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 |
|---|---|---|---|---|---|---|---|---|---|---|
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action, phi=0.05):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DanielTakeshi/DCUR | Actor | false | 355 | [
"MIT"
] | 0 | 1cdb00e7e68060ad3bba9a497106c327f6b5a663 | https://github.com/DanielTakeshi/DCUR/tree/1cdb00e7e68060ad3bba9a497106c327f6b5a663 |
SelfAttention | import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
def __init__(self, num_q_channels: 'int', num_kv_channels: 'int',
num_heads: 'int', dropout: 'float'):
super().__init__()
self.attention = nn.MultiheadAttention(embed_dim=num_q_channels,
num_heads=num_head... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DartingMelody/perceiver-io | SelfAttention | false | 356 | [
"Apache-2.0"
] | 0 | fb818b1763f61e259b23b8b014df2ac01c303a54 | https://github.com/DartingMelody/perceiver-io/tree/fb818b1763f61e259b23b8b014df2ac01c303a54 |
SpaceToDepth | import torch
from torch import nn
class SpaceToDepth(nn.Module):
def __init__(self, block_size):
super().__init__()
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, C, H // self.bs, self.bs, W // self.bs, self.bs)
x = x.permute(0, 3, 5, 1... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Dauriel/weather4cast2021 | SpaceToDepth | false | 357 | [
"Apache-2.0"
] | 0 | 29e818c4bcd488ec84b51558bf5392e4a887db70 | https://github.com/Dauriel/weather4cast2021/tree/29e818c4bcd488ec84b51558bf5392e4a887db70 |
CrossAttention | import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
def __init__(self, num_q_channels: 'int', num_kv_channels: 'int',
num_heads: 'int', dropout: 'float'):
super().__init__()
self.attention = nn.MultiheadAttention(embed_dim=num_q_channels,
num_heads=num_head... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DartingMelody/perceiver-io | CrossAttention | false | 358 | [
"Apache-2.0"
] | 0 | fb818b1763f61e259b23b8b014df2ac01c303a54 | https://github.com/DartingMelody/perceiver-io/tree/fb818b1763f61e259b23b8b014df2ac01c303a54 |
Scale | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, nchannels, bias=True, init_scale=1.0):
super().__init__()
self.nchannels = nchannels
self.weight = nn.Parameter(torch.Tensor(1, nchannels, 1, 1))
if bias:
self.bias = nn.Parameter(torch.Tenso... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | CuongNguyen218/ObjectDetection-OneStageDet | Scale | false | 359 | [
"MIT"
] | 0 | 60efe8b0ee6782b2aea20a32264b2ce1fc21901f | https://github.com/CuongNguyen218/ObjectDetection-OneStageDet/tree/60efe8b0ee6782b2aea20a32264b2ce1fc21901f |
Upsample_ | import torch
from torch import nn
class Upsample_(nn.Module):
def __init__(self, scale=2):
super(Upsample_, self).__init__()
self.upsample = nn.Upsample(mode='bilinear', scale_factor=scale)
def forward(self, x):
return self.upsample(x)
def get_inputs():
return [torch.rand([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 import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Dauriel/weather4cast2021 | Upsample_ | false | 360 | [
"Apache-2.0"
] | 0 | 29e818c4bcd488ec84b51558bf5392e4a887db70 | https://github.com/Dauriel/weather4cast2021/tree/29e818c4bcd488ec84b51558bf5392e4a887db70 |
SubPixelConvolutionalBlock | import torch
from torch import nn
class SubPixelConvolutionalBlock(nn.Module):
"""
A subpixel convolutional block, comprising convolutional, pixel-shuffle, and PReLU activation layers.
"""
def __init__(self, kernel_size=3, n_channels=64, scaling_factor=2):
"""
:param kernel_size: kern... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | DanielLiang1/a-PyTorch-Tutorial-to-Super-Resolution | SubPixelConvolutionalBlock | false | 361 | [
"MIT"
] | 0 | cf7b519029687fe9726bb194fe3765934afa18b3 | https://github.com/DanielLiang1/a-PyTorch-Tutorial-to-Super-Resolution/tree/cf7b519029687fe9726bb194fe3765934afa18b3 |
DoubleDense | import torch
from torch import nn
from torch.nn import functional as F
class DoubleDense(nn.Module):
def __init__(self, in_channels, hidden_neurons, output_channels):
super(DoubleDense, self).__init__()
self.dense1 = nn.Linear(in_channels, out_features=hidden_neurons)
self.dense2 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Dauriel/weather4cast2021 | DoubleDense | false | 362 | [
"Apache-2.0"
] | 0 | 29e818c4bcd488ec84b51558bf5392e4a887db70 | https://github.com/Dauriel/weather4cast2021/tree/29e818c4bcd488ec84b51558bf5392e4a887db70 |
BasicDeconv | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicDeconv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
use_bn=False):
super(BasicDeconv, self).__init__()
self.use_bn = use_bn
self.tconv = nn.ConvTranspose2d(in_channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | DecAngel/C-3-Framework | BasicDeconv | false | 363 | [
"MIT"
] | 0 | 00b792b4481a0ec9d7e30e290c66e7020235e79b | https://github.com/DecAngel/C-3-Framework/tree/00b792b4481a0ec9d7e30e290c66e7020235e79b |
LinearNet | import torch
import torch.nn
import torch.optim
class LinearNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
super().__init__()
self.linear1 = torch.nn.Linear(D_in, H)
self.nonlinear = torch.nn.ReLU()
self.linear2 = torch.nn.Linear(H, D_out)
def forward(self, x: 'tor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DavidV17/ReAgent | LinearNet | false | 364 | [
"BSD-3-Clause"
] | 0 | 9b55696849cf133bc8494a5bce57df420ffe7517 | https://github.com/DavidV17/ReAgent/tree/9b55696849cf133bc8494a5bce57df420ffe7517 |
AngleLoss | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from typing import *
class AngleLoss(nn.Module):
def __init__(self):
super(AngleLoss, self).__init__()
def forward(self, angle, an... | 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
i... | DeVriesMatt/pointMLP-pytorch | AngleLoss | false | 365 | [
"Apache-2.0"
] | 0 | e9c09a2038551e83b072353f3fd7e3294463e892 | https://github.com/DeVriesMatt/pointMLP-pytorch/tree/e9c09a2038551e83b072353f3fd7e3294463e892 |
VAE | import torch
import torch.nn as nn
import torch.nn.functional as F
class VAE(nn.Module):
def __init__(self, state_dim, action_dim, latent_dim, max_action):
super(VAE, self).__init__()
self.e1 = nn.Linear(state_dim + action_dim, 750)
