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 |
|---|---|---|---|---|---|---|---|---|---|---|
LSTMAttentionLayer | import torch
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torch.optim
import torch.utils.data
import torch.onnx.operators
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
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
from torch._inductor.runtime.... | PeterouZh/SemiNAS | LSTMAttentionLayer | false | 17,821 | [
"Apache-2.0"
] | 5 | 39731663271b994571160d43d796b2bb93386b3b | https://github.com/PeterouZh/SemiNAS/tree/39731663271b994571160d43d796b2bb93386b3b |
Mean | from torch.nn import Module
import torch
import torch.utils.data
class Mean(Module):
def __init__(self, dim, keep_dim=False):
super(Mean, self).__init__()
self.dim = dim
self.keep_dim = keep_dim
def forward(self, input):
return input.mean(self.dim, self.keep_dim)
def get_in... | 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.nn import Module
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | RL-WWW/ISST | Mean | false | 17,822 | [
"BSD-3-Clause"
] | 5 | 42b656686fa9660794007a0bc00a7177937410e9 | https://github.com/RL-WWW/ISST/tree/42b656686fa9660794007a0bc00a7177937410e9 |
GumbelQuantize | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
class GumbelQuantize(nn.Module):
"""
Reference:
Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016
https://arxiv.org/abs/1611.01144
"""
def __init__(self, hidden_channel, n_e, e_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.... | PeikeLi/pytorch-vector-quantization | GumbelQuantize | false | 17,823 | [
"MIT"
] | 6 | 48ce6a74ec56b9d8c11dde2cd35b055a925c3070 | https://github.com/PeikeLi/pytorch-vector-quantization/tree/48ce6a74ec56b9d8c11dde2cd35b055a925c3070 |
GeneratorLon | import torch
import torch.onnx
import torch.nn as nn
import torch.nn.functional as F
class GeneratorLon(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, d_model, tgt_lon_classes):
super(GeneratorLon, self).__init__()
self.proj = nn.Linear(d_model, 2, tgt_... | 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.... | PhilippeW83440/conv-social-pooling | GeneratorLon | false | 17,824 | [
"MIT"
] | 4 | 93d3a08af8678c3309d75a9bfb37df500da5cc46 | https://github.com/PhilippeW83440/conv-social-pooling/tree/93d3a08af8678c3309d75a9bfb37df500da5cc46 |
C3D | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
from torch.nn.init import *
class C3D(nn.Module):
"""
The C3D network.
"""
def __init__(self, num_classes, pretrained=False, path=None):
super(C3D, self).__init__()
self.conv1 = nn.Conv3d(3, 64, kernel_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
import ... | Luoyadan/MM2020_ABG | C3D | false | 17,825 | [
"MIT"
] | 8 | d74cf915deea7bb425518f5bd40e64a9a7341981 | https://github.com/Luoyadan/MM2020_ABG/tree/d74cf915deea7bb425518f5bd40e64a9a7341981 |
GluMlp | import torch
import torch.nn as nn
class GluMlp(nn.Module):
""" MLP w/ GLU style gating
See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202
"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.Sigmoid, drop=0.0):
super().__init_... | 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... | RICE-EIC/Patch-Fool | GluMlp | false | 17,826 | [
"MIT"
] | 7 | 9638ec33a4d13b0c5ff0ec3ee5ce6b46ea7da5a6 | https://github.com/RICE-EIC/Patch-Fool/tree/9638ec33a4d13b0c5ff0ec3ee5ce6b46ea7da5a6 |
AffinityLoss | import torch
from torch import Tensor
import torch.nn as nn
class AffinityLoss(nn.Module):
"""
GNINA affinity loss.
Parameters
----------
reduction: str
Reduction method (mean or sum)
delta: float
Scaling factor
penalty: float
Penalty factor
pseudo_huber: bool
... | 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... | RMeli/gnina-torch | AffinityLoss | false | 17,827 | [
"MIT"
] | 5 | eb57e2a62628d39f2a66e7fa1748e80705366761 | https://github.com/RMeli/gnina-torch/tree/eb57e2a62628d39f2a66e7fa1748e80705366761 |
FingerprintDecoder | import torch
import torch.utils.data
import torch.nn.functional as F
class FingerprintDecoder(torch.nn.Module):
def __init__(self, n_in, n_out, dropout=0.1):
super(FingerprintDecoder, self).__init__()
if n_out > n_in:
n_hidden = n_out // 2
else:
n_hidden = n_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
import torch.utils.data
asser... | Prepaire/MolGNN_fewshot | FingerprintDecoder | false | 17,828 | [
"MIT"
] | 6 | c7c17afdeae7f2ef0c8e3ca2da033091ec7537ca | https://github.com/Prepaire/MolGNN_fewshot/tree/c7c17afdeae7f2ef0c8e3ca2da033091ec7537ca |
CustomGruCell | import torch
import numpy as np
import torch.nn as nn
class CustomGruCell(nn.Module):
"""
A forward only GRU cell.
Input should be: (sequence length x batch size x input_size).
The output is the output of the final forward call.
It's not clear if it would be possible to use the output from each ce... | 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
... | Rahul-160/PySyft | CustomGruCell | false | 17,829 | [
"Apache-2.0"
] | 7 | 182627db2369d6f93aa0667f5ea2abee5b878d58 | https://github.com/Rahul-160/PySyft/tree/182627db2369d6f93aa0667f5ea2abee5b878d58 |
GeneratorLat | import torch
import torch.onnx
import torch.nn as nn
import torch.nn.functional as F
class GeneratorLat(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, d_model, tgt_lat_classes):
super(GeneratorLat, self).__init__()
self.proj = nn.Linear(d_model, tgt_lat... | 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.... | PhilippeW83440/conv-social-pooling | GeneratorLat | false | 17,830 | [
"MIT"
] | 4 | 93d3a08af8678c3309d75a9bfb37df500da5cc46 | https://github.com/PhilippeW83440/conv-social-pooling/tree/93d3a08af8678c3309d75a9bfb37df500da5cc46 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, input_dim, output_dim):
super(Actor, self).__init__()
self.fc1 = nn.Linear(input_dim, 128)
self.fc2 = nn.Linear(128, output_dim)
def forward(self, x):
x = F.relu(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.... | PaulPan00/donkey_wrapper | Actor | false | 17,831 | [
"MIT"
] | 6 | a03cf0f42f65625fbce792b06c98acd153c5d6c8 | https://github.com/PaulPan00/donkey_wrapper/tree/a03cf0f42f65625fbce792b06c98acd153c5d6c8 |
RevPaddingLayer | import torch
import torch.nn as nn
class RevPaddingLayer(nn.Module):
def __init__(self, stride):
super().__init__()
self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
def forward(self, x):
x = self.pool(x)
zeros = torch.zeros_like(x)
zeros_left, zeros_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... | RKorzeniowski/BigBiGAN-PyTorch | RevPaddingLayer | false | 17,832 | [
"MIT"
] | 5 | caaaf69b094ae45e9fa3608577fde32dafa1f16e | https://github.com/RKorzeniowski/BigBiGAN-PyTorch/tree/caaaf69b094ae45e9fa3608577fde32dafa1f16e |
AvgPool2d | from torch.nn import Module
import torch
import torch as th
class AvgPool2d(Module):
"""
This class is the beginning of an exact python port of the torch.nn.AvgPool2d
module. Because PySyft cannot hook into layers which are implemented in C++,
our special functionalities (such as encrypted computation... | 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.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | Rahul-160/PySyft | AvgPool2d | false | 17,833 | [
"Apache-2.0"
] | 7 | 182627db2369d6f93aa0667f5ea2abee5b878d58 | https://github.com/Rahul-160/PySyft/tree/182627db2369d6f93aa0667f5ea2abee5b878d58 |
myEncoder | import torch
import torch.nn.functional as F
class myEncoder(torch.nn.Module):
def __init__(self, fomSize, romSize):
super(myEncoder, self).__init__()
self.fc1 = torch.nn.Linear(fomSize, 200)
self.fc2 = torch.nn.Linear(200, 64)
self.fc3 = torch.nn.Linear(64, romSize)
def forw... | 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
assert_size_stride ... | Pressio/pressio4py | myEncoder | false | 17,834 | [
"Unlicense",
"BSD-3-Clause"
] | 4 | 36676dbd112a7c7960ccbf302ff14d4376c819ec | https://github.com/Pressio/pressio4py/tree/36676dbd112a7c7960ccbf302ff14d4376c819ec |
Foo | import torch
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
class Foo(torch.nn.Module):
def __init__(self, size):
super(Foo, self).__init__()
self.n = torch.nn.Parameter(torch.ones(size))
self.m = torch.nn... | 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.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
assert_si... | ROCmSoftwarePlatform/apex | Foo | false | 17,835 | [
"BSD-3-Clause"
] | 6 | db92ee13ca55e284342bdca84bddc38c3812f1ed | https://github.com/ROCmSoftwarePlatform/apex/tree/db92ee13ca55e284342bdca84bddc38c3812f1ed |
FermiDiracDecoder | from torch.nn import Module
import torch
from torch.nn.modules.module import Module
import torch.optim
import torch.nn.modules.loss
class FermiDiracDecoder(Module):
"""Fermi Dirac to compute edge probabilities based on distances."""
