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 |
|---|---|---|---|---|---|---|---|---|---|---|
InceptionA | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=True, **kwargs)
def forward(self, x):
x ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Galaxies99/inception-cuda | InceptionA | false | 11,451 | [
"MIT"
] | 0 | ed8fdbe3caef415e60b52e671273be90e9423e44 | https://github.com/Galaxies99/inception-cuda/tree/ed8fdbe3caef415e60b52e671273be90e9423e44 |
PairwiseRankingLoss | import torch
import torch.nn as nn
class PairwiseRankingLoss(nn.Module):
"""
Pairwise ranking loss
"""
def __init__(self, margin):
super(PairwiseRankingLoss, self).__init__()
self.margin = margin
def forward(self, anchor1, anchor2, img_sentc, sent_imgc):
cost_sent = 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | HUSTLyn/SentEval | PairwiseRankingLoss | false | 11,452 | [
"BSD-3-Clause"
] | 0 | 3aaa8c80681e44d641dccbc1267c2dc6b2e2609f | https://github.com/HUSTLyn/SentEval/tree/3aaa8c80681e44d641dccbc1267c2dc6b2e2609f |
DQN | import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class DQN(nn.Module):
def __init__(self, state_dim, out_dim, capacity, bsz, epsilon):
super().__init__()
self.steps_done = 0
self.position = 0
self.pool = []
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 random
import torch.nn... | Gromy1211/torch-light | DQN | false | 11,453 | [
"MIT"
] | 0 | c7d7a9bc5ab1eab03d800a27d9325859516f01e6 | https://github.com/Gromy1211/torch-light/tree/c7d7a9bc5ab1eab03d800a27d9325859516f01e6 |
MemoryDictionary | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import *
class MemoryDictionary(nn.Module):
"""このクラスでは
M_1 -> M_2
という写像を生成します。
この記憶辞書の最もシンプルな場合である、二層の全結合層によって作成されます。
"""
def __init__(self, num_memory: 'int', num_dims: 'int', device:
'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
import torch.nn.functional as F
from typing import *
asser... | Geson-anko/ThinkingSimulation | MemoryDictionary | false | 11,454 | [
"MIT"
] | 0 | bd4b33c42042a2d8d14e1a9553f19fb4b4bfe8f8 | https://github.com/Geson-anko/ThinkingSimulation/tree/bd4b33c42042a2d8d14e1a9553f19fb4b4bfe8f8 |
AlphaEntropy | import torch
import torch.nn as nn
class AlphaEntropy(nn.Module):
def __init__(self):
super().__init__()
self.v_loss = nn.MSELoss()
def forward(self, props, v, pi, reward):
v_loss = self.v_loss(v, reward)
p_loss = -torch.mean(torch.sum(props * pi, 1))
return p_loss + ... | 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... | Gromy1211/torch-light | AlphaEntropy | false | 11,455 | [
"MIT"
] | 0 | c7d7a9bc5ab1eab03d800a27d9325859516f01e6 | https://github.com/Gromy1211/torch-light/tree/c7d7a9bc5ab1eab03d800a27d9325859516f01e6 |
CMDS_Loss | import torch
from torch import nn
def Covariance(m, bias=False, rowvar=True, inplace=False):
""" Estimate a covariance matrix given data(tensor).
Covariance indicates the level to which two variables vary together.
If we examine N-dimensional samples, `X = [x_1, x_2, ... x_N]^T`,
then the covariance m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Gustoaxel/Cells_Cycle | CMDS_Loss | false | 11,456 | [
"MIT"
] | 0 | d211dea8c05a8d5535e6e72d95c781d6bc02baeb | https://github.com/Gustoaxel/Cells_Cycle/tree/d211dea8c05a8d5535e6e72d95c781d6bc02baeb |
MaxMarginRankingLoss | import torch
import numpy as np
import torch as th
import torch.nn.functional as F
class MaxMarginRankingLoss(th.nn.Module):
def __init__(self, margin=1.0, negative_weighting=False, batch_size=1,
n_pair=1, hard_negative_rate=0.5):
super(MaxMarginRankingLoss, self).__init__()
self.margin =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import torch as th
assert_size_stride = torch._C._dynamo.guards.assert... | HS310164/howto100m | MaxMarginRankingLoss | false | 11,457 | [
"Apache-2.0"
] | 0 | e3952a77c268466de2b9174ae8983c528b91397d | https://github.com/HS310164/howto100m/tree/e3952a77c268466de2b9174ae8983c528b91397d |
DiceLoss | import functools
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
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... | Geoffrey1500/mmsegmentation | DiceLoss | false | 11,458 | [
"Apache-2.0"
] | 0 | 0a5544c46e6ea1e07ed47858d5fcb39a5ae974b1 | https://github.com/Geoffrey1500/mmsegmentation/tree/0a5544c46e6ea1e07ed47858d5fcb39a5ae974b1 |
AtteMatchLay | import torch
import torch.nn as nn
from torch.nn.functional import cosine_similarity
def multi_perspective_expand_for_2D(in_tensor, decompose_params):
"""
Return: [batch_size, decompse_dim, dim]
"""
in_tensor = in_tensor.unsqueeze(1)
decompose_params = decompose_params.unsqueeze(0)
return torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Gromy1211/torch-light | AtteMatchLay | false | 11,459 | [
"MIT"
] | 0 | c7d7a9bc5ab1eab03d800a27d9325859516f01e6 | https://github.com/Gromy1211/torch-light/tree/c7d7a9bc5ab1eab03d800a27d9325859516f01e6 |
LIN | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class LIN(nn.Module):
def __init__(self, num_features, eps=1e-05):
super(LIN, self).__init__()
self.eps = eps
self.rho = Parameter(torch.Tensor(1, num_features, 1, 1))
self.gamma = Parameter(torch.Tensor(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
from torch.nn.parameter import Parameter
assert_size_stri... | Gxx-5/MyPhoto2Cartoon | LIN | false | 11,460 | [
"MIT"
] | 0 | aa05dfa8b7d6c507c33026a2e8b299d5779357be | https://github.com/Gxx-5/MyPhoto2Cartoon/tree/aa05dfa8b7d6c507c33026a2e8b299d5779357be |
MockAccuracy | import torch
class _Metric(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, input: 'torch.Tensor', target: 'torch.Tensor'):
raise NotImplementedError()
class Accuracy(_Metric):
def __init__(self):
super().__init__()
def forward(self, input: '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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | HalleyYoung/MusicTransformer-pytorch | MockAccuracy | false | 11,461 | [
"MIT"
