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 |
|---|---|---|---|---|---|---|---|---|---|---|
SuperPointNet | import torch
import torch.optim
import torch.utils.data
class SuperPointNet(torch.nn.Module):
""" Pytorch definition of SuperPoint Network. """
def __init__(self):
super(SuperPointNet, self).__init__()
self.relu = torch.nn.ReLU(inplace=True)
self.pool = torch.nn.MaxPool2d(kernel_size=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Dai-z/pytorch-superpoint | SuperPointNet | false | 13,572 | [
"MIT"
] | 390 | 90e71045238fdcce13f9f0d02bdd0e1126145a10 | https://github.com/Dai-z/pytorch-superpoint/tree/90e71045238fdcce13f9f0d02bdd0e1126145a10 |
Block | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of
residual blocks).
Args:
drop_prob (float): Drop rate for paths of model. Dropout rate has
to be between 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._inductor.runtime.... | CuttlefishXuan/mmsegmentation-1 | Block | false | 13,573 | [
"Apache-2.0"
] | 789 | 13771312da1a66d5cd642df6aa370affd3f5ceac | https://github.com/CuttlefishXuan/mmsegmentation-1/tree/13771312da1a66d5cd642df6aa370affd3f5ceac |
MeanPoolConv | import torch
import torch.nn as nn
class MeanPoolConv(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True):
super().__init__()
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
padding=kernel_size // 2, bias=biases)
def forward(self,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DeepTitan/PNDM | MeanPoolConv | false | 13,574 | [
"Apache-2.0"
] | 61 | 4037a4f40011c9a0d47b92303e64d47fcc7ed56a | https://github.com/DeepTitan/PNDM/tree/4037a4f40011c9a0d47b92303e64d47fcc7ed56a |
_Gate | import torch
import torch.nn as nn
class _Gate(nn.Module):
"""Utility class to implement a standard sigmoid gate"""
def __init__(self, in_features: 'int', out_features: 'int'):
super(_Gate, self).__init__()
self.fc = nn.Linear(in_features=in_features, out_features=out_features)
self._... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DavidChoi76/neuralhydrology | _Gate | false | 13,575 | [
"BSD-3-Clause"
] | 144 | a4c284b92934ee973c8b3fedf8a60df60c8feae1 | https://github.com/DavidChoi76/neuralhydrology/tree/a4c284b92934ee973c8b3fedf8a60df60c8feae1 |
FSPool | import torch
import torch.nn as nn
import torch.utils.data
def deterministic_sort(s, tau):
"""
"Stochastic Optimization of Sorting Networks via Continuous Relaxations" https://openreview.net/forum?id=H1eSS3CcKX
Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon
s: input elements to be sorted. 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Cyanogenoid/dspn | FSPool | false | 13,576 | [
"MIT"
] | 102 | be3703b470ead46d76b70b4fed656c2e5343aff6 | https://github.com/Cyanogenoid/dspn/tree/be3703b470ead46d76b70b4fed656c2e5343aff6 |
ResidualBlock | import torch
import torch.nn as nn
from functools import partial
def normalization(channels):
"""
Make a standard normalization layer.
:param channels: number of input channels.
:return: an nn.Module for normalization.
"""
return GroupNorm32(32, channels)
def ncsn_conv3x3(in_planes, out_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.triton_helpers import libdevice
import torch.nn as ... | DeepTitan/PNDM | ResidualBlock | false | 13,577 | [
"Apache-2.0"
] | 61 | 4037a4f40011c9a0d47b92303e64d47fcc7ed56a | https://github.com/DeepTitan/PNDM/tree/4037a4f40011c9a0d47b92303e64d47fcc7ed56a |
h_sigmoid | import torch
import torch.nn as nn
from itertools import product as product
import torch.nn.parallel
import torch.utils.data
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):
ret... | 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 itertools import product as product
import torch.nn.parallel
i... | DefTruth/PIPNet | h_sigmoid | false | 13,578 | [
"MIT"
] | 162 | a1fb1e229319dac0069e37eb8fb4278d454edbb0 | https://github.com/DefTruth/PIPNet/tree/a1fb1e229319dac0069e37eb8fb4278d454edbb0 |
UpsampleConv | import torch
import torch.nn as nn
class UpsampleConv(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True):
super().__init__()
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
padding=kernel_size // 2, bias=biases)
self.pixelshuf... | 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... | DeepTitan/PNDM | UpsampleConv | false | 13,579 | [
"Apache-2.0"
] | 61 | 4037a4f40011c9a0d47b92303e64d47fcc7ed56a | https://github.com/DeepTitan/PNDM/tree/4037a4f40011c9a0d47b92303e64d47fcc7ed56a |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(3 * 28 * 28, 512)
self.fc2 = nn.Linear(512, 512)
self.fc3 = nn.Linear(512, 1)
def forward(self, x):
x = x.view... | 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_... | DavyMorgan/invariant-risk-minimization | Net | false | 13,580 | [
"MIT"
] | 77 | d0fe48e75329561e6b2d47dbc97042aa740f77c2 | https://github.com/DavyMorgan/invariant-risk-minimization/tree/d0fe48e75329561e6b2d47dbc97042aa740f77c2 |
CAModule | import torch
from torch import nn
class CAModule(nn.Module):
"""
Re-implementation of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
code reference:
https://github.com/kobiso/CBAM-keras/blob/master/models/attention_module.py
"""
... | 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... | DavidChenL/Chexpert | CAModule | false | 13,581 | [
"Apache-2.0"
] | 202 | 0300057d3a51301cff35a65f79729436678b4a79 | https://github.com/DavidChenL/Chexpert/tree/0300057d3a51301cff35a65f79729436678b4a79 |
h_swish | import torch
import torch.nn as nn
from itertools import product as product
import torch.nn.parallel
import torch.utils.data
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):
ret... | 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 itertools import product as product
import torch.nn.parallel
i... | DefTruth/PIPNet | h_swish | false | 13,582 | [
"MIT"
] | 162 | a1fb1e229319dac0069e37eb8fb4278d454edbb0 | https://github.com/DefTruth/PIPNet/tree/a1fb1e229319dac0069e37eb8fb4278d454edbb0 |
ConvMeanPool | import torch
import torch.nn as nn
class ConvMeanPool(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True,
adjust_padding=False):
super().__init__()
if not adjust_padding:
conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DeepTitan/PNDM | ConvMeanPool | false | 13,583 | [
"Apache-2.0"
