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 |
|---|---|---|---|---|---|---|---|---|---|---|
ContrastiveLoss | import torch
import torch.nn.functional as F
class ContrastiveLoss(torch.nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin=2.0):
super(ContrastiveLoss, 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
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | Rajat-Mehta/Vehicle-Re-identification-UI | ContrastiveLoss | false | 5,756 | [
"MIT"
] | 1 | 9769ae9dac8bd43a3b66f705cb2830fa498649d2 | https://github.com/Rajat-Mehta/Vehicle-Re-identification-UI/tree/9769ae9dac8bd43a3b66f705cb2830fa498649d2 |
ConvLayer | import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
class ConvLayer(nn.Module):
def __init__(self, input_units, output_units, filter_size,
padding_sizes, dropout=0.2):
super(ConvLayer, self).__init__()
self.conv = weight_norm(nn.Conv1d(in_channels=input_units,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RandolphVI/HyperNet | ConvLayer | false | 5,757 | [
"Apache-2.0"
] | 1 | e9f376f5eb087e57360ca41cca2533c3ca967e47 | https://github.com/RandolphVI/HyperNet/tree/e9f376f5eb087e57360ca41cca2533c3ca967e47 |
UnfoldTemporalWindows | import torch
import torch.nn as nn
class UnfoldTemporalWindows(nn.Module):
def __init__(self, window_size, window_stride, window_dilation=1):
super().__init__()
self.window_size = window_size
self.window_stride = window_stride
self.window_dilation = window_dilation
self.pa... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Rgtemze/PersonalityRecognition | UnfoldTemporalWindows | false | 5,758 | [
"MIT"
] | 1 | 90ddd9c02e595d685b8c395ae94d50090288d1f0 | https://github.com/Rgtemze/PersonalityRecognition/tree/90ddd9c02e595d685b8c395ae94d50090288d1f0 |
DeepHeadModule | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from math import sqrt as sqrt
from itertools import product as product
import torchvision.transforms.functional as F
from torch.nn import functional ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | RedHenLab/RedHenAnonymizer | DeepHeadModule | false | 5,759 | [
"MIT"
] | 1 | 3560f1ac5cd5b9c6c7ed8bf322b807d57aedc06a | https://github.com/RedHenLab/RedHenAnonymizer/tree/3560f1ac5cd5b9c6c7ed8bf322b807d57aedc06a |
MaskedConv1d | import torch
import torch.nn as nn
class MaskedConv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, dilation=1,
groups=1, bias=True, causal=True):
if causal:
padding = (kernel_size - 1) * dilation
else:
padding = (kernel_size - 1) * dilatio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Raiselimit/TorchBlocks | MaskedConv1d | false | 5,760 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
KdCeLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class KdCeLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits_S, logits_T, temperature=1):
"""
Calculate the cross entropy between logits_S and logits_T
:param logits_S: Tensor of... | 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
... | Raiselimit/TorchBlocks | KdCeLoss | false | 5,761 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
Loss | import torch
import torch.nn as nn
class Loss(nn.Module):
def __init__(self):
super(Loss, self).__init__()
self.BCELoss = nn.BCELoss(reduce=True, size_average=True)
def forward(self, predict_y, input_y):
loss = self.BCELoss(predict_y, input_y)
return loss
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | RandolphVI/HyperNet | Loss | false | 5,762 | [
"Apache-2.0"
] | 1 | e9f376f5eb087e57360ca41cca2533c3ca967e47 | https://github.com/RandolphVI/HyperNet/tree/e9f376f5eb087e57360ca41cca2533c3ca967e47 |
ContentLoss | import torch
from torch import nn
class ContentLoss(nn.Module):
"""Module to compute the content loss. Allows arbitrary size style images
during initialization and updating the content target.
Usage: During loss network definition set compute_loss to False, to allow,
after initialization iterate throu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | RicCu/NeuralStyle | ContentLoss | false | 5,763 | [
"MIT"
] | 1 | 97dc6aec6b2072a9a187276e047aea885566e1be | https://github.com/RicCu/NeuralStyle/tree/97dc6aec6b2072a9a187276e047aea885566e1be |
LRN | import torch
import torch.nn as nn
import torch.utils.data
class LRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=True
):
super(LRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:
self.average = nn.AvgP... | 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... | Richard456/Dann | LRN | false | 5,764 | [
"MIT"
] | 1 | 1971cf1a7b9ecadc17932a8ecb3f0c34609751a3 | https://github.com/Richard456/Dann/tree/1971cf1a7b9ecadc17932a8ecb3f0c34609751a3 |
Conv2dTime | import torch
import torch.nn as nn
class Conv2dTime(nn.Conv2d):
def __init__(self, in_channels, *args, **kwargs):
"""
Code adapted from https://github.com/EmilienDupont/augmented-neural-odes
Conv2d module where time gets concatenated as a feature map.
Makes ODE func aware of the ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Ravimk07/neural-odes-segmentation | Conv2dTime | false | 5,765 | [
"MIT"
] | 1 | aebda2df029e447ed6a649778ea2f8ea5a169081 | https://github.com/Ravimk07/neural-odes-segmentation/tree/aebda2df029e447ed6a649778ea2f8ea5a169081 |
ActivationQuantizer | from torch.autograd import Function
import torch
import torch.nn as nn
class Round(Function):
@staticmethod
def forward(self, input):
output = torch.round(input)
return output
@staticmethod
def backward(self, grad_output):
grad_input = grad_output.clone()
return grad_... | 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.autograd import F... | RiccardoRuggiero/micronet | ActivationQuantizer | false | 5,766 | [
"MIT"
] | 1 | bfdac2a50a5f0f8484a253b356c06a166bf7e6a0 | https://github.com/RiccardoRuggiero/micronet/tree/bfdac2a50a5f0f8484a253b356c06a166bf7e6a0 |
ConvTran | import torch
from torch import nn
from torch.nn import functional as F
class ConvTran(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConvTran, self).__init__()
self.conv_t = nn.ConvTranspose2d(in_channels, out_channels, 3, 2, 1, 1)
self.batch_norm = nn.InstanceNorm2d(out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | RicCu/NeuralStyle | ConvTran | false | 5,767 | [
"MIT"
] | 1 | 97dc6aec6b2072a9a187276e047aea885566e1be | https://github.com/RicCu/NeuralStyle/tree/97dc6aec6b2072a9a187276e047aea885566e1be |
WeightQuantizer | from torch.autograd import Function
import torch
import torch.nn as nn
class Round(Function):
@staticmethod
def forward(self, input):
output = torch.round(input)
return output
@staticmethod
