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 |
|---|---|---|---|---|---|---|---|---|---|---|
GLU | import torch
import torch.nn as nn
import torch.nn.functional
class GLU(nn.Module):
def __init__(self, input_size, gating_size, output_size):
super().__init__()
self.gate = nn.Linear(gating_size, input_size)
self.lin = nn.Linear(input_size, output_size)
def forward(self, x, gating):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._... | MichalOp/StarTrain | GLU | false | 17,720 | [
"MIT"
] | 7 | e8dddf879f103e18239ad37b373c9b51fbbe093b | https://github.com/MichalOp/StarTrain/tree/e8dddf879f103e18239ad37b373c9b51fbbe093b |
TripletSoftmaxLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class TripletSoftmaxLoss(nn.Module):
"""
Triplet loss
Takes embeddings of an anchor sample, a positive sample, a negative sample, logits and class labels
"""
def __init__(self, margin=0.0, size_average=True, lambda_factor=0.0):
... | 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._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | MikeLagunes/Supervised-Triplet-Network | TripletSoftmaxLoss | false | 17,721 | [
"MIT"
] | 6 | 575bcaf8f17affb0ff0e93212dde0f3f634c196f | https://github.com/MikeLagunes/Supervised-Triplet-Network/tree/575bcaf8f17affb0ff0e93212dde0f3f634c196f |
EdgeLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def cross_entropy(logits, labels):
return torch.mean((1 - labels) * logits + torch.log(1 + torch.exp(-logits))
)
class EdgeLoss(nn.Module):
def __init__(self):
super().__init__()
laplace = torch.FloatTensor([[-1, -1,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | Mhaiyang/TCSVT2021_DCENet | EdgeLoss | false | 17,722 | [
"BSD-3-Clause"
] | 4 | aae8c7643402c15847836c0ce4934b743e11fd8a | https://github.com/Mhaiyang/TCSVT2021_DCENet/tree/aae8c7643402c15847836c0ce4934b743e11fd8a |
Model | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_dim, out_dim):
super(Model, self).__init__()
self.out_dim = out_dim
self.fc1 = nn.Linear(in_dim, in_dim // 2)
self.fc2 = nn.Linear(in_dim // 2, in_dim // 4)
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.... | MiscellaneousStuff/tlol-py | Model | false | 17,723 | [
"MIT"
] | 4 | 60477b4f794daa12930d7bbec4cf692bab426a33 | https://github.com/MiscellaneousStuff/tlol-py/tree/60477b4f794daa12930d7bbec4cf692bab426a33 |
ScaleUp | import torch
import torch.nn as nn
from torch.nn import Parameter
class ScaleUp(nn.Module):
"""ScaleUp"""
def __init__(self, scale):
super(ScaleUp, self).__init__()
self.scale = Parameter(torch.tensor(scale))
def forward(self, x):
return x * self.scale
def get_inputs():
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
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = to... | NTDXYG/DeepPseudo | ScaleUp | false | 17,724 | [
"Apache-2.0"
] | 7 | 0d89045ea145f23259306eb024e9bbe261f33d9b | https://github.com/NTDXYG/DeepPseudo/tree/0d89045ea145f23259306eb024e9bbe261f33d9b |
ClampNorm | import torch
from torch import nn
class ClampNorm(nn.Module):
def __init__(self):
super(ClampNorm, self).__init__()
def forward(self, x):
out = x.clamp(0.0, 1.0)
return out / out.sum(1, keepdim=True)
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | NREL/ml-combustion-pdf-models | ClampNorm | false | 17,725 | [
"Apache-2.0"