] | 61 | 4037a4f40011c9a0d47b92303e64d47fcc7ed56a | https://github.com/DeepTitan/PNDM/tree/4037a4f40011c9a0d47b92303e64d47fcc7ed56a |
InstanceNorm2dPlus | import torch
import torch.nn as nn
class InstanceNorm2dPlus(nn.Module):
def __init__(self, num_features, bias=True):
super().__init__()
self.num_features = num_features
self.bias = bias
self.instance_norm = nn.InstanceNorm2d(num_features, affine=False,
track_running_st... | 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_... | DeepTitan/PNDM | InstanceNorm2dPlus | false | 13,584 | [
"Apache-2.0"
] | 61 | 4037a4f40011c9a0d47b92303e64d47fcc7ed56a | https://github.com/DeepTitan/PNDM/tree/4037a4f40011c9a0d47b92303e64d47fcc7ed56a |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(features))
self.beta = nn.Parameter(torch.zeros(features))
self.eps = eps
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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Dodger23/SincNet | LayerNorm | false | 13,585 | [
"MIT"
] | 951 | bf848e88dc8d6cbeb4484e89486ec0a4ab237cb1 | https://github.com/Dodger23/SincNet/tree/bf848e88dc8d6cbeb4484e89486ec0a4ab237cb1 |
ParamSum | import torch
import torch.utils.data
import torch
from torch import nn
def resize(x1, x2, largest=True):
if largest:
if x1.size()[2:] > x2.size()[2:]:
x2 = nn.Upsample(size=x1.size()[2:], mode='bilinear')(x2)
elif x1.size()[2:] < x2.size()[2:]:
x1 = nn.Upsample(size=x2.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.utils.data
import torch
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda... | DominickZhang/NAS-FCOS | ParamSum | false | 13,586 | [
"BSD-2-Clause"
] | 187 | 1f7281478430eaed028e2cc2dfa8be226c63939b | https://github.com/DominickZhang/NAS-FCOS/tree/1f7281478430eaed028e2cc2dfa8be226c63939b |
Loss | import torch
import torch.nn as nn
class Loss(nn.Module):
def __init__(self, lambd):
super(Loss, self).__init__()
self.lambd = lambd
self.lsm = nn.LogSoftmax(dim=1)
def forward(self, O, Y, C):
return (Y * (self.lambd * C - self.lsm(O))).mean(dim=0).sum()
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 math as tl_math
import torch.nn as nn
... | DmZhukov/CrossTask | Loss | false | 13,587 | [
"BSD-3-Clause"
] | 58 | 2d79941d687dc8bd100898acd9c71c476b99def1 | https://github.com/DmZhukov/CrossTask/tree/2d79941d687dc8bd100898acd9c71c476b99def1 |
PositionwiseFeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
class PointwiseConv(nn.Module):
"""
Pointwise Convolution (1x1 Conv)
Convolution 1 Dimension (Faster version)
(cf. https://github.com/huggingface/pytorch-openai-transformer-lm/blob/ eafc28abdfadfa0732f03a0fc65805c5bfb2ffe7/mode... | 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 ... | DongjunLee/claf | PositionwiseFeedForward | false | 13,588 | [
"MIT"
] | 225 | ef548dda27c9aac8ce4db09774c8a1459d25bde1 | https://github.com/DongjunLee/claf/tree/ef548dda27c9aac8ce4db09774c8a1459d25bde1 |
GraphConvolution | import torch
from torch import nn
from torch.nn import init
class GraphConvolution(nn.Module):
def __init__(self, window_size, in_features, out_features):
super(GraphConvolution, self).__init__()
self.weights = nn.Parameter(torch.Tensor(window_size, in_features,
out_features))
... | 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
from torch.nn import init
assert_size_stride = torch._C._dy... | DavidHeSkr/GCN-GAN-pytorch | GraphConvolution | false | 13,589 | [
"MIT"
] | 66 | f8adf82596733464cb63dddf978c244b25aebe46 | https://github.com/DavidHeSkr/GCN-GAN-pytorch/tree/f8adf82596733464cb63dddf978c244b25aebe46 |
InvHuberLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class InvHuberLoss(nn.Module):
"""Inverse Huber Loss for depth estimation.
The setup is taken from https://arxiv.org/abs/1606.00373
Args:
ignore_index (float): value to ignore in the target
when computin... | 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
... | DrSleep/DenseTorch | InvHuberLoss | false | 13,590 | [
"MIT"
] | 69 | f90bef075429d763fc08338dea8222d28b0a4516 | https://github.com/DrSleep/DenseTorch/tree/f90bef075429d763fc08338dea8222d28b0a4516 |
VPReLU | import torch
import torch.nn as nn
import torch.nn.functional as F
class VPReLU(nn.Module):
__constants__ = ['inplace']
inplace: 'bool'
def __init__(self, inplace: 'bool'=False):
super(VPReLU, self).__init__()
self.inplace = inplace
def forward(self, input: 'torch.Tensor') ->torch.Te... | 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... | DucNguyen183/nfnet_f5 | VPReLU | false | 13,591 | [
"Apache-2.0"
] | 133 | 567a1126ff6ea09b33ffa5dacfac9c983fd48713 | https://github.com/DucNguyen183/nfnet_f5/tree/567a1126ff6ea09b33ffa5dacfac9c983fd48713 |
VPGELU | import torch
import torch.nn as nn
import torch.nn.functional as F
class VPGELU(nn.Module):
def forward(self, input: 'torch.Tensor') ->torch.Tensor:
return F.gelu(input) * 1.7015043497085571
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | DucNguyen183/nfnet_f5 | VPGELU | false | 13,592 | [
"Apache-2.0"
] | 133 | 567a1126ff6ea09b33ffa5dacfac9c983fd48713 | https://github.com/DucNguyen183/nfnet_f5/tree/567a1126ff6ea09b33ffa5dacfac9c983fd48713 |
ACLoss | import torch
import torch.utils.data
class ACLoss(torch.nn.Module):
"""Active Contour loss
http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Learning_Active_Contour_Models_for_Medical_Image_Segmentation_CVPR_2019_paper.pdf
Supports 2D and 3D data, as long as all spatial dimensions have the same
... | 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... | ELEKTRONN/elektronn3 | ACLoss | false | 13,593 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
Argmax | import torch
from torch import nn
import torch.utils.data
class Argmax(nn.Module):
def __init__(self, dim=1, unsqueeze=True):
super().__init__()
self.dim = dim
self.unsqueeze = unsqueeze
def forward(self, x):
argmax = torch.argmax(x, self.dim)
if self.unsqueeze:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | ELEKTRONN/elektronn3 | Argmax | false | 13,594 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
LayerNorm | import torch
import torch.utils.data
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, features, eps=1e-06, gamma=1.0, beta=0.0, learnable=
False):
super(LayerNorm, self).__init__()
if learnable:
self.gamma = nn.Parameter(torch.ones(features))
se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | E18301194/DepthAwareCNN | LayerNorm | false | 13,595 | [