def __init__(self, r, t):
super(FermiDiracDecoder, 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.triton_helpers import math as tl_math
from torch.nn import Module
from torch.nn.modules.module import Module
im... | RingBDStack/ACE-HGNN | FermiDiracDecoder | false | 17,836 | [
"MIT"
] | 5 | afc610dd838951dcd6c3910795b472566f0c23ca | https://github.com/RingBDStack/ACE-HGNN/tree/afc610dd838951dcd6c3910795b472566f0c23ca |
Fusion2_GateLayer | import torch
from torch import nn
class Fusion2_GateLayer(nn.Module):
def __init__(self, input_dim):
super(Fusion2_GateLayer, self).__init__()
self._norm_layer1 = nn.Linear(input_dim * 2, input_dim)
self._norm_layer2 = nn.Linear(input_dim, 1)
def forward(self, input1, input2):
... | 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... | RUCAIBox/WSDM2022-C2CRS | Fusion2_GateLayer | false | 17,837 | [
"MIT"
] | 4 | 8ef2fa7c44bdba1799ab79f379ae7394bd468c02 | https://github.com/RUCAIBox/WSDM2022-C2CRS/tree/8ef2fa7c44bdba1799ab79f379ae7394bd468c02 |
CrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
def _is_long(x):
return isinstance(x, torch.LongTensor) or isinstance(x, torch.LongTensor)
def onehot(indexes, N=None, ignore_index=None):
"""
Creates a one-representati... | 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
... | Randl/Ranger_Mish_reimplementation | CrossEntropyLoss | false | 17,838 | [
"MIT"
] | 7 | 36f580ce8a02fae1929e101c9bd6987ccd2a5843 | https://github.com/Randl/Ranger_Mish_reimplementation/tree/36f580ce8a02fae1929e101c9bd6987ccd2a5843 |
BasicBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=0, bias=True)
class BasicBlock(nn.Module):
"""
Residual BasicBlock... | 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.... | RaoUmer/ISRResCNet | BasicBlock | false | 17,839 | [
"MIT"
] | 6 | 8175bb9efa5bba2cce4ad86616219209c20b7244 | https://github.com/RaoUmer/ISRResCNet/tree/8175bb9efa5bba2cce4ad86616219209c20b7244 |
HiResPose | import torch
import torch.nn as nn
from collections import OrderedDict
from typing import Tuple
import torch.nn.functional as F
class HiResPose(nn.Module):
"""
GNINA HiResPose model architecture.
Parameters
----------
input_dims: tuple
Model input dimensions (channels, depth, height, widt... | 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.... | RMeli/gnina-torch | HiResPose | false | 17,840 | [
"MIT"
] | 5 | eb57e2a62628d39f2a66e7fa1748e80705366761 | https://github.com/RMeli/gnina-torch/tree/eb57e2a62628d39f2a66e7fa1748e80705366761 |
GraphAttentionLayer | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha):
super(GraphAtt... | 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.... | RidongHan/GHE-LPC | GraphAttentionLayer | false | 17,841 | [
"MIT"
] | 4 | 2a10f423d747aa28560a3bcbf29f7ec87422beb8 | https://github.com/RidongHan/GHE-LPC/tree/2a10f423d747aa28560a3bcbf29f7ec87422beb8 |
Fusion2_MinusFCLayer | import torch
from torch import nn
class Fusion2_MinusFCLayer(nn.Module):
def __init__(self, input_dim):
super(Fusion2_MinusFCLayer, self).__init__()
self._norm_layer1 = nn.Linear(input_dim * 3, input_dim)
def forward(self, input1, input2):
norm_input = self._norm_layer1(torch.cat([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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | RUCAIBox/WSDM2022-C2CRS | Fusion2_MinusFCLayer | false | 17,842 | [
"MIT"
] | 4 | 8ef2fa7c44bdba1799ab79f379ae7394bd468c02 | https://github.com/RUCAIBox/WSDM2022-C2CRS/tree/8ef2fa7c44bdba1799ab79f379ae7394bd468c02 |
BertLinear | import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
... | 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 ... | Receiling/ENPAR | BertLinear | false | 17,843 | [
"MIT"
] | 5 | decd2945d21a7be5a0f73c37cfc5e252301aab15 | https://github.com/Receiling/ENPAR/tree/decd2945d21a7be5a0f73c37cfc5e252301aab15 |
Fusion2_FCLayer | import torch
from torch import nn
class Fusion2_FCLayer(nn.Module):
def __init__(self, input_dim):
super(Fusion2_FCLayer, self).__init__()
self._norm_layer1 = nn.Linear(input_dim * 2, input_dim)
def forward(self, input1, input2):
norm_input = self._norm_layer1(torch.cat([input1, inpu... | 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... | RUCAIBox/WSDM2022-C2CRS | Fusion2_FCLayer | false | 17,844 | [
"MIT"
] | 4 | 8ef2fa7c44bdba1799ab79f379ae7394bd468c02 | https://github.com/RUCAIBox/WSDM2022-C2CRS/tree/8ef2fa7c44bdba1799ab79f379ae7394bd468c02 |
Fusion3_FCLayer | import torch
from torch import nn
class Fusion3_FCLayer(nn.Module):
def __init__(self, input_dim):
super(Fusion3_FCLayer, self).__init__()
self._norm_layer1 = nn.Linear(input_dim * 3, input_dim)
def forward(self, input1, input2, input3):
norm_input = self._norm_layer1(torch.cat([inpu... | 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... | RUCAIBox/WSDM2022-C2CRS | Fusion3_FCLayer | false | 17,845 | [
"MIT"
] | 4 | 8ef2fa7c44bdba1799ab79f379ae7394bd468c02 | https://github.com/RUCAIBox/WSDM2022-C2CRS/tree/8ef2fa7c44bdba1799ab79f379ae7394bd468c02 |
DenseAtt | import torch
import torch.nn as nn
import torch.optim
import torch.nn.modules.loss
class DenseAtt(nn.Module):
def __init__(self, in_features, dropout):
super(DenseAtt, self).__init__()
self.dropout = dropout
self.linear = nn.Linear(2 * in_features, 1, bias=True)
self.in_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
import torch.nn as nn
import torch.optim
import torch.nn.modules.loss
assert_siz... | RingBDStack/ACE-HGNN | DenseAtt | false | 17,846 | [
"MIT"
] | 5 | afc610dd838951dcd6c3910795b472566f0c23ca | https://github.com/RingBDStack/ACE-HGNN/tree/afc610dd838951dcd6c3910795b472566f0c23ca |
SelfAttentionBatch | import torch
from torch import nn
import torch.nn.functional as F
class SelfAttentionBatch(nn.Module):
def __init__(self, dim, da, alpha=0.2, dropout=0.5):
super(SelfAttentionBatch, self).__init__()
self.dim = dim
self.da = da