def backward(self, grad_output):
grad_input = grad_output.clone()
return grad_... | 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.... | RiccardoRuggiero/micronet | WeightQuantizer | false | 5,768 | [
"MIT"
] | 1 | bfdac2a50a5f0f8484a253b356c06a166bf7e6a0 | https://github.com/RiccardoRuggiero/micronet/tree/bfdac2a50a5f0f8484a253b356c06a166bf7e6a0 |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
.. math::
\\begin{array}{ll}
x = context*output \\\\
attn = exp(x_i) / sum_j exp(x_j) \\\\
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Replie/replie-pythorch | Attention | false | 5,769 | [
"Apache-2.0"
] | 1 | b432f88fcd0b3275d18abee7e2909b997570a5dc | https://github.com/Replie/replie-pythorch/tree/b432f88fcd0b3275d18abee7e2909b997570a5dc |
Generator_mnist | from _paritybench_helpers import _mock_config
import torch
import torch.utils.data
from torch import nn
import torch.nn.parallel
from collections import OrderedDict
class Generator_mnist(nn.Module):
def __init__(self, opt):
super(Generator_mnist, self).__init__()
self.decoder = nn.Sequential(Orde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | RicoFio/disentangle_mlp | Generator_mnist | false | 5,770 | [
"MIT"
] | 1 | 1fb3b6070b5846051b8b9e9333e8ee61418f4893 | https://github.com/RicoFio/disentangle_mlp/tree/1fb3b6070b5846051b8b9e9333e8ee61418f4893 |
FocalLoss | import torch
class FocalLoss(torch.nn.Module):
def __init__(self, gamma=2, alpha=0.5, size_average=True):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.alpha = alpha
self.size_average = size_average
self.elipson = 1e-06
def forward(self, logits, labels):
... | 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... | RuiBai1999/HiMatch | FocalLoss | false | 5,771 | [
"MIT"
] | 1 | 199ebc6b06b3cce2b3f2298cb9e20f81c01dc7a6 | https://github.com/RuiBai1999/HiMatch/tree/199ebc6b06b3cce2b3f2298cb9e20f81c01dc7a6 |
GCNdecoder | from torch.nn import Module
import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
from torch.nn import functional as F
class GCN(Module):
"""
Graph Convolutional Network
"""
def __init__(self, in_features, out_features, bias... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | Roxbili/topoGAN | GCNdecoder | false | 5,772 | [
"MIT"
] | 1 | 25cc397bf8925e485d3a39837b8bce552118f5dc | https://github.com/Roxbili/topoGAN/tree/25cc397bf8925e485d3a39837b8bce552118f5dc |
MultiHeadAttention | import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
"""
input:
query --- [N, T_q, query_dim]
key --- [N, T_k, key_dim]
output:
out --- [N, T_q, num_units]
"""
def __init__(self, query_dim, key_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.... | Regnac/Emotional_TTS | MultiHeadAttention | false | 5,773 | [
"BSD-3-Clause"
] | 1 | 38158f622d6a3e14e4b5539f2c2ee34e7cd88885 | https://github.com/Regnac/Emotional_TTS/tree/38158f622d6a3e14e4b5539f2c2ee34e7cd88885 |
Residual | import torch
from torch import nn
from torch.nn import functional as F
class Residual(nn.Module):
"""Unlinke other blocks, this module receives unpadded inputs."""
def __init__(self, channels, kernel_size=3):
super(Residual, self).__init__()
padding = int((kernel_size - 1) / 2)
self.p... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RicCu/NeuralStyle | Residual | false | 5,774 | [
"MIT"
] | 1 | 97dc6aec6b2072a9a187276e047aea885566e1be | https://github.com/RicCu/NeuralStyle/tree/97dc6aec6b2072a9a187276e047aea885566e1be |
_BoundaryRefineModule | import torch
import torch.nn as nn
from torch.optim.lr_scheduler import *
class _BoundaryRefineModule(nn.Module):
def __init__(self, dim):
super(_BoundaryRefineModule, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | Rocketbase-AI/rockets-mobilepose | _BoundaryRefineModule | false | 5,775 | [
"MIT"
] | 1 | be7273dff7fcf7d1023f431f4b63ac8d82978182 | https://github.com/Rocketbase-AI/rockets-mobilepose/tree/be7273dff7fcf7d1023f431f4b63ac8d82978182 |
Discriminator | from torch.nn import Module
import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
from torch.nn import functional as F
class GCN(Module):
"""
Graph Convolutional Network
"""
def __init__(self, in_features, out_features, bias... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Roxbili/topoGAN | Discriminator | false | 5,776 | [
"MIT"
] | 1 | 25cc397bf8925e485d3a39837b8bce552118f5dc | https://github.com/Roxbili/topoGAN/tree/25cc397bf8925e485d3a39837b8bce552118f5dc |
_TextureConvGroup | import torch
from torch import nn
from torch.nn import functional as F
def reflect_padding(x, f, s, half=False):
if half:
denom = 2
else:
denom = 1
_, _, h, w = x.shape
pad_w = w * (s / denom - 1) + f - s
pad_h = h * (s / denom - 1) + f - s
if pad_w % 2 == 1:
pad_l = in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | RicCu/NeuralStyle | _TextureConvGroup | false | 5,777 | [
"MIT"
] | 1 | 97dc6aec6b2072a9a187276e047aea885566e1be | https://github.com/RicCu/NeuralStyle/tree/97dc6aec6b2072a9a187276e047aea885566e1be |
MLP3_clamp_eval | 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 MLP3_clamp_eval(nn.Module):
def __init__(self):
super(MLP3_clamp_eval, self).__init__()
self.fc1 = nn.Linear(32 * 32, 51... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RuokaiYin/UnarySim | MLP3_clamp_eval | false | 5,778 | [
"MIT"
] | 1 | 343ff9abf356a63d526b1df8eb946ad528690a27 | https://github.com/RuokaiYin/UnarySim/tree/343ff9abf356a63d526b1df8eb946ad528690a27 |
HUBHardsigmoid | import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class HUBHardsigmoid(torch.nn.Module):
"""
This is a hub scaled addition (x+1)/2.
"""
def __init__(self, scale=3):
super(HUBHardsigmoid, self).__init__()
self.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 import triton_helpers
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.da... | RuokaiYin/UnarySim | HUBHardsigmoid | false | 5,779 | [
"MIT"
] | 1 | 343ff9abf356a63d526b1df8eb946ad528690a27 | https://github.com/RuokaiYin/UnarySim/tree/343ff9abf356a63d526b1df8eb946ad528690a27 |
MLP3 | 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 MLP3(nn.Module):
def __init__(self, width=512, p=0.5):
super(MLP3, self).__init__()
self.fc1 = nn.Linear(32 * 32, width)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RuokaiYin/UnarySim | MLP3 | false | 5,780 | [
"MIT"
] | 1 | 343ff9abf356a63d526b1df8eb946ad528690a27 | https://github.com/RuokaiYin/UnarySim/tree/343ff9abf356a63d526b1df8eb946ad528690a27 |
MLP3_clamp_train | 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 MLP3_clamp_train(nn.Module):
"""
For unary training, activation clamp is better to be after relu.
