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 |
|---|---|---|---|---|---|---|---|---|---|---|
spatial_attn_layer | import torch
import torch.nn as nn
import torch.onnx
import torch.nn.parallel
class BasicConv(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1,
padding=0, dilation=1, groups=1, relu=True, bn=False, bias=False):
super(BasicConv, self).__init__()
self.out_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 ... | Ganzooo/soil_segmentation | spatial_attn_layer | false | 2,296 | [
"MIT"
] | 0 | 56f410e3e184f24e52dd4b542ea309f0d203ca00 | https://github.com/Ganzooo/soil_segmentation/tree/56f410e3e184f24e52dd4b542ea309f0d203ca00 |
AvgPoolPad | import torch
import torch.utils.data
import torch.nn as nn
from torch import optim as optim
import torch.nn.parallel
class AvgPoolPad(nn.Module):
def __init__(self, stride=2, padding=1):
super(AvgPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.AvgPool2d(3, s... | 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.nn as nn
from torch import optim as optim
import torch.nn.parallel
assert_size_stride = torch._C._dynam... | Exir-lxr/crldr-prune-pytorch | AvgPoolPad | false | 2,297 | [
"Apache-2.0"
] | 0 | adeb5e0b24ce66ff9531d4d947f72412c1b5c033 | https://github.com/Exir-lxr/crldr-prune-pytorch/tree/adeb5e0b24ce66ff9531d4d947f72412c1b5c033 |
Contract | import torch
import torch.nn as nn
class Contract(nn.Module):
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
b, c, h, w = x.size()
s = self.gain
x = x.view(b, c, h // s, s, w // s, s)
x = x.permute(0, 3, 5, 1, 2, 4).conti... | 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... | GoalballAnalysis/GUI | Contract | false | 2,298 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
Quantizing_cossim | import torch
import torch.nn as nn
from typing import Tuple
class Quantizing_cossim(nn.Module):
"""
This is quantizing layer.
"""
__initialized: 'bool' = True
def __init__(self, num_quantizing: 'int', quantizing_dim: 'int',
_weight: 'torch.Tensor'=None, initialize_by_dataset: 'bool'=True,... | 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 ... | Geson-anko/VQ_AutoEncoder | Quantizing_cossim | false | 2,299 | [
"MIT"
] | 0 | 62e1694de38ea6f152891e19abc190ad4048e587 | https://github.com/Geson-anko/VQ_AutoEncoder/tree/62e1694de38ea6f152891e19abc190ad4048e587 |
MultiHeadAttentionLayer | import math
import torch
import torch.nn as nn
class MultiHeadAttentionLayer(nn.Module):
def __init__(self, hidden_dim, n_heads, dropout=0.1):
super().__init__()
assert hidden_dim % n_heads == 0
self.hidden_dim = hidden_dim
self.n_heads = n_heads
self.head_dim = 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._inductor.runtime.... | GaroneHuang/pan_pp.pytorch | MultiHeadAttentionLayer | false | 2,300 | [
"Apache-2.0"
] | 0 | dde41ad652179433ad8a9650f671dc6742b783f9 | https://github.com/GaroneHuang/pan_pp.pytorch/tree/dde41ad652179433ad8a9650f671dc6742b783f9 |
MaxPoolPad | import torch
import torch.nn as nn
class MaxPoolPad(nn.Module):
def __init__(self):
super(MaxPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.MaxPool2d(3, stride=2, padding=1)
def forward(self, x):
x = self.pad(x)
x = self.pool(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | GoalballAnalysis/GUI | MaxPoolPad | false | 2,301 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
AconC | import torch
import torch.nn as nn
class AconC(nn.Module):
""" ACON activation (activate or not).
AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"""
def __i... | 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... | GoalballAnalysis/GUI | AconC | false | 2,302 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
Encoder | import torch
import torch.nn.functional as F
from torch import nn
class Encoder(nn.Module):
def __init__(self, input_size: 'int', output_size: 'int', max_temp:
'float'=10.0, min_temp: 'float'=0.1, reg_threshold: 'float'=3.0,
reg_eps: 'float'=1e-10) ->None:
"""Feature selection encoder
... | 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.functional as F
from torch import nn
assert_size_stride = torch.... | GewoonMaarten/spherical-dmri-conv | Encoder | false | 2,303 | [
"MIT"
] | 0 | 6a5bbb31cf70a5f8b839f92e534f49664001ea09 | https://github.com/GewoonMaarten/spherical-dmri-conv/tree/6a5bbb31cf70a5f8b839f92e534f49664001ea09 |
Expand | import torch
import torch.nn as nn
class Expand(nn.Module):
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
b, c, h, w = x.size()
s = self.gain
x = x.view(b, s, s, c // s ** 2, h, w)
x = x.permute(0, 3, 4, 1, 5, 2).contigu... | 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... | GoalballAnalysis/GUI | Expand | false | 2,304 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
SEModule | import torch
import torch.utils.data
import torch.nn as nn
from torch import optim as optim
import torch.nn.parallel
class SEModule(nn.Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=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.utils.data
impor... | Exir-lxr/crldr-prune-pytorch | SEModule | false | 2,305 | [
"Apache-2.0"
] | 0 | adeb5e0b24ce66ff9531d4d947f72412c1b5c033 | https://github.com/Exir-lxr/crldr-prune-pytorch/tree/adeb5e0b24ce66ff9531d4d947f72412c1b5c033 |
PANNsLoss | import torch
import torch.nn as nn
class PANNsLoss(nn.Module):
def __init__(self):
super().__init__()
self.bce = nn.BCEWithLogitsLoss()
self.cel = nn.CrossEntropyLoss()
def forward(self, input, target):
"""
input_ = input
input_ = torch.where(
torc... | 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... | Gopi-Durgaprasad/Kaggle-Cornell-Birdcall-Identification | PANNsLoss | false | 2,306 | [
"Apache-2.0"
] | 0 | 9eafbcba3323c29b0f9271911debc2f18af78f23 | https://github.com/Gopi-Durgaprasad/Kaggle-Cornell-Birdcall-Identification/tree/9eafbcba3323c29b0f9271911debc2f18af78f23 |
AvgPoolPad | import torch
import torch.nn as nn
class AvgPoolPad(nn.Module):
def __init__(self, stride=2, padding=1):
