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 |
|---|---|---|---|---|---|---|---|---|---|---|
FocalLoss | import torch
from torch import nn
class FocalLoss(nn.Module):
def __init__(self, alpha=0.5, gamma=1.0):
super().__init__()
self.alpha = alpha
self.gamma = gamma
def forward(self, inputs, targets, **kwargs):
CEloss = nn.CrossEntropyLoss(reduction='none')(inputs, targets)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | gurucharanmk/Fruits-360_Image_Classification | FocalLoss | false | 10,129 | [
"MIT"
] | 0 | 9d26bba972ed3eca762ff225b33bd70e82edc7f0 | https://github.com/gurucharanmk/Fruits-360_Image_Classification/tree/9d26bba972ed3eca762ff225b33bd70e82edc7f0 |
AdaptiveFeatureNorm | import torch
import torch.nn as nn
import torch.utils.data
class AdaptiveFeatureNorm(nn.Module):
"""
The `Stepwise Adaptive Feature Norm loss (ICCV 2019) <https://arxiv.org/pdf/1811.07456v2.pdf>`_
Instead of using restrictive scalar R to match the corresponding feature norm, Stepwise Adaptive Feature Nor... | 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... | XianyuanLiu/Transfer-Learning-Library | AdaptiveFeatureNorm | false | 10,130 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
ConvertTHWCtoTCHW | import torch
import torch.utils.data
class ConvertTHWCtoTCHW(torch.nn.Module):
"""
Convert a torch.FloatTensor of shape (TIME x HEIGHT x WIDTH x CHANNEL) to
a torch.FloatTensor of shape (TIME x CHANNELS x HEIGHT x WIDTH).
"""
def forward(self, tensor):
return tensor.permute(0, 3, 1, 2).co... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | XianyuanLiu/Transfer-Learning-Library | ConvertTHWCtoTCHW | false | 10,131 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
MinusRbfHSIC | import torch
import torch.nn as nn
import torch.utils.data.distributed
class HSIC(nn.Module):
"""Base class for the finite sample estimator of Hilbert-Schmidt Independence Criterion (HSIC)
..math:: HSIC (X, Y) := || C_{x, y} ||^2_{HS}, where HSIC (X, Y) = 0 iif X and Y are independent.
Empirically, we us... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | derwind/mxfont | MinusRbfHSIC | false | 10,132 | [
"MIT"
] | 0 | 0b6d4554a1e2208906230d3121d792d450ed28dd | https://github.com/derwind/mxfont/tree/0b6d4554a1e2208906230d3121d792d450ed28dd |
BasicCNN2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicCNN2(nn.Module):
def __init__(self):
super().__init__()
self.layer_names = ['conv11', 'conv12', 'conv21', 'conv22',
'conv31', 'conv32', 'fc1', 'output_layer']
self.conv11 = nn.Conv2d(3, 32, 3, paddin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | fnc11/CosDefence | BasicCNN2 | false | 10,133 | [
"MIT"
] | 0 | 94f451b7d4b36cb3b9fcc85098dae242f311532b | https://github.com/fnc11/CosDefence/tree/94f451b7d4b36cb3b9fcc85098dae242f311532b |
SimpleNeuralNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleNeuralNet(nn.Module):
def __init__(self, n_in, n_hidden, n_out):
super().__init__()
self.linear1 = nn.Linear(n_in, n_hidden)
self.linear2 = nn.Linear(n_hidden, n_out)
def forward(self, x):
x = x.vi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gjwgit/mnist | SimpleNeuralNet | false | 10,134 | [
"MIT"
] | 0 | 77551a2600a3df06228546cfe6729df4803b6521 | https://github.com/gjwgit/mnist/tree/77551a2600a3df06228546cfe6729df4803b6521 |
EMDLoss | import torch
import torch.nn as nn
class EMDLoss(nn.Module):
"""EMDLoss class
"""
def __init__(self):
super(EMDLoss, self).__init__()
def forward(self, p_pred: 'torch.Tensor', p_true: 'torch.Tensor'):
assert p_true.shape == p_pred.shape, 'Length of the two distribution must be the sa... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | groundzhou/Image-aesthetic-assesment | EMDLoss | false | 10,135 | [
"MIT"
] | 0 | 0b22f60cdae11650153027c768a6a488b02ff9e4 | https://github.com/groundzhou/Image-aesthetic-assesment/tree/0b22f60cdae11650153027c768a6a488b02ff9e4 |
CorrelationAlignmentLoss | import torch
import torch.nn as nn
import torch.utils.data
class CorrelationAlignmentLoss(nn.Module):
"""The `Correlation Alignment Loss` in
`Deep CORAL: Correlation Alignment for Deep Domain Adaptation (ECCV 2016) <https://arxiv.org/pdf/1607.01719.pdf>`_.
Given source features :math:`f_S` and target fea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | XianyuanLiu/Transfer-Learning-Library | CorrelationAlignmentLoss | false | 10,136 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
GaussianKernel | import torch
import torch.nn as nn
from typing import Optional
import torch.utils.data
class GaussianKernel(nn.Module):
"""Gaussian Kernel Matrix
Gaussian Kernel k is defined by
.. math::
k(x_1, x_2) = \\exp \\left( - \\dfrac{\\| x_1 - x_2 \\|^2}{2\\sigma^2} \\right)
where :math:`x_1, x_2 \... | 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
... | XianyuanLiu/Transfer-Learning-Library | GaussianKernel | false | 10,137 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
BatchSpectralShrinkage | import torch
import torch.nn as nn
import torch.utils.data
class BatchSpectralShrinkage(nn.Module):
"""
The regularization term in `Catastrophic Forgetting Meets Negative Transfer:
Batch Spectral Shrinkage for Safe Transfer Learning (NIPS 2019) <https://proceedings.neurips.cc/paper/2019/file/c6bff625bdb03... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | XianyuanLiu/Transfer-Learning-Library | BatchSpectralShrinkage | false | 10,138 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
BridgeFeatLoss | import torch
import torch.nn as nn
import torch.utils.data
class BridgeFeatLoss(nn.Module):
"""Bridge loss on feature space.
"""
def __init__(self):
super(BridgeFeatLoss, self).__init__()
def forward(self, f_s, f_t, f_mixed, lam):
dist_mixed2s = ((f_mixed - f_s) ** 2).sum(1, keepdim=... | 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... | XianyuanLiu/Transfer-Learning-Library | BridgeFeatLoss | false | 10,139 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
DivLoss | import torch
import torch.nn as nn
import torch.utils.data
class DivLoss(nn.Module):
"""Diversity loss, which is defined as negative of standard deviation.
