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 |
|---|---|---|---|---|---|---|---|---|---|---|
ReshapeF | import torch
import torch.utils.data
import torch
from torch import nn
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
out =... | 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.utils.data
import torch
from torch import nn
assert_size_stride = ... | guyii54/Contrastive-I2I | ReshapeF | false | 6,767 | [
"BSD-3-Clause"
] | 1 | e73daa0f9d3770c2280a304c39678d5b22440647 | https://github.com/guyii54/Contrastive-I2I/tree/e73daa0f9d3770c2280a304c39678d5b22440647 |
SCConv_Layer | import torch
import torch.nn as nn
import torch.nn.functional as F
class SCConv_Layer(nn.Module):
def __init__(self, num_node_feats, num_edge_feats, num_triangle_feats,
output_size, bias=True, f=F.relu):
super().__init__()
self.n2n_weights = nn.Linear(num_node_feats, output_size, bias=bia... | 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 ... | ggoh29/Simplicial-neural-network-benchmark | SCConv_Layer | false | 6,768 | [
"MIT"
] | 1 | 9a12bcd054251790d85e3971f5473dcffaa5664b | https://github.com/ggoh29/Simplicial-neural-network-benchmark/tree/9a12bcd054251790d85e3971f5473dcffaa5664b |
FusedLeakyReLU | import torch
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return F.leaky_relu(input + bias, negative_slope) * scale
class FusedLeakyReLU(nn.Module):
def __init__(self, channel, negative_slope... | 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
from torch import nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.asser... | guyii54/Contrastive-I2I | FusedLeakyReLU | false | 6,769 | [
"BSD-3-Clause"
] | 1 | e73daa0f9d3770c2280a304c39678d5b22440647 | https://github.com/guyii54/Contrastive-I2I/tree/e73daa0f9d3770c2280a304c39678d5b22440647 |
DCRBranch | import torch
import torch.nn as nn
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
class DCRBranch(nn.Module):
"""Branch Network for DCR"""
def __init__(self, num_classes, in_channels, mid_channels,
normalized_embeddings=False):
super().__init__()
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 as nn
import torch.utils.data
import torch.optim
import torch.ut... | gyfastas/CS7319E1G16 | DCRBranch | false | 6,770 | [
"MIT"
] | 1 | 03126af04766abcb269d0c8db481c96c856d21ef | https://github.com/gyfastas/CS7319E1G16/tree/03126af04766abcb269d0c8db481c96c856d21ef |
LinearAttentionLayer | import torch
import torch.nn.functional as F
from torch import nn
class LinearAttentionLayer(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.linear = nn.Linear(input_dim, 1)
def forward(self, question, question_mask):
qtn = question.view(-1, question.shape[-1])
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gustavhartz/legal-contract-elements | LinearAttentionLayer | false | 6,771 | [
"MIT"
] | 1 | 7a1e1f0024f9d336c7166f51b4325acf03db86a2 | https://github.com/gustavhartz/legal-contract-elements/tree/7a1e1f0024f9d336c7166f51b4325acf03db86a2 |
DownsampleA | import torch
import torch.nn as nn
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
assert stride == 2
self.avg = nn.AvgPool2d(kernel_size=1, stride=stride)
def forward(self, x):
x = self.avg(x)
return torch.cat(... | 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... | hamedomidvar/associativeconv | DownsampleA | false | 6,772 | [
"MIT"
] | 1 | 9930915abd3625871354df676865fc44eb92abf3 | https://github.com/hamedomidvar/associativeconv/tree/9930915abd3625871354df676865fc44eb92abf3 |
Reverse | import torch
class Reverse(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, audio):
return torch.flip(audio, dims=[1])
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | h0ngwen/torchaudio-augmentations | Reverse | false | 6,773 | [
"MIT"
] | 1 | d044f9d020e12032ab9280acf5f34a337e72d212 | https://github.com/h0ngwen/torchaudio-augmentations/tree/d044f9d020e12032ab9280acf5f34a337e72d212 |
PolarityInversion | import torch
class PolarityInversion(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, audio):
audio = torch.neg(audio)
return audio
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | h0ngwen/torchaudio-augmentations | PolarityInversion | false | 6,774 | [
"MIT"
] | 1 | d044f9d020e12032ab9280acf5f34a337e72d212 | https://github.com/h0ngwen/torchaudio-augmentations/tree/d044f9d020e12032ab9280acf5f34a337e72d212 |
ToRGB | import math
import torch
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if len(k.shape) == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(input, kernel, up_x, up... | 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 math
import torch.utils.data
import torch
from torch import nn
import tor... | guyii54/Contrastive-I2I | ToRGB | false | 6,775 | [
"BSD-3-Clause"
] | 1 | e73daa0f9d3770c2280a304c39678d5b22440647 | https://github.com/guyii54/Contrastive-I2I/tree/e73daa0f9d3770c2280a304c39678d5b22440647 |
ResidualMLP | import torch
import torch.nn as nn
class ResidualMLP(nn.Module):
def __init__(self, input_dim, target_dim, hidden_dim=64):
super(ResidualMLP, self).__init__()
self.linear1 = nn.Linear(input_dim, hidden_dim)
self.linear2 = nn.Linear(hidden_dim, hidden_dim)
self.linear3 = 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_... | greydanus/piecewise_node | ResidualMLP | false | 6,776 | [
"Apache-2.0"
] | 1 | 9d218d4ec1bab486ae954ad2e84732a5f952770f | https://github.com/greydanus/piecewise_node/tree/9d218d4ec1bab486ae954ad2e84732a5f952770f |
ModulatedConv2d | import math
import torch
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if len(k.shape) == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(input, kernel, up_x, up... | 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 ... | guyii54/Contrastive-I2I | ModulatedConv2d | false | 6,777 | [
"BSD-3-Clause"
] | 1 | e73daa0f9d3770c2280a304c39678d5b22440647 | https://github.com/guyii54/Contrastive-I2I/tree/e73daa0f9d3770c2280a304c39678d5b22440647 |
Multiply | import torch
from abc import ABC
class BaseOperator(ABC):
"""
Abstract class defining the basic structure for operator implementations in Hummingbird.
"""
def __init__(self, regression=False, classification=False, transformer=
False, anomaly_detection=False, **kwargs):
"""
Arg... | 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 abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stri... | hannahaih/hummingbird | Multiply | false | 6,778 | [
"MIT"
] | 1 | b8ec670b3c90ec7e87d3ae4a2b268075bd5eae65 | https://github.com/hannahaih/hummingbird/tree/b8ec670b3c90ec7e87d3ae4a2b268075bd5eae65 |
PoolingF | import torch
import torch.utils.data
import torch
from torch import nn
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
out =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | guyii54/Contrastive-I2I | PoolingF | false | 6,779 | [
"BSD-3-Clause"
] | 1 | e73daa0f9d3770c2280a304c39678d5b22440647 | https://github.com/guyii54/Contrastive-I2I/tree/e73daa0f9d3770c2280a304c39678d5b22440647 |
HierarchicalPolicy | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as f
class HierarchicalPolicy(nn.Module):
def __init__(self, args):
super(HierarchicalPolicy, self).__init__()
self.fc_1 = nn.Linear(args.state_shape, 128)
self.fc_2 = nn.Linear(128... | 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.... | hanhanAnderson/LSF-SAC | HierarchicalPolicy | false | 6,780 | [
"MIT"
] | 1 | 3e2daf0da23b0ea08e92948c87f7e583f3fb1ed9 | https://github.com/hanhanAnderson/LSF-SAC/tree/3e2daf0da23b0ea08e92948c87f7e583f3fb1ed9 |
NumericLabelEncoder | import torch
from abc import ABC
class BaseOperator(ABC):
"""
Abstract class defining the basic structure for operator implementations in Hummingbird.
