id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
7,570 | import numpy as np
import torch
import torch.nn as nn
def combined_shape(length, shape=None):
if shape is None:
return (length,)
return (length, shape) if np.isscalar(shape) else (length, *shape) | null |
7,571 | import numpy as np
import torch
import torch.nn as nn
def mlp(sizes, activation, output_activation=nn.Identity):
layers = []
for j in range(len(sizes)-1):
act = activation if j < len(sizes)-2 else output_activation
layers += [nn.Linear(sizes[j], sizes[j+1]), act()]
return nn.Sequential(*lay... | null |
7,572 | import numpy as np
import torch
import torch.nn as nn
def count_vars(module):
return sum([np.prod(p.shape) for p in module.parameters()]) | null |
7,573 | from copy import deepcopy
import itertools
import numpy as np
import random
import torch
import torch.nn as nn
from torch.optim import Adam
import gym
import pickle
import os
from spikingjelly.activation_based import functional
from replay_buffer_norm import ReplayBuffer
from ilcsan import PopSpikeActor
from core_cuda ... | null |
7,574 | import gym
import math
import random
import numpy as np
from collections import namedtuple
from itertools import count
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as T
from torch.utils.tensorboard import SummaryWriter
import argparse
def ... | null |
7,575 | import gym
import math
import random
import numpy as np
from collections import namedtuple
from itertools import count
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as T
from torch.utils.tensorboard import SummaryWriter
import argparse
def ... | null |
7,576 | import gym
import math
import random
import numpy as np
from collections import namedtuple
from itertools import count
import matplotlib
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from spikingjelly.activation_based import monitor, neuro... | null |
7,577 | import gym
import math
import random
import numpy as np
from collections import namedtuple
from itertools import count
import matplotlib
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from spikingjelly.activation_based import monitor, neuro... | null |
7,578 | import torch
import torch.nn as nn
from spikingjelly.activation_based import neuron, layer, learning
from matplotlib import pyplot as plt
torch.manual_seed(0)
def f_weight(x):
return torch.clamp(x, -1, 1.) | null |
7,581 | import torch
from torch import Tensor, nn
from torch.optim import Adam
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torchvision.transforms
from torchaudio.transforms import Spectrogram
from spikingjelly.activation_based import neuron, surrogate
from spikingjelly.datas... | null |
7,582 | import torch
from torch import Tensor, nn
from torch.optim import Adam
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torchvision.transforms
from torchaudio.transforms import Spectrogram
from spikingjelly.activation_based import neuron, surrogate
from spikingjelly.datas... | null |
7,583 | import numpy as np
from multiprocessing import Process, Pipe
def worker(remote, parent_remote, env_fn_wrapper):
parent_remote.close()
env = env_fn_wrapper.x()
while True:
cmd, data = remote.recv()
if cmd == 'step':
ob, reward, done, info = env.step(data)
if done:
... | null |
7,584 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from spikingjelly.activation_based import rnn
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import glob
import unicodedata
import string
import random
import time
import math
def findFiles(path):
return g... | null |
7,585 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from spikingjelly.activation_based import rnn
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import glob
import unicodedata
import string
import random
import time
import math
if __name__ == '__main__':
all_let... | null |
7,586 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from spikingjelly.activation_based import rnn
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import glob
import unicodedata
import string
import random
import time
import math
def categoryFromOutput(output):
... | null |
7,587 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from spikingjelly.activation_based import rnn
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import glob
import unicodedata
import string
import random
import time
import math
if __name__ == '__main__':
all_let... | Args: sampleSource: 'train', 'test', 'all' Returns: category, line, category_tensor, line_tensor |