"""
def __init__(self):
super(DivLoss, self).__init__()
def forward(self, lam):
mu = lam.mean(0)
std = ((lam - mu) ** 2)... | 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... | XianyuanLiu/Transfer-Learning-Library | DivLoss | false | 10,140 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
VanillaGenerativeAdversarialLoss | import torch
import torch.nn as nn
import torch.utils.data
class VanillaGenerativeAdversarialLoss(nn.Module):
"""
Loss for `Vanilla Generative Adversarial Network <https://arxiv.org/abs/1406.2661>`_
Args:
reduction (str, optional): Specifies the reduction to apply to the output:
``'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 libdevice, math as tl_math
import torc... | XianyuanLiu/Transfer-Learning-Library | VanillaGenerativeAdversarialLoss | false | 10,141 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
Theta | from torch.autograd import Function
import torch
import torch.nn as nn
from typing import Tuple
from typing import Optional
from typing import Any
import torch.utils.data
class GradientReverseFunction(Function):
@staticmethod
def forward(ctx: 'Any', input: 'torch.Tensor', coeff: 'Optional[float]'=1.0
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import torch.nn as nn
from typing import Tup... | XianyuanLiu/Transfer-Learning-Library | Theta | false | 10,142 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
LeastSquaresGenerativeAdversarialLoss | import torch
import torch.nn as nn
import torch.utils.data
class LeastSquaresGenerativeAdversarialLoss(nn.Module):
"""
Loss for `Least Squares Generative Adversarial Network (LSGAN) <https://arxiv.org/abs/1611.04076>`_
Args:
reduction (str, optional): Specifies the reduction to apply to the outpu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | XianyuanLiu/Transfer-Learning-Library | LeastSquaresGenerativeAdversarialLoss | false | 10,143 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
BatchSpectralPenalizationLoss | import torch
import torch.nn as nn
import torch.utils.data
class BatchSpectralPenalizationLoss(nn.Module):
"""Batch spectral penalization loss from `Transferability vs. Discriminability: Batch
Spectral Penalization for Adversarial Domain Adaptation (ICML 2019)
<http://ise.thss.tsinghua.edu.cn/~mlong/doc/b... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | XianyuanLiu/Transfer-Learning-Library | BatchSpectralPenalizationLoss | false | 10,144 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
Vgg16 | import torch
from torch import nn
import torch.nn.functional as F
class Vgg16(nn.Module):
def __init__(self):
super(Vgg16, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | chaitrasj/GAN-based-Visible-Thermal-Person-ReID | Vgg16 | false | 10,145 | [
"MIT"
] | 0 | 8fd65ce3ab5403056fbe6e3574d1a7d02a315e62 | https://github.com/chaitrasj/GAN-based-Visible-Thermal-Person-ReID/tree/8fd65ce3ab5403056fbe6e3574d1a7d02a315e62 |
SimpleNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleNet(nn.Module):
def __init__(self, ni):
super().__init__()
self.linear1 = nn.Linear(ni, 128)
self.linear2 = nn.Linear(128, 128)
self.linear3 = nn.Linear(128, 64)
self.linear4 = nn.Linear(64, 64)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | haakonrob/AI-Feynman | SimpleNet | false | 10,146 | [
"MIT"
] | 0 | 445b68e9a260dcea67a94eed6e0aeb267f25d2ef | https://github.com/haakonrob/AI-Feynman/tree/445b68e9a260dcea67a94eed6e0aeb267f25d2ef |
TripletLossXBM | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms.functional as F
import torch.utils.data
def hard_examples_mining(dist_mat, identity_mat, return_idxes=False):
"""Select hard positives and hard negatives according to `In defense of the Triplet Loss for Person
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | XianyuanLiu/Transfer-Learning-Library | TripletLossXBM | false | 10,147 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
TripletLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms.functional as F
import torch.utils.data
def hard_examples_mining(dist_mat, identity_mat, return_idxes=False):
"""Select hard positives and hard negatives according to `In defense of the Triplet Loss for Person
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | XianyuanLiu/Transfer-Learning-Library | TripletLoss | false | 10,148 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
PytorchMultiClass | import torch
import torch.nn as nn
import torch.nn.functional as F
class PytorchMultiClass(nn.Module):
"""num_features as input parameter
attributes:
layer_1: fully-connected layer with 32 neurons
layer_out: fully-connected layer with 4 neurons
softmax: softmax function
methods:
forward() ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | freescania/advdsi_at2 | PytorchMultiClass | false | 10,149 | [
"MIT"
] | 0 | 13fa0b8beaeccc28975aea40ee5a1db3dd3e33be | https://github.com/freescania/advdsi_at2/tree/13fa0b8beaeccc28975aea40ee5a1db3dd3e33be |
PytorchBinary | import torch
import torch.nn as nn
import torch.nn.functional as F
class PytorchBinary(nn.Module):
def __init__(self, num_features):
super(PytorchBinary, self).__init__()
self.layer_1 = nn.Linear(num_features, 256)
self.layer_out = nn.Linear(256, 1)
self.sigmoid = nn.Sigmoid()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | freescania/advdsi_at2 | PytorchBinary | false | 10,150 | [
"MIT"
] | 0 | 13fa0b8beaeccc28975aea40ee5a1db3dd3e33be | https://github.com/freescania/advdsi_at2/tree/13fa0b8beaeccc28975aea40ee5a1db3dd3e33be |
PytorchRegression | import torch
import torch.nn as nn
import torch.nn.functional as F
class PytorchRegression(nn.Module):
def __init__(self, num_features):
super(PytorchRegression, self).__init__()
self.layer_1 = nn.Linear(num_features, 128)
self.layer_out = nn.Linear(128, 1)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | freescania/advdsi_at2 | PytorchRegression | false | 10,151 | [
"MIT"
] | 0 | 13fa0b8beaeccc28975aea40ee5a1db3dd3e33be | https://github.com/freescania/advdsi_at2/tree/13fa0b8beaeccc28975aea40ee5a1db3dd3e33be |
AvgPoolHead | import torch
import torch.nn as nn
import torch.optim
class AvgPoolHead(nn.Module):
def __init__(self, in_channels, out_channels, fea_map_size):
super(AvgPoolHead, self).__init__()
self.avgpool = nn.AvgPool2d(fea_map_size, stride=1)
self.fc = nn.Linear(in_channels, out_channels)
def ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.g... | harshitbansal05/integral-human-pose | AvgPoolHead | false | 10,152 | [
"MIT"
] | 0 | 50c32b59d765afe3ab2c3873068d3adfb8fd9b13 | https://github.com/harshitbansal05/integral-human-pose/tree/50c32b59d765afe3ab2c3873068d3adfb8fd9b13 |
KarankEtAl | import torch
import torch.nn as nn
import torch.nn.functional as F
class KarankEtAl(nn.Module):
def __init__(self, input_channels, n_classes, patch_size=5):
super(KarankEtAl, self).__init__()
self.patch_size = patch_size
self.input_channels = input_channels
self.n_classes = n_clas... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | giorgosouz/HSI-classification-makantasis-cnn | KarankEtAl | false | 10,153 | [
"MIT"
] | 0 | 95f18274d7cb67babb971db71f358a73dee2affc | https://github.com/giorgosouz/HSI-classification-makantasis-cnn/tree/95f18274d7cb67babb971db71f358a73dee2affc |
FactorTransfer | import torch
from torch import nn
import torch.nn.functional as F
class FactorTransfer(nn.Module):
"""Paraphrasing Complex Network: Network Compression via Factor Transfer, NeurIPS 2018"""
def __init__(self, p1=2, p2=1):
super(FactorTransfer, self).__init__()
self.p1 = p1
self.p2 = p2... | 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 ... | bobo0810/RepDistiller | FactorTransfer | false | 10,154 | [
"BSD-2-Clause"
] | 0 | 0a4cea2142221b9b31c8e995920273f5619b37f8 | https://github.com/bobo0810/RepDistiller/tree/0a4cea2142221b9b31c8e995920273f5619b37f8 |
RepresentationSubspaceDistance | import torch
import torch.nn as nn
import torch.utils.data
class RepresentationSubspaceDistance(nn.Module):
"""
`Representation Subspace Distance (ICML 2021) <http://ise.thss.tsinghua.edu.cn/~mlong/doc/Representation-Subspace-Distance-for-Domain-Adaptation-Regression-icml21.pdf>`_
Args:
trade_off... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | XianyuanLiu/Transfer-Learning-Library | RepresentationSubspaceDistance | false | 10,155 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
Correlation | import torch
from torch import nn
class Correlation(nn.Module):
"""Correlation Congruence for Knowledge Distillation, ICCV 2019.