self.alpha = alpha
self.dropout = dropout
... | 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.... | RUCAIBox/WSDM2022-C2CRS | SelfAttentionBatch | false | 17,847 | [
"MIT"
] | 4 | 8ef2fa7c44bdba1799ab79f379ae7394bd468c02 | https://github.com/RUCAIBox/WSDM2022-C2CRS/tree/8ef2fa7c44bdba1799ab79f379ae7394bd468c02 |
SelfAttentionPooling | import torch
import torch.nn as nn
class SelfAttentionPooling(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super(SelfAttentio... | 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.... | RayTzeng/s3m-membership-inference | SelfAttentionPooling | false | 17,848 | [
"MIT"
] | 9 | ec1ed9438afc4fd3d7a55fd10e6065d2ecc861c4 | https://github.com/RayTzeng/s3m-membership-inference/tree/ec1ed9438afc4fd3d7a55fd10e6065d2ecc861c4 |
Fusion3_MinusFCLayer | import torch
from torch import nn
class Fusion3_MinusFCLayer(nn.Module):
def __init__(self, input_dim):
super(Fusion3_MinusFCLayer, self).__init__()
self._norm_layer1 = nn.Linear(input_dim * 6, input_dim)
def forward(self, input1, input2, input3):
norm_input = self._norm_layer1(torch... | 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... | RUCAIBox/WSDM2022-C2CRS | Fusion3_MinusFCLayer | false | 17,850 | [
"MIT"
] | 4 | 8ef2fa7c44bdba1799ab79f379ae7394bd468c02 | https://github.com/RUCAIBox/WSDM2022-C2CRS/tree/8ef2fa7c44bdba1799ab79f379ae7394bd468c02 |
BinaryNLLEntropy | import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.nn.modules.loss import _Loss
import torch.jit
class BinaryNLLEntropy(_Loss):
def __init__(self, size_average=True):
super(BinaryNLLEntropy, self).__init__()
self.size_average = size_average
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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | RoderickGu/Pretraining_GPT | BinaryNLLEntropy | false | 17,851 | [
"Apache-2.0"
] | 4 | 0a3ecd38116dc271e273f57490b9b45b660bf401 | https://github.com/RoderickGu/Pretraining_GPT/tree/0a3ecd38116dc271e273f57490b9b45b660bf401 |
GAT | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha):
super(GraphAtt... | 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.... | RidongHan/GHE-LPC | GAT | false | 17,852 | [
"MIT"
] | 4 | 2a10f423d747aa28560a3bcbf29f7ec87422beb8 | https://github.com/RidongHan/GHE-LPC/tree/2a10f423d747aa28560a3bcbf29f7ec87422beb8 |
NormKLLoss | import torch
import torch.utils.checkpoint
import torch as th
from torch.nn.modules.loss import _Loss
import torch.jit
class NormKLLoss(_Loss):
def __init__(self, unit_average=False):
super(NormKLLoss, self).__init__()
self.unit_average = unit_average
def forward(self, recog_mu, recog_logvar... | 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.checkpoint
from torch.nn.modules.loss import _Loss
imp... | RoderickGu/Pretraining_GPT | NormKLLoss | false | 17,853 | [
"Apache-2.0"
] | 4 | 0a3ecd38116dc271e273f57490b9b45b660bf401 | https://github.com/RoderickGu/Pretraining_GPT/tree/0a3ecd38116dc271e273f57490b9b45b660bf401 |
first_conv | import torch
import torch.nn as nn
import torch.nn.functional as F
class first_conv(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=False):
super(first_conv, self).__init__(in_channels, out_channels,
kernel_size, 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.... | RuiLin0212/BATMANN | first_conv | false | 17,854 | [
"MIT"
] | 6 | 5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 | https://github.com/RuiLin0212/BATMANN/tree/5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 |
Hidden2Discrete | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
import torch.jit
class Hidden2Discrete(nn.Module):
def __init__(self, input_size, y_size, k_size, is_lstm=False, has_bias=True
):
super(Hidden2Discrete, self).__init__()
self.y_size = y_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
from torch._inductor.runtime.... | RoderickGu/Pretraining_GPT | Hidden2Discrete | false | 17,855 | [
"Apache-2.0"
] | 4 | 0a3ecd38116dc271e273f57490b9b45b660bf401 | https://github.com/RoderickGu/Pretraining_GPT/tree/0a3ecd38116dc271e273f57490b9b45b660bf401 |
Generator | import torch
import torch.distributed
import torch
import torch.nn as nn
def gumbel_softmax(logits, tau=1.0, hard=False, log_mode=True, dim=-1):
while True:
gumbels = -torch.empty_like(logits).exponential_().log()
gumbels = (logits + gumbels) / tau
if log_mode:
y_soft = gumbels... | 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.... | RowitZou/CG-nAR | Generator | false | 17,856 | [
"MIT"
] | 8 | 8e2debeb3170045592b3b674ea6f9b56251e71f4 | https://github.com/RowitZou/CG-nAR/tree/8e2debeb3170045592b3b674ea6f9b56251e71f4 |
last_fc | import torch
import torch.nn as nn
import torch.nn.functional as F
class last_fc(nn.Linear):
def __init__(self, in_features, out_features, bias=True):
super(last_fc, self).__init__(in_features, out_features, bias)
self.layer_type = 'LFC'
self.transform = None
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
from torch._inductor.runtime.... | RuiLin0212/BATMANN | last_fc | false | 17,857 | [
"MIT"
] | 6 | 5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 | https://github.com/RuiLin0212/BATMANN/tree/5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 |
TransformerEncoderFeedForward | import torch
import torch.nn as nn
class Dense(nn.Module):
def __init__(self, in_dim, out_dim, use_bias=True, activation=None,
name=None):
super(Dense, self).__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.use_bias = use_bias
self.activation = activatio... | 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.... | RoySadaka/lpd | TransformerEncoderFeedForward | false | 17,858 | [
"MIT"
] | 4 | 921454d9730d8228f4b0ca5349b0558ebd123c65 | https://github.com/RoySadaka/lpd/tree/921454d9730d8228f4b0ca5349b0558ebd123c65 |
MultiHeadAttention | import torch
import torch as th
import torch.nn as nn
class MultiHeadAttention(nn.Module):