no difference for inference whe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RuokaiYin/UnarySim | MLP3_clamp_train | false | 5,781 | [
"MIT"
] | 1 | 343ff9abf356a63d526b1df8eb946ad528690a27 | https://github.com/RuokaiYin/UnarySim/tree/343ff9abf356a63d526b1df8eb946ad528690a27 |
FEM | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from math import sqrt as sqrt
from itertools import product as product
import torchvision.transforms.functional as F
from torch.nn import functional ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | RedHenLab/RedHenAnonymizer | FEM | false | 5,782 | [
"MIT"
] | 1 | 3560f1ac5cd5b9c6c7ed8bf322b807d57aedc06a | https://github.com/RedHenLab/RedHenAnonymizer/tree/3560f1ac5cd5b9c6c7ed8bf322b807d57aedc06a |
TVLoss | import torch
from torch import nn
class TVLoss(nn.Module):
"""Implements Anisotropic Total Variation regularization"""
def __init__(self):
super(TVLoss, self).__init__()
self.criterion = nn.L1Loss()
def forward(self, x):
X = x.detach()
XX = x
_b, _c, h, w = X.shap... | 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_... | RicCu/NeuralStyle | TVLoss | false | 5,783 | [
"MIT"
] | 1 | 97dc6aec6b2072a9a187276e047aea885566e1be | https://github.com/RicCu/NeuralStyle/tree/97dc6aec6b2072a9a187276e047aea885566e1be |
MLP3_hardsig | 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 MLP3_hardsig(nn.Module):
def __init__(self, width=512, p=0.5):
super(MLP3_hardsig, self).__init__()
self.fc1 = nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | RuokaiYin/UnarySim | MLP3_hardsig | false | 5,784 | [
"MIT"
] | 1 | 343ff9abf356a63d526b1df8eb946ad528690a27 | https://github.com/RuokaiYin/UnarySim/tree/343ff9abf356a63d526b1df8eb946ad528690a27 |
EntropyLoss | import torch
import numpy as np
from torch import nn
from torch.nn import functional as F
class EntropyLoss(nn.Module):
""" Module to compute entropy loss """
def __init__(self, normalize):
super(EntropyLoss, self).__init__()
self.normalize = normalize
def forward(self, x):
eps =... | 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 ... | SAP-samples/emnlp2021-attention-contrastive-learning | EntropyLoss | false | 5,785 | [
"Apache-2.0"
] | 1 | dfad1c7c416d963b1b9b018d4182bebbb11ecf1c | https://github.com/SAP-samples/emnlp2021-attention-contrastive-learning/tree/dfad1c7c416d963b1b9b018d4182bebbb11ecf1c |
PKT | import torch
import torch.nn as nn
import torch.optim
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):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | RylanSchaeffer/RepDistiller | PKT | false | 5,786 | [
"BSD-2-Clause"
] | 1 | 3612d9d8f6f913527c7aaec7e5ea557e72ed7c5e | https://github.com/RylanSchaeffer/RepDistiller/tree/3612d9d8f6f913527c7aaec7e5ea557e72ed7c5e |
HardMGUCell | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
def truncated_normal(t, mean=0.0, std=0.01):
torch.nn.init.normal_(t, mean=mea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | RuokaiYin/UnarySim | HardMGUCell | false | 5,787 | [
"MIT"
] | 1 | 343ff9abf356a63d526b1df8eb946ad528690a27 | https://github.com/RuokaiYin/UnarySim/tree/343ff9abf356a63d526b1df8eb946ad528690a27 |
FactorTransfer | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
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
... | 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... | RylanSchaeffer/RepDistiller | FactorTransfer | false | 5,788 | [
"BSD-2-Clause"
] | 1 | 3612d9d8f6f913527c7aaec7e5ea557e72ed7c5e | https://github.com/RylanSchaeffer/RepDistiller/tree/3612d9d8f6f913527c7aaec7e5ea557e72ed7c5e |
RKDLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class RKDLoss(nn.Module):
"""Relational Knowledge Disitllation, CVPR2019"""
def __init__(self, w_d=25, w_a=50):
super(RKDLoss, self).__init__()
self.w_d = w_d
self.w_a = w_a
def forward(self, 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._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | RylanSchaeffer/RepDistiller | RKDLoss | false | 5,789 | [
"BSD-2-Clause"
] | 1 | 3612d9d8f6f913527c7aaec7e5ea557e72ed7c5e | https://github.com/RylanSchaeffer/RepDistiller/tree/3612d9d8f6f913527c7aaec7e5ea557e72ed7c5e |
PA | import torch
import torch.nn as nn
class PA(nn.Module):
def __init__(self, dim):
super().__init__()
self.pa_conv = nn.Conv3d(dim, dim, kernel_size=3, padding=1, groups=dim
)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
return x * self.sigmoid(self.pa_conv(x))... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | SLKaMiHi/ResT-UNet-unsupervised-medical-image-registration-network-based-on-Transformer-and-CNN | PA | false | 5,790 | [
"MIT"
] | 1 | 728624f978f345a1e713046a7dde12d6f84fd3dd | https://github.com/SLKaMiHi/ResT-UNet-unsupervised-medical-image-registration-network-based-on-Transformer-and-CNN/tree/728624f978f345a1e713046a7dde12d6f84fd3dd |
MLP | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLP(nn.Module):
def __init__(self, input_size, output_size):
super(MLP, self).__init__()
self.fc1 = nn.Linear(input_size, 100)
self.policy = nn.Linear(100, output_size)
self.value = nn.Linear(100, 1)
def... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | SaneBow/AttentionAgentCarRacing | MLP | false | 5,791 | [
"Apache-2.0"
] | 1 | 944dc18b99b2c51a25c206f722a0bbc43cb7bbb0 | https://github.com/SaneBow/AttentionAgentCarRacing/tree/944dc18b99b2c51a25c206f722a0bbc43cb7bbb0 |
Mlp | import torch
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | SCIIX/CV-Backbones | Mlp | false | 5,792 | [
"Apache-2.0"
] | 1 | c76acf0742d8c0b7be9bd061ae2a7b247fa618ef | https://github.com/SCIIX/CV-Backbones/tree/c76acf0742d8c0b7be9bd061ae2a7b247fa618ef |
SPoC_pooling | import torch
import torch.nn as nn
class SPoC_pooling(nn.Module):
def __init__(self):
super(SPoC_pooling, self).__init__()
def forward(self, x):
dim = x.size()
pool = nn.AvgPool2d(dim[-1])
x = pool(x)
return x.view(dim[0], dim[1])
def get_inputs():
return [torch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | SIJIEJI/2020-ai-meets-beauty_ntubeauty | SPoC_pooling | false | 5,793 | [
"MIT"
] | 1 | fede564fb3e3029f3fadfe107484c5c7e39c29c5 | https://github.com/SIJIEJI/2020-ai-meets-beauty_ntubeauty/tree/fede564fb3e3029f3fadfe107484c5c7e39c29c5 |
ConcatAvgMaxPooling | import torch
import torch.nn as nn
class ConcatAvgMaxPooling(nn.Module):
def __init__(self, kernel_size=12, stride=1):
super(ConcatAvgMaxPooling, self).__init__()
self.avgpool = nn.AvgPool2d(kernel_size, stride=1)
self.maxpool = nn.MaxPool2d(kernel_size, stride=1)
def forward(self, x... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | SamitHuang/CELNet | ConcatAvgMaxPooling | false | 5,794 | [
"MIT"
] | 1 | 51e067fdb16e723a45a0a60399d568b58cdc011d | https://github.com/SamitHuang/CELNet/tree/51e067fdb16e723a45a0a60399d568b58cdc011d |
SelfAttention | import torch
import torch.nn as nn
class SelfAttention(nn.Module):
"""A simple self-attention solution."""