The authors nicely shared the code with me. I restructured their code to be
compatible with my running framework. Credits go to the original author"""
def __init__(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 math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | bobo0810/RepDistiller | Correlation | false | 10,156 | [
"BSD-2-Clause"
] | 0 | 0a4cea2142221b9b31c8e995920273f5619b37f8 | https://github.com/bobo0810/RepDistiller/tree/0a4cea2142221b9b31c8e995920273f5619b37f8 |
GATMutiHeadAttLayer | import torch
import torch.nn as nn
from torch.nn import functional as F
class GATMutiHeadAttLayer(nn.Module):
def __init__(self, in_features, out_features, heads, dropout=0.4, alpha
=0.2, concat=True):
super(GATMutiHeadAttLayer, self).__init__()
self.dropout = dropout
self.in_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gitubee/pyGAT | GATMutiHeadAttLayer | false | 10,157 | [
"MIT"
] | 0 | bc4cc2b6565b7f2ad99daf88013207f64991c273 | https://github.com/gitubee/pyGAT/tree/bc4cc2b6565b7f2ad99daf88013207f64991c273 |
FocalLoss | import torch
from torch import nn
class FocalLoss(nn.Module):
def __init__(self, gamma=0, eps=1e-07):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.eps = eps
self.ce = torch.nn.CrossEntropyLoss(reduction='none')
def forward(self, input, target):
logp = 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | h8c2/kaggle-landmark-recognition-2020-1st-place | FocalLoss | false | 10,158 | [
"MIT"
] | 0 | 3285b6c9548d100b14800ea3927f5974b25facd9 | https://github.com/h8c2/kaggle-landmark-recognition-2020-1st-place/tree/3285b6c9548d100b14800ea3927f5974b25facd9 |
BasicNN | import torch
import numpy as np
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
class BasicNN(nn.Module):
def __init__(self):
super(BasicNN, self).__init__()
self.net = nn.Linear(28 * 28, 2)
def forward(self, x):
if type(x) == np.ndarray:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gtfierro/clipper | BasicNN | false | 10,159 | [
"Apache-2.0"
] | 0 | 88d7c238d51d5cf66d118bffca0c17edee84755e | https://github.com/gtfierro/clipper/tree/88d7c238d51d5cf66d118bffca0c17edee84755e |
PositionalEncoding | import torch
from torch import nn
class PositionalEncoding(nn.Module):
"""Implement the PE function."""
def __init__(self, d_model, dropout, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = nn.Parameter(torch.randn(1, max_len, d_model... | 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... | hedinang/ocr2 | PositionalEncoding | false | 10,160 | [
"MIT"
] | 0 | 09cc4c71190e900c6ad5aba9485a804139281fec | https://github.com/hedinang/ocr2/tree/09cc4c71190e900c6ad5aba9485a804139281fec |
NormalizationLayer | import torch
import torch.utils.data
class NormalizationLayer(torch.nn.Module):
"""Class for normalization layer."""
def __init__(self, normalize_scale=1.0, learn_scale=True):
super(NormalizationLayer, self).__init__()
self.norm_s = float(normalize_scale)
if learn_scale:
s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_siz... | hmtrii/tirg | NormalizationLayer | false | 10,161 | [
"Apache-2.0"
] | 0 | e404020795bb46fb01b6bd82a2618f9370174012 | https://github.com/hmtrii/tirg/tree/e404020795bb46fb01b6bd82a2618f9370174012 |
LabelSmoothCrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class LabelSmoothCrossEntropyLoss(nn.modules.loss._WeightedLoss):
def __init__(self, weight=None, reduction='mean', smoothing=0.0):
super().__init__(weight=weight, reduction=reduction)
self.smoothing = smoothing
self.weigh... | 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
... | gosiqueira/dog-breed-recognition | LabelSmoothCrossEntropyLoss | false | 10,162 | [
"MIT"
] | 0 | 27d3499f4922e6e36219f47af08c34e30c929e12 | https://github.com/gosiqueira/dog-breed-recognition/tree/27d3499f4922e6e36219f47af08c34e30c929e12 |
SoftArgmax2D | import torch
import torch.nn as nn
from typing import Optional
def create_meshgrid(x: 'torch.Tensor', normalized_coordinates: 'Optional[bool]'
) ->torch.Tensor:
assert len(x.shape) == 4, x.shape
_, _, height, width = x.shape
_device, _dtype = x.device, x.dtype
if normalized_coordinates:
xs... | 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
... | godspeed5/Human-Path-Prediction | SoftArgmax2D | false | 10,163 | [
"MIT"
] | 0 | 1f451f3750fbd4e37a567f1574cfea1456608be8 | https://github.com/godspeed5/Human-Path-Prediction/tree/1f451f3750fbd4e37a567f1574cfea1456608be8 |
GAT | import torch
import torch.nn as nn
from torch.nn import functional as F
class GATMutiHeadAttLayer(nn.Module):
def __init__(self, in_features, out_features, heads, dropout=0.4, alpha
=0.2, concat=True):
super(GATMutiHeadAttLayer, self).__init__()
self.dropout = dropout
self.in_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gitubee/pyGAT | GAT | false | 10,164 | [
"MIT"
] | 0 | bc4cc2b6565b7f2ad99daf88013207f64991c273 | https://github.com/gitubee/pyGAT/tree/bc4cc2b6565b7f2ad99daf88013207f64991c273 |
DistillKL | import torch
from torch import nn
import torch.nn.functional as F
class DistillKL(nn.Module):
"""Distilling the Knowledge in a Neural Network"""
def __init__(self, T):
super(DistillKL, self).__init__()
self.T = T
def forward(self, y_s, y_t):
p_s = F.log_softmax(y_s / self.T, 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 libdevice, math as tl_math
from torch ... | bobo0810/RepDistiller | DistillKL | false | 10,165 | [
"BSD-2-Clause"
] | 0 | 0a4cea2142221b9b31c8e995920273f5619b37f8 | https://github.com/bobo0810/RepDistiller/tree/0a4cea2142221b9b31c8e995920273f5619b37f8 |
PKT | import torch
from torch import nn
class PKT(nn.Module):
"""Probabilistic Knowledge Transfer for deep representation learning
Code from author: https://github.com/passalis/probabilistic_kt"""
def __init__(self):
super(PKT, self).__init__()
def forward(self, f_s, f_t):
return self.cosi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
fr... | bobo0810/RepDistiller | PKT | false | 10,166 | [
"BSD-2-Clause"