"""
def __init__(self, regression=False, classification=False, transformer=
False, anomaly_detection=False, **kwargs):
"""
Arg... | 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 abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stri... | hannahaih/hummingbird | NumericLabelEncoder | false | 6,781 | [
"MIT"
] | 1 | b8ec670b3c90ec7e87d3ae4a2b268075bd5eae65 | https://github.com/hannahaih/hummingbird/tree/b8ec670b3c90ec7e87d3ae4a2b268075bd5eae65 |
PositionalEncoding | import torch
from torch import nn
class PositionalEncoding(nn.Module):
def __init__(self, patch_num, d_model, dropout=0.1):
super(PositionalEncoding, self).__init__()
self.pe = nn.Parameter(torch.rand(patch_num + 1, d_model))
self.add_positional_encoding = lambda x: x + self.pe[:x.size(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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | hankyul2/ImageClassification | PositionalEncoding | false | 6,782 | [
"Apache-2.0"
] | 1 | c4df6bf3dc1ee804f9885d586aa581ebb4d7ca05 | https://github.com/hankyul2/ImageClassification/tree/c4df6bf3dc1ee804f9885d586aa581ebb4d7ca05 |
StdConv | import torch
from torch import nn
class StdConv(nn.Conv2d):
def forward(self, x):
return self._conv_forward(x, self.standarize(self.weight), self.bias)
def standarize(self, x):
return (x - x.mean(dim=(1, 2, 3), keepdim=True)) / (x.std(dim=(1, 2,
3), keepdim=True) + 1e-06)
def 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 import n... | hankyul2/ImageClassification | StdConv | false | 6,783 | [
"Apache-2.0"
] | 1 | c4df6bf3dc1ee804f9885d586aa581ebb4d7ca05 | https://github.com/hankyul2/ImageClassification/tree/c4df6bf3dc1ee804f9885d586aa581ebb4d7ca05 |
rec_attention | from _paritybench_helpers import _mock_config
import torch
import torch.utils.data
import torch.nn as nn
def batch_product(iput, mat2):
result = None
for i in range(iput.size()[0]):
op = torch.mm(iput[i], mat2)
op = op.unsqueeze(0)
if result is None:
result = op
els... | 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.... | gzerveas/TransformChrome | rec_attention | false | 6,784 | [
"MIT"
] | 1 | ab1046009aff2ec863aa65223dcfcd750d41ab86 | https://github.com/gzerveas/TransformChrome/tree/ab1046009aff2ec863aa65223dcfcd750d41ab86 |
ConvertPointsToHomogeneous | import torch
import torch.nn as nn
def convert_points_to_homogeneous(points):
"""Function that converts points from Euclidean to homogeneous space.
See :class:`~torchgeometry.ConvertPointsToHomogeneous` for details.
Examples::
>>> input = torch.rand(2, 4, 3) # BxNx3
>>> output = tgm.co... | 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... | hassony2/frankmocap | ConvertPointsToHomogeneous | false | 6,785 | [
"BSD-3-Clause"
] | 1 | 50aae41d9b41d2f344ae1709bbf1b25974209fa9 | https://github.com/hassony2/frankmocap/tree/50aae41d9b41d2f344ae1709bbf1b25974209fa9 |
Hidden2Discrete | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init
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
self.k_size = k_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
from torch._inductor.runtime.... | haojiepan1/CrossWOZ | Hidden2Discrete | false | 6,786 | [
"Apache-2.0"
] | 1 | 6d7b4c4cfb73a528b76074764687906abecc90b6 | https://github.com/haojiepan1/CrossWOZ/tree/6d7b4c4cfb73a528b76074764687906abecc90b6 |
ConvertPointsFromHomogeneous | import torch
import torch.nn as nn
def convert_points_from_homogeneous(points):
"""Function that converts points from homogeneous to Euclidean space.
See :class:`~torchgeometry.ConvertPointsFromHomogeneous` for details.
Examples::
>>> input = torch.rand(2, 4, 3) # BxNx3
>>> output = tg... | 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... | hassony2/frankmocap | ConvertPointsFromHomogeneous | false | 6,787 | [
"BSD-3-Clause"
] | 1 | 50aae41d9b41d2f344ae1709bbf1b25974209fa9 | https://github.com/hassony2/frankmocap/tree/50aae41d9b41d2f344ae1709bbf1b25974209fa9 |
GCN_conv | import math
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class GCN_conv(nn.Module):
def __init__(self, in_ft, out_ft, bias=False, dropout=0.0, activation=F
.relu):
super(GCN_conv, 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 math
import torch.util... | haoyfan/Dual-SVDAE | GCN_conv | false | 6,788 | [
"MIT"
] | 1 | 1fcb61960606d743438f33b740cb434dbfcfd727 | https://github.com/haoyfan/Dual-SVDAE/tree/1fcb61960606d743438f33b740cb434dbfcfd727 |
SelfAttn | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init
import torch as th
class SelfAttn(nn.Module):
def __init__(self, hidden_size):
super(SelfAttn, self).__init__()
self.query = nn.Linear(hidden_size, 1)
def forward(self, keys, values, attn_mask=None):
... | 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.... | haojiepan1/CrossWOZ | SelfAttn | false | 6,789 | [
"Apache-2.0"
] | 1 | 6d7b4c4cfb73a528b76074764687906abecc90b6 | https://github.com/haojiepan1/CrossWOZ/tree/6d7b4c4cfb73a528b76074764687906abecc90b6 |
NormKLLoss | import torch
import torch.nn.init
import torch as th
from torch.nn.modules.loss import _Loss
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, prior_mu, prior_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.nn.init
from torch.nn.modules.loss import _Loss
assert_size_... | haojiepan1/CrossWOZ | NormKLLoss | false | 6,790 | [
"Apache-2.0"
] | 1 | 6d7b4c4cfb73a528b76074764687906abecc90b6 | https://github.com/haojiepan1/CrossWOZ/tree/6d7b4c4cfb73a528b76074764687906abecc90b6 |
Zeronet | import torch
import torch.nn as nn
class Zeronet(nn.Module):
def forward(self, x):
"""
Return a zero-out copy of x
:param x: torch.Tensor
:return: x*0, type torch.Tensor
"""
return torch.zeros_like(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def ... | 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... | hedixia/xhd_source | Zeronet | false | 6,791 | [
"MIT"
] | 1 | cb176bceb5f5349d68206aaf60014e251de36300 | https://github.com/hedixia/xhd_source/tree/cb176bceb5f5349d68206aaf60014e251de36300 |
BinaryDiceLoss | import torch
import torch.nn as nn
class BinaryDiceLoss(nn.Module):
"""二分类版本的Dice Loss"""
def __init__(self, smooth: 'int'=1, exponent: 'int'=1, reduction: 'str'
='mean', loss_weight: 'float'=1.0, balance_weight: 'float'=1.0,
activation: 'bool'=False) ->None:
super(BinaryDiceLoss, 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | hehaoming/RSI-ChangeDetection | BinaryDiceLoss | false | 6,792 | [
"MIT"
] | 1 | f24a1d79c03fb9fefc49bc91bc94b3c120992496 | https://github.com/hehaoming/RSI-ChangeDetection/tree/f24a1d79c03fb9fefc49bc91bc94b3c120992496 |
EqualizedLinear | import torch
from torch import nn
import torch.nn.functional as F
class EqualizedLinear(nn.Module):
def __init__(self, input_size, output_size, gain=2 ** 0.5, lrmul=0.01):
super().__init__()
he_std = gain * input_size ** -0.5
init_std = 1.0 / lrmul
self.w_mul = he_std * lrmul
... | 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... | hejj16/Landscape-StyleGAN | EqualizedLinear | false | 6,793 | [
"MIT"
] | 1 | a93cd32b588ab21da9d7589e705ca6f09db18408 | https://github.com/hejj16/Landscape-StyleGAN/tree/a93cd32b588ab21da9d7589e705ca6f09db18408 |
Classifier | import torch
import torch.nn as nn
import torch.nn.functional as F
class Classifier(nn.Module):
def __init__(self):
super(Classifier, self).__init__()
self.fc1 = nn.Linear(900, 3)
def forward(self, x):
x = F.avg_pool2d(x, 8)
x = x.view(-1, 900)
x = self.fc1(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.... | helinwang/pytorch-semseg | Classifier | false | 6,794 | [
"MIT"
] | 1 | 117e5fb8afbad87d6968de1683867854ddec5885 | https://github.com/helinwang/pytorch-semseg/tree/117e5fb8afbad87d6968de1683867854ddec5885 |
ClassificationLogSoftmax | import torch
import torch.nn as nn
class ClassificationLogSoftmax(nn.Module):
"""
Classifier on top of the hidden representation of the first token, which
is usually [CLS] token in BERT-like architectures.