"MIT"
] | 278 | 8ae98f7f18b69f79e7df03397dec2543d3d0c8eb | https://github.com/E18301194/DepthAwareCNN/tree/8ae98f7f18b69f79e7df03397dec2543d3d0c8eb |
GAPTripletMarginLoss | import torch
from torch import nn
import torch.utils.data
import torch.nn.functional as F
from torch.functional import F
def global_average_pooling(inp: 'torch.Tensor') ->torch.Tensor:
if inp.ndim == 5:
return F.adaptive_avg_pool3d(inp, 1)
elif inp.ndim == 4:
return F.adaptive_avg_pool2d(inp, ... | 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
import ... | ELEKTRONN/elektronn3 | GAPTripletMarginLoss | false | 13,596 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
TransformerEncoderLayer | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Linear(nn.Linear):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
super(Linear, self).__init__(in_dim, out_dim, bias)
nn.init.xavier_uniform_(self.weight, gain=nn.init.calculate... | 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.... | Deepest-Project/AlignTTS | TransformerEncoderLayer | false | 13,597 | [
"MIT"
] | 70 | ed9c29d845f65ceb44c87f293b2919b9bbc6a6de | https://github.com/Deepest-Project/AlignTTS/tree/ed9c29d845f65ceb44c87f293b2919b9bbc6a6de |
TransformerDecoderLayer | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Linear(nn.Linear):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
super(Linear, self).__init__(in_dim, out_dim, bias)
nn.init.xavier_uniform_(self.weight, gain=nn.init.calculate... | 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.... | Deepest-Project/AlignTTS | TransformerDecoderLayer | false | 13,598 | [
"MIT"
] | 70 | ed9c29d845f65ceb44c87f293b2919b9bbc6a6de | https://github.com/Deepest-Project/AlignTTS/tree/ed9c29d845f65ceb44c87f293b2919b9bbc6a6de |
DQN | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN(nn.Module):
"""
Deep neural network with represents an agent.
"""
def __init__(self, input_size, num_actions):
super(DQN, self).__init__()
self.linear1 = nn.Linear(input_size, 50)
self.head = nn.Linea... | 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... | Dookas/Robust-Multitask-RL | DQN | false | 13,599 | [
"MIT"
] | 106 | 7970e20cbdf91703c88edcb84568d7354e2525bc | https://github.com/Dookas/Robust-Multitask-RL/tree/7970e20cbdf91703c88edcb84568d7354e2525bc |
SeqAttnMatch | import torch
import torch.nn as nn
import torch.nn.functional as F
class SeqAttnMatch(nn.Module):
"""
Given sequences X and Y, match sequence Y to each element in X.
* o_i = sum(alpha_j * y_j) for i in X
* alpha_j = softmax(y_j * x_i)
"""
def __init__(self, embed_dim, identity=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.... | DongjunLee/claf | SeqAttnMatch | false | 13,600 | [
"MIT"
] | 225 | ef548dda27c9aac8ce4db09774c8a1459d25bde1 | https://github.com/DongjunLee/claf/tree/ef548dda27c9aac8ce4db09774c8a1459d25bde1 |
Classifier | import torch
from torch import nn
class Classifier(nn.Module):
def __init__(self, num_inputs1, num_inputs2):
super().__init__()
self.network = nn.Bilinear(num_inputs1, num_inputs2, 1)
def forward(self, x1, x2):
return self.network(x1, x2)
def get_inputs():
return [torch.rand([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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterpr... | DuaneNielsen/atari-representation-learning | Classifier | false | 13,601 | [
"MIT"
] | 175 | fe34f389768416deaa6a6ff0bdebba3d05762a55 | https://github.com/DuaneNielsen/atari-representation-learning/tree/fe34f389768416deaa6a6ff0bdebba3d05762a55 |
Foo | import torch
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
class Foo(torch.nn.Module):
def __init__(self, size):
super(Foo, self).__init__()
self.n = torch.nn.Parameter(torch.ones(size))
self.m = torch.nn... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
assert_si... | DonnieKim411/apex | Foo | false | 13,602 | [
"BSD-3-Clause"
] | 6,523 | fb00a5a1d569c7b118aa672b3dacac3663ca3911 | https://github.com/DonnieKim411/apex/tree/fb00a5a1d569c7b118aa672b3dacac3663ca3911 |
StableBCELoss | import torch
import torch.utils.data
class StableBCELoss(torch.nn.modules.Module):
def __init__(self):
super(StableBCELoss, self).__init__()
def forward(self, input, target):
neg_abs = -input.abs()
loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
return ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | ELEKTRONN/elektronn3 | StableBCELoss | false | 13,603 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
DistanceWeightedMSELoss | import torch
from torch import nn
import torch.utils.data
class DistanceWeightedMSELoss(nn.Module):
"""Weighted MSE loss for signed euclidean distance transform targets.
By setting ``fg_weight`` to a high value, the errors in foreground
regions are more strongly penalized.
If ``fg_weight=1``, this lo... | 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.utils.data
assert_size_stride = torch._C._dynamo.guards... | ELEKTRONN/elektronn3 | DistanceWeightedMSELoss | false | 13,604 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
CoAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class CoAttention(nn.Module):
"""
CoAttention encoder
in Dynamic Coattention Networks For Question Answering (https://arxiv.org/abs/1611.01604)
check the Figure 2 in paper
* Args:
embed_dim: the number of input embedd... | 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.... | DongjunLee/claf | CoAttention | false | 13,605 | [
"MIT"
] | 225 | ef548dda27c9aac8ce4db09774c8a1459d25bde1 | https://github.com/DongjunLee/claf/tree/ef548dda27c9aac8ce4db09774c8a1459d25bde1 |
SoftmaxBCELoss | import torch
import torch.utils.data
class SoftmaxBCELoss(torch.nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
self.bce = torch.nn.BCELoss(*args, **kwargs)
def forward(self, output, target):
probs = torch.nn.functional.softmax(output, dim=1)
return self.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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ELEKTRONN/elektronn3 | SoftmaxBCELoss | false | 13,606 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
LovaszHingeLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class LovaszHingeLoss(nn.Module):
"""
This class implements the lovasz hinge loss which is the continuous of the IoU for binary segmentation.
