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 |
|---|---|---|---|---|---|---|---|---|---|---|
_Transition | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision.transforms import *
class _Transition(nn.Module):
def __init__(self, in_channels, args):
super(_Transit... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvisi... | HelenR6/robustness-1 | _Transition | false | 11,142 | [
"Apache-2.0"
] | 0 | 5527250df02195dff37628a9d76ae7d76c3c51d1 | https://github.com/HelenR6/robustness-1/tree/5527250df02195dff37628a9d76ae7d76c3c51d1 |
EmbedNet | from _paritybench_helpers import _mock_config
import torch
from torchvision.transforms import functional as F
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class EmbedNet(nn.Module):
def __init__(self, cfg):
super(EmbedNet, self).__init__()
self.embed_conv1 = nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | ZJU-lishuang/mega.pytorch | EmbedNet | false | 11,143 | [
"BSD-2-Clause"
] | 0 | d655e51084d0cbeaf8ab46f63491191dfe3a1ab9 | https://github.com/ZJU-lishuang/mega.pytorch/tree/d655e51084d0cbeaf8ab46f63491191dfe3a1ab9 |
VGGBase | import torch
import torchvision
from torch import nn
import torch.nn.functional as F
from itertools import product as product
import torch.optim
import torch.utils.data
def decimate(tensor, m):
"""
Decimate a tensor by a factor 'm', i.e. downsample by keeping every 'm'th value.
This is used when we conve... | 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 torchvision
from torch... | mosevg/ssd | VGGBase | false | 11,144 | [
"MIT"
] | 0 | 8fd9f6cc376c027427531bcf475188ae43c4b2d6 | https://github.com/mosevg/ssd/tree/8fd9f6cc376c027427531bcf475188ae43c4b2d6 |
SSD300 | import torch
import torchvision
from torch import nn
import torch.nn.functional as F
from math import sqrt
from itertools import product as product
import torch.optim
import torch.utils.data
def decimate(tensor, m):
"""
Decimate a tensor by a factor 'm', i.e. downsample by keeping every 'm'th value.
This... | 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.... | mosevg/ssd | SSD300 | false | 11,145 | [
"MIT"
] | 0 | 8fd9f6cc376c027427531bcf475188ae43c4b2d6 | https://github.com/mosevg/ssd/tree/8fd9f6cc376c027427531bcf475188ae43c4b2d6 |
Model | from torch.nn import Module
import torch
import torch.nn.functional
from torch.nn.parameter import Parameter
from torch.nn.modules import Module
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
from torch.nn import Parameter
from torch.nn import Module
class Mode... | 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.nn.functional
from torch.nn.parameter import Parameter
from torch.nn.modules import Module
import t... | DeanChan/apex | Model | false | 11,146 | [
"BSD-3-Clause"
] | 0 | a03267e5e1209f559a6671da56c479a216f418d1 | https://github.com/DeanChan/apex/tree/a03267e5e1209f559a6671da56c479a216f418d1 |
Normalize | 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 = x.div(norm)
return out
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
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Alan-Paul/ECN | Normalize | false | 11,147 | [
"Apache-2.0"
] | 0 | 5e9a9081ff0c1e36cc0381df3ce9038a79a537e9 | https://github.com/Alan-Paul/ECN/tree/5e9a9081ff0c1e36cc0381df3ce9038a79a537e9 |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class GlobalAvgPool2d(nn.Module):
def __init__(self):
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
N = x.data.size(0)
C = x.data.size(1)
H = x.data.size(2)
W = x.data.size(3)
x... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Abdul-Mukit/ssp_with_hand_tracking | GlobalAvgPool2d | false | 11,148 | [
"MIT"
] | 0 | 04429ac9789283694a9176b94f70ab4e5a8c0727 | https://github.com/Abdul-Mukit/ssp_with_hand_tracking/tree/04429ac9789283694a9176b94f70ab4e5a8c0727 |
velocity_adding_neuron | import torch
import torch.nn as nn
class velocity_adding_neuron(nn.Module):
def __init__(self, weight):
super(velocity_adding_neuron, self).__init__()
self.w = weight
self.nl = nn.Tanh()
def forward(self, x):
return self.nl(self.w * x)
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AgamChopra/simulation-in-a-box | velocity_adding_neuron | false | 11,149 | [
"MIT"
] | 0 | 2a346a2fc83d79e542b64f1bd45c338d27a1934d | https://github.com/AgamChopra/simulation-in-a-box/tree/2a346a2fc83d79e542b64f1bd45c338d27a1934d |
Sum | import torch
import torch.nn as nn
class Sum(nn.Module):
def __init__(self, n, weight=False):
super(Sum, self).__init__()
self.weight = weight
self.iter = range(n - 1)
if weight:
self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True
)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Alex-Beh/hand_tracking | Sum | false | 11,150 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
BCEBlurWithLogitsLoss | import torch
import torch.nn as nn
class BCEBlurWithLogitsLoss(nn.Module):
def __init__(self, alpha=0.05):
super(BCEBlurWithLogitsLoss, self).__init__()
self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')
self.alpha = alpha
def forward(self, pred, true):
loss = self.loss_f... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Alex-Beh/hand_tracking | BCEBlurWithLogitsLoss | false | 11,151 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
Relu | from torch.nn import Module
import torch
class Relu(Module):
def forward(self, inp):
return inp.clamp_min(0.0) - 0.5
def bwd(self, out, inp):
inp.g = (inp > 0).float() * out.g
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 import triton_helpers
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | Akramz/Impractical-DL | Relu | false | 11,152 | [
"MIT"
] | 0 | ff909e369fb765c0857800925e39c433057ae8ac | https://github.com/Akramz/Impractical-DL/tree/ff909e369fb765c0857800925e39c433057ae8ac |
Mse | from torch.nn import Module
import torch
class Mse(Module):
def forward(self, inp, targ):
return (inp.squeeze() - targ).pow(2).mean()