] | 0 | bbfb7050f4a81675b089cd826d4476cf29bf19c2 | https://github.com/HalleyYoung/MusicTransformer-pytorch/tree/bbfb7050f4a81675b089cd826d4476cf29bf19c2 |
adaLIN | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class adaLIN(nn.Module):
def __init__(self, num_features, eps=1e-05):
super(adaLIN, self).__init__()
self.eps = eps
self.rho = Parameter(torch.Tensor(1, num_features, 1, 1))
self.rho.data.fill_(0.9)
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
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stri... | Gxx-5/MyPhoto2Cartoon | adaLIN | false | 11,462 | [
"MIT"
] | 0 | aa05dfa8b7d6c507c33026a2e8b299d5779357be | https://github.com/Gxx-5/MyPhoto2Cartoon/tree/aa05dfa8b7d6c507c33026a2e8b299d5779357be |
ActorCritic | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.distributions import Categorical
class ActorCritic(nn.Module):
def __init__(self):
super().__init__()
self.affine1 = nn.Linear(4, 128)
self.action_head = nn.Linear(128, 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.... | Gromy1211/torch-light | ActorCritic | false | 11,463 | [
"MIT"
] | 0 | c7d7a9bc5ab1eab03d800a27d9325859516f01e6 | https://github.com/Gromy1211/torch-light/tree/c7d7a9bc5ab1eab03d800a27d9325859516f01e6 |
Sentence_Maxpool | import torch
import torch as th
import torch.nn.functional as F
import torch.nn as nn
class Sentence_Maxpool(nn.Module):
def __init__(self, word_dimension, output_dim, relu=True):
super(Sentence_Maxpool, self).__init__()
self.fc = nn.Linear(word_dimension, output_dim)
self.out_dim = 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_... | HS310164/howto100m | Sentence_Maxpool | false | 11,464 | [
"Apache-2.0"
] | 0 | e3952a77c268466de2b9174ae8983c528b91397d | https://github.com/HS310164/howto100m/tree/e3952a77c268466de2b9174ae8983c528b91397d |
Fp32LayerNorm | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Fp32LayerNorm(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
impor... | Ashprakash/roberta | Fp32LayerNorm | false | 11,465 | [
"MIT"
] | 0 | 5ee7abda64d752a467218c247855ddc20c09a779 | https://github.com/Ashprakash/roberta/tree/5ee7abda64d752a467218c247855ddc20c09a779 |
TransitionUp | import torch
from torch import nn
import torch.distributions
import torch.nn.parallel
import torch.optim
def center_crop(layer, max_height, max_width):
_, _, h, w = layer.size()
xy1 = (w - max_width) // 2
xy2 = (h - max_height) // 2
return layer[:, :, xy2:xy2 + max_height, xy1:xy1 + max_width]
class... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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.distributions
import torch.nn.parallel
import ... | Haijunlv/swa_gaussian | TransitionUp | false | 11,466 | [
"BSD-2-Clause"
] | 0 | 412a1f0a18f8607c2493e48275abe5345cd3eb1e | https://github.com/Haijunlv/swa_gaussian/tree/412a1f0a18f8607c2493e48275abe5345cd3eb1e |
CategoricalAccuracy | import torch
class _Metric(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, input: 'torch.Tensor', target: 'torch.Tensor'):
raise NotImplementedError()
class Accuracy(_Metric):
def __init__(self):
super().__init__()
def forward(self, input: 'torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | HalleyYoung/MusicTransformer-pytorch | CategoricalAccuracy | false | 11,467 | [
"MIT"
] | 0 | bbfb7050f4a81675b089cd826d4476cf29bf19c2 | https://github.com/HalleyYoung/MusicTransformer-pytorch/tree/bbfb7050f4a81675b089cd826d4476cf29bf19c2 |
ResidualDenseBlock_5C | import torch
import torch.nn as nn
class ResidualDenseBlock_5C(nn.Module):
def __init__(self, nf=64, gc=32, bias=True):
super(ResidualDenseBlock_5C, self).__init__()
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
self.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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Geeta-Landmark/Super-Resolution-Image | ResidualDenseBlock_5C | false | 11,468 | [
"Apache-2.0"
] | 0 | fb5d71ec9a4673409ecd28189e97056943ca308b | https://github.com/Geeta-Landmark/Super-Resolution-Image/tree/fb5d71ec9a4673409ecd28189e97056943ca308b |
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_... | HabilBhagat/MiniProject---Sem_6 | SeperableConv | false | 11,469 | [
"Apache-2.0"
] | 0 | bbc329a4844921cc04be58f704057bb70ad9dfe2 | https://github.com/HabilBhagat/MiniProject---Sem_6/tree/bbc329a4844921cc04be58f704057bb70ad9dfe2 |
ZeroPad1d | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ZeroPad1d(nn.Module):
def __init__(self, pad_left, pad_right):
super().__init__()
self.pad_left = pad_left
self.p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
assert_size_str... | Ashprakash/roberta | ZeroPad1d | false | 11,470 | [
"MIT"
] | 0 | 5ee7abda64d752a467218c247855ddc20c09a779 | https://github.com/Ashprakash/roberta/tree/5ee7abda64d752a467218c247855ddc20c09a779 |
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_... | HabilBhagat/MiniProject---Sem_6 | InputConv | false | 11,471 | [
"Apache-2.0"
] | 0 | bbc329a4844921cc04be58f704057bb70ad9dfe2 | https://github.com/HabilBhagat/MiniProject---Sem_6/tree/bbc329a4844921cc04be58f704057bb70ad9dfe2 |
RRDB | import torch
import torch.nn as nn
class ResidualDenseBlock_5C(nn.Module):
def __init__(self, nf=64, gc=32, bias=True):
super(ResidualDenseBlock_5C, self).__init__()
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
self.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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Geeta-Landmark/Super-Resolution-Image | RRDB | false | 11,472 | [
"Apache-2.0"
] | 0 | fb5d71ec9a4673409ecd28189e97056943ca308b | https://github.com/Geeta-Landmark/Super-Resolution-Image/tree/fb5d71ec9a4673409ecd28189e97056943ca308b |
AUXModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class AUXModule(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.linear = nn.Linear(in_features, out_features)
def forward(self, x):