super(AvgPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.AvgPool2d(3, stride=stride, padding=padding,
count_include_pad=False)
def forwa... | 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... | GoalballAnalysis/GUI | AvgPoolPad | false | 2,307 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
Classifier | import torch
import torch.distributed
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, hidden_size):
super(Classifier, self).__init__()
self.linear1 = nn.Linear(hidden_size, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x, mask_cls):
h = self.linear1(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.distributed
import torch.nn as nn
assert_size_stride = torch._C._dy... | GraphGrailAi/summ-abs-dev | Classifier | false | 2,308 | [
"MIT"
] | 0 | 512f253bf72b6529589b29d06959b560b79f1cde | https://github.com/GraphGrailAi/summ-abs-dev/tree/512f253bf72b6529589b29d06959b560b79f1cde |
BCEBlurWithLogitsLoss | import torch
import torch.nn as nn
class BCEBlurWithLogitsLoss(nn.Module):
def __init__(self, alpha=0.05):
super().__init__()
self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')
self.alpha = alpha
def forward(self, pred, true):
loss = self.loss_fcn(pred, true)
pred... | 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... | GoalballAnalysis/GUI | BCEBlurWithLogitsLoss | false | 2,309 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
BAP | import torch
import torch.nn as nn
class BAP(nn.Module):
def __init__(self, **kwargs):
super(BAP, self).__init__()
def forward(self, feature_maps, attention_maps):
feature_shape = feature_maps.size()
attention_shape = attention_maps.size()
phi_I = torch.einsum('imjk,injk->imn... | 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.... | GunjanChourasia/WS_DAN_PyTorch | BAP | false | 2,310 | [
"MIT"
] | 0 | 6c12a1b5b0b8980e3b69d44474e0b5edb455570c | https://github.com/GunjanChourasia/WS_DAN_PyTorch/tree/6c12a1b5b0b8980e3b69d44474e0b5edb455570c |
SpatialAttention | import torch
from torch import nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, kernel_si... | 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... | HT-hlf/mmdetection_miner-2.22.0 | SpatialAttention | false | 2,311 | [
"Apache-2.0"
] | 0 | 76eb94d6547f9f95cd58f41bb5c91941e82322b9 | https://github.com/HT-hlf/mmdetection_miner-2.22.0/tree/76eb94d6547f9f95cd58f41bb5c91941e82322b9 |
MeanVoxelFeatureExtractor | import torch
import torch.nn as nn
class VoxelFeatureExtractor(nn.Module):
def __init__(self, **kwargs):
super().__init__()
def get_output_feature_dim(self):
raise NotImplementedError
def forward(self, **kwargs):
raise NotImplementedError
class MeanVoxelFeatureExtractor(VoxelF... | 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... | GuilinZ/PCDet | MeanVoxelFeatureExtractor | false | 2,312 | [
"Apache-2.0"
] | 0 | f39769160854871bec9954630b9a4369b603391d | https://github.com/GuilinZ/PCDet/tree/f39769160854871bec9954630b9a4369b603391d |
HardAttn | import torch
from torch.nn import functional as F
import torch.nn as nn
class HardAttn(nn.Module):
"""Hard Attention (Sec. 3.1.II)"""
def __init__(self, in_channels):
super(HardAttn, self).__init__()
self.fc = nn.Linear(in_channels, 4 * 2)
self.init_params()
def init_params(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 ... | GoalballAnalysis/GUI | HardAttn | false | 2,313 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
MetaAconC | import torch
import torch.nn as nn
class MetaAconC(nn.Module):
""" ACON activation (activate or not).
MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"... | 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... | GoalballAnalysis/GUI | MetaAconC | false | 2,314 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
TransformerLayer | import torch
import torch.nn as nn
class TransformerLayer(nn.Module):
def __init__(self, c, num_heads):
super().__init__()
self.q = nn.Linear(c, c, bias=False)
self.k = nn.Linear(c, c, bias=False)
self.v = nn.Linear(c, c, bias=False)
self.ma = nn.MultiheadAttention(embed_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | GoalballAnalysis/GUI | TransformerLayer | false | 2,315 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
ResizeCat | import torch
import torch.nn as nn
class ResizeCat(nn.Module):
def __init__(self, **kwargs):
super(ResizeCat, self).__init__()
def forward(self, at1, at3, at5):
_N, _C, H, W = at1.size()
resized_at3 = nn.functional.interpolate(at3, (H, W))
resized_at5 = nn.functional.interpol... | 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... | GunjanChourasia/WS_DAN_PyTorch | ResizeCat | false | 2,316 | [
"MIT"
] | 0 | 6c12a1b5b0b8980e3b69d44474e0b5edb455570c | https://github.com/GunjanChourasia/WS_DAN_PyTorch/tree/6c12a1b5b0b8980e3b69d44474e0b5edb455570c |
MMD_loss | import torch
import torch.utils.data
import torch
import torch.nn as nn
class MMD_loss(nn.Module):
def __init__(self, kernel_mul=2.0, kernel_num=5):
super(MMD_loss, self).__init__()
self.kernel_num = kernel_num
self.kernel_mul = kernel_mul
self.fix_sigma = None
def guassian_k... | 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.utils.data
import torch
import torch.nn as nn
assert_size_st... | HC-Feynman/10708-proj | MMD_loss | false | 2,317 | [
"BSD-3-Clause"
] | 0 | 592ed86671539b6e910dac72391ef0d3ae8e79ef | https://github.com/HC-Feynman/10708-proj/tree/592ed86671539b6e910dac72391ef0d3ae8e79ef |
ChannelAttention_avg | import torch
from torch import nn
class ChannelAttention_avg(nn.Module):
def __init__(self, in_planes, ratio=8):
super(ChannelAttention_avg, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(in_planes, ratio, 1, bias=False)
self.relu1 = nn.ReLU()
... | 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... | HT-hlf/mmdetection_miner-2.22.0 | ChannelAttention_avg | false | 2,318 | [
"Apache-2.0"
] | 0 | 76eb94d6547f9f95cd58f41bb5c91941e82322b9 | https://github.com/HT-hlf/mmdetection_miner-2.22.0/tree/76eb94d6547f9f95cd58f41bb5c91941e82322b9 |