7,588 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from spikingjelly.activation_based import rnn
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import glob
import unicodedata
import string
import random
import time
import math
def timeSince(since):
now = t... | null |
7,589 | 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 |
7,590 | 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 |
7,591 | 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 |
7,594 | from copy import deepcopy
import itertools
import numpy as np
import random
import gym
import pickle
import os
import torch
import torch.nn as nn
from torch.optim import Adam
from spikingjelly.activation_based import functional
from replay_buffer_norm import ReplayBuffer
from noisysan import NoisyPopSpikeActor
from cor... | null |
7,595 | import numpy as np
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 |
7,596 | import numpy as np
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 |
7,597 | import ptan
import argparse, os
from itertools import count
import numpy as np
import torch
import torch.optim as optim
from spikingjelly.activation_based import functional
from tensorboardX import SummaryWriter
from utils import model, common, atari_wrappers
def make_env(params):
env = atari_wrappers.make_atari(p... | null |
7,598 | import ptan
import argparse, os
from itertools import count
import numpy as np
import torch
import torch.optim as optim
from spikingjelly.activation_based import functional
from tensorboardX import SummaryWriter
from utils import model, common, atari_wrappers
def make_test_env(params):
env = atari_wrappers.make_at... | null |
7,599 | import ptan
import argparse, os
from itertools import count
import numpy as np
import torch
import torch.optim as optim
from spikingjelly.activation_based import functional
from tensorboardX import SummaryWriter
from utils import model, common, atari_wrappers
EVAL_LEN = 125000
def eval_Q(env, net, cuda):
device = ... | null |
7,600 | import sys
import time
import numpy as np
import torch
import torch.nn as nn
def unpack_batch(batch):
states, actions, rewards, dones, last_states = [], [], [], [], []
for exp in batch:
state = np.array(exp.state, copy=False)
states.append(state)
actions.append(exp.action)
reward... | null |
7,601 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline
from torch.cuda.amp import custom_fwd, custom_bwd
import logging
from . import tensor_cache
from torch import Tensor
from typing import Optional, Union
from torch.types import _int, _size
from torch.nn.m... | * :ref:`API in English <spike_linear-en>` .. _spike_linear-cn: :class:`torch.nn.functional.linear` 在输入为脉冲时的特例。 .. note:: 在CUDA设备上训练时拥有比 :class:`torch.nn.functional.linear` 更低的显存消耗。 .. warning:: `spike` 中的任何元素都必须为0或1。 * :ref:`中文API <spike_linear-cn>` .. _spike_linear-en: A specific case of :class:`torch.nn.functional.li... |
7,602 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline
from torch.cuda.amp import custom_fwd, custom_bwd
import logging
from . import tensor_cache
from torch import Tensor
from typing import Optional, Union
from torch.types import _int, _size
from torch.nn.m... | * :ref:`API in English <spike_conv1d-en>` .. _spike_conv1d-cn: :class:`torch.nn.functional.conv1d` 在输入为脉冲时的特例。 .. note:: 在CUDA设备上训练时拥有比 :class:`torch.nn.functional.conv1d` 更低的显存消耗。 .. warning:: `spike` 中的任何元素都必须为0或1。 * :ref:`中文API <spike_conv1d-cn>` .. _spike_conv1d-en: A specific case of :class:`torch.nn.functional.co... |
7,603 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline
from torch.cuda.amp import custom_fwd, custom_bwd
import logging
from . import tensor_cache
from torch import Tensor
from typing import Optional, Union
from torch.types import _int, _size
from torch.nn.m... | * :ref:`API in English <spike_conv2d-en>` .. _spike_conv2d-cn: :class:`torch.nn.functional.conv2d` 在输入为脉冲时的特例。 .. note:: 在CUDA设备上训练时拥有比 :class:`torch.nn.functional.conv2d` 更低的显存消耗。 .. warning:: `spike` 中的任何元素都必须为0或1。 * :ref:`中文API <spike_conv2d-cn>` .. _spike_conv2d-en: A specific case of :class:`torch.nn.functional.co... |
7,604 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline
from torch.cuda.amp import custom_fwd, custom_bwd
import logging
from . import tensor_cache
from torch import Tensor
from typing import Optional, Union
from torch.types import _int, _size
from torch.nn.m... | * :ref:`API in English <spike_conv3d-en>` .. _spike_conv3d-cn: :class:`torch.nn.functional.conv3d` 在输入为脉冲时的特例。 .. note:: 在CUDA设备上训练时拥有比 :class:`torch.nn.functional.conv3d` 更低的显存消耗。 .. warning:: `spike` 中的任何元素都必须为0或1。 * :ref:`中文API <spike_conv3d-cn>` .. _spike_conv3d-en: A specific case of :class:`torch.nn.functional.co... |