x = F.adaptive_max_pool2d(x, output_size=(1, 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_... | HamzaFarhan/segmentation_models.pytorch | AUXModule | false | 11,473 | [
"MIT"
] | 0 | b7803df1d17027f329e267ba4c55144adfdd4da9 | https://github.com/HamzaFarhan/segmentation_models.pytorch/tree/b7803df1d17027f329e267ba4c55144adfdd4da9 |
MeanStd | import torch
import torch.nn as nn
class MeanStd(nn.Module):
def __init__(self):
super(MeanStd, self).__init__()
def forward(self, x):
x = x.view(x.size(0), x.size(1), -1)
mean_x = torch.mean(x, dim=2)
var_x = torch.mean(x ** 2, dim=2) - mean_x * mean_x
return torch.c... | 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... | GiangHLe/pytorch_GAN_zoo | MeanStd | false | 11,474 | [
"BSD-3-Clause"
] | 0 | 7a3db2a88032f357b3f262abd6204b560caa9f2c | https://github.com/GiangHLe/pytorch_GAN_zoo/tree/7a3db2a88032f357b3f262abd6204b560caa9f2c |
ConvReg | import torch
import torch.nn as nn
class ConvReg(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 64, 3, stride=2, padding=1)
self.conv2 = nn.Conv2d(64, 128, 3, stride=2, padding=1)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Guru-Uni-siegen/Domain-Shifting-Network | ConvReg | false | 11,475 | [
"MIT"
] | 0 | dd9eb7bda07634874497a335151b5e967aaad874 | https://github.com/Guru-Uni-siegen/Domain-Shifting-Network/tree/dd9eb7bda07634874497a335151b5e967aaad874 |
InceptionE | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=True, **kwargs)
def forward(self, x):
x ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Galaxies99/inception-cuda | InceptionE | false | 11,476 | [
"MIT"
] | 0 | ed8fdbe3caef415e60b52e671273be90e9423e44 | https://github.com/Galaxies99/inception-cuda/tree/ed8fdbe3caef415e60b52e671273be90e9423e44 |
AdaIN | import math
import torch
import torch.nn as nn
from numpy import prod
def getLayerNormalizationFactor(x):
"""
Get He's constant for the given layer
https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
"""
size = x.weight.size()
fan_in = pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | GiangHLe/pytorch_GAN_zoo | AdaIN | false | 11,477 | [
"BSD-3-Clause"
] | 0 | 7a3db2a88032f357b3f262abd6204b560caa9f2c | https://github.com/GiangHLe/pytorch_GAN_zoo/tree/7a3db2a88032f357b3f262abd6204b560caa9f2c |
ZSSRNet | import torch
import torch.nn as nn
class ZSSRNet(nn.Module):
def __init__(self, input_channels=3, kernel_size=3, channels=64):
super(ZSSRNet, self).__init__()
self.conv0 = nn.Conv2d(input_channels, channels, kernel_size=
kernel_size, padding=kernel_size // 2, bias=True)
self.c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | HaiTMai/pytorch-zssr | ZSSRNet | false | 11,478 | [
"Apache-2.0"
] | 0 | 433143ef7bcc036648e2d4294699c6ce15c21a7c | https://github.com/HaiTMai/pytorch-zssr/tree/433143ef7bcc036648e2d4294699c6ce15c21a7c |
AsymmetricLoss | 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 libdevice, math as tl_math
import torc... | HexaFarms/MMClassification | AsymmetricLoss | false | 11,479 | [
"Apache-2.0"
] | 0 | d61d0448b6bcd2fd4c0a408688f603a53ab16ca2 | https://github.com/HexaFarms/MMClassification/tree/d61d0448b6bcd2fd4c0a408688f603a53ab16ca2 |
FocalLoss | 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 libdevice, math as tl_math
import torc... | HexaFarms/MMClassification | FocalLoss | false | 11,480 | [
"Apache-2.0"
] | 0 | d61d0448b6bcd2fd4c0a408688f603a53ab16ca2 | https://github.com/HexaFarms/MMClassification/tree/d61d0448b6bcd2fd4c0a408688f603a53ab16ca2 |
GlobalAveragePooling | import torch
import torch.nn as nn
class GlobalAveragePooling(nn.Module):
"""Global Average Pooling neck.
Note that we use `view` to remove extra channel after pooling. We do not
use `squeeze` as it will also remove the batch dimension when the tensor
has a batch dimension of size 1, which can lead t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | HexaFarms/MMClassification | GlobalAveragePooling | false | 11,481 | [
"Apache-2.0"
] | 0 | d61d0448b6bcd2fd4c0a408688f603a53ab16ca2 | https://github.com/HexaFarms/MMClassification/tree/d61d0448b6bcd2fd4c0a408688f603a53ab16ca2 |
Fp32GroupNorm | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Fp32GroupNorm(nn.GroupNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
impor... | Ashprakash/roberta | Fp32GroupNorm | false | 11,482 | [
"MIT"
] | 0 | 5ee7abda64d752a467218c247855ddc20c09a779 | https://github.com/Ashprakash/roberta/tree/5ee7abda64d752a467218c247855ddc20c09a779 |
VarifocalLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
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 libdevice, math as tl_math
import torc... | Huuush/deepfashion2-det | VarifocalLoss | false | 11,483 | [
"Apache-2.0"
] | 0 | 46af0ada8d6f534de2de6a9c069580cd1bf609ec | https://github.com/Huuush/deepfashion2-det/tree/46af0ada8d6f534de2de6a9c069580cd1bf609ec |
Reorg | import torch
import torch.nn as nn
class Reorg(nn.Module):
def __init__(self, stride=2):
super(Reorg, self).__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert x.data.dim() == 4
B = x.data.size(0)
C = x.data.size(1)
H = 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... | Hydroxy-OH/deep_sort_pytorch | Reorg | false | 11,484 | [
"MIT"
] | 0 | 040656566d9f52fefa4ef02ca58f039ff591211b | https://github.com/Hydroxy-OH/deep_sort_pytorch/tree/040656566d9f52fefa4ef02ca58f039ff591211b |
ModMSELoss | import torch
class ModMSELoss(torch.nn.Module):
def __init__(self, shape_r_gt, shape_c_gt):
super(ModMSELoss, self).__init__()
self.shape_r_gt = shape_r_gt
self.shape_c_gt = shape_c_gt
def forward(self, output, label, prior):
prior_size = prior.shape
output_max = 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | HeosSacer/saliency_web_mapper | ModMSELoss | false | 11,485 | [
"MIT"
] | 0 | a2fd744b821086dc1a0af0498361207f7bcddee6 | https://github.com/HeosSacer/saliency_web_mapper/tree/a2fd744b821086dc1a0af0498361207f7bcddee6 |
CosNorm_Classifier | import math
import torch