] | 0 | 0a4cea2142221b9b31c8e995920273f5619b37f8 | https://github.com/bobo0810/RepDistiller/tree/0a4cea2142221b9b31c8e995920273f5619b37f8 |
GeM | import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.parameter import Parameter
def gem(x, p=3, eps=1e-06):
return F.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, p_trainable=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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
from to... | h8c2/kaggle-landmark-recognition-2020-1st-place | GeM | false | 10,167 | [
"MIT"
] | 0 | 3285b6c9548d100b14800ea3927f5974b25facd9 | https://github.com/h8c2/kaggle-landmark-recognition-2020-1st-place/tree/3285b6c9548d100b14800ea3927f5974b25facd9 |
Attention | import torch
from torch import nn
import torch.nn.functional as F
class Attention(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
"""
def __init__(self, dim):
super(Attention, self).__init__()
self.dim = dim
self.linear1 = nn.Linear(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.... | gluver/video-caption.pytorch | Attention | false | 10,168 | [
"MIT"
] | 0 | 15000246980e43f71a254ab3deeb91f0957309bb | https://github.com/gluver/video-caption.pytorch/tree/15000246980e43f71a254ab3deeb91f0957309bb |
EnDown | import torch
import torch.nn as nn
import torch.optim
class EnDown(nn.Module):
def __init__(self, in_channels, out_channels):
super(EnDown, self).__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=3,
stride=2, padding=1)
def forward(self, x):
y = self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.g... | felixquinton1/TransBTS | EnDown | false | 10,169 | [
"Apache-2.0"
] | 0 | 6992c902413ba15f40ebfe9f6d5d0e3594051033 | https://github.com/felixquinton1/TransBTS/tree/6992c902413ba15f40ebfe9f6d5d0e3594051033 |
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... | htn274/siamese-triplet | ContrastiveLoss | false | 10,170 | [
"BSD-3-Clause"
] | 0 | d468fb939a7ab072a0e1cf1c507a87df1a901852 | https://github.com/htn274/siamese-triplet/tree/d468fb939a7ab072a0e1cf1c507a87df1a901852 |
HuberLoss | import torch
import torch.nn as nn
class HuberLoss(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.SmoothL1Loss()
def forward(self, logits, labels):
loss = self.loss(logits, labels)
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | hslrock/Reinforcement-Learning-Implementation | HuberLoss | false | 10,171 | [
"MIT"
] | 0 | 31db7e31c92f8e01609bf51d3f8f22211ec0fd5d | https://github.com/hslrock/Reinforcement-Learning-Implementation/tree/31db7e31c92f8e01609bf51d3f8f22211ec0fd5d |
CoorsNorm | import torch
from torch import nn
class CoorsNorm(nn.Module):
def __init__(self, eps=1e-08, scale_init=1.0):
super().__init__()
self.eps = eps
scale = torch.zeros(1).fill_(scale_init)
self.scale = nn.Parameter(scale)
def forward(self, coors):
norm = coors.norm(dim=-1,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | hypnopump/En-transformer | CoorsNorm | false | 10,172 | [
"MIT"
] | 0 | b52f0e5d79a886512f9d438de345fc8a9eae6420 | https://github.com/hypnopump/En-transformer/tree/b52f0e5d79a886512f9d438de345fc8a9eae6420 |
InitConv | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class InitConv(nn.Module):
def __init__(self, in_channels=4, out_channels=16, dropout=0.2):
super(InitConv, self).__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=3,
padding=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
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.g... | felixquinton1/TransBTS | InitConv | false | 10,173 | [
"Apache-2.0"
] | 0 | 6992c902413ba15f40ebfe9f6d5d0e3594051033 | https://github.com/felixquinton1/TransBTS/tree/6992c902413ba15f40ebfe9f6d5d0e3594051033 |
DQN_Simple | import math
import torch
from torch.autograd import Variable
import torch.nn.functional as F
import torch.nn as nn
class NoisyLinear(nn.Module):
def __init__(self, in_features, out_features, std_init=0.4):
super(NoisyLinear, self).__init__()
self.in_features = in_features
self.out_feature... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch.autograd import Variable
import torch.nn.functional as F
... | exe1023/GA-final | DQN_Simple | false | 10,174 | [
"MIT"
] | 0 | dad84cda665ef24e9568a79a2e7ff0a00edf5851 | https://github.com/exe1023/GA-final/tree/dad84cda665ef24e9568a79a2e7ff0a00edf5851 |
ContrastiveLoss | import torch
import torch.nn.functional as F
class ContrastiveLoss(torch.nn.Module):
"""Contrastive loss function"""
def __init__(self, margin=1.0):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, output1, output2, label):
euclidean_distance = F.pai... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | hz512/Smart-Parking-Enforcement-System | ContrastiveLoss | false | 10,175 | [
"MIT"
] | 0 | e990903de545693ad6e2536bf167c69ab672d16a | https://github.com/hz512/Smart-Parking-Enforcement-System/tree/e990903de545693ad6e2536bf167c69ab672d16a |
REINFORCE | import torch
import torch.nn.functional as F
import torch.nn as nn
class REINFORCE(nn.Module):
def __init__(self, input_size, num_actions):
super(REINFORCE, self).__init__()
self.fc = nn.Linear(input_size, 256)
self.head = nn.Linear(256, num_actions)
self.relu = nn.ReLU()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | exe1023/GA-final | REINFORCE | false | 10,176 | [
"MIT"
] | 0 | dad84cda665ef24e9568a79a2e7ff0a00edf5851 | https://github.com/exe1023/GA-final/tree/dad84cda665ef24e9568a79a2e7ff0a00edf5851 |
LanguageModelCriterion | import torch
import torch.nn as nn
from torch.autograd import *
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
if target.ndim == 3:
target = target.reshape(-1, target.shape[2])
... | 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.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | helloMickey/self-critical.pytorch | LanguageModelCriterion | false | 10,177 | [
"MIT"
] | 0 | 3a26111012099e13daeb688136fea45186127935 | https://github.com/helloMickey/self-critical.pytorch/tree/3a26111012099e13daeb688136fea45186127935 |
RewardCriterion | import torch
import torch.nn as nn
from torch.autograd import *