] | 6 | 0505b9c54ab4c1e2b7ef8ca9f59f76bfb2e3732d | https://github.com/NREL/ml-combustion-pdf-models/tree/0505b9c54ab4c1e2b7ef8ca9f59f76bfb2e3732d |
RelErrorLoss | import torch
from torch import nn
class RelErrorLoss(nn.Module):
def __init__(self):
super(RelErrorLoss, self).__init__()
self.eps = 1e-06
def forward(self, input, target):
return torch.mean(torch.abs(target - input) / (target + self.eps))
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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | NREL/ml-combustion-pdf-models | RelErrorLoss | false | 17,726 | [
"Apache-2.0"
] | 6 | 0505b9c54ab4c1e2b7ef8ca9f59f76bfb2e3732d | https://github.com/NREL/ml-combustion-pdf-models/tree/0505b9c54ab4c1e2b7ef8ca9f59f76bfb2e3732d |
SSP | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
def ssp(*args, **kwargs):
return F.softplus(*args, **kwargs) - np.log(2)
class SSP(nn.Softplus):
def forward(self, xs):
return ssp(xs, self.beta, self.threshold)
def get_inputs():
return [torch.rand([4, 4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
from torch import nn
import torch.nn.functi... | MikeEntwistle/deepqmc | SSP | false | 17,727 | [
"MIT"
] | 4 | b5c20bf1768f04227becd5079c6b40aefc97d26c | https://github.com/MikeEntwistle/deepqmc/tree/b5c20bf1768f04227becd5079c6b40aefc97d26c |
SoftTargetCrossEntropy | import torch
import torch.nn as nn
import torch.nn.functional as F
class SoftTargetCrossEntropy(nn.Module):
def __init__(self, reduce='mean'):
super(SoftTargetCrossEntropy, self).__init__()
self.criterion = nn.KLDivLoss(reduction=reduce)
self.reduce = reduce
def forward(self, x, targ... | 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... | MichalBusta/OpenCitiesAIC | SoftTargetCrossEntropy | false | 17,728 | [
"MIT"
] | 7 | 2358118a782edde27a588d6adaf79941cbd90de6 | https://github.com/MichalBusta/OpenCitiesAIC/tree/2358118a782edde27a588d6adaf79941cbd90de6 |
SoftmaxImage | import torch
from torch import nn
class SoftmaxImage(nn.Module):
"""Apply Softmax on an image.
Softmax2d applies on second dimension (i.e. channels), which is
not what I want. This applies along the H and W dimensions, where
(N, C, H, W) is the size of the input.
"""
def __init__(self, chan... | 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... | NREL/ml-combustion-pdf-models | SoftmaxImage | false | 17,729 | [
"Apache-2.0"
] | 6 | 0505b9c54ab4c1e2b7ef8ca9f59f76bfb2e3732d | https://github.com/NREL/ml-combustion-pdf-models/tree/0505b9c54ab4c1e2b7ef8ca9f59f76bfb2e3732d |
ElectronicAsymptotic | import torch
from torch import nn
class ElectronicAsymptotic(nn.Module):
"""Jastrow factor with a correct electronic cusp.
The Jastrow factor is calculated from distances between all pairs of
electrons, :math:`d_{ij}`,
.. math::
\\mathrm \\gamma
:=\\sum_{ij}-\\frac{c}{\\alpha(1+\\alp... | 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... | MikeEntwistle/deepqmc | ElectronicAsymptotic | false | 17,730 | [
"MIT"
] | 4 | b5c20bf1768f04227becd5079c6b40aefc97d26c | https://github.com/MikeEntwistle/deepqmc/tree/b5c20bf1768f04227becd5079c6b40aefc97d26c |
CNNLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNNLayer(nn.Module):
def __init__(self, input_size, in_channels, out_channels, kernel_width,
act_fun=nn.ReLU, drop_prob=0.1):
"""Initilize CNN layer.