from torch import nn
from torch.nn.parameter import Parameter
class CosNorm_Classifier(nn.Module):
def __init__(self, in_dims, out_dims, scale=16, margin=0.5, init_std=0.001
):
super(CosNorm_Classifier, self).__init__()
self.in_dims = in_dims
self.out_dims... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | HoganZhang/OpenLongTailRecognition-OLTR | CosNorm_Classifier | false | 11,486 | [
"BSD-3-Clause"
] | 0 | 94b7e9fc93e7c96218e801007aa4d09a3f5fc69d | https://github.com/HoganZhang/OpenLongTailRecognition-OLTR/tree/94b7e9fc93e7c96218e801007aa4d09a3f5fc69d |
GaussianFocalLoss | import functools
import torch
import torch.nn.functional as F
import torch.nn as nn
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... | Huuush/deepfashion2-det | GaussianFocalLoss | false | 11,487 | [
"Apache-2.0"
] | 0 | 46af0ada8d6f534de2de6a9c069580cd1bf609ec | https://github.com/Huuush/deepfashion2-det/tree/46af0ada8d6f534de2de6a9c069580cd1bf609ec |
BartClassificationHead | import torch
from torch import nn
import torch.utils.checkpoint
class BartClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, input_dim: 'int', inner_dim: 'int', num_classes:
'int', pooler_dropout: 'float'):
super().__init__()
self.den... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Clemens123/transformers | BartClassificationHead | false | 11,488 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
Upsample | import torch
import torch.nn as nn
class Upsample(nn.Module):
def __init__(self, stride=2):
super(Upsample, self).__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert x.data.dim() == 4
B = x.data.size(0)
C = x.data.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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Hydroxy-OH/deep_sort_pytorch | Upsample | false | 11,489 | [
"MIT"
] | 0 | 040656566d9f52fefa4ef02ca58f039ff591211b | https://github.com/Hydroxy-OH/deep_sort_pytorch/tree/040656566d9f52fefa4ef02ca58f039ff591211b |
ConvDropoutLayerNorm | import torch
from torch import nn
import torch.utils.checkpoint
class SqueezeBertLayerNorm(nn.LayerNorm):
"""
This is a nn.LayerNorm subclass that accepts NCW data layout and performs normalization in the C dimension.
N = batch C = channels W = sequence length
"""
def __init__(self, hidden_size,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Clemens123/transformers | ConvDropoutLayerNorm | false | 11,490 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
PositionwiseFeedForward | import math
import torch
from torch import nn
import torch.utils.checkpoint
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class PositionwiseFeedForward(nn.Module):
"""A two-layer Feed-Forward-Network with residual layer norm.
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | Clemens123/transformers | PositionwiseFeedForward | false | 11,491 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
FastGuidedFilter | import torch
from torch import nn
from torch.nn import functional as F
class BoxFilter(nn.Module):
def __init__(self, r):
super(BoxFilter, self).__init__()
self.r = r
def forward(self, x):
kernel_size = 2 * self.r + 1
kernel_x = torch.full((x.data.shape[1], 1, 1, 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
from torch import nn
from tor... | HyeongminMoon/copy-paste-aug | FastGuidedFilter | false | 11,492 | [
"MIT"
] | 0 | 38fcd770d70b5d4291de0cbb42073b37d7188537 | https://github.com/HyeongminMoon/copy-paste-aug/tree/38fcd770d70b5d4291de0cbb42073b37d7188537 |
exponential | import torch
from torch import nn
class exponential(nn.Module):
def __init__(self):
super(exponential, self).__init__()
def forward(self, x):
return torch.exp(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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | Hyunmok-Park/modular-metalearning-master | exponential | false | 11,493 | [
"MIT"
] | 0 | a7be61d7c48a62ec8c333b1031521977baed792b | https://github.com/Hyunmok-Park/modular-metalearning-master/tree/a7be61d7c48a62ec8c333b1031521977baed792b |
MultiheadAttention | import torch
import torch.nn as nn
class MultiheadAttention(nn.Module):
"""A warpper for torch.nn.MultiheadAttention.
This module implements MultiheadAttention with residual connection,
and positional encoding used in DETR is also passed as input.
Args:
embed_dims (int): The embedding dimens... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Huuush/deepfashion2-det | MultiheadAttention | false | 11,494 | [
"Apache-2.0"
] | 0 | 46af0ada8d6f534de2de6a9c069580cd1bf609ec | https://github.com/Huuush/deepfashion2-det/tree/46af0ada8d6f534de2de6a9c069580cd1bf609ec |
LINEAR_LOGSOFTMAX_CLASSIFIER | import torch
import torch.nn as nn
class LINEAR_LOGSOFTMAX_CLASSIFIER(nn.Module):
def __init__(self, input_dim, nclass):
super(LINEAR_LOGSOFTMAX_CLASSIFIER, self).__init__()
self.fc = nn.Linear(input_dim, nclass)
self.logic = nn.LogSoftmax(dim=1)
def forward(self, x):
o = 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.... | IacoSimoncini/tfvaegan | LINEAR_LOGSOFTMAX_CLASSIFIER | false | 11,495 | [
"MIT"
] | 0 | 157b526d65d0b0d5412f4be6fed02fc7d6325827 | https://github.com/IacoSimoncini/tfvaegan/tree/157b526d65d0b0d5412f4be6fed02fc7d6325827 |
ConvUnit | import torch
import torch.nn as nn
class ConvUnit(nn.Module):
def __init__(self):
super(ConvUnit, self).__init__()
self.conv = nn.Conv2d(in_channels=256, out_channels=32, kernel_size
=5, stride=1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Gromy1211/torch-light | ConvUnit | false | 11,496 | [
"MIT"
] | 0 | c7d7a9bc5ab1eab03d800a27d9325859516f01e6 | https://github.com/Gromy1211/torch-light/tree/c7d7a9bc5ab1eab03d800a27d9325859516f01e6 |
SqueezeBertLayerNorm | import torch
from torch import nn
import torch.utils.checkpoint
class SqueezeBertLayerNorm(nn.LayerNorm):
"""
This is a nn.LayerNorm subclass that accepts NCW data layout and performs normalization in the C dimension.