class RewardCriterion(nn.Module):
def __init__(self):
super(RewardCriterion, self).__init__()
def forward(self, input, seq, reward):
input = input.gather(2, seq.unsqueeze(2)).squeeze(2)
input = input.reshape(-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
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | helloMickey/self-critical.pytorch | RewardCriterion | false | 10,178 | [
"MIT"
] | 0 | 3a26111012099e13daeb688136fea45186127935 | https://github.com/helloMickey/self-critical.pytorch/tree/3a26111012099e13daeb688136fea45186127935 |
Upsample | import torch
import torch.nn as nn
import torch._utils
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
class Upsample(nn.Module):
""" nn.Upsample is deprecated """
def __init__(self, scale_factor, mode='linear'):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch._utils
import torch.utils.data
import torch.utils.data... | fsImageries/video-to-pose3D | Upsample | false | 10,179 | [
"MIT"
] | 0 | 098c87ce19dc3331da03e6eac0b9744684eb66f6 | https://github.com/fsImageries/video-to-pose3D/tree/098c87ce19dc3331da03e6eac0b9744684eb66f6 |
EPELoss | import torch
import torch.nn as nn
class EPELoss(nn.Module):
def __init__(self):
super(EPELoss, self).__init__()
def forward(self, output, target):
lossvalue = torch.norm(output - target + 1e-16, p=2, dim=1).mean()
return lossvalue
def get_inputs():
return [torch.rand([4, 4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | haochen23/GeoProj | EPELoss | false | 10,180 | [
"MIT"
] | 0 | 4b31f51789f9cc41ea7dc977cee057b8bc8a83cc | https://github.com/haochen23/GeoProj/tree/4b31f51789f9cc41ea7dc977cee057b8bc8a83cc |
TripletLoss | import torch
import torch.nn as nn
import torch._utils
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
class TripletLoss(nn.Module):
"""
Triplet loss
Takes embeddings of an anchor sample, a positive sample and a negati... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch._utils
import torch.utils.data
import torch.utils.data... | fsImageries/video-to-pose3D | TripletLoss | false | 10,181 | [
"MIT"
] | 0 | 098c87ce19dc3331da03e6eac0b9744684eb66f6 | https://github.com/fsImageries/video-to-pose3D/tree/098c87ce19dc3331da03e6eac0b9744684eb66f6 |
JointsMSELoss | import torch
import torch.nn as nn
import torch._utils
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
import torch.optim
class JointsMSELoss(nn.Module):
def __init__(self, use_target_weight):
super(JointsMSELoss, self).__init__()
self.criterion = nn.MSELoss()... | 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
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
import torch.... | fsImageries/video-to-pose3D | JointsMSELoss | false | 10,182 | [
"MIT"
] | 0 | 098c87ce19dc3331da03e6eac0b9744684eb66f6 | https://github.com/fsImageries/video-to-pose3D/tree/098c87ce19dc3331da03e6eac0b9744684eb66f6 |
SpeakerIntegrator | import torch
import torch.nn as nn
import torch.utils.data
class SpeakerIntegrator(nn.Module):
def __init__(self):
super(SpeakerIntegrator, self).__init__()
def forward(self, x, spembs):
"""
x shape : (batch, 39, 256)
spembs shape : (batch, 256)
"""
spemb... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | hwRG/FastSpeech2-Pytorch-old-man_city | SpeakerIntegrator | false | 10,183 | [
"MIT"
] | 0 | c32ee3a09bf2a53fcd17a2d0b74e8d1c93586573 | https://github.com/hwRG/FastSpeech2-Pytorch-old-man_city/tree/c32ee3a09bf2a53fcd17a2d0b74e8d1c93586573 |
SceneParserHead | import torch
import torch.utils.data
from torch import nn
class SceneParserHead(nn.Module):
def __init__(self, in_channels, num_classes):
super(SceneParserHead, self).__init__()
self.conv1x1 = nn.Conv2d(in_channels, 2048, 1, 1)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | hangwudy/pytorch_tutorial | SceneParserHead | false | 10,184 | [
"MIT"
] | 0 | 857b128253bd1e2bd30cb85e995c757e5acbb3a2 | https://github.com/hangwudy/pytorch_tutorial/tree/857b128253bd1e2bd30cb85e995c757e5acbb3a2 |
ConvTemporalGraphical | import torch
import torch.nn as nn
import torch._utils
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
import torch.optim
class ConvTemporalGraphical(nn.Module):
"""The basic module for applying a graph convolution.
Args:
in_channels (int): Number of channels in 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
import torch._utils
import torch.utils.data
import torch.u... | fsImageries/video-to-pose3D | ConvTemporalGraphical | false | 10,185 | [
"MIT"
] | 0 | 098c87ce19dc3331da03e6eac0b9744684eb66f6 | https://github.com/fsImageries/video-to-pose3D/tree/098c87ce19dc3331da03e6eac0b9744684eb66f6 |
Maxout | import torch
import torch.nn as nn
class Maxout(nn.Module):
def __init__(self, pool_size):
super().__init__()
self._pool_size = pool_size
def forward(self, x):
assert x.shape[-1
] % self._pool_size == 0, 'Wrong input last dim size ({}) for Maxout({})'.format(
... | 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... | hekaplex/FocusSeq2Seq | Maxout | false | 10,186 | [
"MIT"
] | 0 | 9bab5d3aa020b4d587add9d7a070335cf0feb2d6 | https://github.com/hekaplex/FocusSeq2Seq/tree/9bab5d3aa020b4d587add9d7a070335cf0feb2d6 |
RNN | import torch
import torch.nn as nn
from torch.autograd import Variable
class RNN(nn.Module):
def __init__(self, category_size, input_size, hidden_size, output_size):
super(RNN, self).__init__()
self.category_size = category_size
self.input_size = input_size
self.hidden_size = hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.autograd import Variable
assert_size_stride = t... | iclementine/practical-pytorch | RNN | false | 10,187 | [
"MIT"
] | 0 | 88e2e53e47328cdb3ec23573aec3ff0421f1a2b7 | https://github.com/iclementine/practical-pytorch/tree/88e2e53e47328cdb3ec23573aec3ff0421f1a2b7 |
TVLoss | import torch
from typing import Tuple
from torch.nn.modules.loss import _Loss
from typing import List
from typing import Optional
def _reduce(x: 'torch.Tensor', reduction: 'str'='mean') ->torch.Tensor:
"""Reduce input in batch dimension if needed.
