entry_point stringlengths 1 65 | original_triton_python_code stringlengths 208 619k | optimised_triton_code stringlengths 1.15k 275k | repo_name stringlengths 7 115 | module_name stringlengths 1 65 | synthetic bool 1
class | uuid int64 0 18.5k | licenses listlengths 1 6 | stars int64 0 19.8k | sha stringlengths 40 40 | repo_link stringlengths 72 180 |
|---|---|---|---|---|---|---|---|---|---|---|
Expand | import torch
import torch.nn as nn
import torch.utils.data
class Expand(nn.Module):
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
N, C, H, W = x.size()
s = self.gain
x = x.view(N, s, s, C // s ** 2, H, W)
x = x.permute(0... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | ChaokunChang/SVAS | Expand | false | 248 | [
"Apache-2.0"
] | 0 | 61af6eb39269edff8ea5147311628b3200c3a3d2 | https://github.com/ChaokunChang/SVAS/tree/61af6eb39269edff8ea5147311628b3200c3a3d2 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch.autograd
class Actor(nn.Module):
def __init__(self, input_size, output_size):
super(Actor, self).__init__()
self.fc1 = nn.Linear(input_size, 128)
self.fc2 = nn.Linear(128, 256)
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ChangQingAAS/Deep-Reinforcement-Learning | Actor | false | 249 | [
"MIT"
] | 0 | 3bc1381c632b1730a48e63e972aea62086c4287c | https://github.com/ChangQingAAS/Deep-Reinforcement-Learning/tree/3bc1381c632b1730a48e63e972aea62086c4287c |
L2Norm | import torch
import torch.nn as nn
class L2Norm(nn.Module):
def __init__(self, n_dims, scale=20.0, eps=1e-10):
"""L2 normalization layer.
Args:
n_dims (int): Number of dimensions to be normalized
scale (float, optional): Defaults to 20..
eps (float, optional):... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | ChengBo5/mask-text-detector | L2Norm | false | 250 | [
"Apache-2.0"
] | 0 | ce93e45ed1d982ec0ef6ad977c02e49326bf255a | https://github.com/ChengBo5/mask-text-detector/tree/ce93e45ed1d982ec0ef6ad977c02e49326bf255a |
duelingdqnNet | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch.autograd
class duelingdqnNet(nn.Module):
def __init__(self, STATE_NUM, ACTION_NUM):
super(duelingdqnNet, self).__init__()
self.ACTION_NUM = ACTION_NUM
self.fc1_a = nn.Linear(in_features=STAT... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ChangQingAAS/Deep-Reinforcement-Learning | duelingdqnNet | false | 251 | [
"MIT"
] | 0 | 3bc1381c632b1730a48e63e972aea62086c4287c | https://github.com/ChangQingAAS/Deep-Reinforcement-Learning/tree/3bc1381c632b1730a48e63e972aea62086c4287c |
Reorg | import torch
from torch.optim.lr_scheduler import *
import torch.optim
import torch.nn as nn
import torch.utils.data
import torch.utils.model_zoo
class Reorg(nn.Module):
def __init__(self, stride=2):
super(Reorg, self).__init__()
self.stride = stride
def forward(self, x):
stride = se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.optim.lr_scheduler import *
import torch.optim
import torch.nn as nn
import torch.utils.data
import torch.utils.model_zoo
assert_... | ChitienSun/NCTU_DLSR_final_project | Reorg | false | 252 | [
"MIT"
] | 0 | 9d647426c274afc7651ea4fe9a11f2a0a0fd1fba | https://github.com/ChitienSun/NCTU_DLSR_final_project/tree/9d647426c274afc7651ea4fe9a11f2a0a0fd1fba |
CausalConv1d | import torch
import torch.nn as nn
class CausalConv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=1,
**kwargs):
super(CausalConv1d, self).__init__(in_channels, out_channels,
kernel_size, padding=dilation * (kernel_size - 1), dilation=
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ChesterHuynh/Wavenet-CPC-Music-Translation | CausalConv1d | false | 253 | [
"MIT"
] | 0 | 60632b0330a61a10bac1a129826c55372f685427 | https://github.com/ChesterHuynh/Wavenet-CPC-Music-Translation/tree/60632b0330a61a10bac1a129826c55372f685427 |
Sum | import torch
import torch.nn as nn
import torch.utils.data
class Sum(nn.Module):
def __init__(self, n, weight=False):
super(Sum, self).__init__()
self.weight = weight
self.iter = range(n - 1)
if weight:
self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=Tru... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | ChaokunChang/SVAS | Sum | false | 254 | [
"Apache-2.0"
] | 0 | 61af6eb39269edff8ea5147311628b3200c3a3d2 | https://github.com/ChaokunChang/SVAS/tree/61af6eb39269edff8ea5147311628b3200c3a3d2 |
BPNet | import torch
import torch.nn.functional as F
import torch.nn as nn
class BPNet(nn.Module):
def __init__(self, input_dim, output_dim, level1, level2):
super(BPNet, self).__init__()
self.fc1 = nn.Linear(input_dim, level1)
self.fc2 = nn.Linear(level1, level2)
self.fc3 = nn.Linear(lev... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Cheemion/Sitting_Posture_Recognization_Experiments | BPNet | false | 255 | [
"MIT"
] | 0 | 3a96377fe36b97e9867b5c32fe4d7434ddd370e2 | https://github.com/Cheemion/Sitting_Posture_Recognization_Experiments/tree/3a96377fe36b97e9867b5c32fe4d7434ddd370e2 |
BCEBlurWithLogitsLoss | import torch
import torch.nn as nn
import torch.utils.data
class BCEBlurWithLogitsLoss(nn.Module):
def __init__(self, alpha=0.05):
super(BCEBlurWithLogitsLoss, self).__init__()
self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')
self.alpha = alpha
def forward(self, pred, true):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ChaokunChang/SVAS | BCEBlurWithLogitsLoss | false | 256 | [
"Apache-2.0"
] | 0 | 61af6eb39269edff8ea5147311628b3200c3a3d2 | https://github.com/ChaokunChang/SVAS/tree/61af6eb39269edff8ea5147311628b3200c3a3d2 |
SELayer | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class SELayer(nn.Module):
def __init__(self, in_channels, reduction):
super(SELayer, self).__init__()
mid_channels = in_channels // reduction
self.fc1 = nn.Linear(in_channels, mid_channels)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ChengBin1997/pytorch_image_classification | SELayer | false | 257 | [
"MIT"
] | 0 | f7c39efceb86d961489514917d11b96f44699094 | https://github.com/ChengBin1997/pytorch_image_classification/tree/f7c39efceb86d961489514917d11b96f44699094 |
GHMC | import torch
import torch.nn.functional as F
import torch.nn as nn
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero((labels >= 0) & (labels < label_channels),
as_tuple=False).squeeze()
if inds.n... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ChengBo5/mask-text-detector | GHMC | false | 258 | [
"Apache-2.0"
] | 0 | ce93e45ed1d982ec0ef6ad977c02e49326bf255a | https://github.com/ChengBo5/mask-text-detector/tree/ce93e45ed1d982ec0ef6ad977c02e49326bf255a |
Classify | import torch