N = batch C = channels W = sequence length
"""
def __init__(self, hidden_size,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.utils.checkpoint
assert_size_stride = torch._... | Clemens123/transformers | SqueezeBertLayerNorm | false | 11,497 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
GroupedLinearLayer | import torch
from torch import nn
import torch.utils.checkpoint
class GroupedLinearLayer(nn.Module):
def __init__(self, input_size, output_size, num_groups):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.num_groups = num_groups
self.gr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.checkpoint
assert_size_stride = torch._C... | Clemens123/transformers | GroupedLinearLayer | false | 11,498 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
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, 16, 3, padding=1)
self.conv2 = nn.Conv2d(16, 16, 3, padding=1)
self.conv3 = nn.Conv2d(16, 20, 3, padding=1)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Hunkzer/DLplayground | Net | false | 11,499 | [
"Apache-2.0"
] | 0 | c85238e00052a80e6a59e5d1c705014c45eeb6aa | https://github.com/Hunkzer/DLplayground/tree/c85238e00052a80e6a59e5d1c705014c45eeb6aa |
NoNorm | import torch
from torch import nn
import torch.utils.checkpoint
class NoNorm(nn.Module):
def __init__(self, feat_size, eps=None):
super().__init__()
self.bias = nn.Parameter(torch.zeros(feat_size))
self.weight = nn.Parameter(torch.ones(feat_size))
def forward(self, input_tensor):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Clemens123/transformers | NoNorm | false | 11,500 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
Net | import torch
from torch.nn import functional as F
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.hidden_two = torch.nn.Linear(n_hidden, n_hidden)
self.hidden_3 = tor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | Hyunmok-Park/modular-metalearning-master | Net | false | 11,501 | [
"MIT"
] | 0 | a7be61d7c48a62ec8c333b1031521977baed792b | https://github.com/Hyunmok-Park/modular-metalearning-master/tree/a7be61d7c48a62ec8c333b1031521977baed792b |
GeLU | import torch
import torch.nn as nn
class GeLU(nn.Module):
def forward(self, x):
return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 +
0.044715 * x * 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | IamHimon/re2 | GeLU | false | 11,502 | [
"Apache-2.0"
] | 0 | d16b0ffc385f7b118a6160d035250da8d6320534 | https://github.com/IamHimon/re2/tree/d16b0ffc385f7b118a6160d035250da8d6320534 |
MLP | import torch
import torch.nn as nn
class MLP(nn.Module):
"""
MLP
"""
def __init__(self, hidden_layers, input_size, output_size, seed=1):
"""
`hidden_layers`: list, the number of neurons for every layer;
`input_size`: number of states;
`output_size`: number of actions;
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ILABUTK/MLePOMDP_Early_Sepsis_Detection | MLP | false | 11,503 | [
"MIT"
] | 0 | 7e6fdb1e425ee3cd5aa4142287c1e7dba28a126f | https://github.com/ILABUTK/MLePOMDP_Early_Sepsis_Detection/tree/7e6fdb1e425ee3cd5aa4142287c1e7dba28a126f |
BoxFilter | import torch
from torch import nn
from torch.nn import functional as F
class BoxFilter(nn.Module):
def __init__(self, r):
super(BoxFilter, self).__init__()
self.r = r
def forward(self, x):
kernel_size = 2 * self.r + 1
kernel_x = torch.full((x.data.shape[1], 1, 1, 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | HyeongminMoon/copy-paste-aug | BoxFilter | false | 11,504 | [
"MIT"
] | 0 | 38fcd770d70b5d4291de0cbb42073b37d7188537 | https://github.com/HyeongminMoon/copy-paste-aug/tree/38fcd770d70b5d4291de0cbb42073b37d7188537 |
AconC | import torch
import torch.nn as nn
class AconC(nn.Module):
""" ACON activation (activate or not).
AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"""
def __i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | IanVzs/labelImg | AconC | false | 11,505 | [
"MIT"
] | 0 | 3d3dfbf9cf385f38c60376826fdce1f178f563a6 | https://github.com/IanVzs/labelImg/tree/3d3dfbf9cf385f38c60376826fdce1f178f563a6 |
XOR | import torch
import torch.utils.data.distributed
import torch.nn as nn
import torch.utils.data
class XOR(nn.Module):
def __init__(self, input_dim, output_dim):
super(XOR, self).__init__()
self.lin1 = nn.Linear(input_dim, 8)
self.lin2 = nn.Linear(8, output_dim)
def forward(self, featu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | IST-DASLab/horovod | XOR | false | 11,506 | [
"Apache-2.0"
] | 0 | d2611353c33b299f04e47fae0de741702de3130e | https://github.com/IST-DASLab/horovod/tree/d2611353c33b299f04e47fae0de741702de3130e |
TransformerBlock | import torch
import torch.nn as nn
class TransformerBlock(nn.Module):
def __init__(self, max_len, hidden_size, hidden_dropout,
attention_heads, feed_forward_size):
super().__init__()
self.pre_layer_norm_1 = nn.LayerNorm([max_len, hidden_size])
self.dropout_1 = nn.Dropout(p=hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HyeyeonKoo/RoBERTa_PLD_pytorch | TransformerBlock | false | 11,507 | [
"MIT"
] | 0 | 836db92b5570e3671371119aca0f864109b142fb | https://github.com/HyeyeonKoo/RoBERTa_PLD_pytorch/tree/836db92b5570e3671371119aca0f864109b142fb |
MultiheadAttentionWrapper | import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils import weight_norm
import torch.nn.utils
from torch.optim.lr_scheduler import *
def linear(x):
return x
def activation(func_a):
"""Activation function wrapper
"""
try:
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 import nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils import weight_norm
import torch... | FalconX777/CharacterBert_Multitasking | MultiheadAttentionWrapper | false | 11,508 | [
"BSD-3-Clause"
] | 0 | eab566975871fffd0ec875a05ba478f1bce9b0ab | https://github.com/FalconX777/CharacterBert_Multitasking/tree/eab566975871fffd0ec875a05ba478f1bce9b0ab |
LayerNorm | import torch
from torch import nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.nn.utils
from torch.optim.lr_scheduler import *
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=0.0001):
super(LayerNorm, self).__init__()
self.alpha = Parameter(... | 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
from torch.nn import Parameter