"""
def __init__(self, hidden_size, num_classes):
super().__init__()
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.... | harisankarh/NeMo | ClassificationLogSoftmax | false | 6,795 | [
"Apache-2.0"
] | 1 | 27bfb1aed24a786626e1c27c37417ebcd226ca8a | https://github.com/harisankarh/NeMo/tree/27bfb1aed24a786626e1c27c37417ebcd226ca8a |
BalancedBinaryCrossEntropy | import torch
import torch.nn as nn
from typing import Any
import torch.nn.functional as F
class BalancedBinaryCrossEntropy(nn.Module):
"""二分类加权交叉熵"""
def __init__(self, reduction: 'str'='mean', class_weight: 'Any'=None,
loss_weight: 'float'=1.0, activation: 'bool'=False) ->None:
super(Balance... | 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... | hehaoming/RSI-ChangeDetection | BalancedBinaryCrossEntropy | false | 6,796 | [
"MIT"
] | 1 | f24a1d79c03fb9fefc49bc91bc94b3c120992496 | https://github.com/hehaoming/RSI-ChangeDetection/tree/f24a1d79c03fb9fefc49bc91bc94b3c120992496 |
SeperableConv | import torch
import torch.nn as nn
import torch.nn.functional as F
def _get_padding(kernel_size, stride, dilation):
padding = (stride - 1 + dilation * (kernel_size - 1)) // 2
return padding
class SeperableConv(nn.Module):
def __init__(self, inp, outp, k=3, stride=1, dilation=1):
super(Seperable... | 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_... | henningpohl/body-based-ar | SeperableConv | false | 6,797 | [
"MIT"
] | 1 | dc7d5d6eaf8dd4427de0f2b1cfdcc415cbfffdfb | https://github.com/henningpohl/body-based-ar/tree/dc7d5d6eaf8dd4427de0f2b1cfdcc415cbfffdfb |
HybridLoss | import torch
import torch.nn as nn
from typing import Any
import torch.nn.functional as F
class BalancedBinaryCrossEntropy(nn.Module):
"""二分类加权交叉熵"""
def __init__(self, reduction: 'str'='mean', class_weight: 'Any'=None,
loss_weight: 'float'=1.0, activation: 'bool'=False) ->None:
super(Balance... | 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... | hehaoming/RSI-ChangeDetection | HybridLoss | false | 6,798 | [
"MIT"
] | 1 | f24a1d79c03fb9fefc49bc91bc94b3c120992496 | https://github.com/hehaoming/RSI-ChangeDetection/tree/f24a1d79c03fb9fefc49bc91bc94b3c120992496 |
BalancedBinaryCrossEntropyWithLogits | import torch
import torch.nn as nn
from typing import Any
class BalancedBinaryCrossEntropyWithLogits(nn.Module):
"""二分类加权交叉熵"""
def __init__(self, reduction: 'str'='mean', class_weight: 'Any'=None,
loss_weight: 'float'=1.0, activation: 'bool'=False, eposion:
'float'=1e-10) ->None:
sup... | 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... | hehaoming/RSI-ChangeDetection | BalancedBinaryCrossEntropyWithLogits | false | 6,799 | [
"MIT"
] | 1 | f24a1d79c03fb9fefc49bc91bc94b3c120992496 | https://github.com/hehaoming/RSI-ChangeDetection/tree/f24a1d79c03fb9fefc49bc91bc94b3c120992496 |
InputConv | import torch
import torch.nn as nn
import torch.nn.functional as F
def _get_padding(kernel_size, stride, dilation):
padding = (stride - 1 + dilation * (kernel_size - 1)) // 2
return padding
class InputConv(nn.Module):
def __init__(self, inp, outp, k=3, stride=1, dilation=1):
super(InputConv, 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_... | henningpohl/body-based-ar | InputConv | false | 6,800 | [
"MIT"
] | 1 | dc7d5d6eaf8dd4427de0f2b1cfdcc415cbfffdfb | https://github.com/henningpohl/body-based-ar/tree/dc7d5d6eaf8dd4427de0f2b1cfdcc415cbfffdfb |
ActQuant_PACT | import torch
import torch.nn as nn
def uniform_quantize(k):
class qfn(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
if k == 32:
out = input
elif k == 1:
out = torch.sign(input)
else:
n = float... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | heymesut/SJTU_microe | ActQuant_PACT | false | 6,801 | [
"BSD-3-Clause"
] | 1 | 7a862d03b4d8fe4c8608173a16082f44001f3f13 | https://github.com/heymesut/SJTU_microe/tree/7a862d03b4d8fe4c8608173a16082f44001f3f13 |
Mish | import torch
import torch.nn as nn
import torch.nn.functional as F
class Mish(nn.Module):
def forward(self, x):
return x.mul_(F.softplus(x).tanh())
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, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | heymesut/SJTU_microe | Mish | false | 6,802 | [
"BSD-3-Clause"
] | 1 | 7a862d03b4d8fe4c8608173a16082f44001f3f13 | https://github.com/heymesut/SJTU_microe/tree/7a862d03b4d8fe4c8608173a16082f44001f3f13 |
activation_quantize_fn | import torch
import torch.nn as nn
def uniform_quantize(k):
class qfn(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
if k == 32:
out = input
elif k == 1:
out = torch.sign(input)
else:
n = float... | 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_... | heymesut/SJTU_microe | activation_quantize_fn | false | 6,803 | [
"BSD-3-Clause"
] | 1 | 7a862d03b4d8fe4c8608173a16082f44001f3f13 | https://github.com/heymesut/SJTU_microe/tree/7a862d03b4d8fe4c8608173a16082f44001f3f13 |
weightedFeatureFusion | import torch
import torch.nn as nn
class weightedFeatureFusion(nn.Module):
def __init__(self, layers, weight=False):
super(weightedFeatureFusion, self).__init__()
self.layers = layers
self.weight = weight
self.n = len(layers) + 1
if weight:
self.w = torch.nn.Pa... | 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... | heymesut/SJTU_microe | weightedFeatureFusion | false | 6,804 | [
"BSD-3-Clause"
] | 1 | 7a862d03b4d8fe4c8608173a16082f44001f3f13 | https://github.com/heymesut/SJTU_microe/tree/7a862d03b4d8fe4c8608173a16082f44001f3f13 |
ClipGlobalAvgPool2d | import torch
from torch import nn
class FastGlobalAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super(FastGlobalAvgPool2d, self).__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
in_size = x.size()
return x.view((in_size[0], 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
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | hfyer/NAIC2020_ReID_R1 | ClipGlobalAvgPool2d | false | 6,805 | [
"Apache-2.0"
] | 1 | 240f0c9f65e482e6b0090f01d9f9e3373a337033 | https://github.com/hfyer/NAIC2020_ReID_R1/tree/240f0c9f65e482e6b0090f01d9f9e3373a337033 |
GeneralizedMeanPooling | import torch
from torch import nn
class GeneralizedMeanPooling(nn.Module):
"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
- At p = infinity, one gets Max Pooling
- At p = 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
from torch import nn
assert_... | hfyer/NAIC2020_ReID_R1 | GeneralizedMeanPooling | false | 6,806 | [
"Apache-2.0"
] | 1 | 240f0c9f65e482e6b0090f01d9f9e3373a337033 | https://github.com/hfyer/NAIC2020_ReID_R1/tree/240f0c9f65e482e6b0090f01d9f9e3373a337033 |