self.e2 = nn.Linear(750, 750)
self.mean = nn.Lin... | 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... | DanielTakeshi/DCUR | VAE | false | 366 | [
"MIT"
] | 0 | 1cdb00e7e68060ad3bba9a497106c327f6b5a663 | https://github.com/DanielTakeshi/DCUR/tree/1cdb00e7e68060ad3bba9a497106c327f6b5a663 |
Upsample | import torch
from torch import nn
class Upsample(nn.Module):
def __init__(self, input_dim, output_dim, kernel, stride):
super(Upsample, self).__init__()
self.upsample = nn.ConvTranspose2d(input_dim, output_dim,
kernel_size=kernel, stride=stride)
def forward(self, x):
retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Dauriel/weather4cast2021 | Upsample | false | 367 | [
"Apache-2.0"
] | 0 | 29e818c4bcd488ec84b51558bf5392e4a887db70 | https://github.com/Dauriel/weather4cast2021/tree/29e818c4bcd488ec84b51558bf5392e4a887db70 |
DECLoss | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class DECLoss(nn.Module):
def __init__(self):
super(DECLoss, self).__init__()
def target_distribution(self, q):
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
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.u... | Crazy-Jack/SpatialExpGeneCluster | DECLoss | false | 368 | [
"MIT"
] | 0 | 9e57c308d1c577a936a2358d0641c65b8130034f | https://github.com/Crazy-Jack/SpatialExpGeneCluster/tree/9e57c308d1c577a936a2358d0641c65b8130034f |
SAModule_Head | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv(nn.Module):
def __init__(self, in_channels, out_channels, use_bn=False, **kwargs):
super(BasicConv, self).__init__()
self.use_bn = use_bn
self.conv = nn.Conv2d(in_channels, out_channels, bias=not self.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DecAngel/C-3-Framework | SAModule_Head | false | 369 | [
"MIT"
] | 0 | 00b792b4481a0ec9d7e30e290c66e7020235e79b | https://github.com/DecAngel/C-3-Framework/tree/00b792b4481a0ec9d7e30e290c66e7020235e79b |
TripletLoss | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import *
from torch.optim.lr_scheduler import *
def _batch_hard(mat_distance, mat_similarity, indice=False):
sorted_mat_distance, positive_indices = torch.sort(mat_distance + -
9999999.0 * (1 - mat_similarity), dim=1, descendi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Dingyuan-Zheng/ctf-UDA | TripletLoss | false | 370 | [
"MIT"
] | 0 | 3e3c67f68d7eb0b52a16a259e5a77e153062c4fd | https://github.com/Dingyuan-Zheng/ctf-UDA/tree/3e3c67f68d7eb0b52a16a259e5a77e153062c4fd |
MultiHeadAttention | import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
def __init__(self, num_q_channels: 'int', num_kv_channels: 'int',
num_heads: 'int', dropout: 'float'):
super().__init__()
self.attention = nn.MultiheadAttention(embed_dim=num_q_channels,
num_heads=num_head... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DartingMelody/perceiver-io | MultiHeadAttention | false | 371 | [
"Apache-2.0"
] | 0 | fb818b1763f61e259b23b8b014df2ac01c303a54 | https://github.com/DartingMelody/perceiver-io/tree/fb818b1763f61e259b23b8b014df2ac01c303a54 |
AUGLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class AUGLoss(nn.Module):
def __init__(self):
super(AUGLoss, self).__init__()
def forward(self, x1, x2):
b = x1 - x2
b = b * b
b = b.sum(1)
b = torch.sqrt(b)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | DonghyunAhn/sadvirus | AUGLoss | false | 372 | [
"MIT"
] | 0 | cdcc98812d613962a7003ff0c6013d0805bde024 | https://github.com/DonghyunAhn/sadvirus/tree/cdcc98812d613962a7003ff0c6013d0805bde024 |
Conv2d_fw | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class Conv2d_fw(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, bias=True):
super(Conv2d_fw, self).__init__(in_channels, out_channels,
kernel_size, stride=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | DingYuan0118/Meta-Fine-Tuning | Conv2d_fw | false | 373 | [
"MIT"
] | 0 | 531b7418420c072844216ec5217f1f03f6419a79 | https://github.com/DingYuan0118/Meta-Fine-Tuning/tree/531b7418420c072844216ec5217f1f03f6419a79 |
ContrastiveDistanceLoss | import torch
import torch.nn as nn
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.distributed
import torch.multiprocessing
import torch.backends
class ContrastiveDistanceLoss(nn.Module):
"""The Contrastive distance l... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.nn.modules.loss import *
from torch.nn.modules import *
... | Dokholyan/catalyst | ContrastiveDistanceLoss | false | 374 | [
"Apache-2.0"
] | 0 | de8e681676d76741fdb722d4cd77274ba616915d | https://github.com/Dokholyan/catalyst/tree/de8e681676d76741fdb722d4cd77274ba616915d |
ContrastiveLoss | import torch
class ContrastiveLoss(torch.nn.Module):
def __init__(self, margin=1.0):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, anchor, positive, negative):
distance_anchor_positive = (anchor - positive).pow(2).sum(1)
distance_anchor_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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | DongChengdongHangZhou/Siamese-Network-tiff | ContrastiveLoss | false | 375 | [
"MIT"
] | 0 | aaf923ad59301af1b3237e605964341a90dc414b | https://github.com/DongChengdongHangZhou/Siamese-Network-tiff/tree/aaf923ad59301af1b3237e605964341a90dc414b |
ContrastiveEmbeddingLoss | import torch
import torch.nn as nn
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.distributed
import torch.multiprocessing
import torch.backends
class ContrastiveEmbeddingLoss(nn.Module):
"""The Contrastive embedding... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | Dokholyan/catalyst | ContrastiveEmbeddingLoss | false | 376 | [
"Apache-2.0"
] | 0 | de8e681676d76741fdb722d4cd77274ba616915d | https://github.com/Dokholyan/catalyst/tree/de8e681676d76741fdb722d4cd77274ba616915d |
SoftEntropy | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import *
from torch.optim.lr_scheduler import *
class SoftEntropy(nn.Module):
def __init__(self):
super(SoftEntropy, self).__init__()
self.logsoftmax = nn.LogSoftmax(dim=1)
def forward(self, 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
f... | Dingyuan-Zheng/ctf-UDA | SoftEntropy | false | 377 | [
"MIT"
] | 0 | 3e3c67f68d7eb0b52a16a259e5a77e153062c4fd | https://github.com/Dingyuan-Zheng/ctf-UDA/tree/3e3c67f68d7eb0b52a16a259e5a77e153062c4fd |