def bwd(self, out, inp, targ):
inp.g = 2 * (inp.squeeze() - targ).unsqueeze(-1) / targ.shape[0]
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | Akramz/Impractical-DL | Mse | false | 11,153 | [
"MIT"
] | 0 | ff909e369fb765c0857800925e39c433057ae8ac | https://github.com/Akramz/Impractical-DL/tree/ff909e369fb765c0857800925e39c433057ae8ac |
pheramon_output_neuron | import torch
import torch.nn as nn
class pheramon_output_neuron(nn.Module):
def __init__(self, weight):
super(pheramon_output_neuron, self).__init__()
self.w = weight
self.nl = nn.Sigmoid()
def forward(self, x):
return self.nl(self.w * x)
def get_inputs():
return [torch... | 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... | AgamChopra/simulation-in-a-box | pheramon_output_neuron | false | 11,154 | [
"MIT"
] | 0 | 2a346a2fc83d79e542b64f1bd45c338d27a1934d | https://github.com/AgamChopra/simulation-in-a-box/tree/2a346a2fc83d79e542b64f1bd45c338d27a1934d |
MaxPoolStride1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class MaxPoolStride1(nn.Module):
def __init__(self):
super(MaxPoolStride1, self).__init__()
def forward(self, x):
x = F.max_pool2d(F.pad(x, (0, 1, 0, 1), mode='replicate'), 2, stride=1)
return x
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Abdul-Mukit/ssp_with_hand_tracking | MaxPoolStride1 | false | 11,155 | [
"MIT"
] | 0 | 04429ac9789283694a9176b94f70ab4e5a8c0727 | https://github.com/Abdul-Mukit/ssp_with_hand_tracking/tree/04429ac9789283694a9176b94f70ab4e5a8c0727 |
GeM | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
def gem(x, p=3, eps=1e-06):
return nn.functional.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x
.size(-1))).pow(1.0 / p)
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-06):
super(GeM, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | AlessandroRigoli/project_vg | GeM | false | 11,156 | [
"MIT"
] | 0 | cb1323bee60cdb4108fe0aab68791321c7974832 | https://github.com/AlessandroRigoli/project_vg/tree/cb1323bee60cdb4108fe0aab68791321c7974832 |
Expand | import torch
import torch.nn as nn
class Expand(nn.Module):
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
N, C, H, W = x.size()
s = self.gain
x = x.view(N, s, s, C // s ** 2, H, W)
x = x.permute(0, 3, 4, 1, 5, 2).contigu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Alex-Beh/hand_tracking | Expand | false | 11,157 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
SigmoidFocalClassificationLoss | import torch
import torch.nn as nn
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init__(self, gamma: 'float'=2.0, alpha: 'float'=0.25):
"""
Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
... | 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... | AhmedMoamen62/OpenPCDet | SigmoidFocalClassificationLoss | false | 11,158 | [
"Apache-2.0"
] | 0 | 4d61d099819f40096f795def2c012990d03711cd | https://github.com/AhmedMoamen62/OpenPCDet/tree/4d61d099819f40096f795def2c012990d03711cd |
Classify | import torch
import torch.nn as nn
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Classify(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
super(Classify, self).__init__()
self.aap = nn.AdaptiveAvgP... | 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... | Alex-Beh/hand_tracking | Classify | false | 11,159 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
L2Norm | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
import torch.nn.init as init
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.gamma = scale or None
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from math import sqrt as sqrt
from itertools import produ... | AbhiprayaDash/models | L2Norm | false | 11,160 | [
"Apache-2.0"
] | 0 | ed679a701ccb5891ca4a02f9379c636c50cb9209 | https://github.com/AbhiprayaDash/models/tree/ed679a701ccb5891ca4a02f9379c636c50cb9209 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as functional
class Net(nn.Module):
def __init__(self):
super().__init__()
self.layer1 = nn.Linear(28 ** 2, 64)
self.layer2 = nn.Linear(64, 10)
def forward(self, x):
x = x.view(-1, 28 ** 2)
x = self.layer1(... | 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_... | AlexTaguchi/ml-tutorial | Net | false | 11,161 | [
"MIT"
] | 0 | 5b2693cd1648a72e9bcd6cee1223eedadba4b837 | https://github.com/AlexTaguchi/ml-tutorial/tree/5b2693cd1648a72e9bcd6cee1223eedadba4b837 |
Hardswish | import torch
import torch.nn as nn
import torch.nn.functional as F
class Hardswish(nn.Module):
@staticmethod
def forward(x):
return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0
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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Alex-Beh/hand_tracking | Hardswish | false | 11,162 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
Contract | import torch
import torch.nn as nn
class Contract(nn.Module):
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
N, C, H, W = x.size()
s = self.gain
x = x.view(N, C, H // s, s, W // s, s)
x = x.permute(0, 3, 5, 1, 2, 4).conti... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Alex-Beh/hand_tracking | Contract | false | 11,163 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
ShuffleBlock | import torch
import torch.nn as nn
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
self.groups = groups
def forward(self, x):
"""Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]"""
N, C, H, W = x.size(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AlexHoffman9/HAET-2021-competition-baseline-code | ShuffleBlock | false | 11,164 | [
"MIT"
] | 0 | 1d71c94c68c9903854eceda6caf07442930caa44 | https://github.com/AlexHoffman9/HAET-2021-competition-baseline-code/tree/1d71c94c68c9903854eceda6caf07442930caa44 |
WeightedCrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class WeightedCrossEntropyLoss(nn.Module):
"""
Transform input to fit the fomation of PyTorch offical cross entropy loss
with anchor-wise weighting.