weight_quantize_fn | import torch
import torch.nn as nn
def uniform_quantize(k):
class qfn(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
if k == 32:
out = input
elif k == 1:
out = torch.sign(input)
else:
n = float... | 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... | heymesut/SJTU_microe | weight_quantize_fn | false | 6,807 | [
"BSD-3-Clause"
] | 1 | 7a862d03b4d8fe4c8608173a16082f44001f3f13 | https://github.com/heymesut/SJTU_microe/tree/7a862d03b4d8fe4c8608173a16082f44001f3f13 |
TLU | import torch
from torch import nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
class TLU(nn.Module):
def __init__(self, num_features):
"""max(y, tau) = max(y - tau, 0) + tau = ReLU(y - tau) + tau"""
super(TLU, self).__init__()
self.num_features = num_features
... | 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
from torch.nn import Parameter
from torch.nn.parameter import Parame... | hfyer/NAIC2020_ReID_R1 | TLU | false | 6,808 | [
"Apache-2.0"
] | 1 | 240f0c9f65e482e6b0090f01d9f9e3373a337033 | https://github.com/hfyer/NAIC2020_ReID_R1/tree/240f0c9f65e482e6b0090f01d9f9e3373a337033 |
YOLOLayer | import torch
import numpy as np
import torch.nn as nn
class YOLOLayer(nn.Module):
"""
Detection Layer
"""
def __init__(self, in_ch, n_anchors, n_classes):
super(YOLOLayer, self).__init__()
self.n_anchors = n_anchors
self.n_classes = n_classes
self.conv = nn.Conv2d(in_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | hiroki-kawauchi/SHAPObjectDetection | YOLOLayer | false | 6,809 | [
"MIT"
] | 1 | 3667d026949137cf710fc627672809c8564f5c6f | https://github.com/hiroki-kawauchi/SHAPObjectDetection/tree/3667d026949137cf710fc627672809c8564f5c6f |
AdaptiveAvgMaxPool2d | import torch
from torch import nn
class FastGlobalAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super(FastGlobalAvgPool2d, self).__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
in_size = x.size()
return x.view((in_size[0], 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
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | hfyer/NAIC2020_ReID_R1 | AdaptiveAvgMaxPool2d | false | 6,810 | [
"Apache-2.0"
] | 1 | 240f0c9f65e482e6b0090f01d9f9e3373a337033 | https://github.com/hfyer/NAIC2020_ReID_R1/tree/240f0c9f65e482e6b0090f01d9f9e3373a337033 |
KeyValueAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn.init
class KeyValueAttention(nn.Module):
def __init__(self, query_size, key_size, value_size, hid_size, init_range):
super(KeyValueAttention, self).__init__()
self.key2hid = nn.L... | 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.... | haojiepan1/CrossWOZ | KeyValueAttention | false | 6,811 | [
"Apache-2.0"
] | 1 | 6d7b4c4cfb73a528b76074764687906abecc90b6 | https://github.com/haojiepan1/CrossWOZ/tree/6d7b4c4cfb73a528b76074764687906abecc90b6 |
LandmarksLoss | import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
class WingLoss(nn.Module):
def __init__(self, w=10, e=2):
super(WingLoss, self).__init__()
self.w = w
self.e = e
self.C = self.w - self.w * np.log(1 + self.w / self.e)
def forward(self, x, t, sigma=... | 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 numpy as np
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | homomorfism/wise-programming | LandmarksLoss | false | 6,812 | [
"MIT"
] | 1 | e0589e8900237ddc9c3abf54c85be532cacf2d33 | https://github.com/homomorfism/wise-programming/tree/e0589e8900237ddc9c3abf54c85be532cacf2d33 |
Decoder1 | import torch
import torch.nn as nn
class Decoder1(nn.Module):
def __init__(self):
super(Decoder1, self).__init__()
self.reflecPad2 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv3 = nn.Conv2d(64, 3, 3, 1, 0)
def forward(self, x):
out = self.reflecPad2(x)
out = self.conv3... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | hologerry/wct_experiment | Decoder1 | false | 6,814 | [
"MIT"
] | 1 | 890d885561dc8df8c4ae732aebd902aa838257e6 | https://github.com/hologerry/wct_experiment/tree/890d885561dc8df8c4ae732aebd902aa838257e6 |
QuantMeasure | import torch
from torch import nn
from torch.autograd.function import InplaceFunction
def quantize(x, num_bits=8, min_value=None, max_value=None, num_chunks=None,
stochastic=False, inplace=False, quantize=False, layer_num=-1, multi=
False, index=[], is_act=False):
return UniformQuantize().apply(x, num_bit... | 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 import nn
from to... | hoseung2/DNAS-Compression | QuantMeasure | false | 6,815 | [
"MIT"
] | 1 | 645407fc572045f33278c935091a07e0ccfce87f | https://github.com/hoseung2/DNAS-Compression/tree/645407fc572045f33278c935091a07e0ccfce87f |
SmooothLabelCELoss | import torch
import torch.nn as nn
class SmooothLabelCELoss(nn.Module):
def __init__(self, smooth=0.1, use_uniform=False, reduction='mean'):
super(SmooothLabelCELoss, self).__init__()
self.smooth_coef = smooth
self.smooth_std = 0.5
self.reduction = reduction
self.use_unifo... | 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... | hekq/3DFingerPose | SmooothLabelCELoss | false | 6,816 | [
"MIT"
] | 1 | 385c672408e2fd29ed0373a842727c9fcfd0fc59 | https://github.com/hekq/3DFingerPose/tree/385c672408e2fd29ed0373a842727c9fcfd0fc59 |
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
... | hekq/3DFingerPose | DiceLoss | false | 6,817 | [
"MIT"
] | 1 | 385c672408e2fd29ed0373a842727c9fcfd0fc59 | https://github.com/hekq/3DFingerPose/tree/385c672408e2fd29ed0373a842727c9fcfd0fc59 |
StdConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class StdConv2d(nn.Conv2d):
def forward(self, x):
w = self.weight
v = torch.var(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
m = torch.mean(w, dim=[1, 2, 3], keepdim=True)
w = (w - m) / torch.sqrt(v + 1e-10)
... | 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 ... | hrlblab/Glo-In-One | StdConv2d | false | 6,818 | [
"Apache-2.0"
] | 1 | 7daef49c557bccd6f5c956b88603357346dc78a2 | https://github.com/hrlblab/Glo-In-One/tree/7daef49c557bccd6f5c956b88603357346dc78a2 |
ScaledDotProductAttention | import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim
class Bottle(nn.Module):
""" Perform the reshape routine before and after an operation """
def forward(self, input):
if len(input.size()) <= 2:
return super(Bottle, self).forward(input)
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.... | howardchenhd/Transformer-pytorch | ScaledDotProductAttention | false | 6,819 | [
"MIT"