ContrastivePairwiseEmbeddingLoss | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.distributed
import torch.multiprocessing
import torch.backends
class ContrastivePairwiseEmbeddingLoss... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Dokholyan/catalyst | ContrastivePairwiseEmbeddingLoss | false | 378 | [
"Apache-2.0"
] | 0 | de8e681676d76741fdb722d4cd77274ba616915d | https://github.com/Dokholyan/catalyst/tree/de8e681676d76741fdb722d4cd77274ba616915d |
PairwiseDistanceMatrix | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class PairwiseDistanceMatrix(nn.Module):
def __init__(self):
super(PairwiseDistanceMatrix, self).__init__()
def forward(self, X, Y):
X2 = (X ** 2).sum(1).view(-1, 1)
Y2 = (Y ** 2).sum(1).view(1, -1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_si... | DreamBlack/APCNet | PairwiseDistanceMatrix | false | 379 | [
"MIT"
] | 0 | d76bc9e46c3b631035c5c67e2367b6fb80621333 | https://github.com/DreamBlack/APCNet/tree/d76bc9e46c3b631035c5c67e2367b6fb80621333 |
GenerateMask | import torch
import torch.nn as nn
import torch.nn.functional as F
class VectorAttention(nn.Module):
"""vector attention"""
def __init__(self, input_dim, hidden_dim):
super(VectorAttention, self).__init__()
self.theta = nn.Linear(input_dim, hidden_dim)
self.phi = nn.Linear(input_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.... | Crazy-Jack/BigGAN-PyTorch | GenerateMask | false | 380 | [
"MIT"
] | 0 | 1a5644e9c87cc399580c96cfeb180052076888da | https://github.com/Crazy-Jack/BigGAN-PyTorch/tree/1a5644e9c87cc399580c96cfeb180052076888da |
MLP | import torch
from torch import nn
from torch.nn import functional as F
class MLP(nn.Module):
"""
fc1, fc2
two fully connected layers
fc3
output layer
relu
activation function for hidden layers
sigmoid
activation function for output layer
"""
def __init__(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | EE559DeepLearningEPFL/Project1 | MLP | false | 381 | [
"MIT"
] | 0 | cbafdfee26771ae0ba3cd36375e68d92e9f108b2 | https://github.com/EE559DeepLearningEPFL/Project1/tree/cbafdfee26771ae0ba3cd36375e68d92e9f108b2 |
SEModule | from torch.nn import Module
import torch
import torch.nn as nn
from torch.nn import Conv2d
from torch.nn import ReLU
from torch.nn import Sigmoid
from torch.nn import AdaptiveAvgPool2d
class SEModule(Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | Dou-Yu-xuan/pykinship | SEModule | false | 382 | [
"MIT"
] | 0 | f81f6667fa08a08fe726736d05476168b2a3e2f0 | https://github.com/Dou-Yu-xuan/pykinship/tree/f81f6667fa08a08fe726736d05476168b2a3e2f0 |
TVLoss | import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
from torch.nn.init import *
class TVLoss(nn.Module):
def __init__(self, tv_loss_weight=1):
super(TVLoss, self).__init__()
self.tv_loss_weight = tv_loss_weight
def forward(self, x):
batch_size = x.size()[0]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.optim
import torch.utils.data
from torch.nn.init import *
assert_size_stride = torch._C._dynamo.guards.as... | EVA4-RS-Group/Phase2 | TVLoss | false | 383 | [
"Apache-2.0"
] | 0 | 7c551e3894979cc425dd51baeddbfa5a51b7878d | https://github.com/EVA4-RS-Group/Phase2/tree/7c551e3894979cc425dd51baeddbfa5a51b7878d |
FullAttention | from torch.nn import Module
import torch
from torch.nn import Dropout
class FullAttention(Module):
def __init__(self, use_dropout=False, attention_dropout=0.1):
super().__init__()
self.use_dropout = use_dropout
self.dropout = Dropout(attention_dropout)
def forward(self, queries, keys... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | EStorm21/kornia | FullAttention | false | 384 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | b2bba7950d748ba0b8ce0cc68035a248799a1044 | https://github.com/EStorm21/kornia/tree/b2bba7950d748ba0b8ce0cc68035a248799a1044 |
PointLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
def array2samples_distance(array1, array2):
"""
arguments:
array1: the array, size: (num_point, num_feature)
array2: the samples, size: (num_point, num_feature)
returns:
distances: each entry is th... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_size_stride... | DreamBlack/APCNet | PointLoss | false | 385 | [
"MIT"
] | 0 | d76bc9e46c3b631035c5c67e2367b6fb80621333 | https://github.com/DreamBlack/APCNet/tree/d76bc9e46c3b631035c5c67e2367b6fb80621333 |
Hflip | import torch
import torch.nn as nn
def hflip(input: 'torch.Tensor') ->torch.Tensor:
"""Horizontally flip a tensor image or a batch of tensor images.
.. image:: _static/img/hflip.png
Input must be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input: input tens... | 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... | EStorm21/kornia | Hflip | false | 386 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | b2bba7950d748ba0b8ce0cc68035a248799a1044 | https://github.com/EStorm21/kornia/tree/b2bba7950d748ba0b8ce0cc68035a248799a1044 |
normalize | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch.utils.data
from torch.nn.init import *
class normalize(nn.Module):
def __init__(self):
super(normalize, self).__init__()
def forward(self, x):
x = F.normalize(x, p=2, dim=1)
return 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | EVA4-RS-Group/Phase2 | normalize | false | 387 | [
"Apache-2.0"
] | 0 | 7c551e3894979cc425dd51baeddbfa5a51b7878d | https://github.com/EVA4-RS-Group/Phase2/tree/7c551e3894979cc425dd51baeddbfa5a51b7878d |
AuxsiameseMLP | import torch
from torch import nn
from torch.nn import functional as F
class AuxsiameseMLP(nn.Module):
"""
basic structure similar to the MLP
input is splited into two 1*14*14 images for separating training, share the same parameters
softmax for the auxiliary output layers
"""
def __init__(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | EE559DeepLearningEPFL/Project1 | AuxsiameseMLP | false | 388 | [
"MIT"
] | 0 | cbafdfee26771ae0ba3cd36375e68d92e9f108b2 | https://github.com/EE559DeepLearningEPFL/Project1/tree/cbafdfee26771ae0ba3cd36375e68d92e9f108b2 |
SevenLayerFC_Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class SevenLayerFC_Net(nn.Module):
def __init__(self, D_in, H, D_out):
"""
In the constructor we instantiate three nn.Linear modules and assign them as
member variables.