NormedConv2d | import torch
from torch import nn
class NormedConv2d(nn.Conv2d):
"""Normalized Conv2d Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value of divisor to
keep ... | 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
from torch import n... | HT-hlf/mmdetection_miner-2.22.0 | NormedConv2d | false | 2,319 | [
"Apache-2.0"
] | 0 | 76eb94d6547f9f95cd58f41bb5c91941e82322b9 | https://github.com/HT-hlf/mmdetection_miner-2.22.0/tree/76eb94d6547f9f95cd58f41bb5c91941e82322b9 |
MultiHeadAttention | import torch
import numpy as np
import torch.nn as nn
class MultiHeadAttention(nn.Module):
"""Multi-Head Attention Layer"""
def __init__(self, hidden_size, num_attention_heads, attention_dropout_prob
):
super(MultiHeadAttention, self).__init__()
self.h = num_attention_heads
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Ginga1892/bert-x | MultiHeadAttention | false | 2,320 | [
"MIT"
] | 0 | 903970ef0a6967aa20a82bcf56b874602e37a04d | https://github.com/Ginga1892/bert-x/tree/903970ef0a6967aa20a82bcf56b874602e37a04d |
GlobalAttention | import torch
import torch.distributed
import torch.nn as nn
import torch.nn.functional as F
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_len).type_as(le... | 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.... | GraphGrailAi/summ-abs-dev | GlobalAttention | false | 2,321 | [
"MIT"
] | 0 | 512f253bf72b6529589b29d06959b560b79f1cde | https://github.com/GraphGrailAi/summ-abs-dev/tree/512f253bf72b6529589b29d06959b560b79f1cde |
ChannelAttention | import torch
from torch import nn
class ChannelAttention(nn.Module):
def __init__(self, in_planes, ratio=8):
super(ChannelAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.fc1 = nn.Conv2d(in_planes, ratio, 1, bias=Fals... | 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... | HT-hlf/mmdetection_miner-2.22.0 | ChannelAttention | false | 2,322 | [
"Apache-2.0"
] | 0 | 76eb94d6547f9f95cd58f41bb5c91941e82322b9 | https://github.com/HT-hlf/mmdetection_miner-2.22.0/tree/76eb94d6547f9f95cd58f41bb5c91941e82322b9 |
NormedLinear | import torch
import torch.nn.functional as F
from torch import nn
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value ... | 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
from torch import n... | HT-hlf/mmdetection_miner-2.22.0 | NormedLinear | false | 2,323 | [
"Apache-2.0"
] | 0 | 76eb94d6547f9f95cd58f41bb5c91941e82322b9 | https://github.com/HT-hlf/mmdetection_miner-2.22.0/tree/76eb94d6547f9f95cd58f41bb5c91941e82322b9 |
ChannelAttention_a | import torch
from torch import nn
class ChannelAttention_a(nn.Module):
def __init__(self, in_planes, ratio=8):
super(ChannelAttention_a, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.fc1 = nn.Conv2d(in_planes, ratio, 1, bias=... | 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... | HT-hlf/mmdetection_miner-2.22.0 | ChannelAttention_a | false | 2,324 | [
"Apache-2.0"
] | 0 | 76eb94d6547f9f95cd58f41bb5c91941e82322b9 | https://github.com/HT-hlf/mmdetection_miner-2.22.0/tree/76eb94d6547f9f95cd58f41bb5c91941e82322b9 |
UpConv | import torch
import torch.nn as nn
from enum import Enum
from enum import auto
class UpsampleType(Enum):
CONV_TRANSPOSE = auto()
NEAREST_NEIGHBOUR = auto()
BILINEAR = auto()
class UpConv(nn.Module):
"""
Custom module to handle a single Upsample + Convolution block used in the decoder layer.
... | 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
from enum import Enum
from enum import auto
assert_size_st... | HalestormAI/efficientnet-unet | UpConv | false | 2,325 | [
"MIT"
] | 0 | b6d5ec86d667ce7ac1f689bc16269dca83a079f0 | https://github.com/HalestormAI/efficientnet-unet/tree/b6d5ec86d667ce7ac1f689bc16269dca83a079f0 |
InstockMask | import torch
import torch.nn as nn
class InstockMask(nn.Module):
def __init__(self, time_step, ltsp, min_instock_ratio=0.5,
eps_instock_dph=0.001, eps_total_dph=0.001, **kwargs):
super(InstockMask, self).__init__(**kwargs)
if not eps_total_dph > 0:
raise ValueError(
... | 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_... | GoldbergData/pytorch-forecasting | InstockMask | false | 2,326 | [
"MIT"
] | 0 | e2ef3794da5d996c9740d932a4f55269bb4003f2 | https://github.com/GoldbergData/pytorch-forecasting/tree/e2ef3794da5d996c9740d932a4f55269bb4003f2 |
EncoderImagePrecomp | import torch
import numpy as np
import torch.nn as nn
import torch.nn.init
from collections import OrderedDict
def l2norm(X):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()
X = torch.div(X, norm)
return X
class EncoderImagePrecomp(nn.Module):
def __i... | 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 numpy as np
... | Harshdeep1996/jina-hub | EncoderImagePrecomp | false | 2,327 | [
"Apache-2.0"
] | 0 | 880ff719715b89969860c70219d26a931a031d10 | https://github.com/Harshdeep1996/jina-hub/tree/880ff719715b89969860c70219d26a931a031d10 |
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... | GuyLor/attention-learn-to-route | Attention | false | 2,328 | [
"MIT"
] | 0 | d07d5c1465f7ee5d18651e23cfae9aa1f52a9c6c | https://github.com/GuyLor/attention-learn-to-route/tree/d07d5c1465f7ee5d18651e23cfae9aa1f52a9c6c |
DiceCoefficientLoss | import torch
import torch.nn as nn
class DiceCoefficientLoss(nn.Module):
def __init__(self, apply_softmax: 'bool'=False, eps: 'float'=1e-06):
super().__init__()
self.apply_softmax = apply_softmax
self.eps = eps
def forward(self, x: 'torch.Tensor', y: 'torch.Tensor', multiclass=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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | HalestormAI/efficientnet-unet | DiceCoefficientLoss | false | 2,329 | [
"MIT"
] | 0 | b6d5ec86d667ce7ac1f689bc16269dca83a079f0 | https://github.com/HalestormAI/efficientnet-unet/tree/b6d5ec86d667ce7ac1f689bc16269dca83a079f0 |
NormalizationLayer | import torch
import torch.nn.init
class NormalizationLayer(torch.nn.Module):
"""Class for normalization layer."""