Args:
input_size [int]: embedding dim or the last 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.... | NUSTM/PyTorch-DNN | CNNLayer | false | 17,731 | [
"MIT"
] | 5 | 3cea33380df60e5db307cab50f273efe9ac445c1 | https://github.com/NUSTM/PyTorch-DNN/tree/3cea33380df60e5db307cab50f273efe9ac445c1 |
SyntacticGCN | import torch
import torch.nn as nn
import torch.nn.functional as F
class SyntacticGCN(nn.Module):
def __init__(self, input_size, hidden_size, num_labels, bias=True):
super(SyntacticGCN, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_labels = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | NLP-Discourse-SoochowU/TDDiscourseParser | SyntacticGCN | false | 17,732 | [
"Apache-2.0"
] | 9 | 2f9c7cef85c564c47b368ee4935caf1fad7c598d | https://github.com/NLP-Discourse-SoochowU/TDDiscourseParser/tree/2f9c7cef85c564c47b368ee4935caf1fad7c598d |
ValueNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class ValueNetwork(nn.Module):
"""
Value network V(s_t) = E[G_t | s_t] to use as a baseline in the reinforce
update. This a Neural Net with 1 hidden layer
"""
def __init__(self, num_inputs, hidden_dim):
super(ValueNetwork,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | NadeemWard/pytorch_simple_policy_gradients | ValueNetwork | false | 17,733 | [
"MIT"
] | 5 | d0ae66b46860504a077fdffdac45b5077c12c480 | https://github.com/NadeemWard/pytorch_simple_policy_gradients/tree/d0ae66b46860504a077fdffdac45b5077c12c480 |
Softmax_Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
class Softmax_Policy(nn.Module):
"""
Simple neural network with softmax action selection
"""
def __init__(self, num_inputs, hidden_size, action_space):
super(Softmax_Policy, self).__init__()
num_outputs = action_space
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | NadeemWard/pytorch_simple_policy_gradients | Softmax_Policy | false | 17,734 | [
"MIT"
] | 5 | d0ae66b46860504a077fdffdac45b5077c12c480 | https://github.com/NadeemWard/pytorch_simple_policy_gradients/tree/d0ae66b46860504a077fdffdac45b5077c12c480 |
Decoder | import torch
from torch import nn
class Decoder(nn.Module):
def __init__(self, layer_sizes, latent_size, nlabels):
super(Decoder, self).__init__()
self.MLP = nn.Sequential()
input_size = latent_size + nlabels
for i, (in_size, out_size) in enumerate(zip([input_size] +
l... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | NREL/ml-combustion-pdf-models | Decoder | false | 17,735 | [
"Apache-2.0"
] | 6 | 0505b9c54ab4c1e2b7ef8ca9f59f76bfb2e3732d | https://github.com/NREL/ml-combustion-pdf-models/tree/0505b9c54ab4c1e2b7ef8ca9f59f76bfb2e3732d |
LossW2V | import torch
import torch.nn as nn
class LossW2V(nn.Module):
"""Triplet loss with hard positive/negative mining.
Reference:
Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.
Code imported from https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | Nabeel-Malkani/Digital-Image-Processing | LossW2V | false | 17,736 | [
"MIT"
] | 4 | dee03cb61c54db55c5a2bfa9ca0f9dea7dba66a6 | https://github.com/Nabeel-Malkani/Digital-Image-Processing/tree/dee03cb61c54db55c5a2bfa9ca0f9dea7dba66a6 |
EntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class EntropyLoss(nn.Module):
def __init__(self):
super(EntropyLoss, self).__init__()
def forward(self, x):
out = F.softmax(x, dim=1) * F.log_softmax(x, dim=1)
out = -1.0 * out.sum(dim=1)
return out.mean()
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation | EntropyLoss | false | 17,737 | [
"MIT"
] | 3 | fd0feab42151c0bae60712480301ea26f627a81d | https://github.com/NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation/tree/fd0feab42151c0bae60712480301ea26f627a81d |
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 ... | NanoGDA/gda-extraction | CNN | false | 17,738 | [
"MIT"
] | 4 | 9dfedc54dab10ee4e90d8af622bcaf97e6dc2422 | https://github.com/NanoGDA/gda-extraction/tree/9dfedc54dab10ee4e90d8af622bcaf97e6dc2422 |