"""
super(SevenLayerFC_Net, self).__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Darkhunter9/10-605_cifar100 | SevenLayerFC_Net | false | 389 | [
"MIT"
] | 0 | 97c171c7b3ad74bcb0376c2ec23083f1a0b9417d | https://github.com/Darkhunter9/10-605_cifar100/tree/97c171c7b3ad74bcb0376c2ec23083f1a0b9417d |
JointsMSELoss | import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
from torch.nn.init import *
class JointsMSELoss(nn.Module):
def __init__(self, use_target_weight):
super(JointsMSELoss, self).__init__()
self.criterion = nn.MSELoss(size_average=True)
self.use_target_weight = us... | 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.optim
import torch.utils.data
from torch.nn.init import *
assert_size_stride = torch._C._dynamo.guards.as... | EVA4-RS-Group/Phase2 | JointsMSELoss | false | 390 | [
"Apache-2.0"
] | 0 | 7c551e3894979cc425dd51baeddbfa5a51b7878d | https://github.com/EVA4-RS-Group/Phase2/tree/7c551e3894979cc425dd51baeddbfa5a51b7878d |
SiameseMLP | import torch
from torch import nn
from torch.nn import functional as F
class SiameseMLP(nn.Module):
"""
basic structure similar to the MLP
input is splited into two 1*14*14 images for separating training, share the same parameters
"""
def __init__(self):
super(SiameseMLP, self).__init__()... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | EE559DeepLearningEPFL/Project1 | SiameseMLP | false | 391 | [
"MIT"
] | 0 | cbafdfee26771ae0ba3cd36375e68d92e9f108b2 | https://github.com/EE559DeepLearningEPFL/Project1/tree/cbafdfee26771ae0ba3cd36375e68d92e9f108b2 |
SingleKaistAutoEncoder | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def kaiming_init(module, mode='fan_out', nonlinearity='relu', bias=0,
distribution='normal'):
assert distribution in ['uniform', 'normal']
if distribution == 'uniform':
nn.init.kaiming_uniform_(module.weight, mod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DouCir/mmdetection | SingleKaistAutoEncoder | false | 392 | [
"Apache-2.0"
] | 0 | 44613202c379d85315ed47ca670fd9853f90c3a5 | https://github.com/DouCir/mmdetection/tree/44613202c379d85315ed47ca670fd9853f90c3a5 |
EPE | import torch
import torch.nn as nn
class EPE(nn.Module):
def __init__(self):
super(EPE, self).__init__()
def forward(self, flow, gt, loss_mask):
loss_map = (flow - gt.detach()) ** 2
loss_map = (loss_map.sum(1, True) + 1e-06) ** 0.5
return loss_map * loss_mask
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Egazaga/arXiv2020-RIFE | EPE | false | 393 | [
"MIT"
] | 0 | 84b050e168a682905a9dde8aa15437a4994f2abf | https://github.com/Egazaga/arXiv2020-RIFE/tree/84b050e168a682905a9dde8aa15437a4994f2abf |
BiencoderLoss | import torch
import torch.nn as nn
from torch import Tensor as T
import torch.nn.functional as F
class BiencoderLoss(nn.Module):
def __init__(self):
super(BiencoderLoss, self).__init__()
def forward(self, q_vectors: 'T', p_vectors: 'T'):
score_matrix = torch.mm(q_vectors, torch.transpose(p_v... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Eric-Yang-yz/dense_passage_retrieval | BiencoderLoss | false | 394 | [
"MIT"
] | 0 | 5d6e210d5ff3304b4891c94edd2bd2aee5b34655 | https://github.com/Eric-Yang-yz/dense_passage_retrieval/tree/5d6e210d5ff3304b4891c94edd2bd2aee5b34655 |
Attention | import math
import torch
import torch.nn as nn
class Attention(nn.Module):
"""
Attention mechanism (Luong)
"""
def __init__(self, hidden_size, hidden_size1):
super(Attention, self).__init__()
self.W_h = nn.Linear(hidden_size + hidden_size1, hidden_size, bias=
False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DeepInEvil/kgirnet | Attention | false | 395 | [
"MIT"
] | 0 | 81210907daba7671d3f3fd5814656d1557b7a2b1 | https://github.com/DeepInEvil/kgirnet/tree/81210907daba7671d3f3fd5814656d1557b7a2b1 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, alpha=0.25, gamma=2, logits=False, reduce=True):
super(FocalLoss, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.bce = (logits and F.binary_cross_entropy_... | 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... | EdwardTyantov/siim-isic-melanoma-2020 | FocalLoss | false | 396 | [
"MIT"
] | 0 | ce0ba286244dcf7b5ccb8250505c80350efb0301 | https://github.com/EdwardTyantov/siim-isic-melanoma-2020/tree/ce0ba286244dcf7b5ccb8250505c80350efb0301 |
AuxMLP | import torch
from torch import nn
from torch.nn import functional as F
class AuxMLP(nn.Module):
"""
basic structure similar to the MLP
input is splited into two 1*14*14 images for separating training, use different parameters
softmax for the auxiliary output layers
"""
def __init__(self):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | EE559DeepLearningEPFL/Project1 | AuxMLP | false | 397 | [
"MIT"
] | 0 | cbafdfee26771ae0ba3cd36375e68d92e9f108b2 | https://github.com/EE559DeepLearningEPFL/Project1/tree/cbafdfee26771ae0ba3cd36375e68d92e9f108b2 |
Homography | import torch
import torch.nn as nn
class Homography(nn.Module):
"""Homography geometric model to be used together with ImageRegistrator
module for the optimization-based image
registration."""