] | 1 | ae71ed5767272feb7e717be6d5bfce46f80ec57a | https://github.com/howardchenhd/Transformer-pytorch/tree/ae71ed5767272feb7e717be6d5bfce46f80ec57a |
LeNet_300_100 | import torch
import torch.nn as nn
import torch.nn.functional as F
class LeNet_300_100(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(28 * 28, 300)
self.fc2 = nn.Linear(300, 100)
self.fc3 = nn.Linear(100, 10)
self.relu = nn.ReLU()
self.last... | 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_... | htt-trangtran/smg | LeNet_300_100 | false | 6,820 | [
"MIT"
] | 1 | b7a49055e7d48ec456bac67ab473db2183d2f597 | https://github.com/htt-trangtran/smg/tree/b7a49055e7d48ec456bac67ab473db2183d2f597 |
IA_gate | import torch
import torch.nn as nn
class IA_gate(nn.Module):
def __init__(self, in_dim, out_dim):
super(IA_gate, self).__init__()
self.IA = nn.Linear(in_dim, out_dim)
def forward(self, x, IA_head):
a = self.IA(IA_head)
a = 1.0 + torch.tanh(a)
a = a.unsqueeze(-1).unsqu... | 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 ... | huanglf714/COMatchNet | IA_gate | false | 6,821 | [
"Apache-2.0"
] | 1 | 79023f5be65d354eb9bdac026d7e0d73110bc4aa | https://github.com/huanglf714/COMatchNet/tree/79023f5be65d354eb9bdac026d7e0d73110bc4aa |
ConvBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=strd,
padding=padding, bias=bias)
class ConvBlock(nn.Module):
de... | 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.... | hhj1897/fan_training | ConvBlock | false | 6,822 | [
"MIT"
] | 1 | 5882f9edf2f1a07c80a6d1f3341a7cf1d348e217 | https://github.com/hhj1897/fan_training/tree/5882f9edf2f1a07c80a6d1f3341a7cf1d348e217 |
PositionwiseFeedForward | import torch
import torch.nn as nn
import torch.optim
class LayerNorm(nn.Module):
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))
self.eps = eps
def forward... | 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.... | howardchenhd/Transformer-pytorch | PositionwiseFeedForward | false | 6,823 | [
"MIT"
] | 1 | ae71ed5767272feb7e717be6d5bfce46f80ec57a | https://github.com/howardchenhd/Transformer-pytorch/tree/ae71ed5767272feb7e717be6d5bfce46f80ec57a |
MultiHeadedAttention | import math
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim
class MultiHeadedAttention(nn.Module):
"""
Multi-Head Attention module from
"Attention is All You Need"
:cite:`DBLP:journals/corr/VaswaniSPUJGKP17`.
Similar to standard `dot` attention but uses
... | 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.... | howardchenhd/Transformer-pytorch | MultiHeadedAttention | false | 6,824 | [
"MIT"
] | 1 | ae71ed5767272feb7e717be6d5bfce46f80ec57a | https://github.com/howardchenhd/Transformer-pytorch/tree/ae71ed5767272feb7e717be6d5bfce46f80ec57a |
Decoder2 | import torch
import torch.nn as nn
class Decoder2(nn.Module):
def __init__(self):
super(Decoder2, self).__init__()
self.reflecPad5 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv5 = nn.Conv2d(128, 64, 3, 1, 0)
self.relu5 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingNear... | 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.... | hologerry/wct_experiment | Decoder2 | false | 6,825 | [
"MIT"
] | 1 | 890d885561dc8df8c4ae732aebd902aa838257e6 | https://github.com/hologerry/wct_experiment/tree/890d885561dc8df8c4ae732aebd902aa838257e6 |
HirarchicalAttention | from torch.nn import Module
import torch
from typing import *
import torch.utils.data
import torch.nn as nn
import torch.onnx.operators
import torch.optim
class HirarchicalAttention(Module):
"""
ref: Hierarchical Attention Networks for Document Classification
"""
def __init__(self, hidden_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | hrshy0629/naturalcc | HirarchicalAttention | false | 6,826 | [
"MIT"
] | 1 | 9c3329dd8387c8242deb52bf590ebe3ac795f8de | https://github.com/hrshy0629/naturalcc/tree/9c3329dd8387c8242deb52bf590ebe3ac795f8de |
GCT | import torch
import torch.nn as nn
class GCT(nn.Module):
def __init__(self, num_channels, epsilon=1e-05, mode='l2', after_relu=False
):
super(GCT, self).__init__()
self.alpha = nn.Parameter(torch.ones(1, num_channels, 1, 1))
self.gamma = nn.Parameter(torch.zeros(1, num_channels, 1... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | huanglf714/COMatchNet | GCT | false | 6,827 | [
"Apache-2.0"
] | 1 | 79023f5be65d354eb9bdac026d7e0d73110bc4aa | https://github.com/huanglf714/COMatchNet/tree/79023f5be65d354eb9bdac026d7e0d73110bc4aa |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if (config.hidden_size % config.num_attention_heads != 0 and not
hasattr(config, 'embedding_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.... | hongyuntw/Col-KBERT | BertAttention | false | 6,828 | [
"MIT"
] | 1 | e77ce2585d228a783bf83cc1de53583aff70f7b4 | https://github.com/hongyuntw/Col-KBERT/tree/e77ce2585d228a783bf83cc1de53583aff70f7b4 |
SimpleGFLLoss | import torch
import torch.nn.functional as F
def simple_gfl(pred, target, beta):
"""Simply add a pow of abs difference in front of BCE"""
assert pred.size() == target.size(
), 'simple GFL assume pred and target to have the same shape'
loss = (pred.sigmoid() - target).abs().pow(beta)
loss = F.b... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | huimlight/OpenMMLab-IoUNet | SimpleGFLLoss | false | 6,829 | [
"Apache-2.0"
] | 1 | 00536bac99f4d3d7ad2682dad44f299f714565b6 | https://github.com/huimlight/OpenMMLab-IoUNet/tree/00536bac99f4d3d7ad2682dad44f299f714565b6 |
Conv2dSamePadding | import torch
from torch import nn
import torch.nn.functional as F
def conv2d_same_padding(input, weight, bias=None, stride=1, dilation=1,
groups=1):
input_rows = input.size(2)
filter_rows = weight.size(2)
effective_filter_size_rows = (filter_rows - 1) * dilation[0] + 1
out_rows = (input_rows + str... | 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.nn.functional as F
assert_size_stride = torch.... | hulaba/pycrop-yield-prediction | Conv2dSamePadding | false | 6,830 | [
"MIT"
] | 1 | b4790dc2f87a73e8a0604e8c22466314090c5abf | https://github.com/hulaba/pycrop-yield-prediction/tree/b4790dc2f87a73e8a0604e8c22466314090c5abf |
GCNModelVAE | from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
import torch.nn.modules.loss
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | hurraygong/scGNN | GCNModelVAE | false | 6,831 | [
"MIT"
] | 1 | bc555895fbd5740ddd82e03187171116889cc10e | https://github.com/hurraygong/scGNN/tree/bc555895fbd5740ddd82e03187171116889cc10e |
_ASPPModule | import torch
import torch.nn as nn
class GCT(nn.Module):
def __init__(self, num_channels, epsilon=1e-05, mode='l2', after_relu=False