SimSiamLoss | import torch
import torch.nn as nn
class SimSiamLoss(nn.Module):
"""
Loss function defined in https://arxiv.org/abs/2011.10566
"""
def __init__(self):
super(SimSiamLoss, self).__init__()
def forward(self, zx, zy, px, py):
loss = -(zx.detach() * py).sum(dim=1).mean()
loss ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | NeurAI-Lab/DoGo | SimSiamLoss | false | 17,739 | [
"MIT"
] | 3 | e3038204f15a40a2d5caca20bb171c87a40d95ba | https://github.com/NeurAI-Lab/DoGo/tree/e3038204f15a40a2d5caca20bb171c87a40d95ba |
Smooth_Loss | import torch
import torch.nn as nn
class Smooth_Loss(nn.Module):
def __init__(self):
super(Smooth_Loss, self).__init__()
def forward(self, x):
loss_smooth = torch.mean(torch.abs(x[:, :, :, :-1] - x[:, :, :, 1:])
) + torch.mean(torch.abs(x[:, :, :-1, :] - x[:, :, 1:, :]))
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | NeilDG/SGID-PFF | Smooth_Loss | false | 17,740 | [
"MIT"
] | 8 | e027ac65e63f3c052665290cd0438bb7bdeabf9f | https://github.com/NeilDG/SGID-PFF/tree/e027ac65e63f3c052665290cd0438bb7bdeabf9f |
TFConvNet | import torch
import torch.nn.functional as F
import torch.nn as nn
class TFConvNet(nn.Module):
"""
Network architecture in the Tensorflow image classification tutorial
"""
def __init__(self):
super(TFConvNet, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3)
self.pool = nn.Max... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.functional as... | NVlabs/FedFomo | TFConvNet | false | 17,741 | [
"BSD-3-Clause-Attribution"
] | 7 | fe04f6641407bce4fc58ea3fbf8cb314f9af8629 | https://github.com/NVlabs/FedFomo/tree/fe04f6641407bce4fc58ea3fbf8cb314f9af8629 |
JsdCrossEntropy | import torch
import torch.nn as nn
import torch.nn.functional as F
class JsdCrossEntropy(nn.Module):
def __init__(self):
super(JsdCrossEntropy, self).__init__()
def forward(self, net_1_logits, net_2_logits):
net_1_probs = F.softmax(net_1_logits, dim=1)
net_2_probs = F.softmax(net_2_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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation | JsdCrossEntropy | false | 17,742 | [
"MIT"
] | 3 | fd0feab42151c0bae60712480301ea26f627a81d | https://github.com/NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation/tree/fd0feab42151c0bae60712480301ea26f627a81d |
IBLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class EntropyLoss(nn.Module):
def __init__(self):
super(EntropyLoss, self).__init__()
def forward(self, x):
out = F.softmax(x, dim=1) * F.log_softmax(x, dim=1)
out = -1.0 * out.sum(dim=1)
return out.mean()
c... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation | IBLoss | false | 17,743 | [
"MIT"
] | 3 | fd0feab42151c0bae60712480301ea26f627a81d | https://github.com/NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation/tree/fd0feab42151c0bae60712480301ea26f627a81d |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
def weight_init(m):
"""Custom weight init for Conv2D and Linear layers."""
if isinstance(m, nn.Linear):
nn.init.orthogonal_(m.weight.data)
m.bias.data.fill_(0.0)
elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | LQNew/AUMC | Critic | false | 17,744 | [
"MIT"
] | 5 | c3ce9c289bc8c0912431d68ec4fe260f640df3bc | https://github.com/LQNew/AUMC/tree/c3ce9c289bc8c0912431d68ec4fe260f640df3bc |
CosineSimilarityLoss | from torch.nn import Module
import torch
import torch.nn.functional as F
class BaseLoss(Module):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
class CosineSimilarityLoss(BaseLoss):
def __init__(self, dim=1, eps=1e-08, reduction='mean', *args, **kwargs):
super().__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 libdevice
from torch.nn import Module
... | NullConvergence/torch_temp | CosineSimilarityLoss | false | 17,745 | [
"MIT"