def __init__(self, data_dim, dim_q):
super(SelfAttention, self).__init__()
self._layers = []
self._fc_q = nn.Linear(data_dim, dim_q)
self._layers.append(self._fc_q)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | SaneBow/AttentionAgentCarRacing | SelfAttention | false | 5,795 | [
"Apache-2.0"
] | 1 | 944dc18b99b2c51a25c206f722a0bbc43cb7bbb0 | https://github.com/SaneBow/AttentionAgentCarRacing/tree/944dc18b99b2c51a25c206f722a0bbc43cb7bbb0 |
fullyCon | import torch
import torch.nn as nn
import torch.nn.functional as F
class fullyCon(nn.Module):
def __init__(self):
super(fullyCon, self).__init__()
self.fc1 = nn.Linear(448 * 3 * 448, 500)
self.fc2 = nn.Linear(500, 100)
self.fc3 = nn.Linear(100, 5)
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_... | Lightingooo/- | fullyCon | false | 5,796 | [
"MIT"
] | 1 | 7b48c2689b693617e46992ac081065cf08f14bf8 | https://github.com/Lightingooo/-/tree/7b48c2689b693617e46992ac081065cf08f14bf8 |
DQN | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN(nn.Module):
def __init__(self, inputs, outputs):
super(DQN, self).__init__()
val = int((inputs + outputs) / 2)
self.fc1 = nn.Linear(inputs, val)
self.fc2 = nn.Linear(val, val)
self.fc3 = nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Sai-56/Multi-Agent-DQN-Routing | DQN | false | 5,797 | [
"MIT"
] | 1 | c8e7038bd0dfb69b3bdbdeb60ff9b98bb081e95e | https://github.com/Sai-56/Multi-Agent-DQN-Routing/tree/c8e7038bd0dfb69b3bdbdeb60ff9b98bb081e95e |
SpatialAttention | import torch
import torch.nn as nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=3, multi_branch=False):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
self.conv1 = nn.Conv2d(2, 1, 3, padding=1, bias=False)
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
assert_... | SamitHuang/CELNet | SpatialAttention | false | 5,798 | [
"MIT"
] | 1 | 51e067fdb16e723a45a0a60399d568b58cdc011d | https://github.com/SamitHuang/CELNet/tree/51e067fdb16e723a45a0a60399d568b58cdc011d |
RegWeightedL1Loss | import torch
import torch.nn as nn
import torch.nn.functional as F
def _gather_feat(feat, ind, mask=None):
dim = feat.size(2)
ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
feat = feat.gather(1, ind)
if mask is not None:
mask = mask.unsqueeze(2).expand_as(feat)
feat = fea... | 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
... | SaqibMamoon/GSDT | RegWeightedL1Loss | false | 5,799 | [
"MIT"
] | 1 | e11c52a67291e973016ed34c3c95659e0af32d48 | https://github.com/SaqibMamoon/GSDT/tree/e11c52a67291e973016ed34c3c95659e0af32d48 |
RawScale | import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class RawScale(torch.nn.Module):
"""
Scale raw data to [-1, 1] in a symmetric manner, which meets bipolar/unipolar bitstream requirements.
The remaining data count for 'quantile' quantile o... | 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... | RuokaiYin/UnarySim | RawScale | false | 5,800 | [
"MIT"
] | 1 | 343ff9abf356a63d526b1df8eb946ad528690a27 | https://github.com/RuokaiYin/UnarySim/tree/343ff9abf356a63d526b1df8eb946ad528690a27 |
Base | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Base(nn.Module):
"""docstring for Base"""
def __init__(self, view_space, feature_space, num_actions, hidden_size):
super(Base, self).__init__()
self.view_space = view_space
self.feature_space =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | SJTUwbl/mfrl_pytorch | Base | false | 5,801 | [
"MIT"
] | 1 | 2b385121cc9a8aa16ed6d554d1dc10f02f2fc5d9 | https://github.com/SJTUwbl/mfrl_pytorch/tree/2b385121cc9a8aa16ed6d554d1dc10f02f2fc5d9 |
CrossLayer | import torch
import torch.nn as nn
import torch.optim
class CrossLayer(nn.Module):
def __init__(self, d, dropout):
super().__init__()
self.linear = nn.Linear(d, d)
self.dropout = nn.Dropout(dropout)
def forward(self, x0, x):
return self.dropout(x0 * self.linear(x)) + x
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... | SauravMaheshkar/rtdl | CrossLayer | false | 5,802 | [
"Apache-2.0"
] | 1 | c3f8051210d1cd7fdffc5a63221e3c4e84415ed8 | https://github.com/SauravMaheshkar/rtdl/tree/c3f8051210d1cd7fdffc5a63221e3c4e84415ed8 |
RegLoss | import torch
import torch.nn as nn
def _reg_loss(regr, gt_regr, mask):
""" L1 regression loss
Arguments:
regr (batch x max_objects x dim)
gt_regr (batch x max_objects x dim)
mask (batch x max_objects)
"""
num = mask.float().sum()
mask = mask.unsqueeze(2).expand_as(gt_regr).float()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | SaqibMamoon/GSDT | RegLoss | false | 5,803 | [
"MIT"
] | 1 | e11c52a67291e973016ed34c3c95659e0af32d48 | https://github.com/SaqibMamoon/GSDT/tree/e11c52a67291e973016ed34c3c95659e0af32d48 |
SpRes | import torch
import torch.nn as nn
class SpRes(nn.Module):
def __init__(self, in_channels=31):
super(SpRes, self).__init__()
self.conv1 = nn.Conv2d(in_channels=31, out_channels=3, bias=False,
kernel_size=1, stride=1)
def forward(self, x):
x = self.conv1(x)
x = 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.triton_helpers import libdevice
import torch.nn as ... | SeVEnMY/hyper-reconstruction | SpRes | false | 5,804 | [
"MIT"
] | 1 | 018c34aaf6884650c36a73bd7f4635f927a79da3 | https://github.com/SeVEnMY/hyper-reconstruction/tree/018c34aaf6884650c36a73bd7f4635f927a79da3 |
L2N | import torch
import torch.nn as nn
class L2N(nn.Module):
def __init__(self, eps=1e-06):
super(L2N, self).__init__()
self.eps = eps
def forward(self, x):
return x / (torch.norm(x, p=2, dim=1, keepdim=True) + self.eps
).expand_as(x)
def __repr__(self):
return s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | SIJIEJI/2020-ai-meets-beauty_ntubeauty | L2N | false | 5,805 | [
"MIT"
] | 1 | fede564fb3e3029f3fadfe107484c5c7e39c29c5 | https://github.com/SIJIEJI/2020-ai-meets-beauty_ntubeauty/tree/fede564fb3e3029f3fadfe107484c5c7e39c29c5 |
Correlation | import torch
import torch.nn as nn
import torch.optim
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"""
... | 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
import torch.optim
assert_size_stride = torch._C._d... | RylanSchaeffer/RepDistiller | Correlation | false | 5,806 | [
"BSD-2-Clause"
] | 1 | 3612d9d8f6f913527c7aaec7e5ea557e72ed7c5e | https://github.com/RylanSchaeffer/RepDistiller/tree/3612d9d8f6f913527c7aaec7e5ea557e72ed7c5e |
SobelConv | import torch
import torch.nn as nn
class SobelConv(nn.Module):
def __init__(self, in_channel=31, batch_num=16):
super(SobelConv, self).__init__()
self.bz = batch_num
self.in_channel = in_channel
self.convx = nn.Conv2d(in_channels=31, out_channels=31, kernel_size