def __init__(self, normalize_scale=1.0, learn_scale=True):
super(NormalizationLayer, self).__init__()
self.norm_s = float(normalize_scale)
if learn_scale:
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 libdevice
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Harshdeep1996/jina-hub | NormalizationLayer | false | 2,330 | [
"Apache-2.0"
] | 0 | 880ff719715b89969860c70219d26a931a031d10 | https://github.com/Harshdeep1996/jina-hub/tree/880ff719715b89969860c70219d26a931a031d10 |
LinearZeros | import torch
import torch.nn as nn
class LinearZeros(nn.Linear):
def __init__(self, in_channels, out_channels, logscale_factor=3):
super().__init__(in_channels, out_channels)
self.logscale_factor = logscale_factor
self.register_parameter('logs', nn.Parameter(torch.zeros(out_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.triton_helpers import math as tl_math
import torch.... | GauriJagatap/glow-pytorch | LinearZeros | false | 2,331 | [
"MIT"
] | 0 | e379f524b7cc0b57a9bc2849f4115f97bda5a1de | https://github.com/GauriJagatap/glow-pytorch/tree/e379f524b7cc0b57a9bc2849f4115f97bda5a1de |
LayerNorm | import torch
from torch import nn
from torch.nn import functional as F
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-05):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(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 libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | HalimSD/A-eye | LayerNorm | false | 2,332 | [
"MIT"
] | 0 | 502dcdf47d54d93e8745be7c49897064550db8c7 | https://github.com/HalimSD/A-eye/tree/502dcdf47d54d93e8745be7c49897064550db8c7 |
ResampleNorm | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.functional as F
class TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_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.triton_helpers import libdevice
import torch.nn.functional as F
import torch.nn as nn
import torch.functional a... | GoldbergData/pytorch-forecasting | ResampleNorm | false | 2,333 | [
"MIT"
] | 0 | e2ef3794da5d996c9740d932a4f55269bb4003f2 | https://github.com/GoldbergData/pytorch-forecasting/tree/e2ef3794da5d996c9740d932a4f55269bb4003f2 |
JaccardLoss | import torch
import torch.nn as nn
class JaccardLoss(nn.Module):
def __init__(self, apply_softmax: 'bool'=False, eps: 'float'=1e-06):
super().__init__()
self.apply_softmax = apply_softmax
self.eps = eps
def forward(self, x, y, eps=1e-06):
if self.apply_softmax:
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | HalestormAI/efficientnet-unet | JaccardLoss | false | 2,334 | [
"MIT"
] | 0 | b6d5ec86d667ce7ac1f689bc16269dca83a079f0 | https://github.com/HalestormAI/efficientnet-unet/tree/b6d5ec86d667ce7ac1f689bc16269dca83a079f0 |
TwoMLPHead | import torch
from torch import nn
import torch.nn.functional as F
class TwoMLPHead(nn.Module):
"""
Standard heads for FPN-based models
Arguments:
in_channels (int): number of input channels
representation_size (int): size of the intermediate representation
"""
def __init__(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 import nn
assert_s... | GreenCUBIC/Gas-Prices-of-America | TwoMLPHead | false | 2,335 | [
"MIT"
] | 0 | e2a045db99d061b5d2acbe208da8cc19af12659d | https://github.com/GreenCUBIC/Gas-Prices-of-America/tree/e2a045db99d061b5d2acbe208da8cc19af12659d |
IndexedSegmentationMap | import torch
import torch.nn as nn
class IndexedSegmentationMap(nn.Module):
"""
Takes the raw logits from the n-channel output convolution and uses argmax to convert to an indexed output map.
"""
def __init__(self):
super().__init__()
def forward(self, x: 'torch.Tensor') ->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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | HalestormAI/efficientnet-unet | IndexedSegmentationMap | false | 2,336 | [
"MIT"
] | 0 | b6d5ec86d667ce7ac1f689bc16269dca83a079f0 | https://github.com/HalestormAI/efficientnet-unet/tree/b6d5ec86d667ce7ac1f689bc16269dca83a079f0 |
Attention | import math
import torch
import torch.nn as nn
from abc import *
import torch.nn.functional as F
from torch import optim as optim
class Attention(nn.Module):
"""
Compute 'Scaled Dot Product Attention
"""
def forward(self, query, key, value, mask=None, dropout=None):
scores = torch.matmul(quer... | 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.... | HeegyuKim/RecSys-MovieLens100k | Attention | false | 2,337 | [
"MIT"
] | 0 | aa3a272e6045d8230ecbabbf94a6f68170a26c9e | https://github.com/HeegyuKim/RecSys-MovieLens100k/tree/aa3a272e6045d8230ecbabbf94a6f68170a26c9e |
CBAM | import torch
from torch import nn
class ChannelAttention(nn.Module):
def __init__(self, in_planes, ratio=8):
super(ChannelAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.fc1 = nn.Conv2d(in_planes, ratio, 1, bias=Fals... | 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... | HT-hlf/mmdetection_miner-2.22.0 | CBAM | false | 2,338 | [
"Apache-2.0"
] | 0 | 76eb94d6547f9f95cd58f41bb5c91941e82322b9 | https://github.com/HT-hlf/mmdetection_miner-2.22.0/tree/76eb94d6547f9f95cd58f41bb5c91941e82322b9 |
ActorNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class ActorNetwork(nn.Module):
def __init__(self, state_dim, action_dim, seed, fc1_units=256,
fc2_units=128):
""" Initialize parameters of model and build its.
Parameters:
===========
state_dim (int): State... | 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.... | HatemSelim94/RL-MADDPG | ActorNetwork | false | 2,339 | [
"MIT"
] | 0 | 037a722f59e2e461fe6615685b434365fc5540b1 | https://github.com/HatemSelim94/RL-MADDPG/tree/037a722f59e2e461fe6615685b434365fc5540b1 |
ShuffleBlock | import torch
import torch.nn as nn
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
self.groups = groups
def forward(self, x):
"""Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]"""
N, C, H, W = x.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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Geunwoo-Jeon/pytorch-cifar | ShuffleBlock | false | 2,340 | [
"MIT"
] | 0 | b06eeb65bbc0a4eccd124ed3c5367da70ab1ed20 | https://github.com/Geunwoo-Jeon/pytorch-cifar/tree/b06eeb65bbc0a4eccd124ed3c5367da70ab1ed20 |
ScaledDotProductAttention | import torch
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
def __init__(self, dropout: 'float'=None, scale: 'bool'=True):
super(ScaledDotProductAttention, self).__init__()
if dropout is not None:
self.dropout = nn.Dropout(p=dropout)
else:
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | GoldbergData/pytorch-forecasting | ScaledDotProductAttention | false | 2,341 | [
"MIT"
] | 0 | e2ef3794da5d996c9740d932a4f55269bb4003f2 | https://github.com/GoldbergData/pytorch-forecasting/tree/e2ef3794da5d996c9740d932a4f55269bb4003f2 |
BiInteractionPooling | import torch
import torch.nn as nn
import torch.utils.data
class BiInteractionPooling(nn.Module):
def __init__(self):
super(BiInteractionPooling, self).__init__()
def forward(self, inputs):
concated_embeds_value = inputs
square_of_sum = torch.pow(torch.sum(concated_embeds_value, dim=... | 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.... | Holldean/pytorch-models | BiInteractionPooling | false | 2,342 | [
"MIT"
] | 0 | 9509d0d462b1a98164b266d49ada199071a855ac | https://github.com/Holldean/pytorch-models/tree/9509d0d462b1a98164b266d49ada199071a855ac |
AddNorm | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.functional as F
class TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_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.triton_helpers import libdevice
import torch.nn.functional as F
import torch.nn as nn
import torch.functional a... | GoldbergData/pytorch-forecasting | AddNorm | false | 2,343 | [
"MIT"
] | 0 | e2ef3794da5d996c9740d932a4f55269bb4003f2 | https://github.com/GoldbergData/pytorch-forecasting/tree/e2ef3794da5d996c9740d932a4f55269bb4003f2 |
SE | import torch
import torch.nn as nn
import torch.nn.functional as F
class SE(nn.Module):
"""Squeeze-and-Excitation block."""