def __init__(self) ->None:
super().__init__()
self.model = nn.Parameter(torch.eye(3))
... | 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... | EStorm21/kornia | Homography | false | 398 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | b2bba7950d748ba0b8ce0cc68035a248799a1044 | https://github.com/EStorm21/kornia/tree/b2bba7950d748ba0b8ce0cc68035a248799a1044 |
AuxCNN | import torch
from torch import nn
from torch.nn import functional as F
class AuxCNN(nn.Module):
"""
basic structure similar to the CNN
input is splited into two 1*14*14 images for separating training, use different parameters
softmax for the auxiliary output layers
"""
def __init__(self):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | EE559DeepLearningEPFL/Project1 | AuxCNN | false | 399 | [
"MIT"
] | 0 | cbafdfee26771ae0ba3cd36375e68d92e9f108b2 | https://github.com/EE559DeepLearningEPFL/Project1/tree/cbafdfee26771ae0ba3cd36375e68d92e9f108b2 |
AuxsiameseCNN | import torch
from torch import nn
from torch.nn import functional as F
class AuxsiameseCNN(nn.Module):
"""
basic structure similar to the CNN
input is splited into two 1*14*14 images for separating training, share the same parameters
softmax for the auxiliary output layers
"""
def __init__(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | EE559DeepLearningEPFL/Project1 | AuxsiameseCNN | false | 400 | [
"MIT"
] | 0 | cbafdfee26771ae0ba3cd36375e68d92e9f108b2 | https://github.com/EE559DeepLearningEPFL/Project1/tree/cbafdfee26771ae0ba3cd36375e68d92e9f108b2 |
AddBias | import torch
import torch.nn as nn
class AddBias(nn.Module):
def __init__(self, bias):
super(AddBias, self).__init__()
self.bias = bias
def forward(self, x):
return x + self.bias
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'bias... | 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... | ErikML/convex_adversarial | AddBias | false | 401 | [
"MIT"
] | 0 | 52652943cfdb54199b579dbe70d3be20d2a13f23 | https://github.com/ErikML/convex_adversarial/tree/52652943cfdb54199b579dbe70d3be20d2a13f23 |
Discriminator | import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, n_h):
super(Discriminator, self).__init__()
self.f_k = nn.Bilinear(n_h, n_h, 1)
for m in self.modules():
self.weights_init(m)
def weights_init(self, m):
if isinstance(m, nn.Bilin... | 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... | EchooooAi/gsrl | Discriminator | false | 402 | [
"MIT"
] | 0 | 1cfc73fe052c09fe05df09c92c5232e7341e9ad0 | https://github.com/EchooooAi/gsrl/tree/1cfc73fe052c09fe05df09c92c5232e7341e9ad0 |
Hsigmoid | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guard... | EllisHui/outOfRailWay | Hsigmoid | false | 403 | [
"BSD-2-Clause"
] | 0 | e3bf9aaa18879bee5536740d55006c872f06278f | https://github.com/EllisHui/outOfRailWay/tree/e3bf9aaa18879bee5536740d55006c872f06278f |
DistillKL | import torch
from torch import nn
import torch.nn.functional as F
class DistillKL(nn.Module):
"""KL divergence for distillation"""
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=1)
p_t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Electronicshelf/Few-shot-regularization | DistillKL | false | 404 | [
"MIT"
] | 0 | 3fd0fef52684af77a5e574b5d61cfd8dd557b14b | https://github.com/Electronicshelf/Few-shot-regularization/tree/3fd0fef52684af77a5e574b5d61cfd8dd557b14b |
Normalize | import torch
from torch import nn
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
out = x.div(norm)
return out
def... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Electronicshelf/Few-shot-regularization | Normalize | false | 405 | [
"MIT"
] | 0 | 3fd0fef52684af77a5e574b5d61cfd8dd557b14b | https://github.com/Electronicshelf/Few-shot-regularization/tree/3fd0fef52684af77a5e574b5d61cfd8dd557b14b |
CNN | import torch
from torch import nn
from torch.nn import functional as F
class CNN(nn.Module):
"""
conv1, conv2
two convolution layers.
fc1, fc2
two fully connected layers.
fc3
output layer
relu
activation function for hidden layers
sigmoid
activation func... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | EE559DeepLearningEPFL/Project1 | CNN | false | 406 | [
"MIT"
] | 0 | cbafdfee26771ae0ba3cd36375e68d92e9f108b2 | https://github.com/EE559DeepLearningEPFL/Project1/tree/cbafdfee26771ae0ba3cd36375e68d92e9f108b2 |
RegressionModel | import torch
import torch.nn as nn
class RegressionModel(nn.Module):
def __init__(self, num_features_in, num_anchors=9, feature_size=256):
super(RegressionModel, self).__init__()
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3,
padding=1)
self.act1 = nn.ReL... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | DvdNss/FaceMaskDetection | RegressionModel | false | 407 | [
"Apache-2.0"
] | 0 | 3a4efe2bf8fb224d72636c853684a563b4e7a2e4 | https://github.com/DvdNss/FaceMaskDetection/tree/3a4efe2bf8fb224d72636c853684a563b4e7a2e4 |
SiameseCNN | import torch
from torch import nn
from torch.nn import functional as F
class SiameseCNN(nn.Module):
"""
basic structure similar to the CNN
input is splited into two 1*14*14 images for separating training, share the same parameters
"""
def __init__(self):
super(SiameseCNN, self).__init__()... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | EE559DeepLearningEPFL/Project1 | SiameseCNN | false | 408 | [
"MIT"
] | 0 | cbafdfee26771ae0ba3cd36375e68d92e9f108b2 | https://github.com/EE559DeepLearningEPFL/Project1/tree/cbafdfee26771ae0ba3cd36375e68d92e9f108b2 |
L2Norm | import torch
import torch.nn as nn
class L2Norm(nn.Module):
"""
Scale shall be learnable according to original paper
scale: initial scale number
chan_num: L2Norm channel number (norm over all channels)
"""
def __init__(self, scale=20, chan_num=512):
super(L2Norm, self).__init... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | EldritchJS/inference_results_v0.5 | L2Norm | false | 409 | [
"Apache-2.0"
] | 0 | 5552490e184d9fc342d871fcc410ac423ea49053 | https://github.com/EldritchJS/inference_results_v0.5/tree/5552490e184d9fc342d871fcc410ac423ea49053 |
BasicModel3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel3(nn.Module):
"""
Example model two from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) - ReLU(x2))
"""
def __init__(self):
super().__init__()
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... | Europium248/captum | BasicModel3 | false | 410 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
Conv2dWithConstraint | import torch
from torch import nn
class Conv2dWithConstraint(nn.Conv2d):
def __init__(self, *args, max_norm=1, **kwargs):
self.max_norm = max_norm
super(Conv2dWithConstraint, self).__init__(*args, **kwargs)
def forward(self, x):
self.weight.data = torch.renorm(self.weight.data, p=2, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | ErikBjare/braindecode | Conv2dWithConstraint | false | 411 | [
"BSD-3-Clause"
] | 0 | d77788d4813d353b65f0bfc61c6129015b0390b4 | https://github.com/ErikBjare/braindecode/tree/d77788d4813d353b65f0bfc61c6129015b0390b4 |
EdgeCaseModel | import torch
import torch.nn as nn
class LayerWithRidiculouslyLongNameAndDoesntDoAnything(nn.Module):
def forward(self, x):
return x
class EdgeCaseModel(nn.Module):
def __init__(self, throw_error=False, return_str=False):
super().__init__()
self.throw_error = throw_error
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | EarthLab-Luxembourg/torch-summary | EdgeCaseModel | false | 412 | [
"MIT"
] | 0 | 8ef25aea5e9fb075df27e1e0c77bad56a7254397 | https://github.com/EarthLab-Luxembourg/torch-summary/tree/8ef25aea5e9fb075df27e1e0c77bad56a7254397 |
BasicModel6_MultiTensor | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel6_MultiTensor(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input1, input2):
input = input1 + input2
return 1 - F.relu(1 - input)[:, 1]
def get_inputs():
return [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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Europium248/captum | BasicModel6_MultiTensor | false | 413 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
Attention | import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
from torch.nn.init import *
class Attention(nn.Module):
"""
Attention Network.