7,605 | import torch
import numpy as np
from torch import nn
from typing import Callable, Any
from spikingjelly.activation_based import neuron
import threading
from torch.utils.tensorboard import SummaryWriter
import os
import time
import re
import datetime
def unpack_len1_tuple(x: tuple or torch.Tensor):
if isinstance(x,... | null |
7,606 | import torch
import torch.nn.functional as F
import threading
from .. import configure
from . import cuda_utils
import logging
try:
import cupy
except BaseException as e:
logging.info(f'spikingjelly.activation_based.tensor_cache: {e}')
cupy = None
class DataTypeConvertCUDACode:
float2bool = r'''
ext... | :param spike: a spike tensor whose ``dtype=torch.float`` or ``dtype=torch.half`` and all elements are 0 or 1 :type spike: torch.Tensor :return: (spike_b, s_dtype, s_shape, s_padding) spike_b: a compressed spike tensor with ``dtype=torch.uint8`` and each element stores 8 spikes s_dtype: the dtype of the original spike s... |
7,607 | import torch
import torch.nn.functional as F
import threading
from .. import configure
from . import cuda_utils
import logging
try:
import cupy
except BaseException as e:
logging.info(f'spikingjelly.activation_based.tensor_cache: {e}')
cupy = None
class DataTypeConvertCUDACode:
float2bool = r'''
ext... | :param spike_b: a compressed spike tensor with ``dtype=torch.uint8`` and each element stores 8 spikes :type spike_b: torch.Tensor :param s_dtype: the dtype of the original spike :type s_dtype: torch.dtype :param s_shape: the shape of the original spike :type s_shape: torch.Size :param s_padding: the number of padding e... |
7,608 | import torch
import torch.nn.functional as F
import threading
from .. import configure
from . import cuda_utils
import logging
def tensor_key(x: torch.Tensor):
x = x.flatten()
return x.data_ptr(), x[-1].data_ptr(), x.numel() | null |
7,609 | from spikingjelly.activation_based.auto_cuda.generator import analyse_graph, gen_forward_codes, gen_backward_codes
from spikingjelly.activation_based import surrogate
import torch
def lif_charge(x: torch.Tensor, v_last: torch.Tensor, tau: float, v_reset: float):
h = v_last + (x - (v_last - v_reset)) / tau
... | null |
7,610 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
import re
import sys
import copy
from typing import Callable
import numpy as np
class VarNode:
def __init__(self, prefix: str, name: str, instance: object, value=None):
self.debug_name = name # 原始的name形如 %8, v_last.1
... | null |
7,611 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
import re
import sys
import copy
from typing import Callable
import numpy as np
def hash_str(x: object):
def gen_forward_codes(input_nodes: dict, inter_nodes: dict, output_nodes: dict, cmds: list, hard_reset: bool):
# 暂时只支持单个输出
a... | null |
7,612 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
import re
import sys
import copy
from typing import Callable
import numpy as np
def hash_str(x: object):
hash_code = hash(x)
if hash_code < 0:
return f'_{-hash_code}'
else:
return hash_code
The provided code s... | 用户定义的前向传播函数为 h_seq[t] = fun(x_seq[t], v_v_seq[t], ...) 需要计算出 h_seq[t] -> x_seq[t] 的梯度和 h_seq[t] -> v_v_seq[t]的梯度 还需要考虑 ... 中如果有tensor，可以增加flag，决定是否计算h_seq[t]对其的梯度 |
7,613 | import torch
import torch.nn.functional as F
import numpy as np
import logging
from .. import cuda_utils, surrogate
from ... import configure
from typing import Callable, Iterable
from . import base, cfunction
import math
def if_requires_grad(items: Iterable):
requires_grad = False
for item in items:
i... | null |
7,614 | import torch
import torch.nn.functional as F
import numpy as np
import logging
try:
import cupy
except BaseException as e:
logging.info(f'spikingjelly.activation_based.auto_cuda.ss_neuronal_kernel: {e}')
cupy = None
from .. import cuda_utils, surrogate
from ... import configure
from typing import Callable, ... | null |
7,615 | import torch
import torch.nn.functional as F
import numpy as np
import logging
from .. import cuda_utils, surrogate
from ... import configure
from typing import Callable, Iterable
from . import base, cfunction
import math
def new_tensors(news: tuple, py_dict: dict, ref: str = 'x'):
ref = py_dict[ref]
zero_shap... | null |
7,616 | import torch
import torch.nn.functional as F
import numpy as np
import logging
from .. import cuda_utils, surrogate
from ... import configure
from typing import Callable, Iterable