def __init__(self, in_planes, se_planes):
super(SE, self).__init__()
self.se1 = nn.Conv2d(in_planes, se_planes, kernel_size=1, bias=True)
self.se2 = nn.Conv2d(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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Geunwoo-Jeon/pytorch-cifar | SE | false | 2,344 | [
"MIT"
] | 0 | b06eeb65bbc0a4eccd124ed3c5367da70ab1ed20 | https://github.com/Geunwoo-Jeon/pytorch-cifar/tree/b06eeb65bbc0a4eccd124ed3c5367da70ab1ed20 |
PositionwiseFeedForward | import torch
import torch.nn as nn
class PositionwiseFeedForward(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_hid, d_inner_hid=None, dropout=0):
super(PositionwiseFeedForward, self).__init__()
if d_inner_hid is None:
d_inner_hid = d_hid
self.w_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
assert_... | HeGuanyuan/ABSA-PyTorch | PositionwiseFeedForward | false | 2,345 | [
"MIT"
] | 0 | 8244aeb39007a2714ccbfd54629ddbbb013ea87e | https://github.com/HeGuanyuan/ABSA-PyTorch/tree/8244aeb39007a2714ccbfd54629ddbbb013ea87e |
Conv2dZeros | import torch
import torch.nn as nn
class _ActNorm(nn.Module):
"""
Activation Normalization
Initialize the bias and scale with a given minibatch,
so that the output per-channel have zero mean and unit variance for that.
After initialization, `bias` and `logs` will be trained as parameters.
"""... | 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.... | GauriJagatap/glow-pytorch | Conv2dZeros | false | 2,346 | [
"MIT"
] | 0 | e379f524b7cc0b57a9bc2849f4115f97bda5a1de | https://github.com/GauriJagatap/glow-pytorch/tree/e379f524b7cc0b57a9bc2849f4115f97bda5a1de |
PredictionLayer | import torch
import torch.nn as nn
import torch.utils.data
class PredictionLayer(nn.Module):
def __init__(self, task='binary', use_bias=True, **kwargs):
if task not in ['binary', 'multiclass', 'regression']:
raise ValueError('task must be binary, multiclass or regression')
super(Predi... | 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.... | Holldean/pytorch-models | PredictionLayer | false | 2,347 | [
"MIT"
] | 0 | 9509d0d462b1a98164b266d49ada199071a855ac | https://github.com/Holldean/pytorch-models/tree/9509d0d462b1a98164b266d49ada199071a855ac |
Pool | from torch.nn import Module
import torch
from torch import nn
class Pool(Module):
"""多尺度特征融合，借鉴Inception网络结构"""
def __init__(self):
super(Pool, self).__init__()
self.max1 = nn.MaxPool2d(5, 1, 2)
self.max2 = nn.MaxPool2d(9, 1, 4)
self.max3 = nn.MaxPool2d(13, 1, 6)
def forw... | 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.nn import Module
from torch import nn
assert_size_stride = torch._C._dynamo.gu... | HibikiJie/MONet | Pool | false | 2,348 | [
"Apache-2.0"
] | 0 | 931400df28cb62aab90662abe00acd1d3688073d | https://github.com/HibikiJie/MONet/tree/931400df28cb62aab90662abe00acd1d3688073d |
ConvRelu | import torch
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
class ConvRelu(nn.Module):
"""3x3 convolution followed by ReLU activation building block."""
def __init__(self, num_in, num_out):
super().__init__()
self.block = nn.Conv2d(num_in, num_out, kernel_size=3, pa... | 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
impor... | HugoPopo/robosat.pink | ConvRelu | false | 2,349 | [
"MIT"
] | 0 | daa6a0cd6dff68103b9bcc78a8c9a15d8912c42d | https://github.com/HugoPopo/robosat.pink/tree/daa6a0cd6dff68103b9bcc78a8c9a15d8912c42d |
FM | import torch
import torch.nn as nn
import torch.utils.data
class FM(nn.Module):
def __init__(self):
super(FM, self).__init__()
def forward(self, X):
square_of_sum = torch.pow(torch.sum(X, dim=1, keepdim=True), 2)
sum_of_square = torch.sum(X * X, dim=1, keepdim=True)
cross_ter... | 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.... | Holldean/pytorch-models | FM | false | 2,350 | [
"MIT"
] | 0 | 9509d0d462b1a98164b266d49ada199071a855ac | https://github.com/Holldean/pytorch-models/tree/9509d0d462b1a98164b266d49ada199071a855ac |
ReOrgLayer | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch._utils
class ReOrgLayer(nn.Module):
def __init__(self, stride=2):
super(ReOrgLayer, self).__init__()
self.stride = stride
def forward(self, x):
assert x.data.dim() == 4
... | 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.utils.data.distributed
import torch._utils
assert_size_stride = torch._C._dynamo.... | Humoon/motion_reconstruction | ReOrgLayer | false | 2,351 | [
"BSD-3-Clause"
] | 0 | 9f0d0af3aeafa97455ec19dc4988f1577005c294 | https://github.com/Humoon/motion_reconstruction/tree/9f0d0af3aeafa97455ec19dc4988f1577005c294 |
Attention | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class Attention(nn.Module):
def __init__(self, embed_dim, hidden_dim=None, out_dim=None, n_head=1,
score_function='dot_product', dropout=0):
""" Attention Mechanism
:param embed_dim:
:param 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._inductor.runtime.... | HeGuanyuan/ABSA-PyTorch | Attention | false | 2,352 | [
"MIT"
] | 0 | 8244aeb39007a2714ccbfd54629ddbbb013ea87e | https://github.com/HeGuanyuan/ABSA-PyTorch/tree/8244aeb39007a2714ccbfd54629ddbbb013ea87e |
DecoderBlock | import torch
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
class ConvRelu(nn.Module):
"""3x3 convolution followed by ReLU activation building block."""