"""
def __init__(self):
super(WeightedCrossEntropyLoss, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | AhmedMoamen62/OpenPCDet | WeightedCrossEntropyLoss | false | 11,165 | [
"Apache-2.0"
] | 0 | 4d61d099819f40096f795def2c012990d03711cd | https://github.com/AhmedMoamen62/OpenPCDet/tree/4d61d099819f40096f795def2c012990d03711cd |
MemoryEfficientMish | import torch
import torch.nn as nn
import torch.nn.functional as F
class MemoryEfficientMish(nn.Module):
class F(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return x.mul(torch.tanh(F.softplus(x)))
@staticmethod
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._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
assert_s... | Alex-Beh/hand_tracking | MemoryEfficientMish | false | 11,166 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
ConvAutoencoder | import torch
from torch import nn
class ConvAutoencoder(nn.Module):
def __init__(self, enc_dim=10, channels=1, strides=1):
super().__init__()
self.conv1 = nn.Conv1d(channels, enc_dim, 7, strides, padding=0)
self.dropout = nn.Dropout(0.2)
self.t_conv1 = nn.ConvTranspose1d(enc_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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | AlexMetsai/pytorch-time-series-autoencoder | ConvAutoencoder | false | 11,167 | [
"MIT"
] | 0 | 460e364edcb7c7a84d2e544a22cd48f51cdda4aa | https://github.com/AlexMetsai/pytorch-time-series-autoencoder/tree/460e364edcb7c7a84d2e544a22cd48f51cdda4aa |
L2Norm | import torch
import torch.nn as nn
import torch.nn.functional as F
class L2Norm(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, x):
return F.normalize(x, p=2, dim=self.dim)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | AlessandroRigoli/project_vg | L2Norm | false | 11,168 | [
"MIT"
] | 0 | cb1323bee60cdb4108fe0aab68791321c7974832 | https://github.com/AlessandroRigoli/project_vg/tree/cb1323bee60cdb4108fe0aab68791321c7974832 |
MemoryEfficientSwish | import torch
import torch.nn as nn
class MemoryEfficientSwish(nn.Module):
class F(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return x * torch.sigmoid(x)
@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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Alex-Beh/hand_tracking | MemoryEfficientSwish | false | 11,169 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
PositionwiseFeedForward | import torch
import torch.nn.functional as F
import torch.nn as nn
class PositionwiseFeedForward(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hid)
self.w_2 = nn.Linear(d_hid, d_in)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AbubakrHassan/attention-is-all-you-need-pytorch | PositionwiseFeedForward | false | 11,170 | [
"MIT"
] | 0 | 2bf9a477dea6271b082556069f3665ffed2745cd | https://github.com/AbubakrHassan/attention-is-all-you-need-pytorch/tree/2bf9a477dea6271b082556069f3665ffed2745cd |
RSoftmax | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
class RSoftmax(nn.Module):
"""Radix Softmax module in ``SplitAttentionConv2d``.
Args:
radix (int): Radix of input.
groups (int): Groups of input.
"""
def __init__(self, radix... | 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
... | AlexanderDokuchaev/mmsegmentation | RSoftmax | false | 11,171 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
Conv2dDynamicSamePadding | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
class Conv2dDynamicSamePadding(nn.Conv2d):
"""2D Convolutions like TensorFlow, for a dynamic image size.
The padding is operated in forward function by calculating dynamically.
"""
... | 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._C
import torch.serialization
assert_size_str... | AlexanderDokuchaev/mmsegmentation | Conv2dDynamicSamePadding | false | 11,172 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
orientation_neuron | import torch
import torch.nn as nn
class orientation_neuron(nn.Module):
def __init__(self, weight):
super(orientation_neuron, self).__init__()
self.w = weight
self.nl = nn.Sigmoid()
def forward(self, x):
return self.nl(self.w * x) * 360.0
def get_inputs():
return [torch... | 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... | AgamChopra/simulation-in-a-box | orientation_neuron | false | 11,173 | [
"MIT"
] | 0 | 2a346a2fc83d79e542b64f1bd45c338d27a1934d | https://github.com/AgamChopra/simulation-in-a-box/tree/2a346a2fc83d79e542b64f1bd45c338d27a1934d |
Attention | import torch
from torch import nn
class Attention(nn.Module):
"""
Attention Network.
"""
def __init__(self, encoder_dim, decoder_dim, attention_dim):
"""
:param encoder_dim: feature size of encoded images
:param decoder_dim: size of decoder's RNN
:param attention_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.... | AlexMeinke/serverless-hosting-of-image-captioning | Attention | false | 11,174 | [
"MIT"
] | 0 | 2b539561ac600e6a502ac4ecb25948a50e26cc54 | https://github.com/AlexMeinke/serverless-hosting-of-image-captioning/tree/2b539561ac600e6a502ac4ecb25948a50e26cc54 |
GLU | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class GLU(nn.Module):
def __init__(self):
super(GLU, self).__init__()
def forward(self, x):
nc = x.size(1)
assert nc % 2 == 0, 'channels dont divide 2!'
nc = int(nc / 2)
return... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | Abrantex/finegan | GLU | false | 11,175 | [
"BSD-2-Clause"
] | 0 | 0d60105fd81abaa945cebb2232dbed703fe319f0 | https://github.com/Abrantex/finegan/tree/0d60105fd81abaa945cebb2232dbed703fe319f0 |
PPMConcat | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class PPMConcat(nn.ModuleList):
"""Pyramid Pooling Module that only concat the features of each layer.
Args:
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module.
"""
def __init__(sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | AlexanderDokuchaev/mmsegmentation | PPMConcat | false | 11,176 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
SE | import torch
import torch.nn as nn
import torch.nn.functional as F
class SE(nn.Module):
"""Squeeze-and-Excitation block."""