import torch.nn as nn
import torch.utils.data
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Classify(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
super(Classify, self).__init__()
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | ChaokunChang/SVAS | Classify | false | 259 | [
"Apache-2.0"
] | 0 | 61af6eb39269edff8ea5147311628b3200c3a3d2 | https://github.com/ChaokunChang/SVAS/tree/61af6eb39269edff8ea5147311628b3200c3a3d2 |
PetarVGAT | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class BaseModel(nn.Module):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
pass
@classmethod
def build_model_from_args(cls, args):
"""Build a new ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BruceW91/cogdl | PetarVGAT | false | 260 | [
"MIT"
] | 0 | 1ad524375f5ba062103698a0432fc857572a6933 | https://github.com/BruceW91/cogdl/tree/1ad524375f5ba062103698a0432fc857572a6933 |
ZeroPad1d | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ZeroPad1d(nn.Module):
def __init__(self, pad_left, pad_right):
super().__init__()
self.pad_left = pad_left
self.p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
assert_size_str... | ChanLiang/MAP-BERT | ZeroPad1d | false | 261 | [
"MIT"
] | 0 | c3f95a925002061463dbb68608ff7c67ff353b5d | https://github.com/ChanLiang/MAP-BERT/tree/c3f95a925002061463dbb68608ff7c67ff353b5d |
Conv2d | import torch
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
relu=True, same_padding=False, bn=False):
super(Conv2d, self).__init__()
padding = int((kernel_size - 1) / 2) if same_padding else 0
self.conv = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ChrisKonishi/multi-stream-crowd-counting-extended | Conv2d | false | 262 | [
"MIT"
] | 0 | 4b1590499bd93ac09e62c4c7760b88ae92e6b301 | https://github.com/ChrisKonishi/multi-stream-crowd-counting-extended/tree/4b1590499bd93ac09e62c4c7760b88ae92e6b301 |
Fp32GroupNorm | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Fp32GroupNorm(nn.GroupNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
impor... | ChanLiang/MAP-BERT | Fp32GroupNorm | false | 263 | [
"MIT"
] | 0 | c3f95a925002061463dbb68608ff7c67ff353b5d | https://github.com/ChanLiang/MAP-BERT/tree/c3f95a925002061463dbb68608ff7c67ff353b5d |
MaxPoolStride1 | import torch
from torch.optim.lr_scheduler import *
import torch.nn.functional as F
import torch.optim
import torch.nn as nn
import torch.utils.data
import torch.utils.model_zoo
class MaxPoolStride1(nn.Module):
def __init__(self):
super(MaxPoolStride1, self).__init__()
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.optim.lr_scheduler import *
import torch.optim
import torch.nn as nn
import to... | ChitienSun/NCTU_DLSR_final_project | MaxPoolStride1 | false | 264 | [
"MIT"
] | 0 | 9d647426c274afc7651ea4fe9a11f2a0a0fd1fba | https://github.com/ChitienSun/NCTU_DLSR_final_project/tree/9d647426c274afc7651ea4fe9a11f2a0a0fd1fba |
GlobalAvgPool2d | import torch
from torch.optim.lr_scheduler import *
import torch.nn.functional as F
import torch.optim
import torch.nn as nn
import torch.utils.data
import torch.utils.model_zoo
class GlobalAvgPool2d(nn.Module):
def __init__(self):
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.optim.lr_scheduler import *
import torch.optim
import torch.nn as nn
import torch.utils.data
import torch.utils.model_zoo
assert_... | ChitienSun/NCTU_DLSR_final_project | GlobalAvgPool2d | false | 265 | [
"MIT"
] | 0 | 9d647426c274afc7651ea4fe9a11f2a0a0fd1fba | https://github.com/ChitienSun/NCTU_DLSR_final_project/tree/9d647426c274afc7651ea4fe9a11f2a0a0fd1fba |
DilatedResConv | import torch
import torch.nn as nn
import torch.nn.functional as F
class DilatedResConv(nn.Module):
def __init__(self, channels, dilation=1, activation='relu', padding=1,
kernel_size=3, left_pad=0):
super().__init__()
in_channels = channels
if activation == 'relu':
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ChesterHuynh/Wavenet-CPC-Music-Translation | DilatedResConv | false | 266 | [
"MIT"
] | 0 | 60632b0330a61a10bac1a129826c55372f685427 | https://github.com/ChesterHuynh/Wavenet-CPC-Music-Translation/tree/60632b0330a61a10bac1a129826c55372f685427 |
ClippedLinearQuantization | import torch
from torch.optim.lr_scheduler import *
import torch.optim
import torch.nn as nn
import torch.utils.data
import torch.utils.model_zoo
def linear_dequantize(input, scale_factor, inplace=False):
if inplace:
input.div_(scale_factor)
return input
return input / scale_factor
def linea... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.optim.lr_schedule... | ChitienSun/NCTU_DLSR_final_project | ClippedLinearQuantization | false | 267 | [
"MIT"
] | 0 | 9d647426c274afc7651ea4fe9a11f2a0a0fd1fba | https://github.com/ChitienSun/NCTU_DLSR_final_project/tree/9d647426c274afc7651ea4fe9a11f2a0a0fd1fba |
BahdanauAttention | import math
import torch
from torch import nn
import torch.nn.functional as F
class BahdanauAttention(nn.Module):
def __init__(self, hidden_size):
super(BahdanauAttention, self).__init__()
self.hidden_size = hidden_size
self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Chiang97912/seq2seq | BahdanauAttention | false | 268 | [
"MIT"
] | 0 | 4b544016ecc16fa8e48358021cf486e58494aa0f | https://github.com/Chiang97912/seq2seq/tree/4b544016ecc16fa8e48358021cf486e58494aa0f |
MaxPool2dStaticSamePadding | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MaxPool2dStaticSamePadding(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
self.pool = nn.MaxPool2d(*args, **kwargs)
self.stride = self.pool.stride
self.kernel_size = self.pool... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ChrisLiuxp/efficientdet | MaxPool2dStaticSamePadding | false | 269 | [
"MIT"
] | 0 | 5d52ac491e1dd2a29ee6650bb746f1e840c24fcc | https://github.com/ChrisLiuxp/efficientdet/tree/5d52ac491e1dd2a29ee6650bb746f1e840c24fcc |
PSNRLoss | import torch
import torch.nn as nn
from torch.nn.functional import mse_loss as mse
def psnr(input: 'torch.Tensor', target: 'torch.Tensor', max_val: 'float'
) ->torch.Tensor:
"""Creates a function that calculates the PSNR between 2 images.
PSNR is Peek Signal to Noise Ratio, which is similar to mean squar... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | ChristophReich1996/kornia | PSNRLoss | false | 270 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
RgbaToBgr | import torch
import torch.nn as nn
def bgr_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
"""Convert a BGR image to RGB.
Args:
image (torch.Tensor): BGR Image to be converted to BGR of shape :math:`(*,3,H,W)`.