Source: https://github.com/bermanmaxim/LovaszSoftmax
"""
def __init__(self) ->None:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ChristophReich1996/Cell-DETR | LovaszHingeLoss | false | 13,607 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
Skip | import torch
from torch import nn
class Skip(nn.Module):
def __init__(self, C_in, C_out, stride):
super(Skip, self).__init__()
assert C_out % C_in == 0, 'C_out must be divisible by C_in'
self.repeats = 1, C_out // C_in, 1, 1
def forward(self, x):
return x.repeat(self.repeats)... | 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... | DrSleep/nas-segm-pytorch | Skip | false | 13,608 | [
"BSD-2-Clause"
] | 155 | 5de0c5c60cc05f94305ff59ae9f822656e3e7a96 | https://github.com/DrSleep/nas-segm-pytorch/tree/5de0c5c60cc05f94305ff59ae9f822656e3e7a96 |
CaffeNormalize | import torch
import torch.utils.data
import torch.nn as nn
class CaffeNormalize(nn.Module):
def __init__(self, features, eps=1e-07):
super(CaffeNormalize, self).__init__()
self.scale = nn.Parameter(10.0 * torch.ones(features))
self.eps = eps
def forward(self, x):
x_size = x.s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | E18301194/DepthAwareCNN | CaffeNormalize | false | 13,609 | [
"MIT"
] | 278 | 8ae98f7f18b69f79e7df03397dec2543d3d0c8eb | https://github.com/E18301194/DepthAwareCNN/tree/8ae98f7f18b69f79e7df03397dec2543d3d0c8eb |
PolicyNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class PolicyNetwork(nn.Module):
"""
Deep neural network which represents policy network.
"""
def __init__(self, input_size, num_actions):
super(PolicyNetwork, self).__init__()
self.linear1 = nn.Linear(input_size, 50)
... | 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.... | Dookas/Robust-Multitask-RL | PolicyNetwork | false | 13,610 | [
"MIT"
] | 106 | 7970e20cbdf91703c88edcb84568d7354e2525bc | https://github.com/Dookas/Robust-Multitask-RL/tree/7970e20cbdf91703c88edcb84568d7354e2525bc |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""
Layer Normalization
(https://arxiv.org/abs/1607.06450)
"""
def __init__(self, normalized_shape, eps=1e-05):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(normalized_shape))
self.bet... | 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_... | DongjunLee/claf | LayerNorm | false | 13,611 | [
"MIT"
] | 225 | ef548dda27c9aac8ce4db09774c8a1459d25bde1 | https://github.com/DongjunLee/claf/tree/ef548dda27c9aac8ce4db09774c8a1459d25bde1 |
ResnetBlockConv1D | import torch
import torch.nn as nn
class ResnetBlockConv1D(nn.Module):
def __init__(self, size_in, size_out=None, size_h=None):
super().__init__()
if size_out is None:
size_out = size_in
if size_h is None:
size_h = min(size_in, size_out)
self.size_in = size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | DveloperY0115/texture_fields | ResnetBlockConv1D | false | 13,612 | [
"MIT"
] | 78 | 28c277696e0a658ffff3496892810d5a0ef03f65 | https://github.com/DveloperY0115/texture_fields/tree/28c277696e0a658ffff3496892810d5a0ef03f65 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
def set_init(layers):
for layer in layers:
nn.init.normal(layer.weight, mean=0.0, std=0.1)
nn.init.constant(layer.bias, 0.1)
class Net(nn.Module):
def __init__(self, s_dim, a_dim):
super(Net, 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
import torch.nn as nn
import ... | Dookas/Robust-Multitask-RL | Net | false | 13,613 | [
"MIT"
] | 106 | 7970e20cbdf91703c88edcb84568d7354e2525bc | https://github.com/Dookas/Robust-Multitask-RL/tree/7970e20cbdf91703c88edcb84568d7354e2525bc |
SimpleCNN | import torch
import torch.nn as nn
from collections import OrderedDict
class SimpleCNN(nn.Module):
def __init__(self, input_dim=3, global_pool=False):
super(SimpleCNN, self).__init__()
self.features = nn.Sequential(OrderedDict([('conv1', nn.Conv2d(
input_dim, 64, kernel_size=3, 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | D-X-Y/MSPLD-2018 | SimpleCNN | false | 13,614 | [
"MIT"
] | 63 | 71a6a75830ac84c7a861e63367ad3ace991fae77 | https://github.com/D-X-Y/MSPLD-2018/tree/71a6a75830ac84c7a861e63367ad3ace991fae77 |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
class Policy(nn.Module):
def __init__(self, input_size, num_actions):
super(Policy, self).__init__()
self.affines = nn.Linear(input_size, 100)
self.action_head = nn.Linear(100, num_actions)
self.saved_actions = []
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Dookas/Robust-Multitask-RL | Policy | false | 13,615 | [
"MIT"
] | 106 | 7970e20cbdf91703c88edcb84568d7354e2525bc | https://github.com/Dookas/Robust-Multitask-RL/tree/7970e20cbdf91703c88edcb84568d7354e2525bc |
OnnxErf | import torch
from torch import nn
class OnnxToTorchModule:
"""
Marker class for onnx2torch modules.
"""
pass
class OnnxErf(nn.Module, OnnxToTorchModule):
def forward(self, input_tensor: 'torch.Tensor') ->torch.Tensor:
return torch.erf(input_tensor)
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ENOT-AutoDL/onnx2torch | OnnxErf | false | 13,616 | [
"Apache-2.0"
] | 144 | 2391987b3349bed1670ac3c1bc9062a37323abe3 | https://github.com/ENOT-AutoDL/onnx2torch/tree/2391987b3349bed1670ac3c1bc9062a37323abe3 |
OnnxHardSigmoid | import torch
from torch import nn
class OnnxToTorchModule:
"""
Marker class for onnx2torch modules.