def __init__(self, in_planes, se_planes):
super(SE, self).__init__()
self.se1 = nn.Conv2d(in_planes, se_planes, kernel_size=1, bias=True)
self.se2 = nn.Conv2d(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AlexHoffman9/HAET-2021-competition-baseline-code | SE | false | 11,177 | [
"MIT"
] | 0 | 1d71c94c68c9903854eceda6caf07442930caa44 | https://github.com/AlexHoffman9/HAET-2021-competition-baseline-code/tree/1d71c94c68c9903854eceda6caf07442930caa44 |
Network | import torch
class Network(torch.nn.Module):
def __init__(self):
super(Network, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 64, kernel_size=5)
self.conv2 = torch.nn.Conv2d(64, 512, kernel_size=5)
self.fc1 = torch.nn.Linear(2048, 256)
self.fc2 = torch.nn.Linear(256, 12... | 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.... | AbrahamAcosta/leaves_cnn | Network | false | 11,178 | [
"MIT"
] | 0 | e6be28ef696dc427aa50c7d4581a29a05d1e7a94 | https://github.com/AbrahamAcosta/leaves_cnn/tree/e6be28ef696dc427aa50c7d4581a29a05d1e7a94 |
TD3Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class TD3Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(TD3Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | AkiraHero/rlll | TD3Critic | false | 11,179 | [
"MIT"
] | 0 | f86e1105600629d29b8dca7a7483e7dcb8253056 | https://github.com/AkiraHero/rlll/tree/f86e1105600629d29b8dca7a7483e7dcb8253056 |
TD3Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class TD3Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(TD3Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, action_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.... | AkiraHero/rlll | TD3Actor | false | 11,180 | [
"MIT"
] | 0 | f86e1105600629d29b8dca7a7483e7dcb8253056 | https://github.com/AkiraHero/rlll/tree/f86e1105600629d29b8dca7a7483e7dcb8253056 |
InputInjection | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class InputInjection(nn.Module):
def __init__(self, ratio):
super().__init__()
self.pool = nn.ModuleList()
for i in range(0, ratio):
self.pool.append(nn.AvgPool2d(3, stride=2, padding=1))
def fo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | AlexanderDokuchaev/mmsegmentation | InputInjection | false | 11,181 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
EncoderLayer | import torch
import torch.nn.functional as F
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_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 import triton_helpers
from torch._inductor.runtime.... | AbubakrHassan/attention-is-all-you-need-pytorch | EncoderLayer | false | 11,182 | [
"MIT"
] | 0 | 2bf9a477dea6271b082556069f3665ffed2745cd | https://github.com/AbubakrHassan/attention-is-all-you-need-pytorch/tree/2bf9a477dea6271b082556069f3665ffed2745cd |
ExampleBackbone | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class ExampleBackbone(nn.Module):
def __init__(self):
super(ExampleBackbone, self).__init__()
self.conv = nn.Conv2d(3, 3, 3)
def init_weights(self, pretrained=None):
pass
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
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_str... | AlexanderDokuchaev/mmsegmentation | ExampleBackbone | false | 11,183 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
DecoderLayer | import torch
import torch.nn.functional as F
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_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 import triton_helpers
from torch._inductor.runtime.... | AbubakrHassan/attention-is-all-you-need-pytorch | DecoderLayer | false | 11,184 | [
"MIT"
] | 0 | 2bf9a477dea6271b082556069f3665ffed2745cd | https://github.com/AbubakrHassan/attention-is-all-you-need-pytorch/tree/2bf9a477dea6271b082556069f3665ffed2745cd |
UpSample | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class UpSample(nn.Module):
def __init__(self, n_chan, factor=2):
super(UpSample, self).__init__()
out_chan = n_chan * factor * factor
self.proj = nn.Conv2d(n_chan, out_chan, 1, 1, 0)
self.up = nn.PixelSh... | 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._C
import torch.serialization
assert_size_str... | AlexanderDokuchaev/mmsegmentation | UpSample | false | 11,185 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
IOU | import torch
def _iou(pred, target, size_average=True):
b = pred.shape[0]
IoU = 0.0
for i in range(0, b):
Iand1 = torch.sum(target[i, :, :, :] * pred[i, :, :, :])
Ior1 = torch.sum(target[i, :, :, :]) + torch.sum(pred[i, :, :, :]
) - Iand1
IoU1 = Iand1 / Ior1
IoU... | 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... | AlvinWen428/BASNet | IOU | false | 11,186 | [
"MIT"
] | 0 | 2af21e0333204b8adcb9565b33a0bf72f5471db5 | https://github.com/AlvinWen428/BASNet/tree/2af21e0333204b8adcb9565b33a0bf72f5471db5 |
AngularPWConv | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
def normalize(x, dim, p=2, eps=1e-12):
if torch.onnx.is_in_onnx_export():
return OnnxLpNormalization.apply(x, dim, p, eps)
else:
return F.normalize(x, dim=dim, p=p, eps=eps)
class On... | 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.... | AlexanderDokuchaev/mmsegmentation | AngularPWConv | false | 11,187 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
EdgeGateFree | import torch
import torch.nn as nn
from torch.nn import Parameter
class EdgeGateFree(nn.Module):
"""
Calculate gates for each edge in message passing.
The gates are free parameters.
Note:
This will make the parameters depend on the number of edges, which will limit the model
to work on... | 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 torch.nn import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = to... | AnchoretY/botnet-detection | EdgeGateFree | false | 11,188 | [
"MIT"
] | 0 | e2066ff314f1ea2ccbf4c10ddff819f344a2b715 | https://github.com/AnchoretY/botnet-detection/tree/e2066ff314f1ea2ccbf4c10ddff819f344a2b715 |
Encoding | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
class Encoding(nn.Module):
"""Encoding Layer: a learnable residual encoder.
Input is of shape (batch_size, channels, height, width).
Output is of shape (batch_size, num_codes, channels).