from . import base, cfunction
import math
def neuronal_hard_reset(v_next: str, h: str, spike: str, v_reset: str, dtype: str = 'float'):
... | null |
7,617 | import torch
import torch.nn.functional as F
import numpy as np
import logging
from .. import cuda_utils, surrogate
from ... import configure
from typing import Callable, Iterable
from . import base, cfunction
import math
def neuronal_soft_reset(v_next: str, h: str, spike: str, v_th: str, dtype: str = 'float'):
if... | null |
7,618 | import torch
import torch.nn.functional as F
import numpy as np
import logging
from .. import cuda_utils, surrogate
from ... import configure
from typing import Callable, Iterable
from . import base, cfunction
import math
def neuronal_fire(spike: str, v: str, v_th: str, dtype: str = 'float'):
if dtype == 'float':
... | null |
7,619 | def wrap_return_codes(y: str or None, codes: str):
if y is None:
return f'({codes})'
else:
return f'{y} = {codes};'
def float2half2(y: str or None, x: str):
codes = f'__float2half2_rn({x})'
return wrap_return_codes(y, codes) | null |
7,620 | def wrap_return_codes(y: str or None, codes: str):
if y is None:
return f'({codes})'
else:
return f'{y} = {codes};'
def maximum(z: str or None, x: str, y: str, dtype: str):
if dtype == 'float':
codes = f'max({x}, {y})'
elif dtype == 'half2':
codes = f'__hmax2({x}, {y})'
... | null |
7,621 | def wrap_return_codes(y: str or None, codes: str):
if y is None:
return f'({codes})'
else:
return f'{y} = {codes};'
def neg(y: str or None, x: str, dtype: str):
if dtype == 'float':
codes = f'- {x}'
elif dtype == 'half2':
codes = f'__hneg2({x})'
else:
raise N... | null |
7,622 | def wrap_return_codes(y: str or None, codes: str):
if y is None:
return f'({codes})'
else:
return f'{y} = {codes};'
def exp(y: str or None, x: str, dtype: str):
if dtype == 'float':
codes = f'expf({x})'
elif dtype == 'half2':
codes = f'h2exp({x})'
else:
raise... | null |
7,623 | def constant(y: str or None, x: float, dtype: str):
def atan_backward(y: str, x: str, alpha: float, dtype: str):
assert y is not None
alpha = constant(None, alpha, dtype)
if dtype == 'float':
codes = f'const float atan_backward__alpha_x = ((float) 1.57079632679489661923) * {alpha} * {x};'
c... | null |
7,624 | def constant(y: str or None, x: float, dtype: str):
def abs(y: str or None, x: str, dtype: str):
def if_else(z: str or None, x: str, y: str, mask: str, dtype: str):
def greater_equal(z: str or None, x: str, y: str, dtype: str):
def piecewise_leaky_relu_backward(y: str, x: str, w: float, c: float, dtype: str):
asse... | null |
7,625 | def constant(y: str or None, x: float, dtype: str):
if dtype == 'float':
codes = f'{x}f'
elif dtype == 'half2':
codes = f'__float2half2_rn({x}f)'
else:
raise NotImplementedError(dtype)
return wrap_return_codes(y, codes)
def if_else(z: str or None, x: str, y: str, mask: str, dtype... | null |
7,626 | def constant(y: str or None, x: float, dtype: str):
if dtype == 'float':
codes = f'{x}f'
elif dtype == 'half2':
codes = f'__float2half2_rn({x}f)'
else:
raise NotImplementedError(dtype)
return wrap_return_codes(y, codes)
def power(z: str or None, x: str, y: str, dtype: str):
i... | null |
7,627 | def constant(y: str or None, x: float, dtype: str):
if dtype == 'float':
codes = f'{x}f'
elif dtype == 'half2':
codes = f'__float2half2_rn({x}f)'
else:
raise NotImplementedError(dtype)
return wrap_return_codes(y, codes)
def if_else(z: str or None, x: str, y: str, mask: str, dtype... | null |
7,628 | def constant(y: str or None, x: float, dtype: str):
if dtype == 'float':
codes = f'{x}f'
elif dtype == 'half2':
codes = f'__float2half2_rn({x}f)'
else:
raise NotImplementedError(dtype)
return wrap_return_codes(y, codes)
def greater_equal(z: str or None, x: str, y: str, dtype: str... | null |
7,629 | def constant(y: str or None, x: float, dtype: str):
if dtype == 'float':
codes = f'{x}f'
elif dtype == 'half2':
codes = f'__float2half2_rn({x}f)'
else:
raise NotImplementedError(dtype)
return wrap_return_codes(y, codes)
def if_else_else(w: str or None, x: str, y: str, z: str, mas... | null |
7,634 | import torch
import torch.nn.functional as F
import numpy as np
import logging
try:
import cupy
except BaseException as e:
logging.info(f'spikingjelly.activation_based.auto_cuda.neuronal_kernel: {e}')
cupy = None
from .. import cuda_utils, surrogate
from ... import configure
from typing import Callable, Ite... | null |
7,635 | import torch
import torch.nn.functional as F
import numpy as np
import logging
from .. import cuda_utils, surrogate
from ... import configure