Returns:
torch.Tensor: RGB version of the image with shape of shape :math:`(*,3... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ChristophReich1996/kornia | RgbaToBgr | false | 271 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
GHMR | import torch
import torch.nn as nn
class GHMR(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
`Gradient Harmonized Single-stage Detector
<https://arxiv.org/abs/1811.05181>`_.
Args:
mu (float): The parameter for the Authentic Smooth L1 loss.
b... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ChengBo5/mask-text-detector | GHMR | false | 272 | [
"Apache-2.0"
] | 0 | ce93e45ed1d982ec0ef6ad977c02e49326bf255a | https://github.com/ChengBo5/mask-text-detector/tree/ce93e45ed1d982ec0ef6ad977c02e49326bf255a |
Rot180 | import torch
import torch.nn as nn
def rot180(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-2, -1])
class Rot180(nn.Module):
"""Rotate a tensor image or a batch of tensor images
180 degrees. Input must be a tensor of shape (C, H, W)
or a batch of tensors :math:`(*, C, H, W)`.
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ChristophReich1996/kornia | Rot180 | false | 273 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
SplAtConv2d | import logging
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import ReLU
from torch.nn import Conv2d
from torch.nn.modules.utils import _pair
from torch.optim.lr_scheduler import *
from torch.optim import *
def get_norm(norm, out_channels, **kwargs):
"""
Args:
norm (s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Challyfilio/NAIC2021 | SplAtConv2d | false | 274 | [
"MIT"
] | 0 | 11b38a920dcc902f9b798dc43ae360062862e6e4 | https://github.com/Challyfilio/NAIC2021/tree/11b38a920dcc902f9b798dc43ae360062862e6e4 |
RobertaMaskLeanerHead | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class RobertaMaskLeanerHead(nn.Module):
"""
Head for mask leaner.
input: (batch, src_lens, embed_dim)
output: (batch, src_lens，1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ChanLiang/MAP-BERT | RobertaMaskLeanerHead | false | 275 | [
"MIT"
] | 0 | c3f95a925002061463dbb68608ff7c67ff353b5d | https://github.com/ChanLiang/MAP-BERT/tree/c3f95a925002061463dbb68608ff7c67ff353b5d |
ExtractTensorPatches | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple
from typing import Union
from typing import Optional
from torch.nn.modules.utils import _pair
def _extract_tensor_patchesnd(input: 'torch.Tensor', window_sizes:
'Tuple[int, ...]', strides: 'Tuple[int, ...]') ->torch.Tensor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple
from typing import Union
from typing import Optional
from tor... | ChristophReich1996/kornia | ExtractTensorPatches | false | 276 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
Hflip | import torch
import torch.nn as nn
def hflip(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-1])
class Hflip(nn.Module):
"""Horizontally flip a tensor image or a batch of tensor images. Input must
be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ChristophReich1996/kornia | Hflip | false | 277 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
InverseDepthSmoothnessLoss | import torch
import torch.nn as nn
def _gradient_x(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :, :-1] - img[:, :, :, 1:]
def _gradient_y(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :-1, :] - img[:, :, 1:... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ChristophReich1996/kornia | InverseDepthSmoothnessLoss | false | 278 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
NCutLossOptimized | import torch
from torch import Tensor
import torch.nn as nn
class NCutLossOptimized(nn.Module):
"""Implementation of the continuous N-Cut loss, as in:
'W-Net: A Deep Model for Fully Unsupervised Image Segmentation', by Xia, Kulis (2017)"""
def __init__(self, radius: 'int'=5):
"""
:param r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Christer-L/wnet_pytorch | NCutLossOptimized | false | 279 | [
"MIT"
] | 0 | c7a7d3db0c07d5e2d83fe152ce5fdae31472748b | https://github.com/Christer-L/wnet_pytorch/tree/c7a7d3db0c07d5e2d83fe152ce5fdae31472748b |
RgbaToRgb | import torch
import torch.nn as nn
def rgba_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
"""Convert an image from RGBA to RGB.
Args:
image (torch.Tensor): RGBA Image to be converted to RGB of shape :math:`(*,4,H,W)`.
Returns:
torch.Tensor: RGB version of the image with shape :math:`(*,3... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ChristophReich1996/kornia | RgbaToRgb | false | 280 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
MomentumNetSide | import torch
import torch.utils.data
import torch.utils.data.dataloader
class MomentumNetSide(torch.nn.Module):
def __init__(self, beta: 'float'):
super(MomentumNetSide, self).__init__()
self.beta = beta
def forward(self, inp: 'torch.Tensor'):
return inp * self.beta
def get_inputs(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.utils.data.dataloader
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | ClashLuke/HomebrewNLP | MomentumNetSide | false | 281 | [
"BSD-2-Clause"
] | 0 | 18d9a9a32af4e5e5672a9261ef6ac613dc9194c0 | https://github.com/ClashLuke/HomebrewNLP/tree/18d9a9a32af4e5e5672a9261ef6ac613dc9194c0 |
BinaryFocalLossWithLogits | import torch
import torch.nn as nn
def binary_focal_loss_with_logits(input: 'torch.Tensor', target:
'torch.Tensor', alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
'str'='none', eps: 'float'=1e-08) ->torch.Tensor:
"""Function that computes Binary Focal loss.
.. math::
\\text{FL}(p_t) = -... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ChristophReich1996/kornia | BinaryFocalLossWithLogits | false | 282 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
TotalVariation | import torch
import torch.nn as nn
def total_variation(img: 'torch.Tensor') ->torch.Tensor:
"""Function that computes Total Variation according to [1].
Args:
img (torch.Tensor): the input image with shape :math:`(N, C, H, W)` or :math:`(C, H, W)`.
Return:
torch.Tensor: a scalar with the ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ChristophReich1996/kornia | TotalVariation | false | 283 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
Vflip | import torch
import torch.nn as nn
def vflip(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-2])
class Vflip(nn.Module):
"""Vertically flip a tensor image or a batch of tensor images. Input must
be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ChristophReich1996/kornia | Vflip | false | 284 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
MSE | import torch
import torch.nn as nn
class MSE(nn.Module):
def __init__(self):
super(MSE, self).__init__()
def forward(self, pred, real):
diffs = torch.add(real, -pred)
n = torch.numel(diffs.data)
mse = torch.sum(diffs.pow(2)) / n
return mse
def get_inputs():
retu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Clement25/Multimodal-Attack | MSE | false | 285 | [
"MIT"
] | 0 | bd04ee099d457e87b6e6ee918c03f65a589bcb9a | https://github.com/Clement25/Multimodal-Attack/tree/bd04ee099d457e87b6e6ee918c03f65a589bcb9a |
DiffLoss | import torch
import torch.nn as nn
class DiffLoss(nn.Module):
def __init__(self):
super(DiffLoss, self).__init__()
def forward(self, input1, input2):
batch_size = input1.size(0)
input1 = input1.view(batch_size, -1)
input2 = input2.view(batch_size, -1)
input1_mean = to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Clement25/Multimodal-Attack | DiffLoss | false | 286 | [
"MIT"
] | 0 | bd04ee099d457e87b6e6ee918c03f65a589bcb9a | https://github.com/Clement25/Multimodal-Attack/tree/bd04ee099d457e87b6e6ee918c03f65a589bcb9a |
Fp32LayerNorm | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Fp32LayerNorm(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
impor... | ChanLiang/MAP-BERT | Fp32LayerNorm | false | 287 | [
"MIT"
] | 0 | c3f95a925002061463dbb68608ff7c67ff353b5d | https://github.com/ChanLiang/MAP-BERT/tree/c3f95a925002061463dbb68608ff7c67ff353b5d |
ConcatenateChannels | import torch
import torch.nn
class ConcatenateChannels(torch.nn.Module):
def __init__(self, split_location):
self.split_location = split_location
super(ConcatenateChannels, self).__init__()
def forward(self, x, y):
return torch.cat([x, y], dim=1)
def inverse(self, x):
re... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_... | ClashLuke/memcnn | ConcatenateChannels | false | 288 | [
"MIT"
] | 0 | 1d48132282c02506ca3d35540f819c4c9130eab4 | https://github.com/ClashLuke/memcnn/tree/1d48132282c02506ca3d35540f819c4c9130eab4 |
SplitChannels | import torch
import torch.nn
class SplitChannels(torch.nn.Module):
def __init__(self, split_location):
self.split_location = split_location
super(SplitChannels, self).__init__()
def forward(self, x):
return x[:, :self.split_location, :].clone(), x[:, self.