def __init__(self, hidden_size, attention_dropout_rate, num_heads):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.att_size = att_size = hidden_size // num_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.... | Roestlab/massformer | MultiHeadAttention | false | 17,859 | [
"BSD-2-Clause"
] | 6 | c6324970c392f8ee96651679f49d21e430caa0c9 | https://github.com/Roestlab/massformer/tree/c6324970c392f8ee96651679f49d21e430caa0c9 |
SelfAttn | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
import torch as th
import torch.jit
class SelfAttn(nn.Module):
def __init__(self, hidden_size):
super(SelfAttn, self).__init__()
self.query = nn.Linear(hidden_size, 1)
def forward(self, keys, val... | 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.... | RoderickGu/Pretraining_GPT | SelfAttn | false | 17,860 | [
"Apache-2.0"
] | 4 | 0a3ecd38116dc271e273f57490b9b45b660bf401 | https://github.com/RoderickGu/Pretraining_GPT/tree/0a3ecd38116dc271e273f57490b9b45b660bf401 |
AttentionBlock | import math
import torch
from torch.nn import functional as F
from torch import nn
import torch.utils.data
import torch.optim
def convert_pad_shape(pad_shape):
"""
Used to get arguments for F.pad
"""
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape
... | 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.... | Royeqiu/Nemo_ASR | AttentionBlock | false | 17,861 | [
"Apache-2.0"
] | 10 | 12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e | https://github.com/Royeqiu/Nemo_ASR/tree/12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e |
Classifier | import torch
import torch.distributed
import torch
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 = 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
import torch.distributed
import torch
import torch.nn as nn
assert_size_stride =... | RowitZou/CG-nAR | Classifier | false | 17,862 | [
"MIT"
] | 8 | 8e2debeb3170045592b3b674ea6f9b56251e71f4 | https://github.com/RowitZou/CG-nAR/tree/8e2debeb3170045592b3b674ea6f9b56251e71f4 |
FCN8VGG16 | import torch
import numpy as np
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
def conv3x3(in_planes, out_planes, stride=1, padding=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=(3, 3), stride=(
stride, stride), padding=(padding, padding))
... | 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 numpy as np
import tor... | IssamLaradji/looc | FCN8VGG16 | false | 17,863 | [
"Apache-2.0"
] | 9 | 50a05b9bf2d36cd8770add8cc65f9bab1ad45841 | https://github.com/IssamLaradji/looc/tree/50a05b9bf2d36cd8770add8cc65f9bab1ad45841 |
XNOR_BinarizeConv2d | from torch.autograd import Function
import torch
import torch.nn as nn
import torch.nn.functional as F
class XNOR_BinaryQuantize(Function):
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
out = torch.sign(input)
return out
@staticmethod
def backward(ctx, g... | 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.autograd... | RuiLin0212/BATMANN | XNOR_BinarizeConv2d | false | 17,864 | [
"MIT"
] | 6 | 5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 | https://github.com/RuiLin0212/BATMANN/tree/5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 |
MOTION_ReplaceBlock_B | import torch
import torch.nn.parallel
import torch.optim
import torch
import torch.nn as nn
class MOTION_ReplaceBlock_B(nn.Module):
"""
using diff
"""
def __init__(self, in_channels, n_segment, n_div):
super(MOTION_ReplaceBlock_B, self).__init__()
self.n_div = n_div
self.fold ... | 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.parallel
import torch.optim
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | RongchangLi/DEN | MOTION_ReplaceBlock_B | false | 17,865 | [
"MIT"
] | 4 | f8b744f96a3a68cf0784080ffd561a5279715727 | https://github.com/RongchangLi/DEN/tree/f8b744f96a3a68cf0784080ffd561a5279715727 |
MOTION_Channel_ReplaceBlock | import torch
import torch.nn.parallel
import torch.optim
import torch
import torch.nn as nn
class MOTION_Channel_ReplaceBlock(nn.Module):
def __init__(self, in_channels, n_segment, n_div):
super(MOTION_Channel_ReplaceBlock, self).__init__()
self.n_div = n_div
self.fold = in_channels // 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
import torch.nn.parallel
import torch.optim
import torch
import torch.nn as nn
a... | RongchangLi/DEN | MOTION_Channel_ReplaceBlock | false | 17,866 | [
"MIT"
] | 4 | f8b744f96a3a68cf0784080ffd561a5279715727 | https://github.com/RongchangLi/DEN/tree/f8b744f96a3a68cf0784080ffd561a5279715727 |
DiceBCELoss | import torch
from torch import nn
import torch.nn.functional as F
class DiceBCELoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(DiceBCELoss, self).__init__()
def forward(self, inputs, targets, smooth=1):
inputs = torch.sigmoid(inputs)
inputs = inputs.view(-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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | SH-96/polyp-segmentation-pytorch | DiceBCELoss | false | 17,867 | [
"MIT"
] | 3 | 14ecd2998874a4d26c442bacc3ec69c2d42642f1 | https://github.com/SH-96/polyp-segmentation-pytorch/tree/14ecd2998874a4d26c442bacc3ec69c2d42642f1 |
LayerNorm | import torch
from torch import nn
import torch.utils.data
import torch.optim
class LayerNorm(nn.Module):
def __init__(self, channels, eps=0.0001):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Para... | 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
import torch.utils.data
import torch.optim
assert_size_str... | Royeqiu/Nemo_ASR | LayerNorm | false | 17,868 | [
"Apache-2.0"
] | 10 | 12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e | https://github.com/Royeqiu/Nemo_ASR/tree/12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e |
MultiHeadAttention | import math
import torch
from torch import nn
import torch.utils.data
import torch.optim
class MultiHeadAttention(nn.Module):
"""
Multi-head scaled dot-product attention layer.