):
super(GCT, self).__init__()
self.alpha = nn.Parameter(torch.ones(1, num_channels, 1, 1))
self.gamma = nn.Parameter(torch.zeros(1, num_channels, 1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | huanglf714/COMatchNet | _ASPPModule | false | 6,832 | [
"Apache-2.0"
] | 1 | 79023f5be65d354eb9bdac026d7e0d73110bc4aa | https://github.com/huanglf714/COMatchNet/tree/79023f5be65d354eb9bdac026d7e0d73110bc4aa |
MyMaxPool1dPadSame | import torch
import torch.nn as nn
import torch.nn.functional as F
class MyMaxPool1dPadSame(nn.Module):
"""
extend nn.MaxPool1d to support SAME padding
"""
def __init__(self, kernel_size):
super(MyMaxPool1dPadSame, self).__init__()
self.kernel_size = kernel_size
self.stride = ... | 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... | huynhnhathao/hum_to_find | MyMaxPool1dPadSame | false | 6,833 | [
"MIT"
] | 1 | a0d7ec4bab1a7e2f7175956ff2721e23e2448840 | https://github.com/huynhnhathao/hum_to_find/tree/a0d7ec4bab1a7e2f7175956ff2721e23e2448840 |
AE | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.modules.loss
class AE(nn.Module):
""" Autoencoder for dimensional reduction"""
def __init__(self, dim):
super(AE, self).__init__()
self.dim = dim
self.fc1 = nn.Linear(dim, 512)
self.fc2 = nn.Lin... | 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 ... | hurraygong/scGNN | AE | false | 6,834 | [
"MIT"
] | 1 | bc555895fbd5740ddd82e03187171116889cc10e | https://github.com/hurraygong/scGNN/tree/bc555895fbd5740ddd82e03187171116889cc10e |
VAE | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.modules.loss
class VAE(nn.Module):
""" Variational Autoencoder for dimensional reduction"""
def __init__(self, dim):
super(VAE, self).__init__()
self.dim = dim
self.fc1 = nn.Linear(dim, 400)
sel... | import torch
from torch import device
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... | hurraygong/scGNN | VAE | false | 6,835 | [
"MIT"
] | 1 | bc555895fbd5740ddd82e03187171116889cc10e | https://github.com/hurraygong/scGNN/tree/bc555895fbd5740ddd82e03187171116889cc10e |
eca_block | import math
import torch
import torch.nn as nn
class eca_block(nn.Module):
def __init__(self, channel, b=1, gamma=2):
super(eca_block, self).__init__()
kernel_size = int(abs((math.log(channel, 2) + b) / gamma))
kernel_size = kernel_size if kernel_size % 2 else kernel_size + 1
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
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | huuthieu/pytorch-yolov4-tiny | eca_block | false | 6,836 | [
"MIT"
] | 1 | fac82da75e161221af74b56242272a42cf64c17e | https://github.com/huuthieu/pytorch-yolov4-tiny/tree/fac82da75e161221af74b56242272a42cf64c17e |
IrisClassifier | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
class IrisClassifier(nn.Module):
def __init__(self):
super(IrisClassifier, self).__init__()
self.fc1 = nn.Linear(4, 10)
self.fc2 = nn.Linear(10, 10)
self.fc3 = nn.Linear(10, 3)
def forward(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
import ... | huxuan/mlflow | IrisClassifier | false | 6,837 | [
"Apache-2.0"
] | 1 | 7b4ab0e4cac5d4c2d2cbfcd3d12aa55b2ee83efe | https://github.com/huxuan/mlflow/tree/7b4ab0e4cac5d4c2d2cbfcd3d12aa55b2ee83efe |
DynamicPreHead | import torch
import torch.nn as nn
class DynamicPreHead(nn.Module):
def __init__(self, in_dim=3, embed_dim=100, kernel_size=1):
super(DynamicPreHead, self).__init__()
self.conv = nn.Conv2d(in_dim, embed_dim, kernel_size=kernel_size,
stride=1, padding=int((kernel_size - 1) / 2))
... | 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.... | huanglf714/COMatchNet | DynamicPreHead | false | 6,838 | [
"Apache-2.0"
] | 1 | 79023f5be65d354eb9bdac026d7e0d73110bc4aa | https://github.com/huanglf714/COMatchNet/tree/79023f5be65d354eb9bdac026d7e0d73110bc4aa |
NormalizationLayer | import torch
import torch.utils.data
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:
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.triton_helpers import libdevice
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_siz... | huynhtruc0309/tirg | NormalizationLayer | false | 6,839 | [
"Apache-2.0"
] | 1 | 14ac6dcb41624729a6f4144a7c9e7899074f0eec | https://github.com/huynhtruc0309/tirg/tree/14ac6dcb41624729a6f4144a7c9e7899074f0eec |
decoder2 | import torch
import torch.nn as nn
class decoder2(nn.Module):
def __init__(self, dropout=0.5, act=torch.sigmoid):
super(decoder2, self).__init__()
self.dropout = nn.Dropout(dropout)
self.act = act
def forward(self, z_node, z_hyperedge):
z_node_ = self.dropout(z_node)
... | 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... | iMoonLab/HHDTI | decoder2 | false | 6,840 | [
"MIT"
] | 1 | b2dd0e78818888e676afc91af1425dada5b3258a | https://github.com/iMoonLab/HHDTI/tree/b2dd0e78818888e676afc91af1425dada5b3258a |
node_encoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class node_encoder(nn.Module):
def __init__(self, num_in_node, num_hidden, dropout, act=F.tanh):
super(node_encoder, self).__init__()
self.num_in_node = num_in_node
self.num_hidden = num_hidden
self.dropout = dropo... | 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 ... | iMoonLab/HHDTI | node_encoder | false | 6,841 | [
"MIT"
] | 1 | b2dd0e78818888e676afc91af1425dada5b3258a | https://github.com/iMoonLab/HHDTI/tree/b2dd0e78818888e676afc91af1425dada5b3258a |
hyperedge_encoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class hyperedge_encoder(nn.Module):
def __init__(self, num_in_edge, num_hidden, dropout, act=F.tanh):
super(hyperedge_encoder, self).__init__()
self.num_in_edge = num_in_edge
self.num_hidden = num_hidden
self.dropo... | 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 ... | iMoonLab/HHDTI | hyperedge_encoder | false | 6,842 | [
"MIT"
] | 1 | b2dd0e78818888e676afc91af1425dada5b3258a | https://github.com/iMoonLab/HHDTI/tree/b2dd0e78818888e676afc91af1425dada5b3258a |
kl_loss | from torch.nn import Module
import torch
from torch.nn.modules.module import Module
class kl_loss(Module):
def __init__(self, num_nodes, num_edges):
super(kl_loss, self).__init__()
self.num_nodes = num_nodes
self.num_edges = num_edges
def forward(self, z_node_log_std, z_node_mean, z_... | 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
as... | iMoonLab/HHDTI | kl_loss | false | 6,843 | [
"MIT"
] | 1 | b2dd0e78818888e676afc91af1425dada5b3258a | https://github.com/iMoonLab/HHDTI/tree/b2dd0e78818888e676afc91af1425dada5b3258a |
HGNN_conv | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class HGNN_conv(nn.Module):