"""
pass
class OnnxHardSigmoid(nn.Module, OnnxToTorchModule):
def __init__(self, alpha: 'float'=0.2, beta: 'float'=0.5):
super().__init__()
self.alpha = alpha
self.beta = ... | 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... | ENOT-AutoDL/onnx2torch | OnnxHardSigmoid | false | 13,617 | [
"Apache-2.0"
] | 144 | 2391987b3349bed1670ac3c1bc9062a37323abe3 | https://github.com/ENOT-AutoDL/onnx2torch/tree/2391987b3349bed1670ac3c1bc9062a37323abe3 |
ResnetBlockFC | import torch
import torch.nn as nn
class ResnetBlockFC(nn.Module):
def __init__(self, size_in, size_out=None, size_h=None):
super().__init__()
if size_out is None:
size_out = size_in
if size_h is None:
size_h = min(size_in, size_out)
self.size_in = size_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
import torch.nn as nn
assert_... | DveloperY0115/texture_fields | ResnetBlockFC | false | 13,618 | [
"MIT"
] | 78 | 28c277696e0a658ffff3496892810d5a0ef03f65 | https://github.com/DveloperY0115/texture_fields/tree/28c277696e0a658ffff3496892810d5a0ef03f65 |
LinearPool | import torch
from torch import nn
class LinearPool(nn.Module):
def __init__(self):
super(LinearPool, self).__init__()
def forward(self, feat_map):
"""
Arguments:
feat_map(Tensor): tensor with shape (N, C, H, W)
return(Tensor): tensor with shape (N, C, 1, 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... | DavidChenL/Chexpert | LinearPool | false | 13,619 | [
"Apache-2.0"
] | 202 | 0300057d3a51301cff35a65f79729436678b4a79 | https://github.com/DavidChenL/Chexpert/tree/0300057d3a51301cff35a65f79729436678b4a79 |
hsigmoid | import torch
import torch.nn as nn
import torch.nn.functional as F
class hsigmoid(nn.Module):
def forward(self, x):
out = F.relu6(x + 3, inplace=True) / 6
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Ecalose/dddd_trainer | hsigmoid | false | 13,620 | [
"Apache-2.0"
] | 80 | ef0c6b271cc2898403375f53f813481ffbf6b02c | https://github.com/Ecalose/dddd_trainer/tree/ef0c6b271cc2898403375f53f813481ffbf6b02c |
ResnetBlockConv2d | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def pixel_norm(x):
sigma = x.norm(dim=1, keepdim=True)
out = x / (sigma + 1e-05)
return out
class EqualizedLR(nn.Module):
def __init__(self, module):
super().__init__()
self.module = module
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | DveloperY0115/texture_fields | ResnetBlockConv2d | false | 13,621 | [
"MIT"
] | 78 | 28c277696e0a658ffff3496892810d5a0ef03f65 | https://github.com/DveloperY0115/texture_fields/tree/28c277696e0a658ffff3496892810d5a0ef03f65 |
OnnxGatherElements | import torch
from torch import nn
class OnnxToTorchModule:
"""
Marker class for onnx2torch modules.
"""
pass
class OnnxGatherElements(nn.Module, OnnxToTorchModule):
def __init__(self, axis: 'int'=0):
super().__init__()
self.axis = axis
def forward(self, input_tensor: '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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | ENOT-AutoDL/onnx2torch | OnnxGatherElements | false | 13,622 | [
"Apache-2.0"
] | 144 | 2391987b3349bed1670ac3c1bc9062a37323abe3 | https://github.com/ENOT-AutoDL/onnx2torch/tree/2391987b3349bed1670ac3c1bc9062a37323abe3 |
hswish | import torch
import torch.nn as nn
import torch.nn.functional as F
class hswish(nn.Module):
def forward(self, x):
out = x * F.relu6(x + 3, inplace=True) / 6
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Ecalose/dddd_trainer | hswish | false | 13,623 | [
"Apache-2.0"
] | 80 | ef0c6b271cc2898403375f53f813481ffbf6b02c | https://github.com/Ecalose/dddd_trainer/tree/ef0c6b271cc2898403375f53f813481ffbf6b02c |
ConvBlock | import copy
import torch
from torch import nn
import torch.utils.data
def get_activation(activation):
if isinstance(activation, str):
if activation == 'relu':
return nn.ReLU()
elif activation == 'leaky':
return nn.LeakyReLU(negative_slope=0.1)
elif activation == 'pr... | 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 copy
from torch import... | ELEKTRONN/elektronn3 | ConvBlock | false | 13,624 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
UpsampleConvLayer | import torch
class UpsampleConvLayer(torch.nn.Module):
"""UpsampleConvLayer
Upsamples the input and then does a convolution. This method gives better results
compared to ConvTranspose2d.
ref: http://distill.pub/2016/deconv-checkerboard/
"""
def __init__(self, in_channels, out_channels, kernel... | 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
assert_size_s... | EdenBD/MultiModalStory-demo | UpsampleConvLayer | false | 13,625 | [
"Apache-2.0"
] | 154 | 5e95e2aca766ca7c850e8db4973b8d51dfdba7f8 | https://github.com/EdenBD/MultiModalStory-demo/tree/5e95e2aca766ca7c850e8db4973b8d51dfdba7f8 |
OnnxPow | import torch
from torch import nn
from typing import Optional
def old_style_broadcast(first: 'torch.Tensor', second: 'torch.Tensor', axis:
'int') ->torch.Tensor:
rank = len(first.shape)
axis = axis + rank if axis < 0 else axis
second_shape = [1] * axis + list(second.shape)
second_shape = second_sh... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
from typing import Optional
assert_size_stride = torch._C.... | ENOT-AutoDL/onnx2torch | OnnxPow | false | 13,626 | [
"Apache-2.0"
] | 144 | 2391987b3349bed1670ac3c1bc9062a37323abe3 | https://github.com/ENOT-AutoDL/onnx2torch/tree/2391987b3349bed1670ac3c1bc9062a37323abe3 |
DownConv | import copy
import torch
from torch import nn
import torch.utils.data
def get_activation(activation):
if isinstance(activation, str):
if activation == 'relu':
return nn.ReLU()
elif activation == 'leaky':
return nn.LeakyReLU(negative_slope=0.1)
elif activation == 'pr... | 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 copy
from torch import... | ELEKTRONN/elektronn3 | DownConv | false | 13,627 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
ResnetBlockPointwise | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class EqualizedLR(nn.Module):
def __init__(self, module):
super().__init__()
self.module = module
self._make_params()
def _make_params(self):
weight = self.module.weight
height = wei... | 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... | DveloperY0115/texture_fields | ResnetBlockPointwise | false | 13,628 | [
"MIT"
] | 78 | 28c277696e0a658ffff3496892810d5a0ef03f65 | https://github.com/DveloperY0115/texture_fields/tree/28c277696e0a658ffff3496892810d5a0ef03f65 |
ResizeConv | import torch
from torch import nn
import torch.utils.data
def get_conv(dim=3):
"""Chooses an implementation for a convolution layer."""
if dim == 3:
return nn.Conv3d
elif dim == 2:
return nn.Conv2d
else:
raise ValueError('dim has to be 2 or 3')
def planar_kernel(x):
"""Re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | ELEKTRONN/elektronn3 | ResizeConv | false | 13,629 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
GaussionConvF | import torch
import torch.nn as nn
import torch.nn.functional as F
class GaussionConvF(nn.Module):
"""The first layer in `RobustGCN` that conver node features to distribution (mean, var)"""
def __init__(self, in_features, out_features, bias=False, gamma=1.0):
super().__init__()
self.in_featur... | 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.... | EdisonLeeeee/GraphGallery | GaussionConvF | false | 13,630 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
OnnxSqrt | import torch
from torch import nn
class OnnxToTorchModule:
"""
Marker class for onnx2torch modules.