Args:
hidden_size: size of the embeddings in the model, also known as d_model
num_attention_heads: number of 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.... | Royeqiu/Nemo_ASR | MultiHeadAttention | false | 17,869 | [
"Apache-2.0"
] | 10 | 12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e | https://github.com/Royeqiu/Nemo_ASR/tree/12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e |
binary_last_fc | from torch.autograd import Function
import torch
import torch.nn as nn
import torch.nn.functional as F
class XNOR_BinaryQuantize(Function):
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
out = torch.sign(input)
return out
@staticmethod
def backward(ctx, g... | 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.autograd... | RuiLin0212/BATMANN | binary_last_fc | false | 17,870 | [
"MIT"
] | 6 | 5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 | https://github.com/RuiLin0212/BATMANN/tree/5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 |
ValueNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class ValueNetwork(nn.Module):
def __init__(self, input_dim, output_dim, init_w=0.003):
super(ValueNetwork, self).__init__()
self.fc1 = nn.Linear(input_dim, 256)
self.fc2 = nn.Linear(256, 256)
self.fc3 = nn.Linear(... | 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_... | SAMMiCA/DL_based_E2E_Driving | ValueNetwork | false | 17,871 | [
"MIT"
] | 4 | 01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | https://github.com/SAMMiCA/DL_based_E2E_Driving/tree/01f7d74a0db7ed745cf27b9a1ebab0246015ecbd |
RMSELoss | import torch
import torch.nn as nn
class RMSELoss(torch.nn.Module):
def __init__(self):
super(RMSELoss, self).__init__()
def forward(self, x, y):
criterion = nn.MSELoss()
loss = torch.sqrt(criterion(x, y))
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | SAMMiCA/DL_based_E2E_Driving | RMSELoss | false | 17,872 | [
"MIT"
] | 4 | 01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | https://github.com/SAMMiCA/DL_based_E2E_Driving/tree/01f7d74a0db7ed745cf27b9a1ebab0246015ecbd |
InvConvNear | import torch
from torch.nn import functional as F
from torch import nn
import torch.utils.data
import torch.optim
class InvConvNear(nn.Module):
def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
super().__init__()
assert n_split % 2 == 0
self.channels = 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 import nn
import torch.utils.data
import torch.optim
assert_size_stri... | Royeqiu/Nemo_ASR | InvConvNear | false | 17,873 | [
"Apache-2.0"
] | 10 | 12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e | https://github.com/Royeqiu/Nemo_ASR/tree/12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e |
ConvGLU | import torch
from torch import nn
import torch.utils.data
import torch.optim
def str2act(txt):
"""Translates text to neural network activation"""
return {'sigmoid': nn.Sigmoid(), 'relu': nn.ReLU(), 'none': nn.
Sequential(), 'lrelu': nn.LeakyReLU(0.2), 'selu': nn.SELU()}[txt.
lower()]
class C... | 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
import torch.utils.data
import torch.optim
assert_size_stri... | Royeqiu/Nemo_ASR | ConvGLU | false | 17,874 | [
"Apache-2.0"
] | 10 | 12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e | https://github.com/Royeqiu/Nemo_ASR/tree/12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e |
MOTION_ReplaceBlock_D | import torch
import torch.nn.parallel
import torch.optim
import torch
import torch.nn as nn
class MOTION_ReplaceBlock_D(nn.Module):
"""
reuse conv
"""
def __init__(self, in_channels, n_segment, n_div):
super(MOTION_ReplaceBlock_D, self).__init__()
self.n_div = n_div
self.fold... | 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.parallel
import torch.optim
import torch
import torch.nn as nn
a... | RongchangLi/DEN | MOTION_ReplaceBlock_D | false | 17,875 | [
"MIT"
] | 4 | f8b744f96a3a68cf0784080ffd561a5279715727 | https://github.com/RongchangLi/DEN/tree/f8b744f96a3a68cf0784080ffd561a5279715727 |
TransformerEncoderLayer | import math
import torch
from typing import Callable
from typing import Optional
from typing import Tuple
from typing import List
from typing import Dict
from typing import Union
from typing import Any
import torch.utils.data
import torch.nn.functional as F
import torch.nn
import torch.cuda
import torch.backends.cudnn
... | 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.... | RobertCsordas/tcf | TransformerEncoderLayer | false | 17,876 | [
"MIT"
] | 5 | da20530dfb4336deddfbe5e79d62e72d1dc2580e | https://github.com/RobertCsordas/tcf/tree/da20530dfb4336deddfbe5e79d62e72d1dc2580e |
EncoderLayer | import torch
import torch as th
import torch.nn as nn
class FeedForwardNetwork(nn.Module):
def __init__(self, hidden_size, ffn_size, dropout_rate):
super(FeedForwardNetwork, self).__init__()
self.layer1 = nn.Linear(hidden_size, ffn_size)
self.gelu = nn.GELU()
self.layer2 = 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.... | Roestlab/massformer | EncoderLayer | false | 17,877 | [
"BSD-2-Clause"
] | 6 | c6324970c392f8ee96651679f49d21e430caa0c9 | https://github.com/Roestlab/massformer/tree/c6324970c392f8ee96651679f49d21e430caa0c9 |
SoftQNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class SoftQNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size=256, init_w=0.003):
super(SoftQNetwork, self).__init__()
self.linear1 = nn.Linear(num_inputs + num_actions, hidden_size)
self.linear2 =... | 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_... | SAMMiCA/DL_based_E2E_Driving | SoftQNetwork | false | 17,878 | [
"MIT"
] | 4 | 01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | https://github.com/SAMMiCA/DL_based_E2E_Driving/tree/01f7d74a0db7ed745cf27b9a1ebab0246015ecbd |
PositionwiseFeedForward | import math
import torch
import torch.distributed
import torch
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.
... | 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 ... | RowitZou/CG-nAR | PositionwiseFeedForward | false | 17,879 | [
"MIT"
] | 8 | 8e2debeb3170045592b3b674ea6f9b56251e71f4 | https://github.com/RowitZou/CG-nAR/tree/8e2debeb3170045592b3b674ea6f9b56251e71f4 |
GlobalAvgPool2d | import torch
from torch import nn
import torch.nn.functional as F
class GlobalAvgPool2d(nn.Module):
def __init__(self):
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
return F.avg_pool2d(x, kernel_size=x.size()[2:])
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
d... | 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... | Sandy1230/Dive-into-DL-PyTorch-master | GlobalAvgPool2d | false | 17,880 | [
"Apache-2.0"
] | 4 | eca149f6b706a4e6a7b377707deab22341b014d1 | https://github.com/Sandy1230/Dive-into-DL-PyTorch-master/tree/eca149f6b706a4e6a7b377707deab22341b014d1 |
PolicyNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
class PolicyNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size=256, init_w=
0.003, log_std_min=-20, log_std_max=2):
super(PolicyNetwork, self).__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
import torch.nn as nn
from to... | SAMMiCA/DL_based_E2E_Driving | PolicyNetwork | false | 17,881 | [
"MIT"
] | 4 | 01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | https://github.com/SAMMiCA/DL_based_E2E_Driving/tree/01f7d74a0db7ed745cf27b9a1ebab0246015ecbd |
CorrConv | from torch.autograd import Function