=3, st... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | SeVEnMY/hyper-reconstruction | SobelConv | false | 5,807 | [
"MIT"
] | 1 | 018c34aaf6884650c36a73bd7f4635f927a79da3 | https://github.com/SeVEnMY/hyper-reconstruction/tree/018c34aaf6884650c36a73bd7f4635f927a79da3 |
CustomizedLayer | import torch
import torch.nn as nn
import torch.utils.data
class CustomizedLayer(nn.Module):
def __init__(self, in_dim):
super().__init__()
self.in_dim = in_dim
self.scale = nn.Parameter(torch.Tensor(self.in_dim))
self.bias = nn.Parameter(torch.Tensor(self.in_dim))
def forwar... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | Serjio42/Torch-Pruning | CustomizedLayer | false | 5,808 | [
"MIT"
] | 1 | 8a096df38ddd95a2db39eca5f87b8a26c8d134ef | https://github.com/Serjio42/Torch-Pruning/tree/8a096df38ddd95a2db39eca5f87b8a26c8d134ef |
FastStyle | import torch
from torch import nn
from torch.nn import functional as F
def reflect_padding(x, f, s, half=False):
if half:
denom = 2
else:
denom = 1
_, _, h, w = x.shape
pad_w = w * (s / denom - 1) + f - s
pad_h = h * (s / denom - 1) + f - s
if pad_w % 2 == 1:
pad_l = in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | RicCu/NeuralStyle | FastStyle | false | 5,809 | [
"MIT"
] | 1 | 97dc6aec6b2072a9a187276e047aea885566e1be | https://github.com/RicCu/NeuralStyle/tree/97dc6aec6b2072a9a187276e047aea885566e1be |
ConvNet | import torch
import torch.nn as nn
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=
5, padding=2)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size
=3... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | SGeetansh/dffml | ConvNet | false | 5,810 | [
"MIT"
] | 1 | 04647bdcadef2f7e7b59cdd8ac1e89f17ef1095b | https://github.com/SGeetansh/dffml/tree/04647bdcadef2f7e7b59cdd8ac1e89f17ef1095b |
SoftCrossEntropyLoss | import torch
from torch import Tensor
from typing import List
import torch.nn as nn
import torch.nn.functional as F
class SoftCrossEntropyLoss(nn.Module):
"""
Calculate the CrossEntropyLoss with soft targets
:param weight: Weight to assign to each of the classes. Default: None
:type weight: list of f... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from typing import Lis... | SenWu/fonduer | SoftCrossEntropyLoss | false | 5,811 | [
"MIT"
] | 1 | c4f8d95cec97552b34412c6787eb7370ae17424f | https://github.com/SenWu/fonduer/tree/c4f8d95cec97552b34412c6787eb7370ae17424f |
LocalizationNet | import torch
import torch.utils.data
import torch.nn as nn
class LocalizationNet(nn.Module):
def __init__(self, inplanes, inputsize, nheads=1, use_bn=False):
super(LocalizationNet, self).__init__()
inputH, inputW = inputsize
self.use_bn = use_bn
if self.use_bn:
None
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | Sanny26/indic-htr | LocalizationNet | false | 5,812 | [
"MIT"
] | 1 | c473573b05c251f6e266cbd69acaa7ab18837f37 | https://github.com/Sanny26/indic-htr/tree/c473573b05c251f6e266cbd69acaa7ab18837f37 |
Mac_Pooling | import torch
import torch.nn as nn
class Mac_Pooling(nn.Module):
def __init__(self):
super(Mac_Pooling, self).__init__()
def forward(self, x):
dim = x.size()
pool = nn.MaxPool2d(dim[-1])
x = pool(x)
return x.view(dim[0], dim[1])
def get_inputs():
return [torch.r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | SIJIEJI/2020-ai-meets-beauty_ntubeauty | Mac_Pooling | false | 5,813 | [
"MIT"
] | 1 | fede564fb3e3029f3fadfe107484c5c7e39c29c5 | https://github.com/SIJIEJI/2020-ai-meets-beauty_ntubeauty/tree/fede564fb3e3029f3fadfe107484c5c7e39c29c5 |
TARNetPhi | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class TARNetPhi(nn.Module):
def __init__(self, input_nodes, shared_nodes=200):
super(TARNetPhi, self).__init__()
self.shared1 = nn.Linear(in_features=input_nodes, out_features=
shared_nodes)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Shantanu48114860/PSSAM-GAN | TARNetPhi | false | 5,814 | [
"MIT"
] | 1 | c883431c1d0ebbb42691483f8ac8efaab65410b6 | https://github.com/Shantanu48114860/PSSAM-GAN/tree/c883431c1d0ebbb42691483f8ac8efaab65410b6 |
Mix | import torch
import torch.nn as nn
class Mix(nn.Module):
def __init__(self, m=-0.8):
super(Mix, self).__init__()
w = torch.nn.Parameter(torch.FloatTensor([m]), requires_grad=True)
w = torch.nn.Parameter(w, requires_grad=True)
self.w = w
self.mix_block = nn.Sigmoid()
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ShenZheng2000/Syn2Real-Pytorch | Mix | false | 5,815 | [
"MIT"
] | 1 | 214c800914e2bcd57d4ca74a4c8476a11e1b5905 | https://github.com/ShenZheng2000/Syn2Real-Pytorch/tree/214c800914e2bcd57d4ca74a4c8476a11e1b5905 |
Attention | import torch
import torch.nn.functional as F
import torch.nn as nn
class Attention(nn.Module):
"""
Computing the attention over the words
"""
def __init__(self, input_dim, proj_dim):
super(Attention, self).__init__()
self.input_dim = input_dim
self.proj_dim = proj_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.... | Sein-Jang/R2A | Attention | false | 5,816 | [
"MIT"
] | 1 | f70b69cedb4de3dd60a36963c4b6a881d9d090ee | https://github.com/Sein-Jang/R2A/tree/f70b69cedb4de3dd60a36963c4b6a881d9d090ee |
StochasticGate | import torch
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
class StochasticGate(nn.Module):
"""Stochastically merges features from two levels
with varying size of the receptive field
"""
def __init__(self):
super(StochasticGate, self).__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... | SharhadBashar/1-stage-wseg | StochasticGate | false | 5,817 | [
"Apache-2.0"
] | 1 | 83bf13444f5039ffed2de1605f09b3f90b525586 | https://github.com/SharhadBashar/1-stage-wseg/tree/83bf13444f5039ffed2de1605f09b3f90b525586 |
Network | import torch
import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Network, self).__init__()
self.fc = torch.nn.Linear(n_feature, n_hidden)
self.out = torch.nn.Linear(n_hidden, n_output)
def forward(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ShiZhuming/ChallengeCup | Network | false | 5,818 | [
"MIT"
] | 1 | c422d1e9864e2bc663a3ddb5e3487a04a0525fcc | https://github.com/ShiZhuming/ChallengeCup/tree/c422d1e9864e2bc663a3ddb5e3487a04a0525fcc |
SpatialPyramidPooling | import torch
import torch.nn as nn
class SpatialPyramidPooling(nn.Module):
def __init__(self, pool_sizes=[5, 9, 13]):
super(SpatialPyramidPooling, self).__init__()
self.maxpools = nn.ModuleList([nn.MaxPool2d(pool_size, 1, pool_size //
2) for pool_size in pool_sizes])
def forward(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | SekiroRong/YOLOP | SpatialPyramidPooling | false | 5,819 | [