"""
pass
class OnnxSqrt(nn.Module, OnnxToTorchModule):
def forward(self, input_tensor: 'torch.Tensor') ->torch.Tensor:
return torch.sqrt(input_tensor)
def get_inputs():
return [torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ENOT-AutoDL/onnx2torch | OnnxSqrt | false | 13,631 | [
"Apache-2.0"
] | 144 | 2391987b3349bed1670ac3c1bc9062a37323abe3 | https://github.com/ENOT-AutoDL/onnx2torch/tree/2391987b3349bed1670ac3c1bc9062a37323abe3 |
OnnxGeneralLinear | import torch
from torch import nn
import torch.nn.functional as F
class OnnxToTorchModule:
"""
Marker class for onnx2torch modules.
"""
pass
class OnnxGeneralLinear(nn.Linear, OnnxToTorchModule):
"""General Linear layer with functionality of ONNX GEMM node.
For additional info https://githu... | 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... | ENOT-AutoDL/onnx2torch | OnnxGeneralLinear | false | 13,632 | [
"Apache-2.0"
] | 144 | 2391987b3349bed1670ac3c1bc9062a37323abe3 | https://github.com/ENOT-AutoDL/onnx2torch/tree/2391987b3349bed1670ac3c1bc9062a37323abe3 |
OnnxSoftmaxV1V11 | import torch
from torch import nn
class OnnxToTorchModule:
"""
Marker class for onnx2torch modules.
"""
pass
class OnnxSoftmaxV1V11(nn.Module, OnnxToTorchModule):
def __init__(self, axis: 'int'=1, is_log: 'bool'=False):
super().__init__()
self.axis = axis
self.is_log = 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | ENOT-AutoDL/onnx2torch | OnnxSoftmaxV1V11 | false | 13,633 | [
"Apache-2.0"
] | 144 | 2391987b3349bed1670ac3c1bc9062a37323abe3 | https://github.com/ENOT-AutoDL/onnx2torch/tree/2391987b3349bed1670ac3c1bc9062a37323abe3 |
SAGEAggregator | import torch
import torch.nn as nn
class SAGEAggregator(nn.Module):
def __init__(self, in_features, out_features, agg_method='mean', concat
=False, bias=False):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.concat = concat
... | 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... | EdisonLeeeee/GraphGallery | SAGEAggregator | false | 13,634 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
HingeLoss | import torch
import torch.nn as nn
import torch.utils.data
class HingeLoss(nn.Module):
def __init__(self):
super(HingeLoss, self).__init__()
self.margin = 1.0
def hinge_loss(self, input, target):
output = self.margin - input.mul(target)
output[output.le(0)] = 0
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.utils.data
assert_size_stride = torch._C._dynamo.guard... | Enderdead/BinaryConnect_PyTorch | HingeLoss | false | 13,635 | [
"MIT"
] | 75 | 990e970b1fbd299ff88200db21a9cc3fe44706d3 | https://github.com/Enderdead/BinaryConnect_PyTorch/tree/990e970b1fbd299ff88200db21a9cc3fe44706d3 |
GaussionConvD | import torch
import torch.nn as nn
import torch.nn.functional as F
class GaussionConvD(nn.Module):
"""The subsequent layer in `RobustGCN` that takes node distribution (mean, var) as input"""
def __init__(self, in_features, out_features, bias=False, gamma=1.0):
super().__init__()
self.in_featu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | EdisonLeeeee/GraphGallery | GaussionConvD | false | 13,636 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
APPNProp | import torch
import torch.nn as nn
import torch.nn.functional as F
class SparseDropout(nn.Module):
def __init__(self, p=0.5):
super().__init__()
self.p = p
def forward(self, x):
x_coal = x.coalesce()
drop_val = F.dropout(x_coal._values(), self.p, self.training)
return... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | EdisonLeeeee/GraphGallery | APPNProp | false | 13,637 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
CumulativeLinkLoss | import torch
import numpy as np
from torch import nn
from typing import Optional
def _reduction(loss: 'torch.Tensor', reduction: 'str') ->torch.Tensor:
"""
Reduce loss
Parameters
----------
loss : torch.Tensor, [batch_size, num_classes]
Batch losses.
reduction : str
Method for... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
fro... | EthanRosenthal/spacecutter | CumulativeLinkLoss | false | 13,638 | [
"MIT"
] | 74 | 37a6f7367905b50e7886dc1ef2bfe1d63220347a | https://github.com/EthanRosenthal/spacecutter/tree/37a6f7367905b50e7886dc1ef2bfe1d63220347a |
SinkhornKnopp | import torch
class SinkhornKnopp(torch.nn.Module):
def __init__(self, num_iters=3, epsilon=0.05):
super().__init__()
self.num_iters = num_iters
self.epsilon = epsilon
@torch.no_grad()
def forward(self, logits):
Q = torch.exp(logits / self.epsilon).t()
B = Q.shape[... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | DonkeyShot21/UNO | SinkhornKnopp | false | 13,639 | [
"MIT"
] | 87 | 7613d3f8c58e5f16ee0d68fdd803ef442d819af4 | https://github.com/DonkeyShot21/UNO/tree/7613d3f8c58e5f16ee0d68fdd803ef442d819af4 |
DAGNNConv | import torch
import torch.nn as nn
class DAGNNConv(nn.Module):
def __init__(self, in_features, out_features=1, K=10, bias=False):
super().__init__()
assert out_features == 1, "'out_features' must be 1"
self.in_features = in_features
self.out_features = out_features
self.li... | 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... | EdisonLeeeee/GraphGallery | DAGNNConv | false | 13,640 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
TransitionUp | import torch
from torch import nn
import torch.utils.data
def center_crop(layer, max_height, max_width):
_, _, h, w = layer.size()
xy1 = (w - max_width) // 2
xy2 = (h - max_height) // 2
return layer[:, :, xy2:xy2 + max_height, xy1:xy1 + max_width]
class TransitionUp(nn.Module):
def __init__(sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | ELEKTRONN/elektronn3 | TransitionUp | false | 13,641 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
SSGConv | from torch.nn import Module
import torch
class SSGConv(Module):
def __init__(self, K=16, alpha=0.1, **kwargs):
super().__init__()
assert K > 0
self.K = K
self.alpha = alpha
def forward(self, x, adj):
x_in = x
x_out = torch.zeros_like(x)
for _ in range(... | 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
assert_size_stride = torch._C._dynamo.guards.assert_... | EdisonLeeeee/GraphGallery | SSGConv | false | 13,642 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
WaveletConv | import torch
import torch.nn as nn
class WaveletConv(nn.Module):
def __init__(self, in_features, out_features, num_nodes, bias=False):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.w = nn.Linear(in_features, out_features, bias=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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | EdisonLeeeee/GraphGallery | WaveletConv | false | 13,643 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
ResidualBlock | import torch
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | EdenBD/MultiModalStory-demo | ResidualBlock | false | 13,644 | [
"Apache-2.0"
] | 154 | 5e95e2aca766ca7c850e8db4973b8d51dfdba7f8 | https://github.com/EdenBD/MultiModalStory-demo/tree/5e95e2aca766ca7c850e8db4973b8d51dfdba7f8 |
FocalFrequencyLoss | import torch
import torch.nn as nn
import torch.utils.data
class FocalFrequencyLoss(nn.Module):
"""The torch.nn.Module class that implements focal frequency loss - a
frequency domain loss function for optimizing generative models.