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data
import torch.nn.parallel
class CorrConvFunction(Function):
@staticmethod
def forward(ctx, input, weight, bias=None, stride=1, padding=0, lamda=0.005
):
ctx.save_f... | 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.autograd import Function
import torch.nn as nn
from torch.autograd im... | SCUT-AILab/CorrReg | CorrConv | false | 17,882 | [
"MIT"
] | 5 | 3635d237effd0c7dd1d2a831f8ab14e30edac561 | https://github.com/SCUT-AILab/CorrReg/tree/3635d237effd0c7dd1d2a831f8ab14e30edac561 |
SELECT_fusion_block | import torch
import torch.nn.parallel
import torch.optim
import torch
import torch.nn as nn
class SELECT_fusion_block(nn.Module):
def __init__(self, in_channels, n_segment, n_div):
super(SELECT_fusion_block, self).__init__()
self.n_div = n_div
self.fold = in_channels // n_div
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
import torch.nn.parallel
impo... | RongchangLi/DEN | SELECT_fusion_block | false | 17,883 | [
"MIT"
] | 4 | f8b744f96a3a68cf0784080ffd561a5279715727 | https://github.com/RongchangLi/DEN/tree/f8b744f96a3a68cf0784080ffd561a5279715727 |
CONV1d_FusionBlock | import torch
import torch.nn.parallel
import torch.optim
import torch
import torch.nn as nn
class CONV1d_FusionBlock(nn.Module):
def __init__(self, in_channels, n_segment, n_div):
super(CONV1d_FusionBlock, self).__init__()
self.n_div = n_div
self.fold = in_channels // n_div
self.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
import torch.nn.parallel
import torch.optim
import torch
import torch.nn as nn
a... | RongchangLi/DEN | CONV1d_FusionBlock | false | 17,884 | [
"MIT"
] | 4 | f8b744f96a3a68cf0784080ffd561a5279715727 | https://github.com/RongchangLi/DEN/tree/f8b744f96a3a68cf0784080ffd561a5279715727 |
channel_attention | import torch
from torch import nn
class channel_attention(nn.Module):
def __init__(self, in_channels, feature_size):
super(channel_attention, self).__init__()
self.fc1 = nn.Linear(feature_size * feature_size, feature_size,
bias=False)
self.relu1 = nn.ReLU(inplace=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 import triton_helpers
from torch._inductor.runtime.... | SCUT-AILab/AFA | channel_attention | false | 17,885 | [
"BSD-3-Clause"
] | 7 | acfb42236ce0114d63f22a821fc5954c8c149f45 | https://github.com/SCUT-AILab/AFA/tree/acfb42236ce0114d63f22a821fc5954c8c149f45 |
MLPSoftQNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLPSoftQNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size1=1400,
hidden_size2=1024, hidden_size3=256, init_w=0.003):
super(MLPSoftQNetwork, self).__init__()
self.linear1 = nn.Linear(num_inpu... | 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_... | SAMMiCA/DL_based_E2E_Driving | MLPSoftQNetwork | false | 17,886 | [
"MIT"
] | 4 | 01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | https://github.com/SAMMiCA/DL_based_E2E_Driving/tree/01f7d74a0db7ed745cf27b9a1ebab0246015ecbd |
Fusion | import torch
import torch.nn as nn
class Fusion(nn.Module):
""" Crazy multi-modal fusion: negative squared difference minus relu'd sum
"""
def __init__(self):
super().__init__()
def forward(self, x, y):
return -(x - y) ** 2 + torch.relu(x + y)
def get_inputs():
return [torch.ra... | 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... | Ruiver/CTCNet | Fusion | false | 17,887 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 |
EncoderBlock | import torch
import torch.nn.functional as F
from torch import nn
class EncoderBlock(nn.Module):
"""
Encoder block class
"""
def __init__(self, in_channels, out_channels, k_size, pad_size):
super(EncoderBlock, self).__init__()
self.conv1 = nn.Conv3d(in_channels, out_channels, kernel_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.... | SVRTK/Segmentation_FetalMRI | EncoderBlock | false | 17,888 | [
"Apache-2.0"
] | 6 | 9344a2248cbe8e4cccbe05ca98214626dcf62805 | https://github.com/SVRTK/Segmentation_FetalMRI/tree/9344a2248cbe8e4cccbe05ca98214626dcf62805 |
pixel_attention | import torch
from torch import nn
class pixel_attention(nn.Module):
def __init__(self, in_channels, feature_size):
super(pixel_attention, self).__init__()
self.fc1 = nn.Linear(feature_size * feature_size, feature_size,
bias=False)
self.relu1 = nn.ReLU(inplace=True)
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
from torch._inductor.runtime.... | SCUT-AILab/AFA | pixel_attention | false | 17,889 | [
"BSD-3-Clause"
] | 7 | acfb42236ce0114d63f22a821fc5954c8c149f45 | https://github.com/SCUT-AILab/AFA/tree/acfb42236ce0114d63f22a821fc5954c8c149f45 |
QREmbeddingBag | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class QREmbeddingBag(nn.Module):
"""Computes sums or means over two 'bags' of embeddings, one using the quotient
of the indices and the other using the remainder of the indices, witho... | 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 numpy as np
import torch.nn as nn
from torch.nn.parameter import Paramet... | STAR-Laboratory/Accelerating-RecSys-Training | QREmbeddingBag | false | 17,890 | [
"MIT"
] | 5 | e43cae6fd543813b352b01510e846febd67944ad | https://github.com/STAR-Laboratory/Accelerating-RecSys-Training/tree/e43cae6fd543813b352b01510e846febd67944ad |
Classifier | import torch
import torch.nn as nn
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
self.activate = activate.low... | 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... | Ruiver/CTCNet | Classifier | false | 17,891 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 |
MLPPolicyNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
class MLPPolicyNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size1=1400,
hidden_size2=1024, hidden_size3=256, init_w=0.003, log_std_min=-20,
log_std_max=2):
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
import torch.nn as nn
from to... | SAMMiCA/DL_based_E2E_Driving | MLPPolicyNetwork | false | 17,892 | [
"MIT"
] | 4 | 01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | https://github.com/SAMMiCA/DL_based_E2E_Driving/tree/01f7d74a0db7ed745cf27b9a1ebab0246015ecbd |
HardWeightedSum | import torch
from torch import nn
class HardWeightedSum(nn.Module):
def __init__(self, op_number=2, act=nn.ReLU, eps=0.0001):
super(HardWeightedSum, self).__init__()
shape = op_number, 1, 1, 1, 1
self.weights = nn.Parameter(torch.ones(shape), requires_grad=True)
self.act = act()
... | 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Senyaaa/detection-experiments | HardWeightedSum | false | 17,893 | [
"Apache-2.0"
] | 5 | 5e80dd458e886ca27db5420d25ade8f9d74ae5a8 | https://github.com/Senyaaa/detection-experiments/tree/5e80dd458e886ca27db5420d25ade8f9d74ae5a8 |
DecoderBlock | import torch
from functools import partial
import torch.nn.functional as F
from torch import nn
class DecoderBlock(nn.Module):
"""
Decoder block class
"""
def __init__(self, in_channels, middle_channels, out_channels, k_size,
pad_size):
super(DecoderBlock, self).__init__()
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
from torch._inductor.runtime.... | SVRTK/Segmentation_FetalMRI | DecoderBlock | false | 17,894 | [
"Apache-2.0"
] | 6 | 9344a2248cbe8e4cccbe05ca98214626dcf62805 | https://github.com/SVRTK/Segmentation_FetalMRI/tree/9344a2248cbe8e4cccbe05ca98214626dcf62805 |
SoftMaxWeightedSum | import torch
from torch import nn
class SoftMaxWeightedSum(nn.Module):
def __init__(self, op_number=2):
