_query, response) in enumerate(history):
prompt += old_query + " Answer: " + response + "\nQuestion... | null |
7,485 | from transformers import LlamaTokenizer
import re
import numpy as np
import torch
from functools import partial
def llama2_tokenizer(tokenizer_path, signal_type="base"):
tokenizer = LlamaTokenizer.from_pretrained(tokenizer_path)
if tokenizer.pad_token_id is None:
tokenizer.pad_token_id = 32000
toke... | null |
7,486 | from transformers import LlamaTokenizer
import re
import numpy as np
import torch
from functools import partial
def get_masks_and_position_ids(seq, image_logits_mask):
tokens = seq.unsqueeze(0)
attention_mask = torch.ones((1, len(seq), len(seq)), device=tokens.device)
attention_mask.tril_()
attention_... | null |
7,487 | import os
import logging
import random
import logging
import jsonlines
from io import BytesIO
from PIL import Image
from torch.utils.data import Dataset
from sat.helpers import print_rank0
def find_all_files(path, suffix=".jpg"):
target_files = []
for cur_dir, _, files in os.walk(path, followlinks=True):
... | null |
7,488 | import asyncio
import logging
import time
from signal import SIGINT, SIGTERM, signal
from typing import Optional
import aiohttp
from . import api_helpers, ytlounge
async def finish(devices):
for i in devices:
await i.cancel() | null |
7,489 | import os
import plistlib
from . import config_setup
default_plist = {
"Label": "com.dmunozv04iSponsorBlockTV",
"RunAtLoad": True,
"StartInterval": 20,
"EnvironmentVariables": {"PYTHONUNBUFFERED": "YES"},
"StandardErrorPath": "", # Fill later
"StandardOutPath": "",
"ProgramArguments": "",
... | null |
7,490 | import os
import plistlib
from . import config_setup
def main():
correct_path = os.path.expanduser("~/iSponsorBlockTV")
if os.path.isfile(correct_path + "/iSponsorBlockTV-macos"):
print("Program is on the right path")
print("The launch daemon will now be installed")
create_plist(correct_... | null |
7,491 | import html
from hashlib import sha256
from aiohttp import ClientSession
from cache import AsyncLRU
from . import constants, dial_client
from .conditional_ttl_cache import AsyncConditionalTTL
def list_to_tuple(function):
def wrapper(*args):
args = [tuple(x) if isinstance(x, list) else x for x in args]
... | null |
7,492 | import asyncio
import socket
import ssdp
import xmltodict
from ssdp import network
def get_ip():
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.settimeout(0)
try:
# doesn't even have to be reachable
s.connect(("10.254.254.254", 1))
ip = s.getsockname()[0]
except Exception... | Send out an M-SEARCH request and listening for responses. |
7,493 | import asyncio
import aiohttp
from . import api_helpers, ytlounge
async def pair_device():
try:
lounge_controller = ytlounge.YtLoungeApi("iSponsorBlockTV")
pairing_code = input(
"Enter pairing code (found in Settings - Link with TV code): "
)
pairing_code = int(
... | null |
7,494 | import argparse
import json
import logging
import os
import sys
import time
from appdirs import user_data_dir
from . import config_setup, main, setup_wizard
from .constants import config_file_blacklist_keys
class Config:
def __init__(self, data_dir):
def validate(self):
def __load(self):
def save(se... | null |
7,495 | import asyncio
import copy
import aiohttp
from textual import on
from textual.app import App, ComposeResult
from textual.containers import (
Container,
Grid,
Horizontal,
ScrollableContainer,
Vertical,
)
from textual.events import Click
from textual.screen import Screen
from textual.validation import... | null |
7,496 | import argparse
import pickle
import numpy as np
def get_parser():
parser = argparse.ArgumentParser(description='DouZero: random data generator')
parser.add_argument('--output', default='eval_data', type=str)
parser.add_argument('--num_games', default=10000, type=int)
return parser | null |
7,497 | import argparse
import pickle
import numpy as np
deck = []
deck.extend([17 for _ in range(4)])
deck.extend([20, 30])
def generate():
_deck = deck.copy()
np.random.shuffle(_deck)
card_play_data = {'landlord': _deck[:20],
'landlord_up': _deck[20:37],
'landlord_down... | null |
7,498 | import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `_format_observation` function. Write a Python function `def _format_observation(obs, device)` to solve the following problem:
A utility function to process observations and move them to CUDA.