"""
def __init__(self, encoder_dim, decoder_dim, attention_dim):
"""
:param encoder_dim: feature size of encoded images
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | EVA4-RS-Group/Phase2 | Attention | false | 414 | [
"Apache-2.0"
] | 0 | 7c551e3894979cc425dd51baeddbfa5a51b7878d | https://github.com/EVA4-RS-Group/Phase2/tree/7c551e3894979cc425dd51baeddbfa5a51b7878d |
PredictionHead | import torch
import torch.nn as nn
class PredictionHead(nn.Module):
def __init__(self, in_channels, num_classes, num_anchors):
super(PredictionHead, self).__init__()
self.classification = nn.Conv2d(in_channels, num_classes *
num_anchors, kernel_size=1)
self.regression = nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | EldritchJS/inference_results_v0.5 | PredictionHead | false | 415 | [
"Apache-2.0"
] | 0 | 5552490e184d9fc342d871fcc410ac423ea49053 | https://github.com/EldritchJS/inference_results_v0.5/tree/5552490e184d9fc342d871fcc410ac423ea49053 |
BasicModel2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel2(nn.Module):
"""
Example model one from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1) - 1 - ReLU(x2))
"""
def __init__(self):
super().__init__()
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... | Europium248/captum | BasicModel2 | false | 416 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
BasicModel5_MultiArgs | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel5_MultiArgs(nn.Module):
"""
Slightly modified example model from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) * x3[0] - ReLU(x2) * x3[1])
"""
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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Europium248/captum | BasicModel5_MultiArgs | false | 417 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
RecursiveNet | import torch
import torch.nn as nn
class RecursiveNet(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(64, 64, 3, 1, 1)
def forward(self, x, args1=None, args2=None):
del args1, args2
for _ in range(3):
out = self.conv1(x)
out =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | EarthLab-Luxembourg/torch-summary | RecursiveNet | false | 418 | [
"MIT"
] | 0 | 8ef25aea5e9fb075df27e1e0c77bad56a7254397 | https://github.com/EarthLab-Luxembourg/torch-summary/tree/8ef25aea5e9fb075df27e1e0c77bad56a7254397 |
BasicModel4_MultiArgs | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel4_MultiArgs(nn.Module):
"""
Slightly modified example model from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) - ReLU(x2) / x3)
"""
def __init__(self):
super()... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Europium248/captum | BasicModel4_MultiArgs | false | 419 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
LanguageModelCriterion | import torch
import torch.nn as nn
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
input = input.contiguous().view(-1, input.size(2))
target = target.contiguous().view(-1, 1)
m... | 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... | FYJNEVERFOLLOWS/hw_NLP | LanguageModelCriterion | false | 420 | [
"MIT"
] | 0 | c9d72804128dfed3a53e9df40e94b2d53cccacae | https://github.com/FYJNEVERFOLLOWS/hw_NLP/tree/c9d72804128dfed3a53e9df40e94b2d53cccacae |
BasicModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
input = 1 - F.relu(1 - input)
return input
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Europium248/captum | BasicModel | false | 421 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
ClassificationModel | import torch
import torch.nn as nn
class ClassificationModel(nn.Module):
def __init__(self, num_features_in, num_anchors=9, num_classes=80,
prior=0.01, feature_size=256):
super(ClassificationModel, self).__init__()
self.num_classes = num_classes
self.num_anchors = num_anchors
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | DvdNss/FaceMaskDetection | ClassificationModel | false | 422 | [
"Apache-2.0"
] | 0 | 3a4efe2bf8fb224d72636c853684a563b4e7a2e4 | https://github.com/DvdNss/FaceMaskDetection/tree/3a4efe2bf8fb224d72636c853684a563b4e7a2e4 |
ReLUDeepLiftModel | import torch
import torch.nn as nn
class ReLUDeepLiftModel(nn.Module):
"""
https://www.youtube.com/watch?v=f_iAM0NPwnM
"""
def __init__(self):
super().__init__()
self.relu1 = nn.ReLU()
self.relu2 = nn.ReLU()
def forward(self, x1, x2, x3=2):
return 2 * self.rel... | 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... | Europium248/captum | ReLUDeepLiftModel | false | 423 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
eSEModule | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
... | 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._inductor.runtime import triton_helpers
from torch import nn
import t... | EllisHui/outOfRailWay | eSEModule | false | 424 | [
"BSD-2-Clause"
] | 0 | e3bf9aaa18879bee5536740d55006c872f06278f | https://github.com/EllisHui/outOfRailWay/tree/e3bf9aaa18879bee5536740d55006c872f06278f |
TemporalEmbedding | import math
import torch
import torch.nn as nn
class FixedEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(FixedEmbedding, self).__init__()
w = torch.zeros(c_in, d_model).float()
w.require_grad = False
position = torch.arange(0, c_in).float().unsqueeze(1)
div... | 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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | Fanxingye/Informer2020 | TemporalEmbedding | false | 425 | [
"Apache-2.0"
] | 0 | 94fd05f82ff0882681a9716ae3e980a574fdcbed | https://github.com/Fanxingye/Informer2020/tree/94fd05f82ff0882681a9716ae3e980a574fdcbed |
TanhDeepLiftModel | import torch
import torch.nn as nn
class TanhDeepLiftModel(nn.Module):
"""
Same as the ReLUDeepLiftModel, but with activations
that can have negative outputs
"""
def __init__(self):
super().__init__()
self.tanh1 = nn.Tanh()
self.tanh2 = nn.Tanh()
def forward(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_... | Europium248/captum | TanhDeepLiftModel | false | 426 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
NaiveGroupNorm | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import init
import torch.nn.parallel
class NaiveGroupNorm(Module):
"""NaiveGroupNorm implements Group Normalization with the high-level matrix operations in PyTorch.