"MIT"
] | 1 | e59628925dfaadfa549790cd0cf1c8a7e1139a2c | https://github.com/SekiroRong/YOLOP/tree/e59628925dfaadfa549790cd0cf1c8a7e1139a2c |
M | import torch
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
class M(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
y = torch.cat([x, y])
return y
def get_in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
as... | ShuaihuaLu/examples | M | false | 5,820 | [
"BSD-3-Clause"
] | 1 | 2639cf050493df9d3cbf065d45e6025733add0f4 | https://github.com/ShuaihuaLu/examples/tree/2639cf050493df9d3cbf065d45e6025733add0f4 |
SoftDiceLossSquared | import torch
import numpy as np
from torch import nn
import torch.jit
import torch.nn.functional
def sum_tensor(inp, axes, keepdim=False):
axes = np.unique(axes).astype(int)
if keepdim:
for ax in axes:
inp = inp.sum(int(ax), keepdim=True)
else:
for ax in sorted(axes, reverse=Tr... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
from torch import nn
import torch.jit
import torch.nn.functional
assert_size_stride = torch._C._dynamo.guards.assert_size... | ShishuaiHu/DCAC | SoftDiceLossSquared | false | 5,821 | [
"MIT"
] | 1 | de04d00edde1b38385a8e5aade7541e2c22807e7 | https://github.com/ShishuaiHu/DCAC/tree/de04d00edde1b38385a8e5aade7541e2c22807e7 |
Foo | import torch
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
def add_lowp(a: 'torch.Tensor', b: 'torch.Tensor'):
a, b = a.float(), b.float()
c = a + b
return c.half()
def sigmoid_lowp(x: 'torch.Tensor'):
x = 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.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
as... | ShuaihuaLu/examples | Foo | false | 5,822 | [
"BSD-3-Clause"
] | 1 | 2639cf050493df9d3cbf065d45e6025733add0f4 | https://github.com/ShuaihuaLu/examples/tree/2639cf050493df9d3cbf065d45e6025733add0f4 |
TripletLoss | import torch
from torch import nn
class TripletLoss(nn.Module):
def __init__(self, margin):
super(TripletLoss, self).__init__()
self.margin = margin
self.relu = nn.ReLU()
def forward(self, anchor, positive, negative, size_average=True):
cosine_positive = nn.CosineSimilarity(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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | SeungHeonDoh/music_zeroshot_models | TripletLoss | false | 5,823 | [
"MIT"
] | 1 | 38f80df868da357f3cb30522ad2e2031f0bc184e | https://github.com/SeungHeonDoh/music_zeroshot_models/tree/38f80df868da357f3cb30522ad2e2031f0bc184e |
_Enc | import torch
class _NestedEnc(torch.nn.Module):
def __init__(self, f):
super().__init__()
self.f = f
def forward(self, x):
return self.f(x)
class _Enc(torch.nn.Module):
def __init__(self):
super().__init__()
self.e1 = _NestedEnc(torch.nn.Linear(4, 2))
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | SimonNick/metakbc | _Enc | false | 5,825 | [
"MIT"
] | 1 | b502104e00afcb274c673ecd3aaa0415933e745e | https://github.com/SimonNick/metakbc/tree/b502104e00afcb274c673ecd3aaa0415933e745e |
FocalLossBinary | import torch
import torch.nn.functional as F
import torch.jit
import torch.nn.functional
from functools import partial
from torch.nn.modules.loss import _Loss
def reduced_focal_loss(outputs: 'torch.Tensor', targets: 'torch.Tensor',
threshold: 'float'=0.5, gamma: 'float'=2.0, reduction='mean'):
"""
Compute... | 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... | ShishuaiHu/DCAC | FocalLossBinary | false | 5,826 | [
"MIT"
] | 1 | de04d00edde1b38385a8e5aade7541e2c22807e7 | https://github.com/ShishuaiHu/DCAC/tree/de04d00edde1b38385a8e5aade7541e2c22807e7 |
GDL | import torch
import numpy as np
from torch import nn
import torch.jit
import torch.nn.functional
def sum_tensor(inp, axes, keepdim=False):
axes = np.unique(axes).astype(int)
if keepdim:
for ax in axes:
inp = inp.sum(int(ax), keepdim=True)
else:
for ax in sorted(axes, reverse=Tr... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
from torch import nn
import torch.jit
import torch.nn.functional
assert_size_stride = torch._C._dynamo.guards.assert_size... | ShishuaiHu/DCAC | GDL | false | 5,827 | [
"MIT"
] | 1 | de04d00edde1b38385a8e5aade7541e2c22807e7 | https://github.com/ShishuaiHu/DCAC/tree/de04d00edde1b38385a8e5aade7541e2c22807e7 |
TernaryTanh | import torch
from torch import nn
class TernaryTanh(nn.Module):
def __init__(self, beta=2.0, varying_beta=True):
super(TernaryTanh, self).__init__()
self.beta = beta
self.varying_beta = varying_beta
def forward(self, x):
m = torch.nn.Tanh()
if self.beta >= 1.0:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | SohamMazumder/Federated_Segmentation | TernaryTanh | false | 5,828 | [
"MIT"
] | 1 | d4eb681441003ba20f8b251a42a811c8c436f04e | https://github.com/SohamMazumder/Federated_Segmentation/tree/d4eb681441003ba20f8b251a42a811c8c436f04e |
DiceLoss | import torch
from torch import nn
from torch.autograd import Variable
def expand_as_one_hot(input, C, ignore_index=None):
"""
Converts NxDxHxW label image to NxCxDxHxW, where each label is stored in a separate channel
:param input: 4D input image (NxDxHxW)
:param C: number of channels/labels
:para... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | SohamMazumder/Federated_Segmentation | DiceLoss | false | 5,829 | [
"MIT"
] | 1 | d4eb681441003ba20f8b251a42a811c8c436f04e | https://github.com/SohamMazumder/Federated_Segmentation/tree/d4eb681441003ba20f8b251a42a811c8c436f04e |
ShallowNet | import torch
import torch.nn as nn
class ShallowNet(nn.Module):
def __init__(self, n_features):
super(ShallowNet, self).__init__()
self.a1 = nn.Linear(n_features, 2)
def forward(self, x):
return torch.sigmoid(self.a1(x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | SkBlaz/KBNR | ShallowNet | false | 5,830 | [
"MIT"
] | 1 | 4c37fe3fdfa7719572affd617e2dab43a54ba1d5 | https://github.com/SkBlaz/KBNR/tree/4c37fe3fdfa7719572affd617e2dab43a54ba1d5 |
MyElementwiseModule | import torch
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
class MyElementwiseModule(torch.nn.Module):
def forward(self, x, y):
return x * y + y
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
as... | ShuaihuaLu/examples | MyElementwiseModule | false | 5,831 | [
"BSD-3-Clause"
] | 1 | 2639cf050493df9d3cbf065d45e6025733add0f4 | https://github.com/ShuaihuaLu/examples/tree/2639cf050493df9d3cbf065d45e6025733add0f4 |
SE | import torch
from itertools import chain as chain
import torch.utils.data
import torch.nn as nn
class SwishEfficient(torch.autograd.Function):
"""Swish activation function: x * sigmoid(x)."""