Here is the fu... | A utility function to process observations and move them to CUDA. |
7,500 | import os
import threading
import time
import timeit
import pprint
from collections import deque
import numpy as np
import torch
from torch import multiprocessing as mp
from torch import nn
from .file_writer import FileWriter
from .models import Model
from .utils import get_batch, log, create_env, create_buffers, creat... | This is the main funtion for training. It will first initilize everything, such as buffers, optimizers, etc. Then it will start subprocesses as actors. Then, it will call learning function with multiple threads. |
7,502 | import multiprocessing as mp
import pickle
from douzero.env.game import GameEnv
def mp_simulate(card_play_data_list, card_play_model_path_dict, q):
players = load_card_play_models(card_play_model_path_dict)
env = GameEnv(players)
for idx, card_play_data in enumerate(card_play_data_list):
env.card_pl... | null |
7,513 | import collections
def common_handle(moves, rival_move):
new_moves = list()
for move in moves:
if move[0] > rival_move[0]:
new_moves.append(move)
return new_moves
def filter_type_9_serial_pair(moves, rival_move):
return common_handle(moves, rival_move) | null |
7,520 | from matplotlib import pyplot as plt
import numpy as np
import os
def plot_log(csv_file, x_label=None, y_label=None, plot_max=False, label=None, fontsize=20):
log_data = np.loadtxt(csv_file, delimiter=',', skiprows=1, usecols=(1, 2))
x = log_data[:, 0]
y = log_data[:, 1]
plt.plot(x, y, label=label)
... | null |
7,521 | from matplotlib import pyplot as plt
import numpy as np
from spikingjelly.activation_based.examples.conv_fashion_mnist import Net
from spikingjelly import visualizing
import torch
import torch.nn as nn
import torchvision
def plot_log(csv_file, title, x_label, y_label, figsize=(12, 8), plot_max=False):
log_data = n... | null |
7,523 | from matplotlib import pyplot as plt
import numpy as np
def plot_log(csv_file, title, x_label, y_label, plot_max=False, label=None):
log_data = np.loadtxt(csv_file, delimiter=',', skiprows=1, usecols=(1, 2))
x = log_data[:, 0]
y = log_data[:, 1]
plt.plot(x, y, label=label)
plt.xlabel(x_label, fonts... | null |
7,524 | import numpy as np
from matplotlib import pyplot as plt
plt.style.use(['science'])
for i in range(T.shape[0]):
T_str.append(str(int(T[i])))
plt.title('Execution time of Running Forward with $2^{20}$ Neurons')
plt.xlabel('simulation duration T (step)')
plt.ylabel('execution time (ms)')
plt.xticks(x, T_str)
plt.legen... | null |
7,525 | from matplotlib import pyplot as plt
import numpy as np
from spikingjelly import visualizing
import torch
import torch.nn as nn
import torchvision
def plot_log(csv_file, title, x_label, y_label, plot_max=False, label=None):
log_data = np.loadtxt(csv_file, delimiter=',', skiprows=1, usecols=(1, 2))
x = log_data... | null |
7,526 | from matplotlib import pyplot as plt
import numpy as np
from spikingjelly import visualizing
import torch
import torch.nn as nn
import torchvision
def plot_log(csv_file, title, x_label, y_label, figsize=(12, 8), plot_max=False):
log_data = np.loadtxt(csv_file, delimiter=',', skiprows=1, usecols=(1, 2))
x = log... | null |
7,527 | import torch
from matplotlib import pyplot as plt
def reset_v(h, s):
return h * (1 - s) | null |
7,529 | from matplotlib import pyplot as plt
import numpy as np
def plot_log(csv_file, title, x_label, y_label, plot_max=False):
log_data = np.loadtxt(csv_file, delimiter=',', skiprows=1, usecols=(1, 2))
x = log_data[:, 0]
y = log_data[:, 1]
plt.plot(x, y)
plt.xlabel(x_label, fontsize=20)
plt.ylabel(y_... | null |
7,530 | import requests
import os
from tqdm import tqdm
def download_url(url, dst):
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:67.0) Gecko/20100101 Firefox/67.0'
}
response = requests.get(url, headers=headers, stream=True) # (1)
file_size = int(response.headers['content-lengt... | null |
7,531 | import torch
import torchvision
import torch.nn as nn
import spikingjelly
from spikingjelly.activation_based import ann2snn
from tqdm import tqdm
from spikingjelly.activation_based.ann2snn.sample_models import mnist_cnn
import numpy as np
import matplotlib.pyplot as plt
def val(net, device, data_loader, T=None):
n... | null |
7,532 | import torch
import torchvision
from tqdm import tqdm
import spikingjelly.activation_based.ann2snn as ann2snn
from spikingjelly.activation_based.ann2snn.sample_models import cifar10_resnet
def val(net, device, data_loader, T=None):
net.eval().to(device)
correct = 0.0
total = 0.0
with torch.no_grad():
... | null |
7,533 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=F... | null |
7,534 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=F... | null |
7,535 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d... | null |
7,536 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d... | null |
7,537 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d... | null |
7,538 | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
from typing import Union, Callable
from . import neuron, base, surrogate
def step_quantize_forward(x: torch.Tensor, step: float):
x = x / step
torch.round_(x)
return x * step | null |
7,539 | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
from typing import Union, Callable
from . import neuron, base, surrogate
def step_quantize(x: torch.Tensor, step: float = 1.):
"""
:param x: a float tensor whose range is ``0 <= x <= 1``.