Args:
x: Tensor with shape (N, *).
reduction:... | 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 typing import Tuple
from torch.nn.modules.loss import _Loss
from typing im... | hecoding/piq | TVLoss | false | 10,188 | [
"Apache-2.0"
] | 0 | c72143ce9deb30fefaca434a39e4dfc557673e97 | https://github.com/hecoding/piq/tree/c72143ce9deb30fefaca434a39e4dfc557673e97 |
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 ... | ha55anali/pytorch-nested-unet | BCEDiceLoss | false | 10,189 | [
"MIT"
] | 0 | 444dbd0ff7764478de662723b211c23bd65d99f9 | https://github.com/ha55anali/pytorch-nested-unet/tree/444dbd0ff7764478de662723b211c23bd65d99f9 |
ProtoLoss | import torch
class ProtoLoss(torch.nn.Module):
def __init__(self, num_classes, num_support, num_queries, ndim):
super(ProtoLoss, self).__init__()
self.num_classes = num_classes
self.num_support = num_support
self.num_queries = num_queries
self.ndim = ndim
def euclidea... | 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... | gradjitta/Prototypical-Networks | ProtoLoss | false | 10,191 | [
"MIT"
] | 0 | 9ec344f7299353889e2087224b80a74519ca1a3c | https://github.com/gradjitta/Prototypical-Networks/tree/9ec344f7299353889e2087224b80a74519ca1a3c |
LayerNorm | import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
from torch.nn.modules.normalization import LayerNorm
from torch.optim.lr_scheduler import *
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=0.0001):
super(LayerNorm, self).__init__()... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn import Parameter
from torch.nn.parameter im... | chunhuililili/mt_dnn | LayerNorm | false | 10,192 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
GCN | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class GraphConvolution(nn.Module):
def __init__(self, in_features, out_features):
super(GraphConvolution, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.w... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
from torch.nn.parameter import Parameter
asser... | iDMG-dynamicGCN/DatasetCollection | GCN | false | 10,193 | [
"MIT"
] | 0 | ad761b38bc86af1dd3aee6c72e819d6f00252164 | https://github.com/iDMG-dynamicGCN/DatasetCollection/tree/ad761b38bc86af1dd3aee6c72e819d6f00252164 |
TorchLogCosh | import torch
import torch as _torch
class TorchLogCosh(_torch.nn.Module):
"""
Log(cosh) activation function for PyTorch modules
"""
def __init__(self):
"""
Init method.
"""
super().__init__()
def forward(self, input):
"""
Forward pass of the functi... | 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 as _torch
assert_size_stride = torch._C._dynamo.g... | inailuig/netket | TorchLogCosh | false | 10,194 | [
"Apache-2.0"
] | 0 | ab57a6fb019edb9ac298969950724781f2ae2b22 | https://github.com/inailuig/netket/tree/ab57a6fb019edb9ac298969950724781f2ae2b22 |
AutoEncoder | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class AutoEncoder(nn.Module):
def __init__(self):
super(AutoEncoder, self).__init__()
self.encoder1 = nn.Conv2d(3, 16, 3, padding=1)
self.encoder2 = nn.Conv2d(16, 8, 3, padding=1)
self.encoder3 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | gjustin40/Pytorch-Cookbook | AutoEncoder | false | 10,195 | [
"MIT"
] | 0 | 069514d05b00d07521e1a1a028d0746b65099586 | https://github.com/gjustin40/Pytorch-Cookbook/tree/069514d05b00d07521e1a1a028d0746b65099586 |
DQN | import torch
import torch.nn.functional as F
from torch import nn
class DQN(nn.Module):
"""DQN network, three full connection layers
"""
def __init__(self):
super(DQN, self).__init__()
self.fc1 = nn.Linear(4, 16)
self.fc1.weight.data.normal_(0, 0.1)
self.fc2 = nn.Linear(16... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | ivanwhaf/RL | DQN | false | 10,196 | [
"MIT"
] | 0 | 1610b3684269b1d60543c60460e9ee65309594ee | https://github.com/ivanwhaf/RL/tree/1610b3684269b1d60543c60460e9ee65309594ee |
GeLU | import torch
import torch.nn as nn
import torch.nn.functional as F
class GeLU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return 0.5 * x * (1 + F.tanh(0.7978845608 * (x + 0.044715 * x * x * x))
)
def get_inputs():
return [torch.rand([4, 4, 4, 4]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | irustandi/sentiment-discovery | GeLU | false | 10,197 | [
"BSD-3-Clause"
] | 0 | a2e074f33bbac94ec9dba111a91da026633dad67 | https://github.com/irustandi/sentiment-discovery/tree/a2e074f33bbac94ec9dba111a91da026633dad67 |
Generator | import torch
from torch import nn
import torch.utils.data
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.e_conv1 = nn.Conv2d(3, 3, 1, 1, 0, bias=True)
self.e_conv2 = nn.Conv2d(3, 3, 3, 1, 1, bias=True)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | goldenbili/SRGAN_Test | Generator | false | 10,198 | [
"MIT"
] | 0 | 06705c92abd5b7084ae878a4746060760bcff5c3 | https://github.com/goldenbili/SRGAN_Test/tree/06705c92abd5b7084ae878a4746060760bcff5c3 |
HLCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | 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.... | chunhuililili/mt_dnn | HLCriterion | false | 10,199 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
Cosine | from _paritybench_helpers import _mock_config
import torch
from torch.optim.lr_scheduler import *
class Cosine(torch.nn.Module):
def __init__(self, config):
super().__init__()
def forward(self, src, tgt):
src = src.float()
tgt = tgt.float()
return (torch.matmul(src, tgt.trans... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.optim.lr... | chunhuililili/mt_dnn | Cosine | false | 10,200 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
BiLinearSim | from _paritybench_helpers import _mock_config
import torch
from torch.optim.lr_scheduler import *
class BiLinearSim(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.linear = torch.nn.Linear(config.hidden_size, config.