from typing import Callable, Iterable
from . import base, cfunction
import math
def new_tensors(news: tuple, py_dict: dict, ref: str = 'x_seq'):
ref = py_dict[ref]
zero_... | null |
7,636 | import numpy as np
import logging
import torch
import torch.nn.functional as F
import sys
import logging
from .. import cuda_utils
from ... import configure
def wrap_with_comment(code: str, comment: str):
if logging.DEBUG >= logging.root.level:
return '\n//------' + comment + ' start------\n' + code + '\n/... | null |
7,637 | import numpy as np
import logging
import torch
import torch.nn.functional as F
import sys
import logging
from .. import cuda_utils
from ... import configure
def startswiths(x: str, prefixes: tuple):
ret = False
for prefix in prefixes:
if x.startswith(prefix):
ret = True
return ret | null |
7,638 | import torch
import torch.nn as nn
import torch.nn.functional as F
from spikingjelly.activation_based import surrogate, layer
import math
def directional_rnn_cell_forward(cell: nn.Module, x: torch.Tensor,
states: torch.Tensor):
T = x.shape[0]
ss = states
output = []
... | null |
7,639 | import torch
import torch.nn as nn
import torch.nn.functional as F
from spikingjelly.activation_based import surrogate, layer
import math
The provided code snippet includes necessary dependencies for implementing the `bidirectional_rnn_cell_forward` function. Write a Python function `def bidirectional_rnn_cell_forward... | :param cell: 正向RNN cell，输入是正向序列 :type cell: nn.Module :param cell_reverse: 反向的RNN cell，输入是反向序列 :type cell_reverse: nn.Module :param x: ``shape = [T, batch_size, input_size]`` 的输入 :type x: torch.Tensor :param states: 正向RNN cell的起始状态 若RNN cell只有单个隐藏状态，则 ``shape = [batch_size, hidden_size]`` ； 否则 ``shape = [states_num, ba... |
7,640 | import logging
import torch
import time
import numpy as np
from .. import configure
from typing import Callable
def cpu_timer(f: Callable, *args, **kwargs):
"""
* :ref:`API in English <cpu_timer-en>`
.. _cpu_timer-cn:
计算在CPU上执行 ``f(*args, **kwargs)`` 所需的时间
:param f: 函数
:type f: Callable
:ret... | * :ref:`API in English <cal_fun_t-en>` .. _cal_fun_t-cn: 测量在 ``device`` 上执行 ``n`` 次 ``f(*args, **kwargs)`` 的平均用时 .. note:: 当 ``n > 1`` 时，实际上会执行 ``2n`` 次，然后返回后 ``n`` 次的平均用时，以减小误差。 :param n: 重复的次数 :type n: int :param device: ``f`` 执行的设备，可以为 'cpu' 或CUDA设备 :type device: str or torch.device or int :param f: 函数 :type f: Call... |
7,641 | from abc import abstractmethod
from typing import Callable
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
import logging
from . import surrogate, base
from .auto_cuda import neuron_kernel as ac_neuron_kernel
from .auto_cuda import ss_neuron_kernel as ss_ac_neuron_kerne... | Gaussian (1/f)**beta noise. Based on the algorithm in: Timmer, J. and Koenig, M.: On generating power law noise. Astron. Astrophys. 300, 707-710 (1995) Normalised to unit variance Parameters: ----------- exponent : float The power-spectrum of the generated noise is proportional to S(f) = (1 / f)**beta flicker / pink no... |
7,642 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `set_backend` function. Write a Python function `def s... | * :ref:`API in English <set_backend-en>` .. _set_backend-cn: :param net: 一个神经网络 :type net: nn.Module :param backend: 使用哪个后端 :type backend: str :param instance: 类型为 ``instance`` 的模块后端会被改变 :type instance: nn.Module or tuple[nn.Module] :return: None 将 ``net`` 中 所有类型为 ``instance`` 的模块后端更改为 ``backend`` * :ref:`中文 API <set_b... |
7,643 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
def kernel_dot_product(x: Tensor, y: Tensor, kernel='linear', *args):
"""
* :ref:`API in English <kernel_dot_product-en>`
... | * :ref:`API in English <spike_similar_loss-en>` .. _spike_similar_loss-cn: :param spikes: shape=[N, M, T]，N个数据生成的脉冲 :param labels: shape=[N, C]，N个数据的标签，\ ``labels[i][k] == 1``\ 表示数据i属于第k类，反之亦然，允许多标签 :param str kernel_type: 使用内积来衡量两个脉冲之间的相似性，\ ``kernel_type``\ 是计算内积时，所使用的核函数种类 :param str loss_type: 返回哪种损失，可以为'mse', 'l1'... |
7,644 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `set_threshold_margin` function. Write a Python functi... | * :ref:`API in English <set_threshold_margin-en>` .. _set_threshold_margin-cn: :param output_layer: 用于分类的网络的输出层，输出层输出shape=[batch_size, C] :param label_one_hot: one hot格式的样本标签，shape=[batch_size, C] :param float eval_threshold: 输出层神经元在测试（推理）时使用的电压阈值 :param float threshold0: 输出层神经元在训练时，负样本的电压阈值 :param float threshold1: 输... |