super(SoftMaxWeightedSum, self).__init__()
shape = op_number, 1, 1, 1, 1
self.weights = nn.Parameter(torch.ones(shape), requires_grad=True)
def forward(self, x):
return tor... | 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 import nn
a... | Senyaaa/detection-experiments | SoftMaxWeightedSum | false | 17,895 | [
"Apache-2.0"
] | 5 | 5e80dd458e886ca27db5420d25ade8f9d74ae5a8 | https://github.com/Senyaaa/detection-experiments/tree/5e80dd458e886ca27db5420d25ade8f9d74ae5a8 |
Decoder | import torch
import torch.nn as nn
class Decoder(nn.Module):
def __init__(self, n_features, n_modes, T):
super(Decoder, self).__init__()
self.n_modes = n_modes
self.T = T
self.linear1 = nn.Linear(n_features, 4096)
self.linear2 = nn.Linear(512, n_modes * T * 2)
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.... | SambaranRepo/VectorNet_Waymo | Decoder | false | 17,896 | [
"MIT"
] | 4 | 454016a5020444e78943786c14e4e12a75ce052e | https://github.com/SambaranRepo/VectorNet_Waymo/tree/454016a5020444e78943786c14e4e12a75ce052e |
resBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class resBlock(nn.Module):
def __init__(self, channelDepth, windowSize=3):
super(resBlock, self).__init__()
self.pad = nn.ReflectionPad2d(1)
self.IN_conv1 = nn.InstanceNorm2d(channelDepth)
self.conv1 = nn.Conv2d(ch... | 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.... | SeokjaeLIM/DSSN_release-Pytorch | resBlock | false | 17,897 | [
"Apache-2.0"
] | 7 | fef1dac120d7b83367b4c69f239b089ab5f004d7 | https://github.com/SeokjaeLIM/DSSN_release-Pytorch/tree/fef1dac120d7b83367b4c69f239b089ab5f004d7 |
WeightedFeatureFusion | import torch
import torch.nn as nn
from torchvision.models.resnet import *
import torch.utils.data
class WeightedFeatureFusion(nn.Module):
def __init__(self, layers, weight=False):
super(WeightedFeatureFusion, self).__init__()
self.layers = layers
self.weight = weight
self.n = len... | 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
from torchvision.models.resnet import *
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_si... | PanJason/ML_Proj | WeightedFeatureFusion | false | 17,898 | [
"MIT"
] | 4 | 663be12e8eb6e30e3c902a4984ac0db33bfce605 | https://github.com/PanJason/ML_Proj/tree/663be12e8eb6e30e3c902a4984ac0db33bfce605 |
ConformerFeedForward | import torch
from torch import nn
import torch.utils.data
import torch.optim
class Swish(nn.Module):
"""
Swish activation function introduced in 'https://arxiv.org/abs/1710.05941'
"""
def forward(self, x):
return x * torch.sigmoid(x)
class ConformerFeedForward(nn.Module):
"""
feed-f... | 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
import torch.utils.data
import torch.optim
assert_size_stri... | ShantanuNair/NeMo | ConformerFeedForward | false | 17,899 | [
"Apache-2.0"
] | 10 | d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b | https://github.com/ShantanuNair/NeMo/tree/d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b |
FusionAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
class FusionAttention(nn.Module):
def __init__(self, dim):
super(FusionAttention, self).__init__()
self.attention_matrix = nn.Linear(dim, dim)
self.project_weight = nn.Linear(dim, 1)
def forward(self, inputs):
... | 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.... | Seondong/Customs-Fraud-Detection | FusionAttention | false | 17,900 | [
"MIT"
] | 7 | eb9e4641a78cb32d73787de86dd72ebb09df1452 | https://github.com/Seondong/Customs-Fraud-Detection/tree/eb9e4641a78cb32d73787de86dd72ebb09df1452 |
MultiLayerPerceptron | import torch
import torch.utils.data
import torch.optim
class MultiLayerPerceptron(torch.nn.Module):
"""
A simple MLP that can either be used independently or put on top
of pretrained models (such as BERT) and act as a classifier.
Args:
hidden_size (int): the size of each layer
num_cla... | 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.... | ShantanuNair/NeMo | MultiLayerPerceptron | false | 17,901 | [
"Apache-2.0"
] | 10 | d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b | https://github.com/ShantanuNair/NeMo/tree/d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b |
_Residual_Block | import torch
import torch.nn as nn
class _Residual_Block(nn.Module):
def __init__(self):
super(_Residual_Block, self).__init__()
self.conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size
=3, stride=1, padding=1, bias=False)
self.in1 = nn.InstanceNorm2d(64, affine=Tru... | 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.... | Shandilya21/Improved-Optimization-Tecniques-for-Super-Resoultion-in-Images | _Residual_Block | false | 17,902 | [
"MIT"
] | 10 | d903d99706f557d74e00d4395e7d316172a9f7ee | https://github.com/Shandilya21/Improved-Optimization-Tecniques-for-Super-Resoultion-in-Images/tree/d903d99706f557d74e00d4395e7d316172a9f7ee |
DyIntraModalityUpdate | import torch
import torch.nn as nn
import torch.nn.functional as F
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
... | 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.... | Ruiver/CTCNet | DyIntraModalityUpdate | false | 17,903 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 |
ResnetDecoder | import torch
import torch.nn as nn
class ResnetDecoder(nn.Module):
"""
This class represents the tail of ResNet. It performs a global pooling and maps the output to the
correct class by using a fully connected layer.
"""
def __init__(self, in_features, n_classes):
super().__init__()
... | 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... | SeffyVon/ECG_MICResNet | ResnetDecoder | false | 17,904 | [
"BSD-3-Clause"
] | 5 | 8c6a319b5822ddfb130738eb1d9cdc3c21b24209 | https://github.com/SeffyVon/ECG_MICResNet/tree/8c6a319b5822ddfb130738eb1d9cdc3c21b24209 |
Net | import torch
import torch.nn as nn
import torch.nn.init as init
class Net(nn.Module):
def __init__(self, upscale_factor):
super(Net, self).__init__()
self.upscale_factor = int(upscale_factor)
self.relu = nn.ReLU()
self.conv1 = nn.Conv2d(1, 64, kernel_size=5, padding=2)
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 ... | PiSchool/esa-superresolution-forecasting | Net | false | 17,905 | [
"MIT"
] | 4 | 3c01770dd64749d6b6c40e1068a96a3307c8c035 | https://github.com/PiSchool/esa-superresolution-forecasting/tree/3c01770dd64749d6b6c40e1068a96a3307c8c035 |
OneSideInterModalityUpdate | import torch
import torch.nn as nn
import torch.nn.functional as F
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
... | 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.... | Ruiver/CTCNet | OneSideInterModalityUpdate | false | 17,906 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, input, target):
N = target.size(0)
smooth = 1
input_flat = input.view(N, -1)
target_flat = target.view(N, -1)
intersection = in... | 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... | SeffyVon/ECG_MICResNet | DiceLoss | false | 17,907 | [
"BSD-3-Clause"
] | 5 | 8c6a319b5822ddfb130738eb1d9cdc3c21b24209 | https://github.com/SeffyVon/ECG_MICResNet/tree/8c6a319b5822ddfb130738eb1d9cdc3c21b24209 |
deepmind | import torch
import torch.nn as nn
import torch.nn.functional as F
class deepmind(nn.Module):
def __init__(self):
super(deepmind, self).__init__()
self.conv1 = nn.Conv2d(4, 32, 8, stride=4)
self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