def __init__(self, num_in, num_out):
super().__init__()
self.block = nn.Conv2d(num_in, num_out, kernel_size=3, pa... | 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
impor... | HugoPopo/robosat.pink | DecoderBlock | false | 2,353 | [
"MIT"
] | 0 | daa6a0cd6dff68103b9bcc78a8c9a15d8912c42d | https://github.com/HugoPopo/robosat.pink/tree/daa6a0cd6dff68103b9bcc78a8c9a15d8912c42d |
MaxPoolStride1 | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
import torch._utils
class MaxPoolStride1(nn.Module):
def __init__(self, kernel_size):
super(MaxPoolStride1, self).__init__()
self.kernel_size = kernel_size
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import ... | Humoon/motion_reconstruction | MaxPoolStride1 | false | 2,354 | [
"BSD-3-Clause"
] | 0 | 9f0d0af3aeafa97455ec19dc4988f1577005c294 | https://github.com/Humoon/motion_reconstruction/tree/9f0d0af3aeafa97455ec19dc4988f1577005c294 |
sSEmodule | import torch
import torch.nn as nn
class sSEmodule(nn.Module):
""" ChannelSequeezeExcitationModule
input: [B, C, H, W] torch tensor
output: [B, C, H, W] torch tensor
"""
def __init__(self, in_channel):
super().__init__()
self.conv2d = nn.Conv2d(in_channel, 1, 1)
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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | HwangJohn/feature_representation | sSEmodule | false | 2,355 | [
"MIT"
] | 0 | 27389caacc9c026b65f47ab0cbb4e6d0465e6a60 | https://github.com/HwangJohn/feature_representation/tree/27389caacc9c026b65f47ab0cbb4e6d0465e6a60 |
cSEmodule | import torch
import torch.nn as nn
class cSEmodule(nn.Module):
""" SpatialSequeezeExcitationModule
input: [B, C, H, W] torch tensor
output: [B, C, H, W] torch tensor
"""
def __init__(self, in_channel):
super().__init__()
self.global_avg = nn.AdaptiveAvgPool2d(1)
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | HwangJohn/feature_representation | cSEmodule | false | 2,356 | [
"MIT"
] | 0 | 27389caacc9c026b65f47ab0cbb4e6d0465e6a60 | https://github.com/HwangJohn/feature_representation/tree/27389caacc9c026b65f47ab0cbb4e6d0465e6a60 |
JointsMSELoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class JointsMSELoss(nn.Module):
def __init__(self, use_target_weight):
super(JointsMSELoss, self).__init__()
self.criterion = nn.MSELoss(reduction='mean')
... | 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.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
assert_size_st... | HongJinSeong/COW_KEY_POINT_DETECTION | JointsMSELoss | false | 2,357 | [
"MIT"
] | 0 | ea62ed875e9b8533f1c09b56eb8aefba94b1b906 | https://github.com/HongJinSeong/COW_KEY_POINT_DETECTION/tree/ea62ed875e9b8533f1c09b56eb8aefba94b1b906 |
scSEmodule | import torch
import torch.nn as nn
class cSEmodule(nn.Module):
""" SpatialSequeezeExcitationModule
input: [B, C, H, W] torch tensor
output: [B, C, H, W] torch tensor
"""
def __init__(self, in_channel):
super().__init__()
self.global_avg = nn.AdaptiveAvgPool2d(1)
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | HwangJohn/feature_representation | scSEmodule | false | 2,358 | [
"MIT"
] | 0 | 27389caacc9c026b65f47ab0cbb4e6d0465e6a60 | https://github.com/HwangJohn/feature_representation/tree/27389caacc9c026b65f47ab0cbb4e6d0465e6a60 |
Hsigmoid | import torch
import torch.nn as nn
import torch.nn.functional as F
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
def get_inputs():
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | IgorDavidyuk/pytorch-mobilenet-v3 | Hsigmoid | false | 2,359 | [
"Apache-2.0"
] | 0 | 48678f80d9390b530cb97966db492cf01d1c4a43 | https://github.com/IgorDavidyuk/pytorch-mobilenet-v3/tree/48678f80d9390b530cb97966db492cf01d1c4a43 |
Hswish | import torch
import torch.nn as nn
import torch.nn.functional as F
class Hswish(nn.Module):
def __init__(self, inplace=True):
super(Hswish, self).__init__()
self.inplace = inplace
def forward(self, x):
return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.0
def get_inputs():
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | IgorDavidyuk/pytorch-mobilenet-v3 | Hswish | false | 2,360 | [
"Apache-2.0"
] | 0 | 48678f80d9390b530cb97966db492cf01d1c4a43 | https://github.com/IgorDavidyuk/pytorch-mobilenet-v3/tree/48678f80d9390b530cb97966db492cf01d1c4a43 |
ConvTemporalGraphical | import torch
import torch.nn as nn
class ConvTemporalGraphical(nn.Module):
"""The basic module for applying a graph convolution.
Args:
in_channels (int): Number of channels in the input sequence data
out_channels (int): Number of channels produced by the convolution
kernel_size (int):... | 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... | Hunkzer/mmskeleton | ConvTemporalGraphical | false | 2,361 | [
"Apache-2.0"
] | 0 | 551e3b4fa01330b23caab5815a40fbd848400b15 | https://github.com/Hunkzer/mmskeleton/tree/551e3b4fa01330b23caab5815a40fbd848400b15 |
L1Part | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from itertools import chain as chain
from collections import OrderedDict
import torch.hub
class concatLayer(nn.Module):
def __init__(self, in_channels, out_channels_perSub, i, j, appendix):
super(concat... | 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.nn.parallel
import torch.optim
import torch.u... | EddieMG/LateTemporalModeling3DCNN | L1Part | false | 2,362 | [
"MIT"
] | 0 | 94c87dc1d31d09bc310d0e735a2e55453976cb0d | https://github.com/EddieMG/LateTemporalModeling3DCNN/tree/94c87dc1d31d09bc310d0e735a2e55453976cb0d |
L2Part | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from itertools import chain as chain
from collections import OrderedDict
import torch.hub
class concatLayer(nn.Module):
def __init__(self, in_channels, out_channels_perSub, i, j, appendix):
super(concat... | 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.nn.parallel
import torch.optim
import torch.u... | EddieMG/LateTemporalModeling3DCNN | L2Part | false | 2,363 | [
"MIT"
] | 0 | 94c87dc1d31d09bc310d0e735a2e55453976cb0d | https://github.com/EddieMG/LateTemporalModeling3DCNN/tree/94c87dc1d31d09bc310d0e735a2e55453976cb0d |
BasicBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
def apply_init_(modules):
"""
Initialize NN modules
"""
for m in modules:
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0... | 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 ... | IanYHWu/msc_2021 | BasicBlock | false | 2,364 | [
"MIT"
] | 0 | 0ae09ed392cce5fdf0e85d1f96b7af82900835f8 | https://github.com/IanYHWu/msc_2021/tree/0ae09ed392cce5fdf0e85d1f96b7af82900835f8 |
InputInjection | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class InputInjection(nn.Module):
"""Downsampling module for CGNet."""
def __init__(self, num_downsampling):
super(InputInjection, self).__init__()
self.pool = nn.ModuleList()
for i in range(num_downsampling)... | 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._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | ImportPaddle/APCNet | InputInjection | false | 2,365 | [
"MIT"
] | 0 | 68ade1f83827b4cdd60ee4b6ac25454397100316 | https://github.com/ImportPaddle/APCNet/tree/68ade1f83827b4cdd60ee4b6ac25454397100316 |
MultiHeadAttn | import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0,
pre_lnorm=False):
super(MultiHeadAttn, self).__init__()
self.n_head = n_head
self.d_model = d_model
self.d_hea... | 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.... | HikariNoMJ14/bebopnet-code | MultiHeadAttn | false | 2,366 | [
"MIT"
] | 0 | 9dfa800d3e24c53de5dc948b87a7db2bc2919b54 | https://github.com/HikariNoMJ14/bebopnet-code/tree/9dfa800d3e24c53de5dc948b87a7db2bc2919b54 |
Encoding | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
class Encoding(nn.Module):
"""Encoding Layer: a learnable residual encoder.