It is a temporary solution to export GN by ONNX before the... | 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.nn import Module
from torch.nn import Parameter
from torch.nn import... | EllisHui/outOfRailWay | NaiveGroupNorm | false | 427 | [
"BSD-2-Clause"
] | 0 | e3bf9aaa18879bee5536740d55006c872f06278f | https://github.com/EllisHui/outOfRailWay/tree/e3bf9aaa18879bee5536740d55006c872f06278f |
GEGLU | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.checkpoint
class GEGLU(nn.Module):
def forward(self, x):
x, gate = x.chunk(2, dim=-1)
return F.gelu(gate) * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.utils.checkpoint
assert_size_stride = torch._... | ClashLuke/gpt-neox | GEGLU | false | 428 | [
"MIT"
] | 0 | 3291d0e6c867d9d328b96e8377f5b77c6f66c323 | https://github.com/ClashLuke/gpt-neox/tree/3291d0e6c867d9d328b96e8377f5b77c6f66c323 |
MultiRelu | import torch
from torch import Tensor
from typing import Tuple
import torch.nn as nn
from typing import no_type_check
class MultiRelu(nn.Module):
def __init__(self, inplace: 'bool'=False):
super().__init__()
self.relu1 = nn.ReLU(inplace=inplace)
self.relu2 = nn.ReLU(inplace=inplace)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Europium248/captum | MultiRelu | false | 429 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
BasicModel_MaxPool_ReLU | import torch
import torch.nn as nn
class BasicModel_MaxPool_ReLU(nn.Module):
def __init__(self, inplace=False):
super().__init__()
self.maxpool = nn.MaxPool1d(3)
self.relu = nn.ReLU(inplace=inplace)
def forward(self, x):
return self.relu(self.maxpool(x)).sum(dim=1)
def get_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Europium248/captum | BasicModel_MaxPool_ReLU | false | 430 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
AdaptiveCatAvgMaxPool2d | import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
def adaptive_catavgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_pool2d(x, output_size)
return torch.cat((x_avg, x_max), 1)
class AdaptiveCatAvgMaxPool2d(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
asser... | Fanzhongjie/ARFE | AdaptiveCatAvgMaxPool2d | false | 431 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
GCN_Linear | from torch.nn import Module
import math
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class sparse_dropout(Module):
"""
Sparse dropout implementation
"""
def __init__(self):
super(sparse_dr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Eudialyte/SepGAT | GCN_Linear | false | 432 | [
"MIT"
] | 0 | 6ea77714d1b2f2f5d0857cddcc9f1f5f9c0bcf50 | https://github.com/Eudialyte/SepGAT/tree/6ea77714d1b2f2f5d0857cddcc9f1f5f9c0bcf50 |
GCN | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Conv2D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, padding=
'same', stride=1, dilation=1, groups=1):
super(Conv2D, self).__init__()
assert type(kernel_size) in [int,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
as... | EllisHui/outOfRailWay | GCN | false | 433 | [
"BSD-2-Clause"
] | 0 | e3bf9aaa18879bee5536740d55006c872f06278f | https://github.com/EllisHui/outOfRailWay/tree/e3bf9aaa18879bee5536740d55006c872f06278f |
Linear2 | import torch
import torch.nn.functional as F
from torch import nn
class Linear2(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(Linear2, self).__init__()
self.linear1 = nn.Linear(nfeat, nhid, bias=True)
self.linear2 = nn.Linear(nhid, nclass, bias=True)
self.dro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Eudialyte/SepGAT | Linear2 | false | 434 | [
"MIT"
] | 0 | 6ea77714d1b2f2f5d0857cddcc9f1f5f9c0bcf50 | https://github.com/Eudialyte/SepGAT/tree/6ea77714d1b2f2f5d0857cddcc9f1f5f9c0bcf50 |
BalancedL1Loss | import functools
import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "su... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | Fanzhongjie/ARFE | BalancedL1Loss | false | 435 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
LinearMaxPoolLinearModel | import torch
import torch.nn as nn
class LinearMaxPoolLinearModel(nn.Module):
def __init__(self):
super().__init__()
self.lin1 = nn.Linear(4, 4, bias=False)
self.lin1.weight = nn.Parameter(torch.eye(4, 4))
self.pool1 = nn.MaxPool1d(4)
self.lin2 = nn.Linear(1, 1, bias=False... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Europium248/captum | LinearMaxPoolLinearModel | false | 436 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
AdaptiveAvgMaxPool2d | import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
def adaptive_avgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_pool2d(x, output_size)
return 0.5 * (x_avg + x_max)
class AdaptiveAvgMaxPool2d(nn.Module):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
asser... | Fanzhongjie/ARFE | AdaptiveAvgMaxPool2d | false | 437 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
TinyCnn | import torch
import torch.nn as nn
class TinyCnn(nn.Module):
def __init__(self, feature_extraction=False):
super().__init__()
self.feature_extraction = feature_extraction
self.conv1 = nn.Conv2d(3, 3, 5)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(2, 2)
if not ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Europium248/captum | TinyCnn | false | 438 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
ConvNorm | import torch
import torch.utils.data
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if padding is None:
assert kernel_size % 2 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | FadyKhalaf/tacotron2 | ConvNorm | false | 439 | [
"BSD-3-Clause"
] | 0 | d9bf28a6d286aab42bce46df9f26a9a3d7c2f01f | https://github.com/FadyKhalaf/tacotron2/tree/d9bf28a6d286aab42bce46df9f26a9a3d7c2f01f |
TokenEmbedding | import torch
import torch.nn as nn
class TokenEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(TokenEmbedding, self).__init__()
padding = 1 if torch.__version__ >= '1.5.0' else 2
self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
kernel_size=3, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Fanxingye/Informer2020 | TokenEmbedding | false | 440 | [
"Apache-2.0"
] | 0 | 94fd05f82ff0882681a9716ae3e980a574fdcbed | https://github.com/Fanxingye/Informer2020/tree/94fd05f82ff0882681a9716ae3e980a574fdcbed |
BasicModel_ConvNet_MaxPool3d | import torch
import torch.nn as nn