Ar... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | AlexanderDokuchaev/mmsegmentation | Encoding | false | 11,189 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
DiceLoss | import functools
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AlexanderDokuchaev/mmsegmentation | DiceLoss | false | 11,190 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
PSPModule | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class PSPModule(nn.Module):
"""Reference: https://github.com/MendelXu/ANN
"""
methods = {'max': nn.AdaptiveMaxPool2d, 'avg': nn.AdaptiveAvgPool2d}
def __init__(self, sizes=(1, 3, 6, 8), method='max'):
super().__init... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_stride = tor... | AlexanderDokuchaev/mmsegmentation | PSPModule | false | 11,191 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
Model | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x: 'torch.Tensor', y: 'torch.Tensor'):
return x * y
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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... | Archermmt/tvm | Model | false | 11,192 | [
"Apache-2.0"
] | 0 | 8b900cec1a9c3cb453e159db4d497ebeb26ed289 | https://github.com/Archermmt/tvm/tree/8b900cec1a9c3cb453e159db4d497ebeb26ed289 |
UpsamplingBilinear | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class UpsamplingBilinear(nn.Module):
def __init__(self):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
def forward(self, x):
x = 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
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization im... | Archermmt/tvm | UpsamplingBilinear | false | 11,193 | [
"Apache-2.0"
] | 0 | 8b900cec1a9c3cb453e159db4d497ebeb26ed289 | https://github.com/Archermmt/tvm/tree/8b900cec1a9c3cb453e159db4d497ebeb26ed289 |
Hswish | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Hswish(nn.Module):
def __init__(self, add_stub=False):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
self.add_stub = add_stub
... | 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
from torch.quantization import QuantStub
from torch.quantization im... | Archermmt/tvm | Hswish | false | 11,194 | [
"Apache-2.0"
] | 0 | 8b900cec1a9c3cb453e159db4d497ebeb26ed289 | https://github.com/Archermmt/tvm/tree/8b900cec1a9c3cb453e159db4d497ebeb26ed289 |
SEModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class SEModule(nn.Module):
def __init__(self, planes, compress_rate):
super(SEModule, self).__init__()
self.conv1 = nn.Conv2d(planes, planes // compress_rate, kernel_size
=1, stride=1, bias=True)
self.conv2 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AlexTintin/Face_Recognition_CV_Project | SEModule | false | 11,195 | [
"MIT"
] | 0 | 6becb159dd3d8f547d617983bd422e3f2a9fb52e | https://github.com/AlexTintin/Face_Recognition_CV_Project/tree/6becb159dd3d8f547d617983bd422e3f2a9fb52e |
GCN | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AlexHeffner/pygcn | GCN | false | 11,196 | [
"MIT"
] | 0 | 514f4329209a3bf9c75beba97af42d2c1bf8c129 | https://github.com/AlexHeffner/pygcn/tree/514f4329209a3bf9c75beba97af42d2c1bf8c129 |
TverskyLoss | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
def get_class_weight(class_weight):
"""Get class weight for loss function.
Args:
class_weight (list[float] | str | None): If class_weight is a str,
take it as a... | 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 numpy as np
imp... | AlexanderDokuchaev/mmsegmentation | TverskyLoss | false | 11,197 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
ScaledDotProductAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_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 import triton_helpers
from torch._inductor.runtime.... | AbubakrHassan/attention-is-all-you-need-pytorch | ScaledDotProductAttention | false | 11,198 | [
"MIT"
] | 0 | 2bf9a477dea6271b082556069f3665ffed2745cd | https://github.com/AbubakrHassan/attention-is-all-you-need-pytorch/tree/2bf9a477dea6271b082556069f3665ffed2745cd |
ResNeXtBottleneck | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResNeXtBottleneck(nn.Module):
def __init__(self, in_channels=256, out_channels=256, stride=1,
cardinality=32, dilate=1):
super(ResNeXtBottleneck, self).__init__()
D = out_channels // 2
self.out_channels = out... | 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... | AlexWang000/AlacGAN | ResNeXtBottleneck | false | 11,199 | [
"MIT"
] | 0 | 3b9df7c25c3e95b7727b00fa789cab0cf7d46266 | https://github.com/AlexWang000/AlacGAN/tree/3b9df7c25c3e95b7727b00fa789cab0cf7d46266 |
MultiHeadAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_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 import triton_helpers
from torch._inductor.runtime.... | AbubakrHassan/attention-is-all-you-need-pytorch | MultiHeadAttention | false | 11,200 | [
"MIT"
] | 0 | 2bf9a477dea6271b082556069f3665ffed2745cd | https://github.com/AbubakrHassan/attention-is-all-you-need-pytorch/tree/2bf9a477dea6271b082556069f3665ffed2745cd |
MulScalarNegative | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class MulScalarNegative(nn.Module):
def __init__(self):
super().__init__()
self.float_op = nn.quantized.FloatFunctional()
self.quant = QuantStub()
self.dequant = ... | 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 torch.quantization import QuantStub
from torch.quantization import DeQuantStub
assert_size_stride = torch._C._dyn... | Archermmt/tvm | MulScalarNegative | false | 11,201 | [
"Apache-2.0"
] | 0 | 8b900cec1a9c3cb453e159db4d497ebeb26ed289 | https://github.com/Archermmt/tvm/tree/8b900cec1a9c3cb453e159db4d497ebeb26ed289 |
AvgConsensus | import torch
import torch.nn as nn
class AvgConsensus(nn.Module):
"""Average consensus module.
Args:
dim (int): Decide which dim consensus function to apply.
Default: 1.
"""
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Alexis-Fab/mmaction2 | AvgConsensus | false | 11,202 | [
"Apache-2.0"
] | 0 | 6f76bb465a7164f907318cf58f77fc3d613f8f0f | https://github.com/Alexis-Fab/mmaction2/tree/6f76bb465a7164f907318cf58f77fc3d613f8f0f |
Hsigmoid | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Hsigmoid(nn.Module):
def __init__(self, add_stub=False):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
self.add_stub = add_stub
... | 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
from torch.quantization import QuantStub
from torch.quantization im... | Archermmt/tvm | Hsigmoid | false | 11,203 | [
"Apache-2.0"
] | 0 | 8b900cec1a9c3cb453e159db4d497ebeb26ed289 | https://github.com/Archermmt/tvm/tree/8b900cec1a9c3cb453e159db4d497ebeb26ed289 |
FeedForward_NN | import torch
import torch.nn as nn
class FeedForward_NN(nn.Module):
def __init__(self, input_size, hidden_layer, output_size):
super(FeedForward_NN, self).__init__()
self.layer1 = nn.Linear(input_size, hidden_layer)
self.relu = nn.ReLU()
self.layer2 = nn.Linear(hidden_layer, outpu... | 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_... | AqibJavaid899/PyTorch_Models | FeedForward_NN | false | 11,204 | [
"MIT"
] | 0 | cf81f6ef5d81aed76dca3f1a15be1a308b5d450f | https://github.com/AqibJavaid899/PyTorch_Models/tree/cf81f6ef5d81aed76dca3f1a15be1a308b5d450f |
SpatialGatherModule | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
class SpatialGatherModule(nn.Module):
"""Aggregate the context features according to the initial predicted
probability distribution.