@staticmethod
def forward(ctx, x):
result = x * torch.sigmoid(x)
ctx.save_for_backward(x)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from itertools import chain a... | SheldongChen/SlowFast | SE | false | 5,832 | [
"Apache-2.0"
] | 1 | 298cd1648bcaaafa7d436bf286a2c7f243f36416 | https://github.com/SheldongChen/SlowFast/tree/298cd1648bcaaafa7d436bf286a2c7f243f36416 |
GeneralizedDiceLoss | import torch
from torch import nn
from torch.autograd import Variable
def expand_as_one_hot(input, C, ignore_index=None):
"""
Converts NxDxHxW label image to NxCxDxHxW, where each label is stored in a separate channel
:param input: 4D input image (NxDxHxW)
:param C: number of channels/labels
:para... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | SohamMazumder/Federated_Segmentation | GeneralizedDiceLoss | false | 5,833 | [
"MIT"
] | 1 | d4eb681441003ba20f8b251a42a811c8c436f04e | https://github.com/SohamMazumder/Federated_Segmentation/tree/d4eb681441003ba20f8b251a42a811c8c436f04e |
TwoNet | import torch
import torch.nn as nn
class TwoNet(nn.Module):
def __init__(self, n_features, embedding_dim=256):
super(TwoNet, self).__init__()
self.a1 = nn.Linear(n_features, embedding_dim)
self.a2 = nn.Linear(embedding_dim, 2)
def forward(self, x):
x = torch.relu(self.a1(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_... | SkBlaz/KBNR | TwoNet | false | 5,834 | [
"MIT"
] | 1 | 4c37fe3fdfa7719572affd617e2dab43a54ba1d5 | https://github.com/SkBlaz/KBNR/tree/4c37fe3fdfa7719572affd617e2dab43a54ba1d5 |
ConvBnRelu | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class ConvBnRelu(nn.Module):
"""
A block of convolution, relu, batchnorm
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SkywalkerAtlas/HRGAN | ConvBnRelu | false | 5,835 | [
"MIT"
] | 1 | bf6d58c1f3c6e042c7ea70319a25e3420531d552 | https://github.com/SkywalkerAtlas/HRGAN/tree/bf6d58c1f3c6e042c7ea70319a25e3420531d552 |
GenerativeLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class GenerativeLoss(nn.Module):
def __init__(self):
super(GenerativeLoss, self).__init__()
self.criterion = nn.BCELoss(reduction='mean')
def forward(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 libdevice, math as tl_math
import torc... | SkywalkerAtlas/HRGAN | GenerativeLoss | false | 5,836 | [
"MIT"
] | 1 | bf6d58c1f3c6e042c7ea70319a25e3420531d552 | https://github.com/SkywalkerAtlas/HRGAN/tree/bf6d58c1f3c6e042c7ea70319a25e3420531d552 |
Encoder | import torch
import torch.nn as nn
import torch.nn.parallel
from torch.autograd import Variable
class Encoder(nn.Module):
def __init__(self, x_dim, h_dim, z_dim):
super(Encoder, self).__init__()
self.x_dim = x_dim
self.h_dim = h_dim
self.z_dim = z_dim
self.relu = nn.LeakyR... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch.autograd import Variab... | Shimaa1/group_activity_gcn | Encoder | false | 5,837 | [
"MIT"
] | 1 | 53f86e93eb7a78d537532d48c836ce30cbf7e8d1 | https://github.com/Shimaa1/group_activity_gcn/tree/53f86e93eb7a78d537532d48c836ce30cbf7e8d1 |
ConvTripleBlock | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class ConvBnRelu(nn.Module):
"""
A block of convolution, relu, batchnorm
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SkywalkerAtlas/HRGAN | ConvTripleBlock | false | 5,838 | [
"MIT"
] | 1 | bf6d58c1f3c6e042c7ea70319a25e3420531d552 | https://github.com/SkywalkerAtlas/HRGAN/tree/bf6d58c1f3c6e042c7ea70319a25e3420531d552 |
Residual | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class ConvBnRelu(nn.Module):
"""
A block of convolution, relu, batchnorm
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SkywalkerAtlas/HRGAN | Residual | false | 5,839 | [
"MIT"
] | 1 | bf6d58c1f3c6e042c7ea70319a25e3420531d552 | https://github.com/SkywalkerAtlas/HRGAN/tree/bf6d58c1f3c6e042c7ea70319a25e3420531d552 |
ThreeNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class ThreeNet(nn.Module):
def __init__(self, n_features, e1=2048, e2=1024, e3=640, e4=512, e5=216,
p=0.4):
super(ThreeNet, self).__init__()
self.a1 = nn.Linear(n_features, e1)
self.a2 = nn.Linear(e1, e2)
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.triton_helpers import libdevice
import torch.nn as ... | SkBlaz/KBNR | ThreeNet | false | 5,840 | [
"MIT"
] | 1 | 4c37fe3fdfa7719572affd617e2dab43a54ba1d5 | https://github.com/SkBlaz/KBNR/tree/4c37fe3fdfa7719572affd617e2dab43a54ba1d5 |
AndModule | import torch
import torch.nn as nn
import torch.nn
class AndModule(nn.Module):
def forward(self, attn1, attn2):
out = torch.min(attn1, attn2)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert... | SpyrosMouselinos/DeltaFormers | AndModule | false | 5,841 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
FiveNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class FiveNet(nn.Module):
def __init__(self, n_features, e1=1024, e2=2048, e3=1024, e4=640, e5=
512, p=0.4):
super(FiveNet, self).__init__()
self.a1 = nn.Linear(n_features, e2)
self.a2 = nn.Linear(e2, e3)
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.triton_helpers import libdevice
import torch.nn as ... | SkBlaz/KBNR | FiveNet | false | 5,842 | [
"MIT"
] | 1 | 4c37fe3fdfa7719572affd617e2dab43a54ba1d5 | https://github.com/SkBlaz/KBNR/tree/4c37fe3fdfa7719572affd617e2dab43a54ba1d5 |
OrModule | import torch
import torch.nn as nn
import torch.nn
class OrModule(nn.Module):
def forward(self, attn1, attn2):
out = torch.max(attn1, attn2)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert... | SpyrosMouselinos/DeltaFormers | OrModule | false | 5,843 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
OneLayerFCBodyWithAction | import torch
import torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class OneLayerFCBodyWithAction(nn.Module):
def __init__(self, state_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Sohojoe/UdacityDeepRL-Project2 | OneLayerFCBodyWithAction | false | 5,844 | [
"MIT"
] | 1 | 7137eea0b606ea32d00424d23130ff213f03ecf1 | https://github.com/Sohojoe/UdacityDeepRL-Project2/tree/7137eea0b606ea32d00424d23130ff213f03ecf1 |
QREmbeddingBag | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class QREmbeddingBag(nn.Module):
"""Computes sums or means over two 'bags' of embeddings, one using the quotient
of the indices and the other using the remainder of the indices, witho... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
import torch.nn as nn
from torch.nn.parameter import Paramet... | SplitInfinity/dlrm | QREmbeddingBag | false | 5,845 | [
"MIT"
] | 1 | 726dc9059be94b249d41e9b5a399c991fe687edb | https://github.com/SplitInfinity/dlrm/tree/726dc9059be94b249d41e9b5a399c991fe687edb |
PreActBlockNoBN | import torch
import torch.nn as nn
import torch.nn.functional as F
class PreActBlockNoBN(nn.Module):
"""Pre-activation version of the BasicBlock."""