def __init__(self, in_ft, out_ft, bias=True):
super(HGNN_conv, self).__init__()
self.weight = Parameter(torch.Tensor(in_ft, out_ft))
if bias:
self.bias = Paramet... | 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 math
import torch.nn as nn
from torch.nn.parameter import Parameter
asser... | iMoonLab/HHDTI | HGNN_conv | false | 6,844 | [
"MIT"
] | 1 | b2dd0e78818888e676afc91af1425dada5b3258a | https://github.com/iMoonLab/HHDTI/tree/b2dd0e78818888e676afc91af1425dada5b3258a |
ArcFace | import math
import torch
from itertools import product as product
import torch.nn as nn
import torch.utils.data.distributed
class ArcFace(nn.Module):
def __init__(self, s=64.0, m=0.5):
"""ArcFace formula:
cos(m + theta) = cos(m)cos(theta) - sin(m)sin(theta)
Note that:
0 <=... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import math
from itertools i... | iChenning/face_project | ArcFace | false | 6,845 | [
"MIT"
] | 1 | 8d70858817da4d15c7b513ae492034784f57f35f | https://github.com/iChenning/face_project/tree/8d70858817da4d15c7b513ae492034784f57f35f |
Model | import torch
from torch import nn
class Model(nn.Module):
def forward(self, img: 'torch.Tensor', scale: 'torch.Tensor', mean:
'torch.Tensor'):
return torch.div(torch.sub(img, mean), scale)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | ibaiGorordo/depthai-experiments | Model | false | 6,846 | [
"MIT"
] | 1 | cde67e277120ddac815cbad6360695759cca900f | https://github.com/ibaiGorordo/depthai-experiments/tree/cde67e277120ddac815cbad6360695759cca900f |
Actor | import torch
import torch.nn.functional as F
import torch.nn as nn
class Actor(nn.Module):
def __init__(self, hidden_size, num_inputs, action_space):
super(Actor, self).__init__()
self.action_space = action_space
num_outputs = action_space.shape[0]
self.linear1 = nn.Linear(num_inp... | 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 ... | icml2019-anonymous-author/Action-Robust-Reinforcement-Learning | Actor | false | 6,847 | [
"MIT"
] | 1 | 03f0a1dd5f4a0fc5230c0ad0b41f63161bae862b | https://github.com/icml2019-anonymous-author/Action-Robust-Reinforcement-Learning/tree/03f0a1dd5f4a0fc5230c0ad0b41f63161bae862b |
Block | import torch
import torch._C
import torch.serialization
from torch import nn
import torch.nn.functional as F
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob=None):
super(DropPath, self).__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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | huazai-1994/24th-resolution-for-STAC-Overflow | Block | false | 6,848 | [
"Apache-2.0"
] | 1 | 80bb3b367a126264823ffc597dc01586c262f9d9 | https://github.com/huazai-1994/24th-resolution-for-STAC-Overflow/tree/80bb3b367a126264823ffc597dc01586c262f9d9 |
EncoderBlock | import math
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim
class LayerNorm(nn.Module):
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(fe... | 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.... | howardchenhd/Transformer-pytorch | EncoderBlock | false | 6,849 | [
"MIT"
] | 1 | ae71ed5767272feb7e717be6d5bfce46f80ec57a | https://github.com/howardchenhd/Transformer-pytorch/tree/ae71ed5767272feb7e717be6d5bfce46f80ec57a |
DecoderBlock | import math
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim
class LayerNorm(nn.Module):
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(fe... | 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.... | howardchenhd/Transformer-pytorch | DecoderBlock | false | 6,850 | [
"MIT"
] | 1 | ae71ed5767272feb7e717be6d5bfce46f80ec57a | https://github.com/howardchenhd/Transformer-pytorch/tree/ae71ed5767272feb7e717be6d5bfce46f80ec57a |
Biaffine | import torch
import torch.nn as nn
class Biaffine(nn.Module):
def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
super(Biaffine, self).__init__()
self.n_in = n_in
self.n_out = n_out
self.bias_x = bias_x
self.bias_y = bias_y
self.weight = nn.Parameter(torc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | icewing1996/biaffine-parser | Biaffine | false | 6,851 | [
"MIT"
] | 1 | f5a4ece7ba9a087d81b76dd6a8ea6aa7d90c6c82 | https://github.com/icewing1996/biaffine-parser/tree/f5a4ece7ba9a087d81b76dd6a8ea6aa7d90c6c82 |
Critic | import torch
import torch.nn.functional as F
import torch.nn as nn
class Critic(nn.Module):
def __init__(self, hidden_size, num_inputs, action_space):
super(Critic, self).__init__()
self.action_space = action_space
num_outputs = action_space.shape[0]
self.linear1 = nn.Linear(num_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 torch.nn as ... | icml2019-anonymous-author/Action-Robust-Reinforcement-Learning | Critic | false | 6,852 | [
"MIT"
] | 1 | 03f0a1dd5f4a0fc5230c0ad0b41f63161bae862b | https://github.com/icml2019-anonymous-author/Action-Robust-Reinforcement-Learning/tree/03f0a1dd5f4a0fc5230c0ad0b41f63161bae862b |
ScaledDotProductAttention | import torch
import numpy as np
import torch.utils.data
class ScaledDotProductAttention(torch.nn.Module):
"""
Scaled, softmax attention module for Transformer as defined by
Attention(Q, K, V) on pg 4. Returns the final attention vectors as well as
the attention matrices (pairwise scores). """
def... | 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.... | icemansina/protein-transformer | ScaledDotProductAttention | false | 6,853 | [
"BSD-3-Clause"
] | 1 | 4e73b17f2a4b89ba1a9f6703976d1a31b7a8a5eb | https://github.com/icemansina/protein-transformer/tree/4e73b17f2a4b89ba1a9f6703976d1a31b7a8a5eb |
Actor | import torch
import torch as t
import torch.nn as nn
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, action_range):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, action_dim)
self.action_range ... | 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.... | ikamensh/machin | Actor | false | 6,854 | [
"MIT"
] | 1 | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | https://github.com/ikamensh/machin/tree/af7b423c47bc1412530cf6c96c11bd3af9b3e239 |
MultiHeadedAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadedAttention(nn.Module):
def __init__(self, num_head, d_model, dropout=0.1):
super(MultiHeadedAttention, self).__init__()
assert d_model % num_head == 0
self.d_k = d_model // num_head
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.... | iamxpy/pointer_summarizer | MultiHeadedAttention | false | 6,855 | [
"Apache-2.0"
] | 1 | ebeb2ad32a45162c0da14dac0b6241b0b0d00fa0 | https://github.com/iamxpy/pointer_summarizer/tree/ebeb2ad32a45162c0da14dac0b6241b0b0d00fa0 |