Employ the soft-weighted method to aggregate the context.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AlexanderDokuchaev/mmsegmentation | SpatialGatherModule | false | 11,205 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
BalancedL1Loss | import functools
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tenso... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AtticusJohnson/mmdetection | BalancedL1Loss | false | 11,206 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
WeightNet | import torch
import torch.nn as nn
class WeightNet(nn.Module):
"""WeightNet in Temporal interlace module.
The WeightNet consists of two parts: one convolution layer
and a sigmoid function. Following the convolution layer, the sigmoid
function and rescale module can scale our output to the range (0, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Alexis-Fab/mmaction2 | WeightNet | false | 11,207 | [
"Apache-2.0"
] | 0 | 6f76bb465a7164f907318cf58f77fc3d613f8f0f | https://github.com/Alexis-Fab/mmaction2/tree/6f76bb465a7164f907318cf58f77fc3d613f8f0f |
NN | import torch
import torch.nn as nn
class NN(nn.Module):
def __init__(self, input, hidden, output):
super(NN, self).__init__()
self.lin1 = nn.Linear(input, hidden)
self.lin2 = nn.Linear(hidden, output)
def forward(self, X):
out = torch.sigmoid(self.lin1(X))
out = 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AqibJavaid899/PyTorch_Models | NN | false | 11,208 | [
"MIT"
] | 0 | cf81f6ef5d81aed76dca3f1a15be1a308b5d450f | https://github.com/AqibJavaid899/PyTorch_Models/tree/cf81f6ef5d81aed76dca3f1a15be1a308b5d450f |
TorchModule | import torch
import torch.nn
class TorchLinearModule(torch.nn.Module):
def __init__(self, in_size, out_size):
super(TorchLinearModule, self).__init__()
self._linear = torch.nn.Linear(in_size, out_size)
def forward(self, x):
return self._linear(x)
class TorchModule(torch.nn.Module):... | 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
ass... | AnimeshGurjar/ivy | TorchModule | false | 11,209 | [
"Apache-2.0"
] | 0 | e598872d96b8f7a1db461f005bec99cd0400ecec | https://github.com/AnimeshGurjar/ivy/tree/e598872d96b8f7a1db461f005bec99cd0400ecec |
BinaryLogisticRegressionLoss | import torch
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > threshold).float()
num_positive... | 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
... | Alexis-Fab/mmaction2 | BinaryLogisticRegressionLoss | false | 11,210 | [
"Apache-2.0"
] | 0 | 6f76bb465a7164f907318cf58f77fc3d613f8f0f | https://github.com/Alexis-Fab/mmaction2/tree/6f76bb465a7164f907318cf58f77fc3d613f8f0f |
CrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | AtticusJohnson/mmdetection | CrossEntropyLoss | false | 11,211 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
OutConv | import torch
import torch.nn as nn
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutConv, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AtharvBhat/EstimateDepth | OutConv | false | 11,212 | [
"MIT"
] | 0 | f440a9e8372ca2346cae8634f396bac06d004bf7 | https://github.com/AtharvBhat/EstimateDepth/tree/f440a9e8372ca2346cae8634f396bac06d004bf7 |
OffsetNet | import torch
import torch.nn as nn
class OffsetNet(nn.Module):
"""OffsetNet in Temporal interlace module.
The OffsetNet consists of one convolution layer and two fc layers
with a relu activation following with a sigmoid function. Following
the convolution layer, two fc layers and relu are applied to ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Alexis-Fab/mmaction2 | OffsetNet | false | 11,213 | [
"Apache-2.0"
] | 0 | 6f76bb465a7164f907318cf58f77fc3d613f8f0f | https://github.com/Alexis-Fab/mmaction2/tree/6f76bb465a7164f907318cf58f77fc3d613f8f0f |
PFF | import torch
import torch.nn as nn
class PFF(nn.Module):
def __init__(self, model_dimension, width_mult=4):
super().__init__()
self.linear1 = nn.Linear(model_dimension, width_mult * model_dimension)
self.linear2 = nn.Linear(width_mult * model_dimension, model_dimension)
self.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 import triton_helpers
from torch._inductor.runtime.... | AmitNikhade/MyTransformer | PFF | false | 11,214 | [
"Apache-2.0"
] | 0 | d717ee1db59ba60bb6b3f1b8a705f6ebed6df1e5 | https://github.com/AmitNikhade/MyTransformer/tree/d717ee1db59ba60bb6b3f1b8a705f6ebed6df1e5 |
BertLayerNorm | import torch
import torch.nn as nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AterhiM/BERT-E2E-ABSA | BertLayerNorm | false | 11,215 | [
"Apache-2.0"
] | 0 | 9266a851fd1d7164eb0fc422d3f5eb02e474080b | https://github.com/AterhiM/BERT-E2E-ABSA/tree/9266a851fd1d7164eb0fc422d3f5eb02e474080b |
GHMR | import torch
import torch.nn as nn
class GHMR(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
"Gradient Harmonized Single-stage Detector"
https://arxiv.org/abs/1811.05181
Args:
mu (float): The parameter for the Authentic Smooth L1 loss.