split_location:... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_... | ClashLuke/memcnn | SplitChannels | false | 289 | [
"MIT"
] | 0 | 1d48132282c02506ca3d35540f819c4c9130eab4 | https://github.com/ClashLuke/memcnn/tree/1d48132282c02506ca3d35540f819c4c9130eab4 |
SpatialAttention | import torch
import torch.nn as nn
class SpatialAttention(nn.Module):
def __init__(self, kernel=3):
super(SpatialAttention, self).__init__()
self.conv1 = nn.Conv2d(2, 1, kernel_size=kernel, padding=kernel //
2, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Clayrisee/BanchelorsProject-FAS | SpatialAttention | false | 290 | [
"MIT"
] | 0 | 3da199fb2e7be04eed7f28374ef753383511dbee | https://github.com/Clayrisee/BanchelorsProject-FAS/tree/3da199fb2e7be04eed7f28374ef753383511dbee |
InvDepth | import torch
import torch.nn as nn
class InvDepth(nn.Module):
def __init__(self, height, width, min_depth=0.5, max_depth=25.0):
super(InvDepth, self).__init__()
self._min_range = 1.0 / max_depth
self._max_range = 1.0 / min_depth
self.w = nn.Parameter(self._init_weights(height, wid... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ChristophReich1996/kornia | InvDepth | false | 291 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
DummyModel | import torch
import torch.nn
class DummyModel(torch.nn.Module):
def __init__(self, block):
super(DummyModel, self).__init__()
self.block = block
self.conv = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 1, 64... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | ClashLuke/memcnn | DummyModel | false | 292 | [
"MIT"
] | 0 | 1d48132282c02506ca3d35540f819c4c9130eab4 | https://github.com/ClashLuke/memcnn/tree/1d48132282c02506ca3d35540f819c4c9130eab4 |
Simplenet | import torch
from torch.optim.lr_scheduler import *
import torch.nn.functional as F
import torch.optim
import torch.nn as nn
import torch.utils.data
import torch.utils.model_zoo
class Simplenet(nn.Module):
def __init__(self):
super(Simplenet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.optim.lr_scheduler... | ChitienSun/NCTU_DLSR_final_project | Simplenet | false | 293 | [
"MIT"
] | 0 | 9d647426c274afc7651ea4fe9a11f2a0a0fd1fba | https://github.com/ChitienSun/NCTU_DLSR_final_project/tree/9d647426c274afc7651ea4fe9a11f2a0a0fd1fba |
InvConv | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class InvConv(nn.Module):
"""Invertible 1x1 Convolution for 2D inputs. Originally described in Glow
(https://arxiv.org/abs/1807.03039). Does not support LU-decomposed version.
Args:
num_channels (int): Number of... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | ClaraBing/flow | InvConv | false | 294 | [
"MIT"
] | 0 | 00290326a97235e7d83303f1efff2e14214d0c36 | https://github.com/ClaraBing/flow/tree/00290326a97235e7d83303f1efff2e14214d0c36 |
PixWiseBCELoss | import torch
import torch.nn as nn
class PixWiseBCELoss(nn.Module):
def __init__(self, beta=0.5):
super().__init__()
self.criterion = nn.BCELoss()
self.beta = beta
def forward(self, net_mask, net_label, target_mask, target_label):
pixel_loss = self.criterion(net_mask, target_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Clayrisee/BanchelorsProject-FAS | PixWiseBCELoss | false | 295 | [
"MIT"
] | 0 | 3da199fb2e7be04eed7f28374ef753383511dbee | https://github.com/Clayrisee/BanchelorsProject-FAS/tree/3da199fb2e7be04eed7f28374ef753383511dbee |
SIMSE | import torch
import torch.nn as nn
class SIMSE(nn.Module):
def __init__(self):
super(SIMSE, self).__init__()
def forward(self, pred, real):
diffs = torch.add(real, -pred)
n = torch.numel(diffs.data)
simse = torch.sum(diffs).pow(2) / n ** 2
return simse
def get_input... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Clement25/Multimodal-Attack | SIMSE | false | 296 | [
"MIT"
] | 0 | bd04ee099d457e87b6e6ee918c03f65a589bcb9a | https://github.com/Clement25/Multimodal-Attack/tree/bd04ee099d457e87b6e6ee918c03f65a589bcb9a |
Attention | import math
import torch
from torch import nn
class Attention(nn.Module):
"""A generic attention module for a decoder in seq2seq"""
def __init__(self, dim, use_tanh=False, C=10):
super(Attention, self).__init__()
self.use_tanh = use_tanh
self.project_query = nn.Linear(dim, dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | ChristinaTan0704/transTSP | Attention | false | 297 | [
"MIT"
] | 0 | b97cd7ed8ae97e91b687d5007d13a021781f3d1d | https://github.com/ChristinaTan0704/transTSP/tree/b97cd7ed8ae97e91b687d5007d13a021781f3d1d |
Gate | import torch
import torch.nn as nn
class Gate(torch.nn.Module):
def __init__(self, out_planes):
super(Gate, self).__init__()
self.gate = nn.Parameter(torch.ones(1, out_planes, 1, 1),
requires_grad=False)
def forward(self, x):
return self.gate * x
def get_inputs():
r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | CityU-AIM-Group/GFBS | Gate | false | 298 | [
"MIT"
] | 0 | d71361243f1bcf699e1a20b312b05fe0be4dfd6d | https://github.com/CityU-AIM-Group/GFBS/tree/d71361243f1bcf699e1a20b312b05fe0be4dfd6d |
Block | import torch
from torch import nn
from torch.optim.lr_scheduler import *
from torch.optim import *
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created fo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Challyfilio/NAIC2021 | Block | false | 299 | [
"MIT"
] | 0 | 11b38a920dcc902f9b798dc43ae360062862e6e4 | https://github.com/Challyfilio/NAIC2021/tree/11b38a920dcc902f9b798dc43ae360062862e6e4 |
ConvBlock | import torch
from torch import nn
class ConvBlock(nn.Module):
def __init__(self, nb_in, nb_out):
super(ConvBlock, self).__init__()
self.convolution = nn.Conv2d(in_channels=nb_in, out_channels=nb_out,
kernel_size=5, stride=1, padding=2)
self.ReLU = nn.ReLU()
self.MaxPoo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Clement-W/PT-Activation-Map-Visualiser | ConvBlock | false | 300 | [
"MIT"
] | 0 | 6c71d5225585e5f18c3e73a4775d7816699faeea | https://github.com/Clement-W/PT-Activation-Map-Visualiser/tree/6c71d5225585e5f18c3e73a4775d7816699faeea |
MultiplicationInverse | import torch