Ref:
Focal Frequency Loss for Image Reconstruction and Synthesis. In 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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | EndlessSora/focal-frequency-loss | FocalFrequencyLoss | false | 13,645 | [
"MIT"
] | 364 | dcaa01ecbfbbd9d8f83f7e5993474e1aa087227c | https://github.com/EndlessSora/focal-frequency-loss/tree/dcaa01ecbfbbd9d8f83f7e5993474e1aa087227c |
TAGConv | import torch
import torch.nn as nn
class TAGConv(nn.Module):
def __init__(self, in_features, out_features, K=3, bias=True):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.K = K
self.w = nn.Linear(in_features * (self.K + 1), out_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | EdisonLeeeee/GraphGallery | TAGConv | false | 13,646 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
GlobalAvgPool2d | import torch
import torch.nn as nn
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
return nn.functional.adaptive_avg_pool2d(inputs, 1).view(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Exdenta/torchsat | GlobalAvgPool2d | false | 13,647 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
DiceBCELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class DiceBCELoss(nn.Module):
"""
This loss combines Dice loss with the standard binary cross-entropy (BCE) loss that is generally the default for segmentation models.
Combining the two methods allows for some diversity in the loss, while... | 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... | Exdenta/torchsat | DiceBCELoss | false | 13,648 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
CharbonnierLoss | import torch
import torch.utils.data
import torch.nn as nn
class CharbonnierLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=1e-06):
super(CharbonnierLoss, self).__init__()
self.eps = eps
def forward(self, x, y):
diff = x - y
loss = torch.sum(torch.sqrt... | 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.utils.data
impo... | EvgeneyZ/TMNet | CharbonnierLoss | false | 13,649 | [
"Apache-2.0"
] | 90 | 8a42754747c2fa575e9108c13b5018a884f46099 | https://github.com/EvgeneyZ/TMNet/tree/8a42754747c2fa575e9108c13b5018a884f46099 |
SpectralEigenConv | import torch
import torch.nn as nn
class SpectralEigenConv(nn.Module):
def __init__(self, in_features, out_features, bias=False, K=10, alpha=
0.1, **kwargs):
super().__init__()
assert K > 0
self.K = K
self.alpha = alpha
self.in_features = in_features
self.o... | 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... | EdisonLeeeee/GraphGallery | SpectralEigenConv | false | 13,650 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
MaskedMultiTaskCrossEntropy | import torch
from torch import nn
class MaskedMultiTaskCrossEntropy(nn.Module):
def forward(self, input, target):
scores = torch.sigmoid(input)
target_active = (target == 1).float()
loss_terms = -(target_active * torch.log(scores) + (1 -
target_active) * torch.log(1 - scores))... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | EricBoittier/graph-neural-networks-for-drug-discovery | MaskedMultiTaskCrossEntropy | false | 13,651 | [
"MIT"
] | 69 | 12fed5c6e7bbd716d9f713d34067ed83dd539b50 | https://github.com/EricBoittier/graph-neural-networks-for-drug-discovery/tree/12fed5c6e7bbd716d9f713d34067ed83dd539b50 |
IoULoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class IoULoss(nn.Module):
"""
The IoU metric, or Jaccard Index, is similar to the Dice metric and is calculated as the ratio between the overlap
of the positive instances between two sets, and their mutual combined values
"""
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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Exdenta/torchsat | IoULoss | false | 13,652 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
"""
Focal Loss was introduced by Lin et al of Facebook AI Research in 2017 as a means of combatting extremely imbalanced datasets
where positive cases were relatively rare. Their paper "Focal Loss for Dense Obj... | 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... | Exdenta/torchsat | FocalLoss | false | 13,653 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
rSoftMax | import torch
import torch.nn as nn
import torch.nn.functional as F
class rSoftMax(nn.Module):
def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
if self.radix > 1:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Exdenta/torchsat | rSoftMax | false | 13,654 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
DiceLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class DiceLoss(nn.Module):
"""
The Dice coefficient, or Dice-Sørensen coefficient, is a common metric for pixel segmentation
"""
def __init__(self, weight=None, size_average=True):
super(DiceLoss, self).__init__()
def for... | 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... | Exdenta/torchsat | DiceLoss | false | 13,655 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
FocalTverskyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalTverskyLoss(nn.Module):
"""
A variant on the Tversky loss that also includes the gamma modifier from Focal Loss.
"""
def __init__(self, weight=None, size_average=True):
super(FocalTverskyLoss, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Exdenta/torchsat | FocalTverskyLoss | false | 13,656 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
ConvInRelu | import torch
import numpy as np
import torch.nn as nn
class ConvInRelu(nn.Module):
def __init__(self, channels_in, channels_out, kernel_size, stride=1):
super(ConvInRelu, self).__init__()
self.n_params = 0
self.channels = channels_out
self.reflection_pad = nn.ReflectionPad2d(int(n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ElistratovSemyon/style-augmentation | ConvInRelu | false | 13,657 | [
"MIT"
] | 69 | ac88dcc92d43615e9a63d90ba58cdd8178c5b02c | https://github.com/ElistratovSemyon/style-augmentation/tree/ac88dcc92d43615e9a63d90ba58cdd8178c5b02c |
LabelPropagation | import torch
import torch.nn as nn
import torch.nn.functional as F
class LabelPropagation(nn.Module):
"""label propagation model adapted from https://github.com/CUAI/CorrectAndSmooth
`"Learning from Labeled and
Unlabeled Datawith Label Propagation"
<http://mlg.eng.cam.ac.uk/zoubin/papers/CMU-CALD-02-1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | EdisonLeeeee/GraphGallery | LabelPropagation | false | 13,658 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
MaxPool2dDynamicSamePadding | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MaxPool2dDynamicSamePadding(nn.MaxPool2d):
"""2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size.