bins ... | 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... | AtticusJohnson/mmdetection | GHMR | false | 11,216 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
SEModule | import torch
import torch.nn as nn
class SEModule(nn.Module):
def __init__(self, channels, reduction):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool3d(1)
self.bottleneck = self._round_width(channels, reduction)
self.fc1 = nn.Conv3d(channels, self.bottleneck, kernel_size=1,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Alexis-Fab/mmaction2 | SEModule | false | 11,217 | [
"Apache-2.0"
] | 0 | 6f76bb465a7164f907318cf58f77fc3d613f8f0f | https://github.com/Alexis-Fab/mmaction2/tree/6f76bb465a7164f907318cf58f77fc3d613f8f0f |
GHMC | import torch
import torch.nn as nn
import torch.nn.functional as F
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero((labels >= 0) & (labels < label_channels),
as_tuple=False).squeeze()
if inds.n... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | AtticusJohnson/mmdetection | GHMC | false | 11,218 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
EmbedE | import torch
from torch import nn
from torch.functional import F
class EmbedE(nn.Module):
def __init__(self, l_in, l_h, l_g):
super(EmbedE, self).__init__()
self.fc = nn.Linear(l_in, l_h * l_g)
def forward(self, h):
h = F.relu(self.fc(h))
return h
def get_inputs():
retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | AnnaNikitaML/GraphConvolutionalNetwork | EmbedE | false | 11,219 | [
"MIT"
] | 0 | 2f3153b82fad10cdd33d261a77e08f77fa37d36a | https://github.com/AnnaNikitaML/GraphConvolutionalNetwork/tree/2f3153b82fad10cdd33d261a77e08f77fa37d36a |
ConvWS2d | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv_ws_2d(input, weight, bias=None, stride=1, padding=0, dilation=1,
groups=1, eps=1e-05):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1)
mean = weight_flat.mean(dim=1, keepdim=True).view(c_in, 1, 1, 1)
std = weight... | 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 ... | AtticusJohnson/mmdetection | ConvWS2d | false | 11,220 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
ConvTemporalGraphical | import torch
import torch.nn as nn
class ConvTemporalGraphical(nn.Module):
"""The basic module for applying a graph convolution.
Args:
in_channels (int): Number of channels in the input sequence data
out_channels (int): Number of channels produced by the convolution
kernel_size (int):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Alexis-Fab/mmaction2 | ConvTemporalGraphical | false | 11,221 | [
"Apache-2.0"
] | 0 | 6f76bb465a7164f907318cf58f77fc3d613f8f0f | https://github.com/Alexis-Fab/mmaction2/tree/6f76bb465a7164f907318cf58f77fc3d613f8f0f |
L1Loss | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AtticusJohnson/mmdetection | L1Loss | false | 11,222 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
Model | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(Model, self).__init__()
self.layer1 = nn.Linear(input_dim, hidden_dim)
self.sigmoid = nn.Sigmoid()
self.layer2 = nn.Linear(hidden_dim, output_dim)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AyushSomani001/CreditCardFraud | Model | false | 11,223 | [
"MIT"
] | 0 | 015d4992e543889edb6a47ba13d997ace8d1c51c | https://github.com/AyushSomani001/CreditCardFraud/tree/015d4992e543889edb6a47ba13d997ace8d1c51c |
GlobalAvgPool2d | import torch
import torch.nn as nn
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
in_size = inputs.size()
return inputs.view((in_size[0],... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AwaleSajil/BiSeNet | GlobalAvgPool2d | false | 11,224 | [
"MIT"
] | 0 | 2724941ef4052224c5581e6e42389e71a7c5cd5d | https://github.com/AwaleSajil/BiSeNet/tree/2724941ef4052224c5581e6e42389e71a7c5cd5d |
L2Norm | import torch
import torch.nn as nn
class L2Norm(nn.Module):
def __init__(self, n_dims, scale=20.0, eps=1e-10):
super(L2Norm, self).__init__()
self.n_dims = n_dims
self.weight = nn.Parameter(torch.Tensor(self.n_dims))
self.eps = eps
self.scale = scale
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AtticusJohnson/mmdetection | L2Norm | false | 11,225 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
BMNLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > thr... | import torch
from torch import device
import triton
import triton.language 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_ma... | Alexis-Fab/mmaction2 | BMNLoss | false | 11,226 | [
"Apache-2.0"
] | 0 | 6f76bb465a7164f907318cf58f77fc3d613f8f0f | https://github.com/Alexis-Fab/mmaction2/tree/6f76bb465a7164f907318cf58f77fc3d613f8f0f |
AffineChannel2d | import torch
import torch.utils.data
from torch import nn
class AffineChannel2d(nn.Module):
""" A simple channel-wise affine transformation operation """
def __init__(self, num_channels, eps=1e-05):
super().__init__()
self.num_channels = num_channels
self.eps = eps
self.weight... | 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | BUPT-PRIV/detectron2 | AffineChannel2d | false | 11,227 | [
"Apache-2.0"
] | 0 | 3163664cd5f43d50ea1966f410dc82410b9ccbf4 | https://github.com/BUPT-PRIV/detectron2/tree/3163664cd5f43d50ea1966f410dc82410b9ccbf4 |
Accuracy | import torch
import torch.nn.functional as F
import torch.nn as nn
class Accuracy(nn.Module):
def __init__(self):
super().__init__()
def forward(self, prediction, target, mask=None, token_dim=-1,
sequence_dim=-2):
prediction = F.softmax(prediction, token_dim).argmax(sequence_dim)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | BShennette/Pno-ai | Accuracy | false | 11,228 | [
"MIT"
] | 0 | 486434bfb40887d06e3d12a66831b9e0e7d020c2 | https://github.com/BShennette/Pno-ai/tree/486434bfb40887d06e3d12a66831b9e0e7d020c2 |
TripletLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class TripletLoss(nn.Module):
"""
Triplet loss
Takes embeddings of an anchor sample, a positive sample and a negative sample
"""
def __init__(self, margin):
super(TripletLoss, self).__init__()
self.margin = margin
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | AytacKahveci/siamese-triplet | TripletLoss | false | 11,229 | [
"BSD-3-Clause"
] | 0 | 09860e36d934bb1773a4d49dbad183a5152cb0b0 | https://github.com/AytacKahveci/siamese-triplet/tree/09860e36d934bb1773a4d49dbad183a5152cb0b0 |
ContrastiveLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class ContrastiveLoss(nn.Module):
"""
Contrastive loss
Takes embeddings of two samples and a target label == 1 if samples are from the same class and label == 0 otherwise
"""
def __init__(self, margin):
super(ContrastiveLo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | AytacKahveci/siamese-triplet | ContrastiveLoss | false | 11,230 | [
"BSD-3-Clause"
] | 0 | 09860e36d934bb1773a4d49dbad183a5152cb0b0 | https://github.com/AytacKahveci/siamese-triplet/tree/09860e36d934bb1773a4d49dbad183a5152cb0b0 |
MSELoss | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | 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 functools
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride... | AtticusJohnson/mmdetection | MSELoss | false | 11,231 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
... | 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_... | AlexHoffman9/HAET-2021-competition-baseline-code | Net | false | 11,232 | [
"MIT"
] | 0 | 1d71c94c68c9903854eceda6caf07442930caa44 | https://github.com/AlexHoffman9/HAET-2021-competition-baseline-code/tree/1d71c94c68c9903854eceda6caf07442930caa44 |
ConvNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AndrewAltimit/Scene-Classification-AWS-Serverless | ConvNet | false | 11,234 | [
"MIT"
] | 0 | caa4bff102987338dcfa597b9ec1638e6e5e63f5 | https://github.com/AndrewAltimit/Scene-Classification-AWS-Serverless/tree/caa4bff102987338dcfa597b9ec1638e6e5e63f5 |
CustomizedLayer | import torch
import torch.nn as nn
import torch.utils.data
class CustomizedLayer(nn.Module):
def __init__(self, in_dim):
super().__init__()
self.in_dim = in_dim
self.scale = nn.Parameter(torch.Tensor(self.in_dim))
self.bias = nn.Parameter(torch.Tensor(self.in_dim))
def forwar... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | B06901052/Torch-Pruning | CustomizedLayer | false | 11,235 | [
"MIT"
] | 0 | 43c99e1ea6039c7641e01cd7527492d69bfce35a | https://github.com/B06901052/Torch-Pruning/tree/43c99e1ea6039c7641e01cd7527492d69bfce35a |
DoubleResolutionLayer | import torch
import torch.nn as nn
class DoubleResolutionLayer(nn.Module):
def forward(self, x):
x = nn.functional.interpolate(x, scale_factor=2, mode='nearest')
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | BeningSobariah/ark-stroller | DoubleResolutionLayer | false | 11,236 | [
"Apache-2.0"
] | 0 | af2036a1726523d5aca9b1040bfc1fad5c3420f2 | https://github.com/BeningSobariah/ark-stroller/tree/af2036a1726523d5aca9b1040bfc1fad5c3420f2 |
Net | from torch.nn import Module
import torch
from torch.nn import Conv2d
from torch.nn import Dropout2d
from torch.nn import Linear
from torch.nn.functional import relu
from torch.nn.functional import max_pool2d
from torch.nn.functional import log_softmax
from torch import flatten
class Net(Module):
def __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.... | AhmetTavli/Olivetti-CNN | Net | false | 11,237 | [
"MIT"
] | 0 | 174747382f17e02c0e5f964d08a449429ac6fbd8 | https://github.com/AhmetTavli/Olivetti-CNN/tree/174747382f17e02c0e5f964d08a449429ac6fbd8 |
IIDIsotropicGaussianUVLoss | import math
import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn
class IIDIsotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of iid residuals with isotropic covariance:
$Sigma_i = sigma_i^2 I$
The loss (negative log likelihood) is then:
$1/2 sum_{i=1}^n ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math... | BUPT-PRIV/detectron2 | IIDIsotropicGaussianUVLoss | false | 11,238 | [
"Apache-2.0"
] | 0 | 3163664cd5f43d50ea1966f410dc82410b9ccbf4 | https://github.com/BUPT-PRIV/detectron2/tree/3163664cd5f43d50ea1966f410dc82410b9ccbf4 |
IndepAnisotropicGaussianUVLoss | import math
import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn
class IndepAnisotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of independent residuals with anisotropic covariances:
$Sigma_i = sigma_i^2 I + r_i r_i^T$
The loss (negative log likelihood) is ... | 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 math... | BUPT-PRIV/detectron2 | IndepAnisotropicGaussianUVLoss | false | 11,239 | [
"Apache-2.0"
] | 0 | 3163664cd5f43d50ea1966f410dc82410b9ccbf4 | https://github.com/BUPT-PRIV/detectron2/tree/3163664cd5f43d50ea1966f410dc82410b9ccbf4 |
SpatialPyramidPooling | import torch
import torch.nn as nn
class SpatialPyramidPooling(nn.Module):
def __init__(self, pool_sizes=[5, 9, 13]):
super(SpatialPyramidPooling, self).__init__()
self.maxpools = nn.ModuleList([nn.MaxPool2d(pool_size, 1, pool_size //
2) for pool_size in pool_sizes])
def forward(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Arcofcosmos/MyYolov4_Pytorch | SpatialPyramidPooling | false | 11,240 | [
"MIT"
] | 0 | 14c445503d0fc69b8a8b64ecdc87256ac4c1fce1 | https://github.com/Arcofcosmos/MyYolov4_Pytorch/tree/14c445503d0fc69b8a8b64ecdc87256ac4c1fce1 |
PixelNormLayer | import torch
import torch.nn as nn
class PixelNormLayer(nn.Module):
def __init__(self):
super(PixelNormLayer, self).__init__()
def forward(self, x):
return x / torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) + 1e-08)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_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 libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | BeningSobariah/ark-stroller | PixelNormLayer | false | 11,241 | [
"Apache-2.0"
] | 0 | af2036a1726523d5aca9b1040bfc1fad5c3420f2 | https://github.com/BeningSobariah/ark-stroller/tree/af2036a1726523d5aca9b1040bfc1fad5c3420f2 |
Convolution | import torch
import torch.nn as nn
class Convolution(nn.Module):
def __init__(self, c_in, c_out):
super().__init__()
self.conv = nn.Conv2d(c_in, c_out, 3, stride=1, padding=1)
self.relu = nn.ReLU(True)
def forward(self, x):
return self.relu(self.conv(x))
def get_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
import torch.nn as nn
assert_... | Baymine/Dassl | Convolution | false | 11,242 | [
"MIT"
] | 0 | 0836fb1f08393e2204326618e783d796741f657e | https://github.com/Baymine/Dassl/tree/0836fb1f08393e2204326618e783d796741f657e |
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