import torch.nn
class MultiplicationInverse(torch.nn.Module):
def __init__(self, factor=2):
super(MultiplicationInverse, self).__init__()
self.factor = torch.nn.Parameter(torch.ones(1) * factor)
def forward(self, x):
return x * self.factor
def inverse(self, y):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_... | ClashLuke/memcnn | MultiplicationInverse | false | 301 | [
"MIT"
] | 0 | 1d48132282c02506ca3d35540f819c4c9130eab4 | https://github.com/ClashLuke/memcnn/tree/1d48132282c02506ca3d35540f819c4c9130eab4 |
CrossEntropyLossTF | from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn
from torch.nn.modules.module import Module
def _assert_no_grad(variable):
msg = (
"nn criterions don't compute the gradient w.r.t. targets - please mark these variables as not requiring gradients"
)
assert not varia... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import M... | ClashLuke/memcnn | CrossEntropyLossTF | false | 302 | [
"MIT"
] | 0 | 1d48132282c02506ca3d35540f819c4c9130eab4 | https://github.com/ClashLuke/memcnn/tree/1d48132282c02506ca3d35540f819c4c9130eab4 |
SSE | import torch
import torch.nn as nn
import torch.utils.data
class SSE(nn.Module):
def __init__(self, in_ch):
super(SSE, self).__init__()
self.conv = nn.Conv2d(in_ch, in_ch, kernel_size=1, stride=1)
def forward(self, x):
input_x = x
x = self.conv(x)
x = torch.sigmoid(x)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | ColinWine/Accurate-and-rapid-pulmonary-tuberculosis-diagnosis-system | SSE | false | 303 | [
"Apache-2.0"
] | 0 | 7be433b3a495a7c4db2b850a79dc505e413909c4 | https://github.com/ColinWine/Accurate-and-rapid-pulmonary-tuberculosis-diagnosis-system/tree/7be433b3a495a7c4db2b850a79dc505e413909c4 |
ExpNormalBasis | import torch
import numpy as np
import torch.nn as nn
class ExpNormalBasis(nn.Module):
def __init__(self, n_rbf, cutoff, learnable_mu, learnable_beta):
super().__init__()
self.mu = torch.linspace(np.exp(-cutoff), 1, n_rbf)
init_beta = (2 / n_rbf * (1 - np.exp(-cutoff))) ** -2
self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._d... | ClintvanHoesel/MXMNet_adapted | ExpNormalBasis | false | 304 | [
"MIT"
] | 0 | 091aae4a664b5b0944dfe95dbd2f5da441541437 | https://github.com/ClintvanHoesel/MXMNet_adapted/tree/091aae4a664b5b0944dfe95dbd2f5da441541437 |
CosineEnvelope | import torch
import numpy as np
import torch.nn as nn
class CosineEnvelope(nn.Module):
def __init__(self, cutoff):
super().__init__()
self.cutoff = cutoff
def forward(self, d):
output = 0.5 * (torch.cos(np.pi * d / self.cutoff) + 1)
exclude = d >= self.cutoff
output[e... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ClintvanHoesel/MXMNet_adapted | CosineEnvelope | false | 305 | [
"MIT"
] | 0 | 091aae4a664b5b0944dfe95dbd2f5da441541437 | https://github.com/ClintvanHoesel/MXMNet_adapted/tree/091aae4a664b5b0944dfe95dbd2f5da441541437 |
Conv2d | import torch
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding='auto', dilation=1, bias=False, norm=nn.Identity(),
activation=nn.ReLU()):
super(Conv2d, self).__init__()
if padding == 'auto':
ke... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ClementPla/NNTools | Conv2d | false | 306 | [
"MIT"
] | 0 | 61562be2d931a7f720ceee1bd91a37a2b9a329af | https://github.com/ClementPla/NNTools/tree/61562be2d931a7f720ceee1bd91a37a2b9a329af |
MultiHeadedAttention | import math
import torch
from torch import Tensor
import torch.nn as nn
class MultiHeadedAttention(nn.Module):
"""
Multi-Head Attention module from "Attention is All You Need"
Implementation modified from OpenNMT-py.
https://github.com/OpenNMT/OpenNMT-py
"""
def __init__(self, num_heads: 'in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ClementNguyen/slt | MultiHeadedAttention | false | 307 | [
"Apache-2.0"
] | 0 | 20ee90349d1ed0655b99612ffcfae6d079116db6 | https://github.com/ClementNguyen/slt/tree/20ee90349d1ed0655b99612ffcfae6d079116db6 |
PainnRadialBasis | import torch
import numpy as np
import torch.nn as nn
class PainnRadialBasis(nn.Module):
def __init__(self, n_rbf, cutoff, learnable_k):
super().__init__()
self.n = torch.arange(1, n_rbf + 1).float()
if learnable_k:
self.n = nn.Parameter(self.n)
self.cutoff = cutoff
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ClintvanHoesel/MXMNet_adapted | PainnRadialBasis | false | 308 | [
"MIT"
] | 0 | 091aae4a664b5b0944dfe95dbd2f5da441541437 | https://github.com/ClintvanHoesel/MXMNet_adapted/tree/091aae4a664b5b0944dfe95dbd2f5da441541437 |
Net | import torch
from torch import Tensor
from logging import info
from torch import nn
from logging import error
from torch.nn import Linear
from torch.nn.functional import relu
class Net(nn.Module):
def __init__(self, size):
super(Net, self).__init__()
convolutions = [5]
info('CONV LAYERS: ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from logging import info
from... | ChsHub/ai_denoiser | Net | false | 309 | [
"MIT"
] | 0 | abb0852765b10a0f05593a850f9922c5737f5f6a | https://github.com/ChsHub/ai_denoiser/tree/abb0852765b10a0f05593a850f9922c5737f5f6a |
MultiHeadAttention | import math
import torch
import numpy as np
from torch import nn
class MultiHeadAttention(nn.Module):
def __init__(self, n_heads, input_dim, embed_dim, val_dim=None, key_dim
=None):
super(MultiHeadAttention, self).__init__()
if val_dim is None:
val_dim = embed_dim // n_heads
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ChristinaTan0704/transTSP | MultiHeadAttention | false | 311 | [
"MIT"
] | 0 | b97cd7ed8ae97e91b687d5007d13a021781f3d1d | https://github.com/ChristinaTan0704/transTSP/tree/b97cd7ed8ae97e91b687d5007d13a021781f3d1d |
Sine | import torch
import torch.nn as nn
class Sine(nn.Module):
"""for implicit representation"""
def __init__(self, w0=1.0):
super().__init__()
self.w0 = w0
def forward(self, x):
return torch.sin(self.w0 * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inp... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | Crazy-Jack/BigGAN-PyTorch | Sine | false | 312 | [
"MIT"