:type x: torch.Tensor
:param step:... | Denote ``k`` as an ``int``, ``x[i]`` will be quantized to the nearest ``2 * k / scale``, \ and ``k = {-128, -127, ..., 126, 127}``. |
7,540 | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
from typing import Union, Callable
from . import neuron, base, surrogate
def right_shift_to_zero(x: torch.Tensor, bits: int):
def _listep_forward(x: torch.Tensor, decay: torch.Tensor, state: torch.Tensor, w_scale: int,
... | null |
7,541 | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
from typing import Union, Callable
from . import neuron, base, surrogate
def _listep_backward(grad_output: torch.Tensor, decay: torch.Tensor, state: torch.Tensor, hw_bits: int = 12):
grad_state = (1 - decay / (1 << hw_bits)) * grad_o... | null |
7,542 | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
from typing import Union, Callable
from . import neuron, base, surrogate
def TNX_to_NXT(x_seq: torch.Tensor):
# x_seq.shape = [T, N, *]
permute_args = list(range(1, x_seq.dim()))
permute_args.append(0)
ret... | null |
7,543 | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
from typing import Union, Callable
from . import neuron, base, surrogate
def NXT_to_TNX(x_seq: torch.Tensor):
# x_seq.shape = [N, *, T]
permute_args = list(range(x_seq.dim() - 1))
permute_args.insert(0, x_seq.dim(... | null |
7,544 | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
from typing import Union, Callable
from . import neuron, base, surrogate
def lava_neuron_forward(lava_neuron: nn.Module, x_seq: torch.Tensor, v: torch.Tensor or float):
# x_seq.shape = [T, N, *]
# lave uses shape = [*, T]... | null |
7,545 | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
from typing import Union, Callable
from . import neuron, base, surrogate
def step_quantize(x: torch.Tensor, step: float = 1.):
"""
:param x: a float tensor whose range is ``0 <= x <= 1``.
:type x: torch.Tensor
:param step:... | null |
7,546 | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
from typing import Union, Callable
from . import neuron, base, surrogate
try:
import lava.lib.dl.slayer as slayer
# ----------------------------------------
# data reshape function
# ---------------------------------------... | null |
7,547 | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
from typing import Union, Callable
from . import neuron, base, surrogate
try:
import lava.lib.dl.slayer as slayer
# ----------------------------------------
# data reshape function
# ---------------------------------------... | :param fc: a pytorch linear layer without bias :type fc: nn.Linear :return: a lava slayer dense synapse :rtype: slayer.synapse.Dense Codes example: .. code-block:: python T = 4 N = 2 layer_nn = nn.Linear(8, 4, bias=False) layer_sl = lava_exchange.linear_to_lava_synapse_dense(layer_nn) x_seq = torch.rand([T, N, 8]) with... |
7,548 | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
from typing import Union, Callable
from . import neuron, base, surrogate
try:
import lava.lib.dl.slayer as slayer
# ----------------------------------------
# data reshape function
# ---------------------------------------... | :param conv2d_nn: a pytorch conv2d layer without bias :type conv2d_nn: nn.Conv2d :return: a lava slayer conv synapse :rtype: slayer.synapse.Conv Codes example: .. code-block:: python T = 4 N = 2 layer_nn = nn.Conv2d(3, 8, kernel_size=3, stride=1, padding=1, bias=False) layer_sl = lava_exchange.conv2d_to_lava_synapse_co... |
7,549 | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
from typing import Union, Callable
from . import neuron, base, surrogate
try:
import lava.lib.dl.slayer as slayer
# ----------------------------------------
# data reshape function
# ---------------------------------------... | :param pool2d_nn: a pytorch AvgPool2d layer :type pool2d_nn: nn.AvgPool2d :return: a lava slayer pool layer :rtype: slayer.synapse.Pool .. admonition:: Warning :class: warning The lava slayer pool layer applies sum pooling, rather than average pooling. .. code-block:: python T = 4 N = 2 layer_nn = nn.AvgPool2d(kernel_s... |
7,550 | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
from typing import Union, Callable
from . import neuron, base, surrogate
try:
import lava.lib.dl.slayer as slayer
# ----------------------------------------
# data reshape function
# ---------------------------------------... | Supported layer types input : {shape, type} flatten: {shape, type} average: {shape, type} concat : {shape, type, layers} dense : {shape, type, neuron, inFeatures, outFeatures, weight, delay(if available)} pool : {shape, type, neuron, kernelSize, stride, padding, dilation, weight} conv : {shape, type, neuron, inChannels... |
7,551 | import copy
import os
from typing import Dict