7,645 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `redundant_one_hot` function. Write a Python function ... | * :ref:`API in English <redundant_one_hot-en>` .. _redundant_one_hot-cn: :param labels: shape=[batch_size]的tensor，表示\ ``batch_size``\ 个标签 :param int num_classes: 类别总数 :param int n: 表示每个类别所用的编码数量 :return: shape=[batch_size, num_classes * n]的tensor 对数据进行冗余的one-hot编码，每一类用 ``n`` 个1和 ``(num_classes - 1) * n`` 个0来编码。 示例： .. ... |
7,646 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `first_spike_index` function. Write a Python function ... | * :ref:`API in English <first_spike_index-en>` .. _first_spike_index-cn: :param spikes: shape=[*, T]，表示任意个神经元在t=0, 1, ..., T-1，共T个时刻的输出脉冲 :return: index, shape=[*, T]，为 ``True`` 的位置表示该神经元首次释放脉冲的时刻 输入若干个神经元的输出脉冲，返回一个与输入相同shape的 ``bool`` 类型的index。index为 ``True`` 的位置，表示该神经元首次释放脉冲的时刻。 示例： .. code-block:: python >>> spikes ... |
7,647 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `multi_step_forward` function. Write a Python function... | * :ref:`API in English <multi_step_forward-en>` .. _multi_step_forward-cn: :param x_seq: ``shape=[T, batch_size, ...]`` 的输入tensor :type x_seq: Tensor :param single_step_module: 一个或多个单步模块 :type single_step_module: torch.nn.Module or list[nn.Module] or tuple[nn.Module] or torch.nn.Sequential or Callable :return: ``shape=... |
7,648 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `chunk_multi_step_forward` function. Write a Python fu... | * :ref:`API in English <chunk_multi_step_forward-en>` .. _chunk_multi_step_forward-cn: :param split_size: 分割的尺寸 :type split_size: int :param x_seq: 输入 :type x_seq: Tensor :param multi_step_module: 一个使用多步传播模式的网络 :type multi_step_module: nn.Module :return: 输出 :rtype: Tensor 将 ``shape = [T, *]`` 的输入 ``x_seq`` 拆分成多个 ``shap... |
7,649 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `scale_fused_conv2d_weight_of_convbn2d` function. Writ... | * :ref:`API in English <scale_fused_conv2d_weight_of_convbn2d-en>` .. _scale_fused_conv2d_weight_of_convbn2d-cn: :param conv2d: 一个2D卷积层 :type conv2d: torch.nn.Conv2d :param bn2d: 一个2D的BN层 :type bn2d: torch.nn.BatchNorm2d :return: the weight of this fused module :rtype: Tensor ``{Conv2d-BatchNorm2d}`` 模块可以合并为一个单个的 ``{Co... |
7,650 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `scale_fused_conv2d_bias_of_convbn2d` function. Write ... | * :ref:`API in English <scale_fused_conv2d_bias_of_convbn2d-en>` .. _scale_fused_conv2d_bias_of_convbn2d-cn: :param conv2d: 一个2D卷积层 :type conv2d: torch.nn.Conv2d :param bn2d: 一个2D的BN层 :type bn2d: torch.nn.BatchNorm2d :return: the weight of this fused module :rtype: Tensor ``{Conv2d-BatchNorm2d}`` 模块可以合并为一个单个的 ``{Conv2d... |
7,651 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
def fused_conv2d_weight_of_convbn2d(conv2d: nn.Conv2d, bn2d: nn.BatchNorm2d):
"""
* :ref:`API in English <fused_conv2d_weight_... | * :ref:`API in English <fuse_convbn2d-en>` .. _fuse_convbn2d-cn: :param conv2d: 一个2D卷积层 :type conv2d: torch.nn.Conv2d :param bn2d: 一个2D的BN层 :type bn2d: torch.nn.BatchNorm2d :return: the weight of this fused module :rtype: Tensor ``{Conv2d-BatchNorm2d}`` 模块可以合并为一个单个的 ``{Conv2d}``，其中``BatchNorm2d`` 的参数会被吸收进 ``Conv2d``。 本... |
7,652 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `temporal_efficient_training_cross_entropy` function. ... | * :ref:`API in English <temporal_efficient_training_cross_entropy-en>` .. _temporal_efficient_training_cross_entropy-cn: :param x_seq: ``shape=[T, N, C, *]`` 的预测值，其中 ``C`` 是类别总数 :type x_seq: torch.Tensor :param target: ``shape=[N]`` 的真实值，其中 ``target[i]`` 是真实类别 :type target: torch.Tensor :return: the temporal efficient ... |
7,653 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `kaiming_normal_conv_linear_weight` function. Write a ... | * :ref:`API in English <kaiming_normal_conv_linear_weight-en>` .. _kaiming_normal_conv_linear_weight-cn: :param net: 任何属于 ``nn.Module`` 子类的网络 :return: None 使用kaiming normal初始化 ``net` `中的所有 :class:`torch.nn._ConvNd` 和 :class:`torch.nn.Linear` 的权重（不包括偏置项）。参见 :class:`torch.nn.init.kaiming_normal_`。 * :ref:`中文API <kaiming_... |
7,654 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `delay` function. Write a Python function `def delay(x... | * :ref:`API in English <delay.__init__-en>` .. _delay.__init__-cn: :param x_seq: 输入的序列，``shape = [T, *]`` :type x_seq: torch.Tensor :param delay_steps: 延迟的时间步数 :type delay_steps: int :return: 延迟后的序列 :rtype: torch.Tensor 延迟函数，可以用来延迟输入，使得 ``y[t] = x[t - delay_steps]``。缺失的数据用0填充。 代码示例： .. code-block:: python x = torch.ran... |
7,655 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
def fptt_online_training_init_w_ra(optimizer: torch.optim.Optimizer) -> list:
w_ra = []
for item in optimizer.param_groups:
... | null |
7,656 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
def detach_net(net: nn.Module):
"""
* :ref:`API in English <detach_net-en>`
.. _detach_net-cn:
:param net: 任何属于 ``nn.M... | :param model: the neural network :type model: nn.Module :param optimizer: the optimizer for the network :type optimizer: torch.optim.Optimizer :param x_seq: the input sequence :type x_seq: torch.Tensor :param target_seq: the output sequence :type target_seq: torch.Tensor :param f_loss_t: the loss function, which should... |
7,657 | import logging
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Callable
from . import neuron, base
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `ottt_online_training` function. Write a Python functi... | :param model: the neural network :type model: nn.Module :param optimizer: the optimizer for the network :type optimizer: torch.optim.Optimizer :param x_seq: the input sequence :type x_seq: torch.Tensor :param target_seq: the output sequence :type target_seq: torch.Tensor :param f_loss_t: the loss function, which should... |
7,658 | import torch
class round_atgf(torch.autograd.Function):
def forward(ctx, x: torch.Tensor):
return torch.round(x)
def backward(ctx, grad_output: torch.Tensor):
return grad_output
The provided code snippet includes necessary dependencies for implementing the `round` function. Write a Python funct... | :param x: the input tensor :type x: torch.Tensor :return: the output tensor :rtype: torch.Tensor Apply ``y = torch.round(x)`` with re-defining gradient as :math:`\\frac{\\partial y}{\\partial x} = 1`. |
7,659 | import torch
class ceil_atgf(torch.autograd.Function):
def forward(ctx, x: torch.Tensor):
return torch.ceil(x)
def backward(ctx, grad_output: torch.Tensor):
return grad_output
The provided code snippet includes necessary dependencies for implementing the `ceil` function. Write a Python function... | :param x: the input tensor :type x: torch.Tensor :return: the output tensor :rtype: torch.Tensor Apply ``y = torch.ceil(x)`` with re-defining gradient as :math:`\\frac{\\partial y}{\\partial x} = 1`. |
7,660 | import torch
class floor_atgf(torch.autograd.Function):
def forward(ctx, x: torch.Tensor):
return torch.floor(x)
def backward(ctx, grad_output: torch.Tensor):
return grad_output
The provided code snippet includes necessary dependencies for implementing the `floor` function. Write a Python funct... | :param x: the input tensor :type x: torch.Tensor :return: the output tensor :rtype: torch.Tensor Apply ``y = torch.floor(x)`` with re-defining gradient as :math:`\\frac{\\partial y}{\\partial x} = 1`. |
7,661 | import torch
def clamp_backward(grad_output: torch.Tensor, x: torch.Tensor, min_value: float, max_value: float):
mask = (x >= min_value).to(x) * (x <= max_value).to(x)
return grad_output * mask | null |
7,662 | import torch
class clamp_atgf(torch.autograd.Function):
def forward(ctx, x: torch.Tensor, min_value: float, max_value: float):
if x.requires_grad:
ctx.save_for_backward(x)
ctx.min_value = min_value
ctx.max_value = max_value
return torch.clamp(x, min_value, max_val... | :param x: the input tensor :type x: torch.Tensor :param min_value: lower-bound of the range to be clamped to :type min_value: float :param max_value: upper-bound of the range to be clamped to :type max_value: torch.Tensor :return: the output tensor :rtype: torch.Tensor Apply ``y = torch.clamp(x, min_value, max_value)``... |
7,663 | import torch
def step_quantize_forward(x: torch.Tensor, step: float):
return torch.round_(x / step) * step | null |
7,664 | import torch
class step_quantize_atgf(torch.autograd.Function):
def forward(ctx, x: torch.Tensor, step: float):
return step_quantize_forward(x, step)
def backward(ctx, grad_output: torch.Tensor):
return grad_output, None
The provided code snippet includes necessary dependencies for implementing... | :param x: the input tensor :type x: torch.Tensor :param step: the quantize step :type step: float :return: the quantized tensor :rtype: torch.Tensor Quantize ``x`` to the nearest ``i * step``, where ``i`` is an integer. Note that the gradient is defined by :math:`\\frac{\\partial y}{\\partial x} = 1`. .. image:: ../_st... |
7,665 | import torch
def k_bit_quantize_forward(x: torch.Tensor, k: int):
c = float(1 << k) - 1.
x = x * c
torch.round_(x)
return x / c | null |
7,666 | import torch
def k_bit_quantize(x: torch.Tensor, k: int):
"""
:param x: a float tensor whose range is ``[0, 1]``.