from torch.nn.parameter imp... | FalconX777/CharacterBert_Multitasking | LayerNorm | false | 11,509 | [
"BSD-3-Clause"
] | 0 | eab566975871fffd0ec875a05ba478f1bce9b0ab | https://github.com/FalconX777/CharacterBert_Multitasking/tree/eab566975871fffd0ec875a05ba478f1bce9b0ab |
Downsample | import torch
import torch.nn as nn
import torch.nn.parallel
class Downsample(nn.Module):
"""
Image to Patch Embedding, downsampling between stage1 and stage2
"""
def __init__(self, in_embed_dim, out_embed_dim, patch_size):
super().__init__()
self.proj = nn.Conv2d(in_embed_dim, out_emb... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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._dy... | Inch-Z/volo | Downsample | false | 11,510 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 |
Anomaly | import torch
import torch.utils.data
from torch import nn
class Anomaly(nn.Module):
def __init__(self, window=1024):
self.window = window
super(Anomaly, self).__init__()
self.layer1 = nn.Conv1d(window, window, kernel_size=1, stride=1,
padding=0)
self.layer2 = nn.Conv1d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | G-santini/anomalydetector | Anomaly | false | 11,511 | [
"MIT"
] | 0 | f41be86d357cba7c164a02947b28d5c70ee3e451 | https://github.com/G-santini/anomalydetector/tree/f41be86d357cba7c164a02947b28d5c70ee3e451 |
BCEDiceLoss | import torch
from torch import nn
import torch.utils.data
import torch.nn.functional as F
class BCEDiceLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, target):
bce = F.binary_cross_entropy_with_logits(input, target)
smooth = 1e-05
input = torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Information-Fusion-Lab-Umass/pytorch-nested-unet | BCEDiceLoss | false | 11,512 | [
"MIT"
] | 0 | 29b8704795f9d0ab17952b19bf8b4624e7aa16c0 | https://github.com/Information-Fusion-Lab-Umass/pytorch-nested-unet/tree/29b8704795f9d0ab17952b19bf8b4624e7aa16c0 |
MetaAconC | import torch
import torch.nn as nn
class MetaAconC(nn.Module):
""" ACON activation (activate or not).
MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | IanVzs/labelImg | MetaAconC | false | 11,513 | [
"MIT"
] | 0 | 3d3dfbf9cf385f38c60376826fdce1f178f563a6 | https://github.com/IanVzs/labelImg/tree/3d3dfbf9cf385f38c60376826fdce1f178f563a6 |
VAE | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.optim
import torch.utils.data.distributed
import torch.nn.functional as F
import torch.autograd
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Li... | 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... | HolyLow/examples | VAE | false | 11,514 | [
"BSD-3-Clause"
] | 0 | 23b0cb1022cf7a21428883e95fded01d74a059bf | https://github.com/HolyLow/examples/tree/23b0cb1022cf7a21428883e95fded01d74a059bf |
OutlookAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class OutlookAttention(nn.Module):
"""
Implementation of outlook attention
--dim: hidden dim
--num_heads: number of heads
--kernel_size: kernel size in each window for outlook attention
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._inductor.runtime.... | Inch-Z/volo | OutlookAttention | false | 11,515 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 |
PatchEmbed | import torch
import torch.nn as nn
import torch.nn.parallel
class PatchEmbed(nn.Module):
"""
Image to Patch Embedding.
Different with ViT use 1 conv layer, we use 4 conv layers to do patch embedding
"""
def __init__(self, img_size=224, stem_conv=False, stem_stride=1,
patch_size=8, in_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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._dy... | Inch-Z/volo | PatchEmbed | false | 11,516 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 |
PELU | import math
import torch
import torch as th
import torch.nn as nn
class PELU(nn.Module):
def __init__(self, a=None, b=None):
super().__init__()
default_val = math.sqrt(0.1)
a = default_val if a is None else a
b = default_val if b is None else b
self.a = nn.Parameter(th.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.triton_helpers import math as tl_math
import math
import torch as th
import torch.nn as nn
assert_size_stride =... | InzamamRahaman/PELU | PELU | false | 11,517 | [
"MIT"
] | 0 | ee2598c32f3596f18d957417c97c03e8862086bf | https://github.com/InzamamRahaman/PELU/tree/ee2598c32f3596f18d957417c97c03e8862086bf |
AdjMSELoss | import torch
import torch.nn as nn
class AdjMSELoss(nn.Module):
def __init__(self):
super(AdjMSELoss, self).__init__()
def forward(self, outputs, labels):
loss = torch.abs(outputs - labels)
adj_fact = torch.mean(torch.abs(labels)) ** 2
adj = torch.exp(-outputs * labels / adj_... | 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
... | JDE65/Adjusted-MAE-loss-function | AdjMSELoss | false | 11,518 | [
"MIT"
] | 0 | e0b54c41a499f68791b731e29e31b5e0f410ac5c | https://github.com/JDE65/Adjusted-MAE-loss-function/tree/e0b54c41a499f68791b731e29e31b5e0f410ac5c |
Transformer | import torch
import torch.nn as nn
import torch.nn.parallel
class Mlp(nn.Module):
"""Implementation of MLP"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Inch-Z/volo | Transformer | false | 11,519 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 |
ClassBlock | import torch
import torch.nn as nn
import torch.nn.parallel
class Mlp(nn.Module):
"""Implementation of MLP"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Inch-Z/volo | ClassBlock | false | 11,520 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 |
DummyModelWithSharedSubmodule | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class DummyDenseWithRelu(nn.Module):
def __init__(self, input_size, output... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Donfa1con/distiller | DummyModelWithSharedSubmodule | false | 11,521 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 |
LocalConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class LocalConv2d(nn.Module):
def __init__(self, num_rows, num_feats_in, num_feats_out, kernel=1,
padding=0):
super(LocalConv2d, self).__init__()
self.num_rows = num_rows
self.out_channels = num_feats_out
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | JSharpClone/M3D-RPN- | LocalConv2d | false | 11,522 | [