] | 0 | 1a5644e9c87cc399580c96cfeb180052076888da | https://github.com/Crazy-Jack/BigGAN-PyTorch/tree/1a5644e9c87cc399580c96cfeb180052076888da |
CatConv | import torch
import torch.nn as nn
class CatConv(nn.Module):
def __init__(self, in_kernels_1, in_kernels_2, kernels):
super(CatConv, self).__init__()
self.conv = nn.Conv2d(in_kernels_1 + in_kernels_2, kernels,
kernel_size=1, bias=True)
def forward(self, x1, x2):
x1 = torc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ColinKohler/ActionDyanmicsNetwork | CatConv | false | 313 | [
"MIT"
] | 0 | 9cb6ffca111bfb1e1efb31cbac9201a98739a6ed | https://github.com/ColinKohler/ActionDyanmicsNetwork/tree/9cb6ffca111bfb1e1efb31cbac9201a98739a6ed |
GELU | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class GELU(nn.Module):
"""Applies the Gaussian Error Linear Units function:
.. math:: ext{GELU}(x) = x * \\Phi(x)
where :math:`\\Phi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | Crazy-Jack/SpatialExpGeneCluster | GELU | false | 315 | [
"MIT"
] | 0 | 9e57c308d1c577a936a2358d0641c65b8130034f | https://github.com/Crazy-Jack/SpatialExpGeneCluster/tree/9e57c308d1c577a936a2358d0641c65b8130034f |
Adv | import torch
import torch.nn as nn
class Adv(nn.Module):
def __init__(self, dim_inputs, dropout):
super(Adv, self).__init__()
self.affine1 = nn.Linear(dim_inputs, 32)
self.affine2 = nn.Linear(32, 32)
self.adv_head = nn.Linear(32, 1)
self.act = nn.LeakyReLU()
self.d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Cranial-XIX/TRPO-and-its-variant | Adv | false | 316 | [
"MIT"
] | 0 | aa74102d013c998a666683667073c22aad8c5bce | https://github.com/Cranial-XIX/TRPO-and-its-variant/tree/aa74102d013c998a666683667073c22aad8c5bce |
ScaledDotProduct | import torch
from typing import Tuple
from typing import Optional
class ScaledDotProduct(torch.nn.Module):
def __init__(self, dropout=0.0):
"""Processes a projected query and key-value pair to apply
scaled dot product attention.
Args:
dropout (float): probability of dropping a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ConnollyLeon/recommenders | ScaledDotProduct | false | 319 | [
"MIT"
] | 0 | 6ada3b6b71380660fec353c11db752b4637aebf5 | https://github.com/ConnollyLeon/recommenders/tree/6ada3b6b71380660fec353c11db752b4637aebf5 |
Block | import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNorm(nn.Module):
""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
shape (batch_size, height, width,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Clayrisee/BanchelorsProject-FAS | Block | false | 320 | [
"MIT"
] | 0 | 3da199fb2e7be04eed7f28374ef753383511dbee | https://github.com/Clayrisee/BanchelorsProject-FAS/tree/3da199fb2e7be04eed7f28374ef753383511dbee |
Net | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.onnx
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 24, kernel_size=5, padding=2)
self.conv2 = nn.Conv2d(24, 48, kernel_size=5, padding=1)
self.conv3 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Charlie839242/MNIST_example | Net | false | 321 | [
"Apache-2.0"
] | 0 | e23d5b0314d8fb2bd38323dbb289a2a1591f105b | https://github.com/Charlie839242/MNIST_example/tree/e23d5b0314d8fb2bd38323dbb289a2a1591f105b |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, n_obs, output_dim, hidden_size, init_w=0.003):
super().__init__()
self.linear1 = nn.Linear(n_obs + output_dim, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Crazyalltnt/RL-Alogorithms-Implement | Critic | false | 322 | [
"MIT"
] | 0 | 27905f1c1890b1aff907564230b4ec0c22e60ba0 | https://github.com/Crazyalltnt/RL-Alogorithms-Implement/tree/27905f1c1890b1aff907564230b4ec0c22e60ba0 |
Policy | import torch
import torch.nn as nn
class Policy(nn.Module):
def __init__(self, dim_inputs, dim_outputs):
super(Policy, self).__init__()
self.affine1 = nn.Linear(dim_inputs, 64)
self.affine2 = nn.Linear(64, 64)
self.action_mean = nn.Linear(64, dim_outputs)
self.action_mean.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Cranial-XIX/TRPO-and-its-variant | Policy | false | 324 | [
"MIT"
] | 0 | aa74102d013c998a666683667073c22aad8c5bce | https://github.com/Cranial-XIX/TRPO-and-its-variant/tree/aa74102d013c998a666683667073c22aad8c5bce |
PMA | import math
import torch
from torch import Tensor
from torch.nn import Linear
from typing import Type
from typing import Optional
from typing import Tuple
from torch.nn import LayerNorm
class MAB(torch.nn.Module):
def __init__(self, dim_Q: 'int', dim_K: 'int', dim_V: 'int', num_heads:
'int', Conv: 'Optio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ClintvanHoesel/MXMNet_adapted | PMA | false | 325 | [
"MIT"
] | 0 | 091aae4a664b5b0944dfe95dbd2f5da441541437 | https://github.com/ClintvanHoesel/MXMNet_adapted/tree/091aae4a664b5b0944dfe95dbd2f5da441541437 |
BatchedVectorAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class BatchedVectorAttention(nn.Module):
"""vector attention"""
def __init__(self, input_dim, hidden_dim):
super(BatchedVectorAttention, self).__init__()
self.theta = nn.Linear(input_dim, hidden_dim)
self.phi = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Crazy-Jack/BigGAN-PyTorch | BatchedVectorAttention | false | 326 | [
"MIT"
] | 0 | 1a5644e9c87cc399580c96cfeb180052076888da | https://github.com/Crazy-Jack/BigGAN-PyTorch/tree/1a5644e9c87cc399580c96cfeb180052076888da |
CReLU | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, nchannels, bias=True, init_scale=1.0):
super().__init__()
self.nchannels = nchannels
self.weight = nn.Parameter(torch.Tensor(1, nchannels, 1, 1))
if bias:
self.bias = nn.Parameter(torch.Tenso... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | CuongNguyen218/ObjectDetection-OneStageDet | CReLU | false | 327 | [
"MIT"
] | 0 | 60efe8b0ee6782b2aea20a32264b2ce1fc21901f | https://github.com/CuongNguyen218/ObjectDetection-OneStageDet/tree/60efe8b0ee6782b2aea20a32264b2ce1fc21901f |
PaddedMaxPool2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class PaddedMaxPool2d(nn.Module):
""" Maxpool layer with a replicating padding.