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(PreActBlockNoBN, self).__init__()
self.conv1 = nn.Conv2d(in_planes, pla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Spijkervet/Greedy_InfoMax | PreActBlockNoBN | false | 5,846 | [
"MIT"
] | 1 | d1784da7995e029d07691ee0977fea49383fb0f8 | https://github.com/Spijkervet/Greedy_InfoMax/tree/d1784da7995e029d07691ee0977fea49383fb0f8 |
SimpleNet | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributions as D
class SimpleNet(nn.Module):
def __init__(self, s_dim, a_dim):
super(SimpleNet, self).__init__()
self.s_dim = s_dim
self.a_dim = a_dim
self.a1 = nn.Linear(s_dim, 100)
self.mu ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SpencerLo-CMU/pytorch-rl-suite | SimpleNet | false | 5,847 | [
"MIT"
] | 1 | 52b215f38cbb4c39a0ccfff48ab8262b1c9ef4a0 | https://github.com/SpencerLo-CMU/pytorch-rl-suite/tree/52b215f38cbb4c39a0ccfff48ab8262b1c9ef4a0 |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn
import torch as torch
import torch.sparse
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.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.... | Somefive/cogdl | BertAttention | false | 5,848 | [
"MIT"
] | 1 | 1c5ab88aafc27529495d0d22f781055619e27cb2 | https://github.com/Somefive/cogdl/tree/1c5ab88aafc27529495d0d22f781055619e27cb2 |
BertIntermediate | from _paritybench_helpers import _mock_config
from torch.nn import Module
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + 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
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | SpyrosMouselinos/NVLR_solver | BertIntermediate | false | 5,849 | [
"Apache-2.0"
] | 1 | 7fe12f9eab980ee6959f0b8797aef779b3270c25 | https://github.com/SpyrosMouselinos/NVLR_solver/tree/7fe12f9eab980ee6959f0b8797aef779b3270c25 |
QueryModule | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class QueryModule(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv1 = nn.Conv2d(dim, dim, kernel_size=(3, 3), padding=1)
self.conv2 = nn.Conv2d(dim, dim, kernel_size=(3, 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | SpyrosMouselinos/DeltaFormers | QueryModule | false | 5,850 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
AttentionModule | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class AttentionModule(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv1 = nn.Conv2d(dim, dim, kernel_size=(3, 3), padding=1)
self.conv2 = nn.Conv2d(dim, dim, kernel_size=(3, 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | SpyrosMouselinos/DeltaFormers | AttentionModule | false | 5,851 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
ComparisonModule | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class ComparisonModule(nn.Module):
def __init__(self, dim):
super().__init__()
self.projection = nn.Conv2d(2 * dim, dim, kernel_size=(1, 1), padding=0
)
self.conv1 = nn.Conv2d(dim, dim, 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 ... | SpyrosMouselinos/DeltaFormers | ComparisonModule | false | 5,852 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
ResBlock | import torch
import torch.nn.functional as F
class ResBlock(torch.nn.Module):
def __init__(self, channels):
super(ResBlock, self).__init__()
self.channels = channels
self.conv1 = torch.nn.Conv2d(channels, channels, kernel_size=(3, 3),
padding=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
assert_size_stride = torch._C... | StarsStation/DeepLearning | ResBlock | false | 5,853 | [
"MIT"
] | 1 | a4c833af93652069f19a8c6f0b1e42cde64bbb79 | https://github.com/StarsStation/DeepLearning/tree/a4c833af93652069f19a8c6f0b1e42cde64bbb79 |
TwoLayerFCBodyWithAction | import torch
import torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class TwoLayerFCBodyWithAction(nn.Module):
def __init__(self, state_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Sohojoe/UdacityDeepRL-Project2 | TwoLayerFCBodyWithAction | false | 5,854 | [
"MIT"
] | 1 | 7137eea0b606ea32d00424d23130ff213f03ecf1 | https://github.com/Sohojoe/UdacityDeepRL-Project2/tree/7137eea0b606ea32d00424d23130ff213f03ecf1 |
Encoder | import torch
import torch.nn as nn
class Encoder(nn.Module):
def __init__(self, input_dim, hidden_dim, latent_dim):
super(Encoder, self).__init__()
self.FC_input = nn.Linear(input_dim, hidden_dim)
self.FC_mean = nn.Linear(hidden_dim, latent_dim)
self.FC_var = nn.Linear(hidden_dim,... | 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... | StefanNa/dtu_mlops | Encoder | false | 5,855 | [
"Apache-2.0"
] | 1 | 148f3427f8d090d39d127857be8a37832f800279 | https://github.com/StefanNa/dtu_mlops/tree/148f3427f8d090d39d127857be8a37832f800279 |
CosineClassifier | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class CosineClassifier(nn.Module):
def __init__(self, classes, channels=512):
super().__init__()
self.channels = channels
self.cls = nn.Conv2d(channels, classes, 1, bias=False)
self.scaler =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SirRob1997/DomainBed | CosineClassifier | false | 5,856 | [
"MIT"
] | 1 | 7399a2b0a63df48f4b67755a3f33901223d5c8fb | https://github.com/SirRob1997/DomainBed/tree/7399a2b0a63df48f4b67755a3f33901223d5c8fb |
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