self.conv3 = nn.Conv2d(64, 32, 3, stride=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_... | Rowing0914/TF2_RL | deepmind | false | 17,908 | [
"MIT"
] | 8 | c1b7f9b376cbecf01deb17f76f8e761035ed336a | https://github.com/Rowing0914/TF2_RL/tree/c1b7f9b376cbecf01deb17f76f8e761035ed336a |
Bias | import torch
import torch.nn as nn
class Bias(nn.Module):
def __init__(self):
super(Bias, self).__init__()
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, feat_img, feat_sound):
B, C, H, W = feat_sound.size()
feat_img = feat_img.view(B, 1, C)
z = torch.bmm(... | 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... | SheldonTsui/Minus-Plus-Network | Bias | false | 17,909 | [
"Apache-2.0"
] | 5 | 7aa281b17f637a9f168aaf250039e560027a3817 | https://github.com/SheldonTsui/Minus-Plus-Network/tree/7aa281b17f637a9f168aaf250039e560027a3817 |
projection_model | import torch
class projection_model(torch.nn.Module):
def __init__(self, neo_hidden, clip_hidden=512):
super(projection_model, self).__init__()
self.fc1 = torch.nn.Linear(neo_hidden, neo_hidden // 2)
self.act = torch.nn.GELU()
self.fc2 = torch.nn.Linear(neo_hidden // 2, clip_hidde... | 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
assert_size_stride ... | ShivanshuPurohit/GPT-Neo-visual-grounding | projection_model | false | 17,910 | [
"Apache-2.0"
] | 4 | 9c938257a688ef5ae8bc1b87b61d943aa158e880 | https://github.com/ShivanshuPurohit/GPT-Neo-visual-grounding/tree/9c938257a688ef5ae8bc1b87b61d943aa158e880 |
DSCLoss | import torch
import torch.nn as nn
class DSCLoss(nn.Module):
def __init__(self):
super(DSCLoss, self).__init__()
def forward(self, input, target):
N = target.size(0)
smooth = 1
input_flat = input.view(N, -1)
target_flat = target.view(N, -1)
input_flat * 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | SeffyVon/ECG_MICResNet | DSCLoss | false | 17,911 | [
"BSD-3-Clause"
] | 5 | 8c6a319b5822ddfb130738eb1d9cdc3c21b24209 | https://github.com/SeffyVon/ECG_MICResNet/tree/8c6a319b5822ddfb130738eb1d9cdc3c21b24209 |
TwoMLPHead | import torch
import torch.nn as 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
import torch.nn as nn
assert_... | Sense-GVT/BigPretrain | TwoMLPHead | false | 17,912 | [
"Apache-2.0"
] | 8 | d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | https://github.com/Sense-GVT/BigPretrain/tree/d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e |
InterModalityUpdate | import torch
import torch.nn as nn
import torch.nn.functional as F
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
... | 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.... | Ruiver/CTCNet | InterModalityUpdate | false | 17,913 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 |
C3D_mini | import torch
import torch.nn as nn
class C3D_mini(nn.Module):
""" The C3D_mini network """
def __init__(self, num_classes=2, pretrained=False):
super(C3D_mini, self).__init__()
self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.pool1 = nn.MaxPool3d(kernel_siz... | 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_... | Ontheway361/C3D | C3D_mini | false | 17,914 | [
"MIT"
] | 7 | 7aa5364d8c0c6bddc17b1b8939b198fe66e282ca | https://github.com/Ontheway361/C3D/tree/7aa5364d8c0c6bddc17b1b8939b198fe66e282ca |
InnerProd | import torch
import torch.nn as nn
class InnerProd(nn.Module):
def __init__(self, fc_dim):
super(InnerProd, self).__init__()
self.scale = nn.Parameter(torch.ones(fc_dim))
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, feat_img, feat_sound):
sound_size = feat_sound... | 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... | SheldonTsui/Minus-Plus-Network | InnerProd | false | 17,915 | [
"Apache-2.0"
] | 5 | 7aa281b17f637a9f168aaf250039e560027a3817 | https://github.com/SheldonTsui/Minus-Plus-Network/tree/7aa281b17f637a9f168aaf250039e560027a3817 |
Actor | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class Actor(nn.Module):
def __init__(self, nb_states, nb_actions, hidden1=400, hidden2=300,
init_w=0.003):
super(Actor, self).__init__()
self.fc1 = nn.Linear(nb_states, hidden1)
... | 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 ... | Sharpiless/HAQ-for-Mobilenetv3-Quantization | Actor | false | 17,916 | [
"MIT"
] | 5 | 76b7d98471adb666ad140abd2518bce6f0de3cfa | https://github.com/Sharpiless/HAQ-for-Mobilenetv3-Quantization/tree/76b7d98471adb666ad140abd2518bce6f0de3cfa |
FeedForward | import math
import torch
import torch.nn as nn
def activation(act_type='swish'):
if act_type == 'swish':
act = swish()
return act
else:
act = nn.ReLU(inplace=True)
return act
class swish(nn.Module):
def __init__(self):
super(swish, self).__init__()
def 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.triton_helpers import libdevice
import math
import ... | Sense-GVT/BigPretrain | FeedForward | false | 17,917 | [
"Apache-2.0"
] | 8 | d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | https://github.com/Sense-GVT/BigPretrain/tree/d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e |
SIMPA | import torch
from typing import Optional
from typing import Tuple
import torch.nn as nn
from torch.nn.parameter import Parameter
from typing import Union
class SIMPA(nn.Module):
"""The signed mixed-path aggregation model.
Args:
hop (int): Number of hops to consider.
directed (bool, optional):... | 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 torch.nn.parameter import Parameter
assert_size_strid... | SherylHYX/SSSNET_Signed_Clustering | SIMPA | false | 17,918 | [
"MIT"
] | 5 | 85736c18e86b396d64177d22b8c7f9859dfd794c | https://github.com/SherylHYX/SSSNET_Signed_Clustering/tree/85736c18e86b396d64177d22b8c7f9859dfd794c |
SparseConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autograd
class Sparse(autograd.Function):
""""
Prune the unimprotant weight for the forwards phase,
but pass the gradient to dense weight using SR-STE in the backwards phase
"""
@staticmethod
def forward(ctx,... | 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.... | Sense-GVT/BigPretrain | SparseConv2d | false | 17,920 | [
"Apache-2.0"
] | 8 | d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | https://github.com/Sense-GVT/BigPretrain/tree/d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e |
GCNConv_diag | import torch
from sklearn.metrics.pairwise import *
from torch.optim.lr_scheduler import *
class GCNConv_diag(torch.nn.Module):
"""
A GCN convolution layer of diagonal matrix multiplication
"""
def __init__(self, input_size, device):
super(GCNConv_diag, self).__init__()
self.W = torch... | 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 sklearn.metrics.pairwise import *
from torch.optim.lr_scheduler import *
as... | STK101/GRCN | GCNConv_diag | false | 17,921 | [
"MIT"
] | 4 | 7389000a13d5969bcc77dc4cf73a4107acc68403 | https://github.com/STK101/GRCN/tree/7389000a13d5969bcc77dc4cf73a4107acc68403 |
Balance_Theory | import torch
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from typing import Union
class Balance_Theory(nn.Module):
"""The signed graph clustering model with balance theory, restricted to 2 hops for fair compari... | 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.... | SherylHYX/SSSNET_Signed_Clustering | Balance_Theory | false | 17,922 | [
"MIT"
] | 5 | 85736c18e86b396d64177d22b8c7f9859dfd794c | https://github.com/SherylHYX/SSSNET_Signed_Clustering/tree/85736c18e86b396d64177d22b8c7f9859dfd794c |
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