Args:
kernel_size (int or tuple): Kernel size for maxpooling
stride (int or tuple, optional): The stride of the window; Default ``kernel_size``
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | CuongNguyen218/ObjectDetection-OneStageDet | PaddedMaxPool2d | false | 328 | [
"MIT"
] | 0 | 60efe8b0ee6782b2aea20a32264b2ce1fc21901f | https://github.com/CuongNguyen218/ObjectDetection-OneStageDet/tree/60efe8b0ee6782b2aea20a32264b2ce1fc21901f |
UnpoolingAsConvolution | import torch
import torch.nn.functional as F
import torch.nn as nn
def getIncomingShape(incoming):
size = incoming.size()
return [size[0], size[1], size[2], size[3]]
def interleave(tensors, axis):
old_shape = getIncomingShape(tensors[0])[1:]
new_shape = [-1] + old_shape
new_shape[axis] *= len(te... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ColinKohler/ActionDyanmicsNetwork | UnpoolingAsConvolution | false | 330 | [
"MIT"
] | 0 | 9cb6ffca111bfb1e1efb31cbac9201a98739a6ed | https://github.com/ColinKohler/ActionDyanmicsNetwork/tree/9cb6ffca111bfb1e1efb31cbac9201a98739a6ed |
PLU | import torch
import torch.nn as nn
class PLU(nn.Module):
"""
y = max(alpha*(x+c)−c, min(alpha*(x−c)+c, x))
from PLU: The Piecewise Linear Unit Activation Function
"""
def __init__(self, alpha=0.1, c=1):
super().__init__()
self.alpha = alpha
self.c = c
def forward(self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | CuongNguyen218/ObjectDetection-OneStageDet | PLU | false | 331 | [
"MIT"
] | 0 | 60efe8b0ee6782b2aea20a32264b2ce1fc21901f | https://github.com/CuongNguyen218/ObjectDetection-OneStageDet/tree/60efe8b0ee6782b2aea20a32264b2ce1fc21901f |
HLoss | import torch
import torch.nn as nn
class HLoss(nn.Module):
def __init__(self):
super(HLoss, self).__init__()
def forward(self, x):
b = torch.exp(x) * x
b = -1.0 * b.sum(dim=1)
return b
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
retu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | DAIZHENWEI/FastGCN_pytorch | HLoss | false | 332 | [
"MIT"
] | 0 | 87efe350d5acbe517a0642e9862ac9676b55c053 | https://github.com/DAIZHENWEI/FastGCN_pytorch/tree/87efe350d5acbe517a0642e9862ac9676b55c053 |
TripletLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class TripletLoss(nn.Module):
"""
Triplet loss
Takes embeddings of an anchor sample, a positive sample and a negative sample
"""
def __init__(self, margin):
super(TripletLoss, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | DanIulian/minigrid_rl | TripletLoss | false | 333 | [
"MIT"
] | 0 | d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa | https://github.com/DanIulian/minigrid_rl/tree/d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa |
MAB | import math
import torch
from torch import Tensor
from torch.nn import Linear
from typing import Type
from typing import Optional
from typing import Tuple
from torch.nn import LayerNorm
class MAB(torch.nn.Module):
def __init__(self, dim_Q: 'int', dim_K: 'int', dim_V: 'int', num_heads:
'int', Conv: 'Optio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ClintvanHoesel/MXMNet_adapted | MAB | false | 334 | [
"MIT"
] | 0 | 091aae4a664b5b0944dfe95dbd2f5da441541437 | https://github.com/ClintvanHoesel/MXMNet_adapted/tree/091aae4a664b5b0944dfe95dbd2f5da441541437 |
L2Norm | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, nchannels, bias=True, init_scale=1.0):
super().__init__()
self.nchannels = nchannels
self.weight = nn.Parameter(torch.Tensor(1, nchannels, 1, 1))
if bias:
self.bias = nn.Parameter(torch.Tenso... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | CuongNguyen218/ObjectDetection-OneStageDet | L2Norm | false | 335 | [
"MIT"
] | 0 | 60efe8b0ee6782b2aea20a32264b2ce1fc21901f | https://github.com/CuongNguyen218/ObjectDetection-OneStageDet/tree/60efe8b0ee6782b2aea20a32264b2ce1fc21901f |
GCN | from torch.nn import Module
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | DAIZHENWEI/FastGCN_pytorch | GCN | false | 336 | [
"MIT"
] | 0 | 87efe350d5acbe517a0642e9862ac9676b55c053 | https://github.com/DAIZHENWEI/FastGCN_pytorch/tree/87efe350d5acbe517a0642e9862ac9676b55c053 |
Reorg | import torch
import torch.nn as nn
class Reorg(nn.Module):
""" This layer reorganizes a tensor according to a stride.
The dimensions 2,3 will be sliced by the stride and then stacked in dimension 1. (input must have 4 dimensions)
Args:
stride (int): stride to divide the input tensor
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | CuongNguyen218/ObjectDetection-OneStageDet | Reorg | false | 337 | [
"MIT"
] | 0 | 60efe8b0ee6782b2aea20a32264b2ce1fc21901f | https://github.com/CuongNguyen218/ObjectDetection-OneStageDet/tree/60efe8b0ee6782b2aea20a32264b2ce1fc21901f |
ValueNetwork | import torch
import torch.nn.functional as F
from torch import nn
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class ValueNetwork(nn.Module):
def __init__(self, num_inputs, hidden_dim):
super(Va... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | DailinH/pytorch-soft-actor-critic | ValueNetwork | false | 338 | [
"MIT"
] | 0 | 0669e22cf2ba1ddd7cd373687a7ed8ba2a65fd8b | https://github.com/DailinH/pytorch-soft-actor-critic/tree/0669e22cf2ba1ddd7cd373687a7ed8ba2a65fd8b |
ScaleReLU | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, nchannels, bias=True, init_scale=1.0):
super().__init__()
self.nchannels = nchannels
self.weight = nn.Parameter(torch.Tensor(1, nchannels, 1, 1))
if bias:
self.bias = nn.Parameter(torch.Tenso... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | CuongNguyen218/ObjectDetection-OneStageDet | ScaleReLU | false | 339 | [
"MIT"
] | 0 | 60efe8b0ee6782b2aea20a32264b2ce1fc21901f | https://github.com/CuongNguyen218/ObjectDetection-OneStageDet/tree/60efe8b0ee6782b2aea20a32264b2ce1fc21901f |
ContrastiveLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class ContrastiveLoss(nn.Module):
"""
Contrastive loss
Takes embeddings of two samples and a target label == 1 if samples are from the same class and label == 0 otherwise
"""
def __init__(self, margin):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | DanIulian/minigrid_rl | ContrastiveLoss | false | 340 | [
"MIT"
] | 0 | d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa | https://github.com/DanIulian/minigrid_rl/tree/d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa |
HLoss | import torch
import torch.nn.functional as F
import torch.utils.data
class HLoss(torch.nn.Module):
def __init__(self):
super(HLoss, self).__init__()
def forward(self, x):
b = F.softmax(x, dim=1) * F.log_softmax(x, dim=1)
b = -1.0 * b.sum()