A2CCritic | import torch
import torch as t
import torch.nn as nn
class A2CCritic(nn.Module):
def __init__(self, state_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, 1)
def forward(self, state):
v = t.relu(self.fc... | 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_... | ikamensh/machin | A2CCritic | false | 6,856 | [
"MIT"
] | 1 | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | https://github.com/ikamensh/machin/tree/af7b423c47bc1412530cf6c96c11bd3af9b3e239 |
DDPGCritic | import torch
import torch as t
import torch.nn as nn
class DDPGCritic(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim + action_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, 1)
def forward(self, state, 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
import torch.nn as nn
assert_... | ikamensh/machin | DDPGCritic | false | 6,857 | [
"MIT"
] | 1 | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | https://github.com/ikamensh/machin/tree/af7b423c47bc1412530cf6c96c11bd3af9b3e239 |
CO_Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class CO_Attention(nn.Module):
def __init__(self, in_dim, co_attention_dim):
super(CO_Attention, self).__init__()
self.leak_relu = nn.LeakyReLU()
self.relu = nn.ReLU()
self.conv1 = nn.Conv2d(in_dim, 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
assert_... | huanglf714/COMatchNet | CO_Attention | false | 6,858 | [
"Apache-2.0"
] | 1 | 79023f5be65d354eb9bdac026d7e0d73110bc4aa | https://github.com/huanglf714/COMatchNet/tree/79023f5be65d354eb9bdac026d7e0d73110bc4aa |
ActorDiscrete | import torch
import torch as t
import torch.nn as nn
class ActorDiscrete(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, action_dim)
def forward(self, 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.... | ikamensh/machin | ActorDiscrete | false | 6,859 | [
"MIT"
] | 1 | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | https://github.com/ikamensh/machin/tree/af7b423c47bc1412530cf6c96c11bd3af9b3e239 |
A2CActorDisc | import torch
from torch.distributions import Categorical
import torch as t
import torch.nn as nn
class A2CActorDisc(nn.Module):
def __init__(self, state_dim, action_num):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, 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.... | ikamensh/machin | A2CActorDisc | false | 6,860 | [
"MIT"
] | 1 | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | https://github.com/ikamensh/machin/tree/af7b423c47bc1412530cf6c96c11bd3af9b3e239 |
MultiHeadedAttention | import torch
import numpy as np
import torch.utils.data
class ScaledDotProductAttention(torch.nn.Module):
"""
Scaled, softmax attention module for Transformer as defined by
Attention(Q, K, V) on pg 4. Returns the final attention vectors as well as
the attention matrices (pairwise scores). """
def... | 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.... | icemansina/protein-transformer | MultiHeadedAttention | false | 6,861 | [
"BSD-3-Clause"
] | 1 | 4e73b17f2a4b89ba1a9f6703976d1a31b7a8a5eb | https://github.com/icemansina/protein-transformer/tree/4e73b17f2a4b89ba1a9f6703976d1a31b7a8a5eb |
QNet | import torch
import torch as t
import torch.nn as nn
class QNet(nn.Module):
def __init__(self, state_dim, action_num, atom_num=10):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, action_num * atom_num)
self.acti... | 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.... | ikamensh/machin | QNet | false | 6,862 | [
"MIT"
] | 1 | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | https://github.com/ikamensh/machin/tree/af7b423c47bc1412530cf6c96c11bd3af9b3e239 |
CosineBasisLinear | import torch
import numpy as np
import torch.nn as nn
def cosine_basis_functions(x, n_basis_functions=64):
"""Cosine basis functions used to embed quantile thresholds.
Args:
x (torch.Tensor): Input.
n_basis_functions (int): Number of cosine basis functions.
Returns:
ndarray: Embe... | 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 numpy ... | imatge-upc/pixelcoordEDL | CosineBasisLinear | false | 6,863 | [
"MIT"
] | 1 | 353632feed6ac8c93758c1a2a1b7a477e7ff053c | https://github.com/imatge-upc/pixelcoordEDL/tree/353632feed6ac8c93758c1a2a1b7a477e7ff053c |
GatedActivation | import torch
import torch.nn as nn
import torch.nn.functional as F
class GatedActivation(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x, y = x.chunk(2, dim=1)
return F.tanh(x) * F.sigmoid(y)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def g... | 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_... | imatge-upc/pixelcoordEDL | GatedActivation | false | 6,864 | [
"MIT"
] | 1 | 353632feed6ac8c93758c1a2a1b7a477e7ff053c | https://github.com/imatge-upc/pixelcoordEDL/tree/353632feed6ac8c93758c1a2a1b7a477e7ff053c |
A2CActorCont | import torch
import torch as t
import torch.nn as nn
from torch.distributions import Normal
import torch.nn.functional as F
class A2CActorCont(nn.Module):
def __init__(self, state_dim, action_dim, action_range):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ikamensh/machin | A2CActorCont | false | 6,865 | [
"MIT"
] | 1 | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | https://github.com/ikamensh/machin/tree/af7b423c47bc1412530cf6c96c11bd3af9b3e239 |
Attention | import torch
import torch.nn as nn
class Attention(nn.Module):
def __init__(self):
super().__init__()
self.softmax = nn.Softmax(dim=-1)
def forward(self, Q, K, V, mask=None, dk=64):
w = torch.bmm(Q, K.transpose(1, 2))
if mask is not None:
assert w.size() == mask.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.... | iml1111/machine-translation | Attention | false | 6,866 | [
"MIT"
] | 1 | a7dd673efbe8a172c1df49e0d50482dc84008c37 | https://github.com/iml1111/machine-translation/tree/a7dd673efbe8a172c1df49e0d50482dc84008c37 |
HSwishV2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class HSwishFunctionV2(torch.autograd.Function):
@staticmethod
def forward(ctx, feat):
act = F.relu6(feat + 3).mul_(feat).div_(6)
ctx.variables = feat
return act
@staticmethod
def backward(ctx, grad_output):
... | 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.nn.functional as F
assert_size_stride = torch._C._dyna... | imvladikon/pytorch-loss | HSwishV2 | false | 6,867 | [
"MIT"
] | 1 | 6cfaabe1be898e1ff000b3dffb46d0ef09096f6b | https://github.com/imvladikon/pytorch-loss/tree/6cfaabe1be898e1ff000b3dffb46d0ef09096f6b |
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