hidden_size, bias=False)
def forward(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.optim.lr_scheduler import *
assert_size_stride = torch._C._dynamo.gua... | chunhuililili/mt_dnn | BiLinearSim | false | 10,201 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
JSCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | 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.... | chunhuililili/mt_dnn | JSCriterion | false | 10,202 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
KlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | 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.... | chunhuililili/mt_dnn | KlCriterion | false | 10,203 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
Mnist_CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Mnist_CNN(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | hongsam123/PyTorch-tutorials-kr | Mnist_CNN | false | 10,204 | [
"BSD-3-Clause"
] | 0 | e48bbbc7088bf6b9da66abb8862b8d0539662bd5 | https://github.com/hongsam123/PyTorch-tutorials-kr/tree/e48bbbc7088bf6b9da66abb8862b8d0539662bd5 |
Pooler | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.optim.lr_scheduler import *
def linear(x):
return x
def activation(func_a):
"""Activation function wrapper
"""
try:
f = eval(func_a)
except:
f = linear
return f
class DropoutWrapper(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
import torch.nn.functional as F
import torch.nn as nn
from torch.optim.lr_schedu... | chunhuililili/mt_dnn | Pooler | false | 10,205 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
NsKlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
def stable_kl(logit, target, epsilon=1e-06, reduce=True):
logit = logit.view(-1, logit.size(-1)).float()
target = target.view(-1, target.size(-1)).float()
bs = logit.size(0)
p = ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | chunhuililili/mt_dnn | NsKlCriterion | false | 10,206 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
CeCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.... | chunhuililili/mt_dnn | CeCriterion | false | 10,207 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
MseCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | 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.modules.loss import _Loss
from torch.optim.lr_scheduler import *
assert_siz... | chunhuililili/mt_dnn | MseCriterion | false | 10,208 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
MultiheadAttentionWrapper | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.utils import weight_norm
from torch.optim.lr_scheduler import *
def linear(x):
return x
def activation(func_a):
"""Activation function wrapper
"""
try:
f = eval(func_a)
except:
f = linear
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.functional as F
import torch.nn as nn
from torch.nn.utils import weight_norm
from torch.optim.lr_scheduler import *
assert_s... | chunhuililili/mt_dnn | MultiheadAttentionWrapper | false | 10,209 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
Network | import torch
import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self):
super().__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 * 10 * 10, 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_... | ibrahimalmakky/py4ai | Network | false | 10,210 | [
"MIT"
] | 0 | 224f54086523314ff9c7133680f119c62f6ea249 | https://github.com/ibrahimalmakky/py4ai/tree/224f54086523314ff9c7133680f119c62f6ea249 |
ComplexConv | import torch
import torch.nn as nn
class ComplexConv(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(ComplexConv, self).__init__()
self.device = torch.device('cuda' if torch.cuda.is_available() else
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | iseeklin/Electromagnetic-Signal-Recognition-Using-Deep-Learning | ComplexConv | false | 10,211 | [
"Apache-2.0"
] | 0 | be78a2d966f33fd90567b21295cda1c1d472e14a | https://github.com/iseeklin/Electromagnetic-Signal-Recognition-Using-Deep-Learning/tree/be78a2d966f33fd90567b21295cda1c1d472e14a |
NsSymKlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
def stable_kl(logit, target, epsilon=1e-06, reduce=True):
logit = logit.view(-1, logit.size(-1)).float()
target = target.view(-1, target.size(-1)).float()
bs = logit.size(0)
p = ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | chunhuililili/mt_dnn | NsSymKlCriterion | false | 10,212 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
Pooling | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Pooling(nn.Module):
def __init__(self, pooling_type=['GAP']):
super(Pooling, self).__init__()
self.pooling = []
... | 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.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils... | heebinYoo/proxy-synthesis-confidence-control-new | Pooling | false | 10,213 | [
"Apache-2.0"
] | 0 | c591cdffc30cf933bd242ba5646d2436a42a3181 | https://github.com/heebinYoo/proxy-synthesis-confidence-control-new/tree/c591cdffc30cf933bd242ba5646d2436a42a3181 |
SymKlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | 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.... | chunhuililili/mt_dnn | SymKlCriterion | false | 10,214 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
Feedforward | import torch
class Feedforward(torch.nn.Module):
def __init__(self, input_size, hidden_size=100):
super(Feedforward, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)
self.relu = torch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | jacob-parnell-rozetta/longformer_coverage | Feedforward | false | 10,215 | [
"Apache-2.0"
] | 0 | 59268bc7ae7eeb962c43080e524eaf1e62100b6c | https://github.com/jacob-parnell-rozetta/longformer_coverage/tree/59268bc7ae7eeb962c43080e524eaf1e62100b6c |
ToMono | import torch
import torch.nn as nn
class ToMono(nn.Module):
def forward(self, waveform: 'torch.Tensor') ->torch.Tensor:
return torch.mean(waveform, dim=0, keepdim=True)
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... | icyda17/very-deep-CNNs | ToMono | false | 10,216 | [
"Apache-2.0"
] | 0 | c275ef222d50dae90e508345ec3be5adfa5e33ce | https://github.com/icyda17/very-deep-CNNs/tree/c275ef222d50dae90e508345ec3be5adfa5e33ce |
VAE_genes | import torch
import torch.utils.data
from torch import nn
from torch.nn import functional as F
class VAE_genes(nn.Module):
def __init__(self):
super(VAE_genes, self).__init__()
self.input_linear = nn.Linear(907, 500)
self.enc_middle = nn.Linear(500, 100)
self.enc_1 = nn.Linear(100... | 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... | helenaandres/adversarial-generation-of-gene-expression-data | VAE_genes | false | 10,217 | [
"MIT"
] | 0 | 9a10f0c364b7daa789ae75ab5b51ed5c7cbcbeb1 | https://github.com/helenaandres/adversarial-generation-of-gene-expression-data/tree/9a10f0c364b7daa789ae75ab5b51ed5c7cbcbeb1 |
Normalize | import torch
import torch.nn as nn
class Normalize(nn.Module):