return b
def get_inputs():
ret... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | DanIulian/minigrid_rl | HLoss | false | 341 | [
"MIT"
] | 0 | d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa | https://github.com/DanIulian/minigrid_rl/tree/d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa |
Entropy_loss | import torch
import torch.nn as nn
import torch.nn.functional as F
class Entropy_loss(nn.Module):
def __init__(self):
super(Entropy_loss, self).__init__()
def forward(self, x):
probs = F.softmax(x, dim=1)
b = torch.log(probs) * probs
b = -1.0 * b.sum(dim=1)
return b
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | DAIZHENWEI/FastGCN_pytorch | Entropy_loss | false | 342 | [
"MIT"
] | 0 | 87efe350d5acbe517a0642e9862ac9676b55c053 | https://github.com/DAIZHENWEI/FastGCN_pytorch/tree/87efe350d5acbe517a0642e9862ac9676b55c053 |
SPU | import torch
import torch.nn as nn
class SPU(nn.Module):
def forward(self, x):
return torch.where(x > 0, x ** 2 - 0.5, torch.sigmoid(-x) - 1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | DanDoge/course_ethz | SPU | false | 343 | [
"MIT"
] | 0 | 73e5f77e3694d6134169127c0500898402683c32 | https://github.com/DanDoge/course_ethz/tree/73e5f77e3694d6134169127c0500898402683c32 |
SAB | import math
import torch
from torch import Tensor
from torch.nn import Linear
from typing import Type
from typing import Optional
from typing import Tuple
from torch.nn import LayerNorm
class MAB(torch.nn.Module):
def __init__(self, dim_Q: 'int', dim_K: 'int', dim_V: 'int', num_heads:
'int', Conv: 'Optio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ClintvanHoesel/MXMNet_adapted | SAB | false | 344 | [
"MIT"
] | 0 | 091aae4a664b5b0944dfe95dbd2f5da441541437 | https://github.com/ClintvanHoesel/MXMNet_adapted/tree/091aae4a664b5b0944dfe95dbd2f5da441541437 |
QNetwork | import torch
import torch.nn.functional as F
from torch import nn
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class QNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_dim):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | DailinH/pytorch-soft-actor-critic | QNetwork | false | 345 | [
"MIT"
] | 0 | 0669e22cf2ba1ddd7cd373687a7ed8ba2a65fd8b | https://github.com/DailinH/pytorch-soft-actor-critic/tree/0669e22cf2ba1ddd7cd373687a7ed8ba2a65fd8b |
GameNet | import torch
import torch.utils.data
class GameNet(torch.nn.Module):
def __init__(self):
super(GameNet, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 16, (3, 3), stride=1, padding=1)
self.conv2 = torch.nn.Conv2d(16, 16, (3, 3), stride=1, padding=1)
self.conv3 = torch.nn.Conv2d(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
asser... | DanIulian/minigrid_rl | GameNet | false | 346 | [
"MIT"
] | 0 | d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa | https://github.com/DanIulian/minigrid_rl/tree/d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa |
PPReLU | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, nchannels, bias=True, init_scale=1.0):
super().__init__()
self.nchannels = nchannels
self.weight = nn.Parameter(torch.Tensor(1, nchannels, 1, 1))
if bias:
self.bias = nn.Parameter(torch.Tenso... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | CuongNguyen218/ObjectDetection-OneStageDet | PPReLU | false | 347 | [
"MIT"
] | 0 | 60efe8b0ee6782b2aea20a32264b2ce1fc21901f | https://github.com/CuongNguyen218/ObjectDetection-OneStageDet/tree/60efe8b0ee6782b2aea20a32264b2ce1fc21901f |
ImageLevelFeaturePooling2D | import torch
from torch import nn
from torch.nn import functional as F
class ImageLevelFeaturePooling2D(nn.Module):
def __init__(self, out_channels):
super().__init__()
self.out_channels = out_channels
def forward(self, x):
x1 = torch.mean(x.view(x.size(0) * 2, x.size(1), -1), dim=2)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Dauriel/weather4cast2021 | ImageLevelFeaturePooling2D | false | 349 | [
"Apache-2.0"
] | 0 | 29e818c4bcd488ec84b51558bf5392e4a887db70 | https://github.com/Dauriel/weather4cast2021/tree/29e818c4bcd488ec84b51558bf5392e4a887db70 |
SRCNN | import torch
from torch import nn
class SRCNN(nn.Module):
def __init__(self, num_channels=1):
super(SRCNN, self).__init__()
self.conv1 = nn.Conv2d(num_channels, 64, kernel_size=9, padding=9 // 2)
self.conv2 = nn.Conv2d(64, 32, kernel_size=5, padding=5 // 2)
self.conv3 = nn.Conv2d(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | DanielLiang1/a-PyTorch-Tutorial-to-Super-Resolution | SRCNN | false | 350 | [
"MIT"
] | 0 | cf7b519029687fe9726bb194fe3765934afa18b3 | https://github.com/DanielLiang1/a-PyTorch-Tutorial-to-Super-Resolution/tree/cf7b519029687fe9726bb194fe3765934afa18b3 |
ConditionalBatchNorm2d | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=0.0001):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super(SpectralNorm, self).__init__()
self.module = module
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Crazy-Jack/BigGAN-PyTorch | ConditionalBatchNorm2d | false | 351 | [
"MIT"
] | 0 | 1a5644e9c87cc399580c96cfeb180052076888da | https://github.com/Crazy-Jack/BigGAN-PyTorch/tree/1a5644e9c87cc399580c96cfeb180052076888da |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | DanielTakeshi/DCUR | Critic | false | 352 | [
"MIT"
] | 0 | 1cdb00e7e68060ad3bba9a497106c327f6b5a663 | https://github.com/DanielTakeshi/DCUR/tree/1cdb00e7e68060ad3bba9a497106c327f6b5a663 |
SEModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class SEModule(nn.Module):
def __init__(self, planes, compress_rate):
super(SEModule, self).__init__()
self.conv1 = nn.Conv2d(planes, planes // compress_rate, kernel_size
=1, stride=1, bias=True)
self.conv2 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Darshan-Ramesh/EmpRecognition | SEModule | false | 353 | [
"MIT"
] | 0 | c85775659bcbb79f62de29a7a764cc72f1de0674 | https://github.com/Darshan-Ramesh/EmpRecognition/tree/c85775659bcbb79f62de29a7a764cc72f1de0674 |
DepthwiseSeparableConv | import torch
from torch import nn
class DepthwiseSeparableConv(nn.Module):
def __init__(self, in_channels, output_channels, kernel_size, padding=0,
kernels_per_layer=1):
super(DepthwiseSeparableConv, self).__init__()
self.depthwise = nn.Conv2d(in_channels, in_channels *
kernel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Dauriel/weather4cast2021 | DepthwiseSeparableConv | false | 354 | [
"Apache-2.0"
] | 0 | 29e818c4bcd488ec84b51558bf5392e4a887db70 | https://github.com/Dauriel/weather4cast2021/tree/29e818c4bcd488ec84b51558bf5392e4a887db70 |
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