:type x: torch.Tensor
:param k: the bit number of output
:type k: int
:return: ``y = round((2 ** k - 1) * x) / (2 ** k - 1)``
:rtype: torch.Tensor
The k-bit quantizer defined... | :param x: a float tensor whose range is ``[0, 1]``. :type x: torch.Tensor :param k: the bit number of output :type k: int :param w: the weight of the affine transform :type w: torch.Tensor :param b: the bias of the affine transform :type b: torch.Tensor :return: ``y = w * round((2 ** k - 1) * x) / (2 ** k - 1) + b`` :r... |
7,667 | import torch
The provided code snippet includes necessary dependencies for implementing the `clamp_by_linear` function. Write a Python function `def clamp_by_linear(x: torch.Tensor, eps: float = 1e-5)` to solve the following problem:
:param x: the input tensor to be normed, whose range is ``(-inf, inf)`` :type x: torc... | :param x: the input tensor to be normed, whose range is ``(-inf, inf)`` :type x: torch.Tensor :param eps: a value added to the denominator for numerical stability. The default value is ``1e-5`` :type eps: float :type max_value: float :return: the normed tensor, whose range is ``[min_value, max_value]`` :rtype: torch.Te... |
7,668 | import logging
import torch
import torch.nn.functional as F
from . import cuda_utils, surrogate, tensor_cache
from .. import configure
import numpy as np
def check_multi_step_neuron_output_and_grad(device, multi_step_neuron, shape = [65, 15, 511], *neu_args, **neu_kwargs):
@torch.no_grad()
def max_error(x, y):... | null |
7,669 | import logging
import torch
import torch.nn.functional as F
from . import cuda_utils, surrogate, tensor_cache
from .. import configure
import numpy as np
def check_single_step_neuron_output_and_grad(device, single_step_neuron, shape = [65, 15, 511], *neu_args, **neu_kwargs):
@torch.no_grad()
def max_error(x, y... | null |
7,670 | import logging
import torch
import torch.nn.functional as F
from . import cuda_utils, surrogate, tensor_cache
from .. import configure
import numpy as np
class MultiStepIFNodePTT(torch.autograd.Function):
def create_fptt_kernel(hard_reset: bool, dtype: str):
kernel_name = f'IFNode_fptt_{"hard" if hard_reset... | null |
7,671 | import torch
import torch.nn as nn
import copy
import logging
from abc import abstractmethod
try:
import cupy
except BaseException as e:
logging.info(f'spikingjelly.activation_based.base: {e}')
cupy = None
The provided code snippet includes necessary dependencies for implementing the `check_backend_library... | * :ref:`API in English <check_backend_library-en>` .. _check_backend_library-cn: :param backend: ``'torch'``, ``'cupy'`` 或 ``'lava'`` :type backend: str 检查某个后端的python库是否已经安装。若未安装则此函数会报错。 * :ref:`中文 API <check_backend_library-cn>` .. _check_backend_library-en: :param backend: ``'torch'``, ``'cupy'`` or ``'lava'`` :type ... |
7,672 | from typing import Callable, Union
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import neuron, monitor, base
def mstdp_linear_single_step(
fc: nn.Linear, in_spike: torch.Tensor, out_spike: torch.Tensor,
trace_pre: Union[float, torch.Tensor, None],
trace_post: Union[... | null |
7,673 | from typing import Callable, Union
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import neuron, monitor, base
def mstdpet_linear_single_step(
fc: nn.Linear, in_spike: torch.Tensor, out_spike: torch.Tensor,
trace_pre: Union[float, torch.Tensor, None],
trace_post: Unio... | null |
7,674 | from typing import Callable, Union
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import neuron, monitor, base
def stdp_linear_single_step(
fc: nn.Linear, in_spike: torch.Tensor, out_spike: torch.Tensor,
trace_pre: Union[float, torch.Tensor, None],
trace_post: Union[fl... | null |
7,675 | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from .auto_cuda import cfunction
The provided code snippet includes necessary dependencies for implementing the `check_manual_grad` function. Write a Python function `def check_manual_grad(primitive_function, spiking_function, *args, **kwar... | :param primitive_function: 梯度替代函数的原函数 :type primitive_function: callable :param spiking_function: 梯度替代函数 :type spiking_function: callable 梯度替代函数的反向传播一般是手写的，可以用此函数去检查手写梯度是否正确。 此函数检查梯度替代函数spiking_function的反向传播，与原函数primitive_function的反向传播结果是否一致。“一致”被定义为，两者的误差不超过eps。 示例代码： .. code-block:: python def s2nn_apply(x, alpha, be... |
7,676 | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from .auto_cuda import cfunction
def check_cuda_grad(neu, surrogate_function, device, *args, **kwargs):
# check_cuda_grad(neuron.IFNode, surrogate.S2NN, device='cuda:1', alpha=4., beta=1.)
for dtype in [torch.float, torch.half]:
... | null |
7,677 | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from .auto_cuda import cfunction
def piecewise_quadratic_backward(grad_output: torch.Tensor, x: torch.Tensor, alpha: float):
x_abs = x.abs()
mask = (x_abs > (1 / alpha))
grad_x = (grad_output * (- (alpha ** 2) * x_abs + alpha)).... | null |
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