import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
import numpy as np
from . import neuron, functional, layer
def to_lynxi_supported_module(m_in: nn.Module, T: int):
def to_lynxi_supported_modules(net: list or tuple or nn.Sequential, T: int):... | null |
7,552 | import copy
import os
from typing import Dict
import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
import numpy as np
from . import neuron, functional, layer
def torch_tensor_to_lynxi(x: torch.Tensor, device_id: int = 0, to_apu: bool = True):
x_size_in_byte = x.element_size() * x.n... | null |
7,553 | import copy
import os
from typing import Dict
import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
import numpy as np
from . import neuron, functional, layer
def lynxi_tensor_to_torch(x: lynpy.Tensor, shape: tuple or list = None, dtype: str = None):
if shape is not None and dtype i... | null |
7,554 | import copy
import os
from typing import Dict
import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
import numpy as np
from . import neuron, functional, layer
try:
'''
适配灵汐科技的芯片
'''
import lyngor
import lynpy
logging.info(f'lynpy.version={lynpy.version}')
logging.... | null |
7,555 | import copy
import os
from typing import Dict
import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
import numpy as np
from . import neuron, functional, layer
def load_lynxi_model(device_id: int, model_path: str):
return lynpy.Model(dev_id=device_id, path=model_path) | null |
7,556 | import gym
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Normal
from torch.utils.tensorboard import SummaryWriter
from spikingjelly.activation_based.examples.common.multiprocessing_env import Su... | null |
7,557 | import gym
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Normal
from torch.utils.tensorboard import SummaryWriter
from spikingjelly.activation_based.examples.common.multiprocessing_env import Su... | null |
7,558 | import gym
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Normal
from torch.utils.tensorboard import SummaryWriter
from spikingjelly.activation_based.examples.common.multiprocessing_env import Su... | null |
7,559 | import gym
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Normal
from torch.utils.tensorboard import SummaryWriter
from spikingjelly.activation_based.examples.common.multiprocessing_env import Su... | null |
7,560 | import torch
import torch.nn as nn
from spikingjelly.activation_based import neuron, layer, learning
from matplotlib import pyplot as plt
def f_pre(x, w_min, alpha=0.):
return (x - w_min) ** alpha | null |
7,561 | import torch
import torch.nn as nn
from spikingjelly.activation_based import neuron, layer, learning
from matplotlib import pyplot as plt
def f_post(x, w_max, alpha=0.):
return (w_max - x) ** alpha | null |
7,562 | import gym
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Categorical
from torch.utils.tensorboard import SummaryWriter
from spikingjelly.activation_based.examples.common.multiprocessing_env impo... | null |
7,563 | import gym
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Categorical
from torch.utils.tensorboard import SummaryWriter
from spikingjelly.activation_based.examples.common.multiprocessing_env impo... | null |
7,564 | import gym
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Categorical
from torch.utils.tensorboard import SummaryWriter
from spikingjelly.activation_based.examples.common.multiprocessing_env impo... | null |
7,565 | import gym
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Normal
from torch.utils.tensorboard import SummaryWriter
from spikingjelly.activation_based.examples.common.multiprocessing_env import Su... | null |
7,566 | import gym
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Normal
from torch.utils.tensorboard import SummaryWriter
from spikingjelly.activation_based.examples.common.multiprocessing_env import Su... | null |
7,567 | import gym
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Normal
from torch.utils.tensorboard import SummaryWriter
from spikingjelly.activation_based.examples.common.multiprocessing_env import Su... | null |
7,568 | import gym
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Normal
from torch.utils.tensorboard import SummaryWriter
from spikingjelly.activation_based.examples.common.multiprocessing_env import Su... | null |
7,569 | import logging
import torch
import torch.nn as nn
import torchvision
from torchvision import transforms
from spikingjelly.activation_based import functional, lava_exchange, surrogate, encoding, neuron
import torch.nn.functional as F
from torch.utils.data import DataLoader
import os
import argparse
import h5py
def expo... | null |
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