The padding is operated in forward function by calculating dynamically.
"""
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... | Exdenta/torchsat | MaxPool2dDynamicSamePadding | false | 13,659 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
Conv2dDynamicSamePadding | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv2dDynamicSamePadding(nn.Conv2d):
"""2D Convolutions like TensorFlow, for a dynamic image size.
The padding is operated in forward function by calculating dynamically.
"""
def __init__(self, in_channels, out_ch... | 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... | Exdenta/torchsat | Conv2dDynamicSamePadding | false | 13,660 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
outconv | import torch
import torch.nn as nn
class outconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(outconv, self).__init__()
self.conv = nn.Conv2d(in_ch, out_ch, 1, padding_mode='reflect')
def forward(self, x):
x = self.conv(x)
return x
def get_inputs():
return [torc... | 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.... | ExplorativeEngineering/LFMNet2 | outconv | false | 13,661 | [
"Apache-2.0"
] | 46 | 3f190be0f047b9e05c69b0a11f99218fd4fc510c | https://github.com/ExplorativeEngineering/LFMNet2/tree/3f190be0f047b9e05c69b0a11f99218fd4fc510c |
SplAtConv2d | from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Conv2d
from torch.nn import ReLU
from torch.nn.modules.utils import _pair
class DropBlock2D(object):
def __init__(self, *args, **kwargs):
raise NotImplementedError
class rSoftMax(nn.Modul... | 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.... | Exdenta/torchsat | SplAtConv2d | false | 13,662 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
VitMlpHead | import torch
def get_args():
parser = argparse.ArgumentParser()
group = parser.add_argument_group(title='input data')
group.add_argument('--input', type=str, required=True, help=
'Path to input JSON')
group.add_argument('--json-keys', nargs='+', default=['text'], help=
'space separate ... | 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
assert_size_stride ... | ExaSearch/Megatron-DeepSpeed | VitMlpHead | false | 13,663 | [
"MIT"
] | 71 | 215dcf9fd4d18d9efa1d15d06c3eb85572957bf3 | https://github.com/ExaSearch/Megatron-DeepSpeed/tree/215dcf9fd4d18d9efa1d15d06c3eb85572957bf3 |
CapOnlyContrastiveLoss | import torch
import torch.nn as nn
import torch.nn.init
def cosine_sim(im, s):
"""Cosine similarity between all the image and sentence pairs
"""
return im.mm(s.t())
def order_sim(im, s):
"""Order embeddings similarity measure $max(0, s-im)$
"""
YmX = s.unsqueeze(1).expand(s.size(0), im.size(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ExplorerFreda/VSE-C | CapOnlyContrastiveLoss | false | 13,664 | [
"MIT"
] | 61 | 52d7742adfe017eacd74f36a5953ea2ace9f5fce | https://github.com/ExplorerFreda/VSE-C/tree/52d7742adfe017eacd74f36a5953ea2ace9f5fce |
NormedLinear | import torch
import torch.nn.functional as F
import torch.nn as nn
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value... | 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 ... | FMsunyh/mmdetection | NormedLinear | false | 13,665 | [
"Apache-2.0"
] | 240 | d3683eb06d1041aa3d55f35ad81d8c37718a4c2d | https://github.com/FMsunyh/mmdetection/tree/d3683eb06d1041aa3d55f35ad81d8c37718a4c2d |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.affine1 = nn.Linear(4, 128)
self.affine2 = nn.Linear(128, 2)
self.saved_log_probs = []
self.rewards = []
def forward(sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Eunjnnn/ignite | Policy | false | 13,666 | [
"BSD-3-Clause"
] | 4,119 | 743089705b2b252aa5e2a0f310da3a8724d6711e | https://github.com/Eunjnnn/ignite/tree/743089705b2b252aa5e2a0f310da3a8724d6711e |
Return | import torch
import numpy as np
class Return(torch.nn.Module):
def __init__(self, discount_factor):
super().__init__()
assert 0 <= discount_factor < 1
self.coefficient = 1 / (1 - discount_factor)
self.min_reward = np.float32(-1)
self.max_reward = np.float32(1)
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
import numpy as np
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | Eyalcohenx/tonic | Return | false | 13,667 | [
"MIT"
] | 350 | afc15c6fa23fed4f696f68f0acf961964b0172dc | https://github.com/Eyalcohenx/tonic/tree/afc15c6fa23fed4f696f68f0acf961964b0172dc |
SegmentationLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class LovaszHingeLoss(nn.Module):
"""
This class implements the lovasz hinge loss which is the continuous of the IoU for binary segmentation.
Source: https://github.com/bermanmaxim/LovaszSoftmax
"""
def __init__(self) ->None:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ChristophReich1996/Cell-DETR | SegmentationLoss | false | 13,668 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
SobLoss | import torch
class SobLoss(torch.nn.Module):
"""
Sobolev norm penalty on function
(sum |x_{i} - x{i+1}|^p)^{1/p}
parameters:
p - dimension of norm
"""
def __init__(self, p):
super(SobLoss, self).__init__()
self.p = p
def forward(self, beta):
hdiff = beta[... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | Filco306/TopologyLayer | SobLoss | false | 13,669 | [
"MIT"
] | 250 | 1d6261017a80cff0ee06bb896ded40777b0989b4 | https://github.com/Filco306/TopologyLayer/tree/1d6261017a80cff0ee06bb896ded40777b0989b4 |
ShiftedConv | import math
import torch
import torch.nn as nn
from numpy import prod
def getLayerNormalizationFactor(x):
"""
Get He's constant for the given layer
https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
"""
size = x.weight.size()
fan_in = pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
from numpy import prod
assert_size_stride = to... | EyalSel/CPC_audio | ShiftedConv | false | 13,670 | [
"MIT"
] | 260 | b98a1bdf1fe9ea219816db7a6c28115d404a3510 | https://github.com/EyalSel/CPC_audio/tree/b98a1bdf1fe9ea219816db7a6c28115d404a3510 |
LogisticCumulativeLink | import torch
from torch import nn
class LogisticCumulativeLink(nn.Module):
"""
Converts a single number to the proportional odds of belonging to a class.
Parameters
----------
num_classes : int
Number of ordered classes to partition the odds into.
init_cutpoints : str (default='ordere... | 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... | EthanRosenthal/medallion | LogisticCumulativeLink | false | 13,671 | [
"MIT"
] | 74 | 063fe875f5122063e6f616512cffd9ffa4df1974 | https://github.com/EthanRosenthal/medallion/tree/063fe875f5122063e6f616512cffd9ffa4df1974 |
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