"Apache-2.0"
] | 0 | 5192b095e921b5c054a66fd0ce948e67aee957be | https://github.com/JSharpClone/M3D-RPN-/tree/5192b095e921b5c054a66fd0ce948e67aee957be |
BahdanauAttention | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
from torch.nn.parameter import Parameter
import torch.onnx
import torch.testing
class EltwiseAdd(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 import triton_helpers
from torch._inductor.runtime.... | Donfa1con/distiller | BahdanauAttention | false | 11,523 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 |
Actor | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class Actor(nn.Module):
def __init__(self, nb_states, nb_actions, hidden1=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Donfa1con/distiller | Actor | false | 11,524 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 |
ModelWithDuplicates | import torch
from collections import OrderedDict
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class ModelWithDuplicates(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 import triton_helpers
from torch._inductor.runtime.... | Donfa1con/distiller | ModelWithDuplicates | false | 11,525 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 |
Mean | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class Mean(nn.Module):
def __init__(self, *args, **kwargs):
super(... | 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.parallel
import torch.optim
import torch.utils.data... | Donfa1con/distiller | Mean | false | 11,526 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 |
policy1 | import torch
import torch.nn as nn
class policy1(nn.Module):
def __init__(self):
super(policy1, self).__init__()
self.sm = nn.Softmax(dim=-1)
self.actor = nn.Parameter(torch.FloatTensor([-0.35, 0.4, 1]))
def forward(self):
mu = self.sm(self.actor)
return mu
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 math as tl_math
import torch.nn as nn
... | JWongDude/FruitLoops | policy1 | false | 11,527 | [
"MIT"
] | 0 | f4346d9db16ba619d71ce5bb819f5da08a88a120 | https://github.com/JWongDude/FruitLoops/tree/f4346d9db16ba619d71ce5bb819f5da08a88a120 |
AlexNet | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class AlexNet(nn.Module):
def __init__(self):
super(AlexNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, (11, 11), stride=(4, 4), padding=(2, 2))
self.conv2 = nn.Conv2d(64, 192, (5, 5), stride=(1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Fritingo/AlexNet_on_browser | AlexNet | false | 11,528 | [
"MIT"
] | 0 | 3e674dd84e25ee74f2efde77882b4faa788907c2 | https://github.com/Fritingo/AlexNet_on_browser/tree/3e674dd84e25ee74f2efde77882b4faa788907c2 |
Norm | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class Norm(nn.Module):
"""
A module wrapper for vector/matrix norm
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | Donfa1con/distiller | Norm | false | 11,529 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 |
TwoMLPHead | import torch
from torch import nn
import torch.nn.functional as F
class TwoMLPHead(nn.Module):
"""
Standard heads for FPN-based models
Arguments:
in_channels (int): number of input channels
representation_size (int): size of the intermediate representation
"""
def __init__(self, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | GerardWalsh/DeepLabv3FineTuning | TwoMLPHead | false | 11,530 | [
"MIT"
] | 0 | 149d4b33a7dc94c56361f559ca67cb0fcf9ae9d5 | https://github.com/GerardWalsh/DeepLabv3FineTuning/tree/149d4b33a7dc94c56361f559ca67cb0fcf9ae9d5 |
ClippedLinearQuantization | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
def linear_dequantize(input, scale, zero_point, inplace=False):
if inplace:... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | Donfa1con/distiller | ClippedLinearQuantization | false | 11,531 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 |
Downsample | import torch
import torch.nn as nn
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Jack000/improved-diffusion | Downsample | false | 11,532 | [
"MIT"
] | 0 | e2abfc8072f9007b558b697b79d2affdae0eca3b | https://github.com/Jack000/improved-diffusion/tree/e2abfc8072f9007b558b697b79d2affdae0eca3b |
Classifier | import torch
import torch.distributed
import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, hidden_size):
super(Classifier, self).__init__()
self.linear1 = nn.Linear(hidden_size, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x, mask_cls):
h = s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.distributed
import torch
import torch.nn as nn
assert_size_stride =... | JackInTaiwan/BertSum | Classifier | false | 11,533 | [
"Apache-2.0"
] | 0 | 5b6f372b13358473d17c49bfc45f1e15c80f9fce | https://github.com/JackInTaiwan/BertSum/tree/5b6f372b13358473d17c49bfc45f1e15c80f9fce |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""Construct a layernorm module in the OpenAI style (epsilon inside the square root)."""
def __init__(self, n_state, e=1e-05):
super(LayerNorm, self).__init__()
self.g = nn.Parameter(torch.ones(n_state))
self.b = nn.Parame... | 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_... | HamoolNizar/RumorDetectionSystem | LayerNorm | false | 11,534 | [
"MIT"
] | 0 | 902ae4d705c0a6db470064f0e7f07f3c167d3eac | https://github.com/HamoolNizar/RumorDetectionSystem/tree/902ae4d705c0a6db470064f0e7f07f3c167d3eac |
DilatedResidualLayer | import torch
from torch import nn
import torch.nn.functional as F
class DilatedResidualLayer(nn.Module):
def __init__(self, dilation, in_channels, out_channels):
super(DilatedResidualLayer, self).__init__()
self.conv_dilated = nn.Conv1d(in_channels, out_channels, 3, padding
=dilation,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Jaakik/hydra-ml | DilatedResidualLayer | false | 11,535 | [
"MIT"
] | 0 | eae54fc478163130c94450a2a2ddea4f204c1ea9 | https://github.com/Jaakik/hydra-ml/tree/eae54fc478163130c94450a2a2ddea4f204c1ea9 |
BiDAFAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
def masked_softmax(logits, mask, dim=-1, log_softmax=False):
"""Take the softmax of `logits` over given dimension, and set
entries to 0 wherever `mask` is 0.
Args:
logits (torch.Tensor): Inputs to the softmax function.