Input is of shape (batch_size, channels, height, width).
Output is of shape (batch_size, num_codes, channels).
Ar... | 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
... | ImportPaddle/APCNet | Encoding | false | 2,367 | [
"MIT"
] | 0 | 68ade1f83827b4cdd60ee4b6ac25454397100316 | https://github.com/ImportPaddle/APCNet/tree/68ade1f83827b4cdd60ee4b6ac25454397100316 |
PositionwiseFeedForward | import math
import torch
import torch.distributed
import torch.nn as nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
Args:
... | 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
import ... | GraphGrailAi/summ-abs-dev | PositionwiseFeedForward | false | 2,368 | [
"MIT"
] | 0 | 512f253bf72b6529589b29d06959b560b79f1cde | https://github.com/GraphGrailAi/summ-abs-dev/tree/512f253bf72b6529589b29d06959b560b79f1cde |
DiceLoss | import functools
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "... | 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 functools
impor... | ImportPaddle/APCNet | DiceLoss | false | 2,369 | [
"MIT"
] | 0 | 68ade1f83827b4cdd60ee4b6ac25454397100316 | https://github.com/ImportPaddle/APCNet/tree/68ade1f83827b4cdd60ee4b6ac25454397100316 |
MultiHeadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
def scaled_dot_product_attention(q, k, v, mask):
"""
q: query = (..., seq_len_q, depth)
k: key = (..., seq_len_k, depth)
v: value = (..., seq_len_v, depth_v)
mask: float tensor with shape broadcastable to
(..., seq_len_q, 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.... | IanYHWu/transformers-for-translation | MultiHeadAttention | false | 2,370 | [
"MIT"
] | 0 | b763e58deb2263507eecd2eb569fbaf5c1dd9df8 | https://github.com/IanYHWu/transformers-for-translation/tree/b763e58deb2263507eecd2eb569fbaf5c1dd9df8 |
BilinearUpsample | import torch
from typing import Union
from typing import List
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class BilinearUpsample(nn.Module):
"""
Overview:
Upsamples the input to the given member varible scale_factor using mode biliner
Interface:
forward
... | 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 typing import Union
from typing import List
import torch.nn as nn
import torch.utils... | Hcnaeg/DI-engine | BilinearUpsample | false | 2,371 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
SpatialGatherModule | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
class SpatialGatherModule(nn.Module):
"""Aggregate the context features according to the initial predicted
probability distribution.
Employ the soft-weighted method to aggregate the context.
... | 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.... | ImportPaddle/APCNet | SpatialGatherModule | false | 2,372 | [
"MIT"
] | 0 | 68ade1f83827b4cdd60ee4b6ac25454397100316 | https://github.com/ImportPaddle/APCNet/tree/68ade1f83827b4cdd60ee4b6ac25454397100316 |
LabelSmoothCELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def one_hot(val: 'torch.LongTensor', num: 'int', num_first: 'bool'=False
) ->torch.FloatTensor:
"""
Overview:
Convert a ``torch.LongTensor`` to one hot encoding.
This implementation can be slightly f... | 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
... | Hcnaeg/DI-engine | LabelSmoothCELoss | false | 2,373 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
DiceLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BinaryDiceLoss(nn.Module):
"""Dice loss of binary class
Args:
smooth: A float number to smooth loss, and avoid NaN error, default: 1
p: Denominator value: \\sum{x^p} + \\sum{y^p}, default: 2
predict: A tensor of s... | 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
... | Ignas-S/retinanet-simple | DiceLoss | false | 2,374 | [
"Apache-2.0"
] | 0 | 81b17f65fa5278e6b9a4918e6a20b77949a7e87d | https://github.com/Ignas-S/retinanet-simple/tree/81b17f65fa5278e6b9a4918e6a20b77949a7e87d |
AttnScore | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_len).type_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.... | IndexFziQ/ASER | AttnScore | false | 2,375 | [
"MIT"
] | 0 | 67dd1a2a25cec175c15675cc1f8a63ca065b447e | https://github.com/IndexFziQ/ASER/tree/67dd1a2a25cec175c15675cc1f8a63ca065b447e |
CNN64x3 | import torch
import torch.nn as nn
class CNN64x3(nn.Module):
def __init__(self, input_channels, output_channels):
super(CNN64x3, self).__init__()
self.conv = nn.Conv2d(in_channels=input_channels, kernel_size=3,
out_channels=output_channels)
self.relu = nn.ReLU()
self.p... | 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_... | InExp123/pytorch-self_driving_car | CNN64x3 | false | 2,376 | [
"MIT"
] | 0 | b4e8c8a76079085bf0471dad1820ee9995cffc74 | https://github.com/InExp123/pytorch-self_driving_car/tree/b4e8c8a76079085bf0471dad1820ee9995cffc74 |
ATOCAttentionUnit | import torch
from typing import Union
import torch.nn as nn
from typing import Dict
import torch.utils.data
class ATOCAttentionUnit(nn.Module):
"""
Overview:
the attention unit of the atoc network. We now implement it as two-layer MLP, same as the original paper
Interface:
__init__, forwa... | 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 ... | Hcnaeg/DI-engine | ATOCAttentionUnit | false | 2,377 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
PositionwiseFeedForward | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""
Layer Normalization class
"""
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(features))
... | 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.... | IndexFziQ/ASER | PositionwiseFeedForward | false | 2,378 | [
"MIT"
] | 0 | 67dd1a2a25cec175c15675cc1f8a63ca065b447e | https://github.com/IndexFziQ/ASER/tree/67dd1a2a25cec175c15675cc1f8a63ca065b447e |
LinearFBSP | import torch
import numpy as np
from typing import Tuple
import torch.nn.functional as F
from typing import cast
def scale(old_value, old_min, old_max, new_min, new_max):
old_range = old_max - old_min
new_range = new_max - new_min
new_value = (old_value - old_min) * new_range / old_range + new_min
ret... | 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, math as tl_math
im... | Gikiman/executors | LinearFBSP | false | 2,379 | [
"Apache-2.0"
] | 0 | 98658b4136859164390cfccbde8cf0f7cf843593 | https://github.com/Gikiman/executors/tree/98658b4136859164390cfccbde8cf0f7cf843593 |
GLU | import torch
import torch.nn as nn
import torch.utils.data
class GLU(nn.Module):
"""
Overview:
Gating Linear Unit.