def forward(self, waveform: 'torch.Tensor') ->torch.Tensor:
return (waveform - waveform.mean()) / waveform.std()
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._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | icyda17/very-deep-CNNs | Normalize | false | 10,218 | [
"Apache-2.0"
] | 0 | c275ef222d50dae90e508345ec3be5adfa5e33ce | https://github.com/icyda17/very-deep-CNNs/tree/c275ef222d50dae90e508345ec3be5adfa5e33ce |
Pad | import torch
import torch.nn as nn
import torch.nn.functional as F
class Pad(nn.Module):
def __init__(self, value: 'float', size: 'int'):
super().__init__()
self.value = value
self.size = size
def forward(self, waveform: 'torch.Tensor') ->torch.Tensor:
return F.pad(waveform, ... | 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... | icyda17/very-deep-CNNs | Pad | false | 10,219 | [
"Apache-2.0"
] | 0 | c275ef222d50dae90e508345ec3be5adfa5e33ce | https://github.com/icyda17/very-deep-CNNs/tree/c275ef222d50dae90e508345ec3be5adfa5e33ce |
SeeInDark | import torch
import torch.nn as nn
class SeeInDark(nn.Module):
def __init__(self, num_classes=10):
super(SeeInDark, self).__init__()
self.conv1_1 = nn.Conv2d(4, 32, kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1)
self.pool1 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | hyeokjae-choi/pytorch-Learning-to-See-in-the-Dark | SeeInDark | false | 10,220 | [
"MIT"
] | 0 | b32bf991072decb3aea348d8cd59acbf34d5da2c | https://github.com/hyeokjae-choi/pytorch-Learning-to-See-in-the-Dark/tree/b32bf991072decb3aea348d8cd59acbf34d5da2c |
HardtanhBoundToPOTNet | import torch
from torch.nn import Conv2d
from torch.nn import Hardtanh
from torch.nn.functional import relu
from torch.nn.functional import hardtanh
import torch.nn.functional
class HardtanhBoundToPOTNet(torch.nn.Module):
def __init__(self):
super(HardtanhBoundToPOTNet, self).__init__()
self.conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 Conv2d
f... | isabella232/model_optimization | HardtanhBoundToPOTNet | false | 10,221 | [
"Apache-2.0"
] | 0 | 074d1dfd8b4d18e57c6186c0ec5e49eb17a0fc7a | https://github.com/isabella232/model_optimization/tree/074d1dfd8b4d18e57c6186c0ec5e49eb17a0fc7a |
Unet | import torch
import torch.nn as nn
def crop(image, new_shape):
plus_h, plus_w = 0, 0
if new_shape[2] % 2 != 0:
plus_h = 1
if new_shape[3] % 2 != 0:
plus_w = 1
middle_height = image.shape[2] // 2
middle_weight = image.shape[3] // 2
go_height = new_shape[2] // 2
go_weight = 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_... | furkannturkmen/pytorch-CNN-architecture | Unet | false | 10,222 | [
"MIT"
] | 0 | 6a864811f51409c1526224c288fe608010e0c888 | https://github.com/furkannturkmen/pytorch-CNN-architecture/tree/6a864811f51409c1526224c288fe608010e0c888 |
Fusion | import torch
import torch.nn as nn
class Fusion(nn.Module):
def __init__(self, input_dim, hidden_dim):
super(Fusion, self).__init__()
self.linear = nn.Linear(input_dim * 4, hidden_dim, bias=True)
self.tanh = nn.Tanh()
def forward(self, x, y):
z = torch.cat([x, y, x * y, x - y... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | hgrhgy/NumSeq2SQL | Fusion | false | 10,223 | [
"MIT"
] | 0 | 6f22fdf108736f979afa2dbd3af14aa9ad4718aa | https://github.com/hgrhgy/NumSeq2SQL/tree/6f22fdf108736f979afa2dbd3af14aa9ad4718aa |
CRF | import torch
import torch.nn as nn
class CRF(nn.Module):
"""
Implements Conditional Random Fields that can be trained via
backpropagation.
"""
def __init__(self, num_tags):
super(CRF, self).__init__()
self.num_tags = num_tags
self.transitions = nn.Parameter(torch.Tensor(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | jbogensperger/DRUG_CROSSNER | CRF | false | 10,224 | [
"MIT"
] | 0 | c82fc4ce6fd6229b48d28bafffe38f5ea3dcd6aa | https://github.com/jbogensperger/DRUG_CROSSNER/tree/c82fc4ce6fd6229b48d28bafffe38f5ea3dcd6aa |
BertLastCLSModule | import torch
from torch import nn
class BertLastCLSModule(nn.Module):
def __init__(self, dropout_prob=0.0):
super().__init__()
self.dropout = nn.Dropout(dropout_prob)
def forward(self, input):
last_hidden = input[-1][:, 0, :]
out = self.dropout(last_hidden)
return out... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | jdunnmon/emmental-tutorials | BertLastCLSModule | false | 10,225 | [
"MIT"
] | 0 | 2aa6c86e2e74943fbf75f4df1e70c5b8614c6c49 | https://github.com/jdunnmon/emmental-tutorials/tree/2aa6c86e2e74943fbf75f4df1e70c5b8614c6c49 |
SelfGating | import torch
import torch as th
import torch.nn as nn
class SelfGating(nn.Module):
def __init__(self, input_dim):
super(SelfGating, self).__init__()
self.fc = nn.Linear(input_dim, input_dim)
def forward(self, input_tensor):
"""Feature gating as used in S3D-G.
"""
spatio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | inbalcroitoru/Information-retrieval-Audio-retrieval-with-text-queries | SelfGating | false | 10,226 | [
"Apache-2.0"
] | 0 | d98ee159c61a8a9a1c433f0bfed14e7005215d5f | https://github.com/inbalcroitoru/Information-retrieval-Audio-retrieval-with-text-queries/tree/d98ee159c61a8a9a1c433f0bfed14e7005215d5f |
QLinear | import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd as A
from torch.autograd.function import once_differentiable
from torch.nn.parameter import Parameter
import torch.nn.parallel
import torch.optim
import torch.utils.data
class WeightQuantization(A.Functio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 Tensor
import torch.nn as nn
import torch.autograd as A
from t... | i207M/pytorch-cifar | QLinear | false | 10,227 | [
"MIT"
] | 0 | df4417b6d0a25515ac82b5aa6151ae2135b2cd5c | https://github.com/i207M/pytorch-cifar/tree/df4417b6d0a25515ac82b5aa6151ae2135b2cd5c |
FusionLayer | import torch
import torch.nn as nn
class FusionLayer(nn.Module):
"""
vector based fusion
m(x, y) = W([x, y, x * y, x - y]) + b
g(x, y) = w([x, y, x * y, x - y]) + b
:returns g(x, y) * m(x, y) + (1 - g(x, y)) * x
"""
def __init__(self, input_dim):
super(FusionLayer, self).__init__(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | hgrhgy/NumSeq2SQL | FusionLayer | false | 10,228 | [
"MIT"
] | 0 | 6f22fdf108736f979afa2dbd3af14aa9ad4718aa | https://github.com/hgrhgy/NumSeq2SQL/tree/6f22fdf108736f979afa2dbd3af14aa9ad4718aa |
QConv2d | import torch
from torch import Tensor
import torch.nn as nn
import torch.autograd as A
from torch.autograd.function import once_differentiable
from torch.nn.parameter import Parameter
import torch.nn.parallel
import torch.optim
import torch.utils.data
class WeightQuantization(A.Function):
@staticmethod
def f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import Tensor
import torch.nn as nn
import torch.autograd as A
from t... | i207M/pytorch-cifar | QConv2d | false | 10,229 | [
"MIT"
] | 0 | df4417b6d0a25515ac82b5aa6151ae2135b2cd5c | https://github.com/i207M/pytorch-cifar/tree/df4417b6d0a25515ac82b5aa6151ae2135b2cd5c |
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