mas... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JNXSTJ/squad | BiDAFAttention | false | 11,536 | [
"MIT"
] | 0 | ed875a90b212e1fe2f05144edb5595cedb5dd42b | https://github.com/JNXSTJ/squad/tree/ed875a90b212e1fe2f05144edb5595cedb5dd42b |
Upsample | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Jack000/improved-diffusion | Upsample | false | 11,537 | [
"MIT"
] | 0 | e2abfc8072f9007b558b697b79d2affdae0eca3b | https://github.com/Jack000/improved-diffusion/tree/e2abfc8072f9007b558b697b79d2affdae0eca3b |
CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self, input_size=50, hidden_size=256, dropout=0,
kernel_size=3, padding=1, activation_function=F.relu):
"""
Args:
input_size: dimention of input embedding
kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | JanKalo/OpenNRE | CNN | false | 11,538 | [
"MIT"
] | 0 | 2842903e5b66c88311820adac50a16ee3dc8ff77 | https://github.com/JanKalo/OpenNRE/tree/2842903e5b66c88311820adac50a16ee3dc8ff77 |
TVLoss | import torch
import torch.nn as nn
class TVLoss(nn.Module):
def __init__(self, strength):
super(TVLoss, self).__init__()
self.strength = strength
def forward(self, input):
self.x_diff = input[:, :, 1:, :] - input[:, :, :-1, :]
self.y_diff = input[:, :, :, 1:] - input[:, :, :,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | JaledMC/neural-style-pt | TVLoss | false | 11,539 | [
"MIT"
] | 0 | ce205c867761e251e86c89722df81c74dad7a221 | https://github.com/JaledMC/neural-style-pt/tree/ce205c867761e251e86c89722df81c74dad7a221 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, ignore_target=-1):
super().__init__()
self.ignore_target = ignore_target
def forward(self, input, target):
"""
:param input: (N), logit
:param target: (N), {0, 1}
: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... | JamesWang007/PointRCNN | DiceLoss | false | 11,540 | [
"MIT"
] | 0 | ea0812c52e6767b976fc50fed61e6b72fa6cdf81 | https://github.com/JamesWang007/PointRCNN/tree/ea0812c52e6767b976fc50fed61e6b72fa6cdf81 |
SigmoidFocalClassificationLoss | import torch
import torch.nn as nn
def _sigmoid_cross_entropy_with_logits(logits, labels):
loss = torch.clamp(logits, min=0) - logits * labels.type_as(logits)
loss += torch.log1p(torch.exp(-torch.abs(logits)))
return loss
class SigmoidFocalClassificationLoss(nn.Module):
"""Sigmoid focal cross entrop... | 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... | JamesWang007/PointRCNN | SigmoidFocalClassificationLoss | false | 11,541 | [
"MIT"
] | 0 | ea0812c52e6767b976fc50fed61e6b72fa6cdf81 | https://github.com/JamesWang007/PointRCNN/tree/ea0812c52e6767b976fc50fed61e6b72fa6cdf81 |
GlobalAvgPool2d | import torch
from torch import nn
import torch.nn.functional as F
class GlobalAvgPool2d(nn.Module):
def __init__(self):
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
return F.avg_pool2d(x, kernel_size=x.size()[2:])
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | JessyLee/Jessy_Dive_into_DL_Pytorch | GlobalAvgPool2d | false | 11,542 | [
"MIT"
] | 0 | 40b7921637b13507057f41485d928f3b59cc6f6a | https://github.com/JessyLee/Jessy_Dive_into_DL_Pytorch/tree/40b7921637b13507057f41485d928f3b59cc6f6a |
PSNRLoss | import torch
import torch.nn as nn
from torch.nn.functional import mse_loss as mse
def psnr(input: 'torch.Tensor', target: 'torch.Tensor', max_val: 'float'
) ->torch.Tensor:
"""Creates a function that calculates the PSNR between 2 images.
PSNR is Peek Signal to Noise Ratio, which is similar to mean squar... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | JoanFM/kornia | PSNRLoss | false | 11,543 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
Conv2d | import torch
import torch.nn as nn
from torch.nn import functional as F
class Conv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2d, self).__init__(in_channels, out_channels, kernel_size,
strid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | JassiGhuman/backgroundSubtraction | Conv2d | false | 11,544 | [
"MIT"
] | 0 | 351a380b34f9d84548bea734a69842227e373e65 | https://github.com/JassiGhuman/backgroundSubtraction/tree/351a380b34f9d84548bea734a69842227e373e65 |
Rot180 | import torch
import torch.nn as nn
def rot180(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-2, -1])
class Rot180(nn.Module):
"""Rotate a tensor image or a batch of tensor images
180 degrees. Input must be a tensor of shape (C, H, W)
or a batch of tensors :math:`(*, C, H, W)`.
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | JoanFM/kornia | Rot180 | false | 11,545 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
BasicBlock | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
def conv3x3(in_planes, out_planes, stride=1, dilation=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation, bias=False)
class BasicBlock(nn.Module):
expansion ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | JiazeWang/6-PACK | BasicBlock | false | 11,546 | [
"MIT"
] | 0 | bce910213cfbf89b4ed7b59ff6c70a59a7c19b99 | https://github.com/JiazeWang/6-PACK/tree/bce910213cfbf89b4ed7b59ff6c70a59a7c19b99 |
LastLevelMaxPool | import torch
import torch.utils.data
from torchvision.transforms import functional as F
from torch import nn
import torch.nn.functional as F
class LastLevelMaxPool(nn.Module):
def forward(self, x):
return [F.max_pool2d(x, 1, 2, 0)]
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
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | Amir4g/maskrcnn-benchmark | LastLevelMaxPool | false | 11,547 | [
"MIT"
] | 0 | c734fef962c3a2782e0055cfb6f825505a4b0c26 | https://github.com/Amir4g/maskrcnn-benchmark/tree/c734fef962c3a2782e0055cfb6f825505a4b0c26 |
Fire | import torch
from torch import nn
class Fire(nn.Module):
def __init__(self, inplanes, squeeze_planes, expand1x1_planes,
expand3x3_planes):
super(Fire, self).__init__()
self.inplanes = inplanes
self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
self.squeeze_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 import nn
assert_s... | GerardWalsh/DeepLabv3FineTuning | Fire | false | 11,548 | [
"MIT"
] | 0 | 149d4b33a7dc94c56361f559ca67cb0fcf9ae9d5 | https://github.com/GerardWalsh/DeepLabv3FineTuning/tree/149d4b33a7dc94c56361f559ca67cb0fcf9ae9d5 |
RgbaToRgb | import torch
import torch.nn as nn
def rgba_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
"""Convert an image from RGBA to RGB.
Args:
image: RGBA Image to be converted to RGB of shape :math:`(*,4,H,W)`.
Returns:
RGB version of the image with shape :math:`(*,3,H,W)`.
Example:
... | 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... | JoanFM/kornia | RgbaToRgb | false | 11,549 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
ExtractTensorPatches | import torch
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import Union
from torch.nn.modules.utils import _pair
def _extract_tensor_patchesnd(input: 'torch.Tensor', window_sizes:
'Tuple[int, ...]', strides: 'Tuple[int, ...]') ->torch.Tensor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import Union
from tor... | JoanFM/kornia | ExtractTensorPatches | false | 11,550 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
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