This class does a thing like this:
.. code:: python
# Inputs: input, context, output_size
# The gate value is a learnt function of the input.
... | 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... | Hcnaeg/DI-engine | GLU | false | 2,380 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_len).type_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.... | IndexFziQ/ASER | Attention | false | 2,381 | [
"MIT"
] | 0 | 67dd1a2a25cec175c15675cc1f8a63ca065b447e | https://github.com/IndexFziQ/ASER/tree/67dd1a2a25cec175c15675cc1f8a63ca065b447e |
SpatialGate3D | import torch
import torch.nn as nn
class BasicConv3D(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1,
padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False):
super(BasicConv3D, self).__init__()
self.out_channels = out_planes
self.conv = nn.Con... | 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.... | Healingl/3DAPRNet | SpatialGate3D | false | 2,382 | [
"BSD-2-Clause"
] | 0 | 7c5e0028ae844df4e1f26327e8b438532ca0745f | https://github.com/Healingl/3DAPRNet/tree/7c5e0028ae844df4e1f26327e8b438532ca0745f |
AvgPool2dSame | import math
import torch
import numpy as np
from typing import List
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def get_same_padding(x: 'int', k: 'int', s: 'int', d: 'int'):
return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0)
def pad_same(x, k: 'List[int]', s: 'List... | 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 math
import numpy as np
from typing import List
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
assert_... | Hcnaeg/DI-engine | AvgPool2dSame | false | 2,383 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
EnsembleFC | import torch
import torch.nn as nn
import torch.utils.data
class EnsembleFC(nn.Module):
__constants__ = ['in_features', 'out_features']
in_features: 'int'
out_features: 'int'
ensemble_size: 'int'
weight: 'torch.Tensor'
def __init__(self, in_features: 'int', out_features: 'int',
ensemb... | 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... | Hcnaeg/DI-engine | EnsembleFC | false | 2,384 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
Encoder | import torch
import torch.nn as nn
import torch.utils.data
class Conv(nn.Module):
def __init__(self, filters0, filters1, kernel_size, bn, bias=True):
super().__init__()
if bn:
bias = False
self.conv = nn.Conv2d(filters0, filters1, kernel_size, stride=1,
padding=ker... | 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... | Hcnaeg/DI-engine | Encoder | false | 2,385 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
Head | import torch
import torch.nn as nn
import torch.utils.data
class Conv(nn.Module):
def __init__(self, filters0, filters1, kernel_size, bn, bias=True):
super().__init__()
if bn:
bias = False
self.conv = nn.Conv2d(filters0, filters1, kernel_size, stride=1,
padding=ker... | 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... | Hcnaeg/DI-engine | Head | false | 2,386 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
PPMConcat | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class PPMConcat(nn.ModuleList):
"""Pyramid Pooling Module that only concat the features of each layer.
Args:
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module.
"""
def __init__(sel... | 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._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | ImportPaddle/APCNet | PPMConcat | false | 2,387 | [
"MIT"
] | 0 | 68ade1f83827b4cdd60ee4b6ac25454397100316 | https://github.com/ImportPaddle/APCNet/tree/68ade1f83827b4cdd60ee4b6ac25454397100316 |
MultiHeadedAttention | import math
import torch
from typing import Optional
from typing import Tuple
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
... | 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.... | InfluencerNGZK/wenet | MultiHeadedAttention | false | 2,388 | [
"Apache-2.0"
] | 0 | 9a3c7f70a78ce675f5e013b1f67a06d1d23fba3e | https://github.com/InfluencerNGZK/wenet/tree/9a3c7f70a78ce675f5e013b1f67a06d1d23fba3e |
SENet | import torch
import torch.nn as nn
import torch.utils.data
class SENet(nn.Module):
"""support estimation network"""
def __init__(self, input_size: 'int', hidden_size: 'int', output_dims:
'int') ->None:
super(SENet, self).__init__()
self.l_1 = nn.Linear(input_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.triton_helpers import libdevice
import torch.nn as ... | Hcnaeg/DI-engine | SENet | false | 2,389 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
ClippedLinearQuantization | import torch
from torch.optim.lr_scheduler import *
import torch.optim
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.testing
def linear_dequantize(input, scale, zero_point, inplace=False):
if inplace:
input.add_(zero_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.optim.lr_schedule... | HatsuneMiku4/distiller | ClippedLinearQuantization | false | 2,390 | [
"Apache-2.0"
] | 0 | 8fbacb01ebcb7d70c5d3ecb6a88093e6c4d42137 | https://github.com/HatsuneMiku4/distiller/tree/8fbacb01ebcb7d70c5d3ecb6a88093e6c4d42137 |
GEGLU | import torch
from torch import nn
import torch.nn.functional as F
class GEGLU(nn.Module):
def forward(self, x):
x, gates = x.chunk(2, dim=-1)
return x * F.gelu(gates)
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
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | JaireYu/perceiver-pytorch | GEGLU | false | 2,391 | [
"MIT"
] | 0 | 23edd66a057bb0a6fc15126461b4409a522ca09e | https://github.com/JaireYu/perceiver-pytorch/tree/23edd66a057bb0a6fc15126461b4409a522ca09e |
ScaledDotProductAttention | import torch
from typing import Optional
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class ScaledDotProductAttention(nn.Module):
"""
Overview:
Implementation of dot product attentionn with scaling.
"""
def __init__(self, d_k: 'int', dropout: 'float'=0.0) ->No... | 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.... | Hcnaeg/DI-engine | ScaledDotProductAttention | false | 2,392 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
RewardModelNetwork | import torch
import torch.nn as nn
import torch.utils.data
class RewardModelNetwork(nn.Module):
def __init__(self, input_size: 'int', hidden_size: 'int', output_size:
'int') ->None:
super(RewardModelNetwork, self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.l2 = 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.triton_helpers import libdevice
import torch.nn as ... | Hcnaeg/DI-engine | RewardModelNetwork | false | 2,393 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
OutputTransition | import torch
from torch import nn
class OutputTransition(nn.Module):
def __init__(self, inChans, n_labels):
super(OutputTransition, self).__init__()
self.final_conv = nn.Conv3d(inChans, n_labels, kernel_size=1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
out = self.sigm... | 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... | JXQI/ModelsGenesis | OutputTransition | false | 2,394 | [
"MIT"
] | 0 | f961288313a78f03bd3045ac27722f791f365bd8 | https://github.com/JXQI/ModelsGenesis/tree/f961288313a78f03bd3045ac27722f791f365bd8 |
ZeroPad1d | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.utils.data
import torch.onnx.operators
import torch.optim
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.optim.lr_scheduler
import torch.utils.data
import torch.onnx.operators
import torch.optim
assert_size_str... | Fei00Wu/espresso | ZeroPad1d | false | 2,395 | [
"MIT"
] | 0 | 4e8e6e2f9151a87448845c5142611c103dd4580c | https://github.com/Fei00Wu/espresso/tree/4e8e6e2f9151a87448845c5142611c103dd4580c |
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