class BasicModel_ConvNet_MaxPool3d(nn.Module):
"""Same as above, but with the MaxPool1d replaced
with a MaxPool3d. This is useful because the MaxPool modules
behave differently to other modules from the perspective
of the DeepLift Attributions
"""
def __init... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Europium248/captum | BasicModel_ConvNet_MaxPool3d | false | 441 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
CrossEntropyLoss | import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Red... | 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
... | Fanzhongjie/ARFE | CrossEntropyLoss | false | 442 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
h_sigmoid | import torch
import torch.nn as nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def g... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Felicia980317/mytorch | h_sigmoid | false | 443 | [
"Apache-2.0"
] | 0 | e463122c0d402878ec5b4c5a823a0feeba8fdbfe | https://github.com/Felicia980317/mytorch/tree/e463122c0d402878ec5b4c5a823a0feeba8fdbfe |
MedianPool2d | import torch
from torch.nn.modules.utils import _pair
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
from torch.nn.modules.utils import _quadruple
class MedianPool2d(nn.Module):
""" Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: si... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.utils import _pair
import torch.nn as nn
import tor... | Fanzhongjie/ARFE | MedianPool2d | false | 444 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
BasicModel_ConvNet_One_Conv | import torch
from torch import Tensor
import torch.nn as nn
from typing import Optional
from typing import no_type_check
class BasicModel_ConvNet_One_Conv(nn.Module):
def __init__(self, inplace: 'bool'=False):
super().__init__()
self.conv1 = nn.Conv2d(1, 2, 3, 1)
self.relu1 = nn.ReLU(inpl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Europium248/captum | BasicModel_ConvNet_One_Conv | false | 445 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
GCN1 | from torch.nn import Module
import math
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class sparse_dropout(Module):
"""
Sparse dropout implementation
"""
def __init__(self):
super(sparse_dr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import math
import torch.nn.functional as F
from tor... | Eudialyte/SepGAT | GCN1 | false | 446 | [
"MIT"
] | 0 | 6ea77714d1b2f2f5d0857cddcc9f1f5f9c0bcf50 | https://github.com/Eudialyte/SepGAT/tree/6ea77714d1b2f2f5d0857cddcc9f1f5f9c0bcf50 |
Tanh | import torch
import torch.nn as nn
import torch.nn.parallel
class Tanh(nn.Module):
def __init__(self, inplace: 'bool'=False):
super(Tanh, self).__init__()
self.inplace = inplace
def forward(self, x):
return x.tanh_() if self.inplace else x.tanh()
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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._d... | Fanzhongjie/ARFE | Tanh | false | 448 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
SoftmaxDeepLiftModel | import torch
import torch.nn as nn
class SoftmaxDeepLiftModel(nn.Module):
"""
Model architecture from:
https://adventuresinmachinelearning.com/pytorch-tutorial-deep-learning/
"""
def __init__(self, num_in, num_hidden, num_out):
super().__init__()
self.num_in = num_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.... | Europium248/captum | SoftmaxDeepLiftModel | false | 449 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
SEModule | import torch
import torch.nn as nn
import torch.nn.parallel
class SEModule(nn.Module):
def __init__(self, channels, reduction=16, act_layer=nn.ReLU):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
reduction_channels = max(channels // reduction, 8)
self.fc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Fanzhongjie/ARFE | SEModule | false | 450 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
MockModule | import torch
import torch.nn as nn
import torch.nn
import torch.optim
class MockModule(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(3, 16, kernel_size=1)
def forward(self, x: 'torch.Tensor'):
return self.conv(x).mean(3).mean(2)
def get_inputs():
retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn
import torch.optim
assert_size_stride = to... | FebruaryBreeze/torch-parameter-groups | MockModule | false | 451 | [
"MIT"
] | 0 | e90c3d451c1afcfe5267801d5cfcc5413777b1d8 | https://github.com/FebruaryBreeze/torch-parameter-groups/tree/e90c3d451c1afcfe5267801d5cfcc5413777b1d8 |
ShuffleBlock | import torch
import torch.nn as nn
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
self.groups = groups
def forward(self, x):
"""Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]"""
N, C, H, W = x.size(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Fede112/pytorch-cifar | ShuffleBlock | false | 452 | [
"MIT"
] | 0 | 2e89028e9aaabb22dd944f0002d29548059c24df | https://github.com/Fede112/pytorch-cifar/tree/2e89028e9aaabb22dd944f0002d29548059c24df |
SplitAttnConv2d | import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
class RadixSoftmax(nn.Module):
def __init__(self, radix, cardinality):
super(RadixSoftmax, self).__init__()
self.radix = radix
self.cardinality = cardinality
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
from torch._inductor.runtime.... | Fanzhongjie/ARFE | SplitAttnConv2d | false | 453 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self, input_size=50, hidden_size=256, dropout=0,
kernel_size=3, padding=1, activation_function=F.relu):
"""
Args:
input_size: dimention of input embedding
kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Fatead/NER | CNN | false | 454 | [
"MIT"
] | 0 | 142fabb2fcb1b730042da7acfde10199c62537d5 | https://github.com/Fatead/NER/tree/142fabb2fcb1b730042da7acfde10199c62537d5 |
BasicModel_ConvNet | import torch
from torch import Tensor
import torch.nn as nn
from typing import no_type_check
class BasicModel_ConvNet(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 2, 3, 1)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(2)
self.conv2 = nn.C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Europium248/captum | BasicModel_ConvNet | false | 455 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
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