] | 3 | 29a0d7190f0be6124f51bd85b8320cd8b3cef29a | https://github.com/NullConvergence/torch_temp/tree/29a0d7190f0be6124f51bd85b8320cd8b3cef29a |
ResBlock | import torch
import torch.nn as nn
def get_same_padding(kernel_size, dilation):
kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
padding = (kernel_size - 1) // 2
return padding
class ResBlock(nn.Module):
def __init__(self, inplanes, planes, kernel_size=3, stride=1, dilation=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_... | NeilDG/SGID-PFF | ResBlock | false | 17,746 | [
"MIT"
] | 8 | e027ac65e63f3c052665290cd0438bb7bdeabf9f | https://github.com/NeilDG/SGID-PFF/tree/e027ac65e63f3c052665290cd0438bb7bdeabf9f |
BinaryTreeGRULayer | import torch
import torch.nn as nn
class BinaryTreeGRULayer(nn.Module):
def __init__(self, hidden_dim):
super(BinaryTreeGRULayer, self).__init__()
self.fc1 = nn.Linear(in_features=2 * hidden_dim, out_features=3 *
hidden_dim)
self.fc2 = nn.Linear(in_features=2 * hidden_dim, out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | NanoGDA/gda-extraction | BinaryTreeGRULayer | false | 17,747 | [
"MIT"
] | 4 | 9dfedc54dab10ee4e90d8af622bcaf97e6dc2422 | https://github.com/NanoGDA/gda-extraction/tree/9dfedc54dab10ee4e90d8af622bcaf97e6dc2422 |
Select | import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
class Select(nn.Module):
def __init__(self, c):
super(Select, self).__init__()
self.weight = Parameter(torch.ones(c, requires_grad=False))
def forward(self, input):
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.a... | Nuctech-AI/LBS_pruning | Select | false | 17,748 | [
"MIT"
] | 6 | d2f67b287b69968b54a55fc3d25e26eef64d29a7 | https://github.com/Nuctech-AI/LBS_pruning/tree/d2f67b287b69968b54a55fc3d25e26eef64d29a7 |
PositionalEncoding | import math
import torch
class PositionalEncoding(torch.nn.Module):
"""
Positional encoding for Transformer
Parameters
----------
hidden_size : `int`, required
Hidden size of positional encoding.
Must match hidden size of input tokens.
dropout : `float`, required
Dropo... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda... | Nemexur/nonauto-lm | PositionalEncoding | false | 17,750 | [
"Apache-2.0"
] | 3 | 6f237e4fc2b3b679cd92126ea5facd58d3cf6e75 | https://github.com/Nemexur/nonauto-lm/tree/6f237e4fc2b3b679cd92126ea5facd58d3cf6e75 |
BinResBlock | import torch
import torch.nn as nn
def get_same_padding(kernel_size, dilation):
kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
padding = (kernel_size - 1) // 2
return padding
class BinResBlock(nn.Module):
def __init__(self, inplanes, kernel_size=3, dilation=1):
super(BinResB... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | NeilDG/SGID-PFF | BinResBlock | false | 17,751 | [
"MIT"
] | 8 | e027ac65e63f3c052665290cd0438bb7bdeabf9f | https://github.com/NeilDG/SGID-PFF/tree/e027ac65e63f3c052665290cd0438bb7bdeabf9f |
ConvReluPool | import torch
import torch.nn as nn
from torch.nn import functional as F
def Conv2d(fIn, fOut, k, stride=1):
"""torch Conv2d with same padding"""
assert k % 2 == 0
pad = int((k - 1) / 2)
return torch.nn.Conv2d(fIn, fOut, k, stride=stride, padding=pad)
def Pool(k, stride=1, pad=0):
return torch.nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | NeuralMMO/baselines | ConvReluPool | false | 17,752 | [
"MIT"
] | 7 | 407004cfd0c0959b871a982adf49e4fe667df8de | https://github.com/NeuralMMO/baselines/tree/407004cfd0c0959b871a982adf49e4fe667df8de |
RNN | import torch
import torch.nn as nn
import torch.nn.init
class RNN(nn.Module):
def __init__(self, data_size, hidden_size, output_size):
super(RNN, self).__init__()
self.hidden_size = hidden_size
input_size = data_size + hidden_size
self.i2h = nn.Linear(input_size, hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo... | OBA9k/Test_dev | RNN | false | 17,753 | [
"Apache-2.0"
] | 4 | bfdd337fb56ca160e1d09b6c310d1e6037d55fcd | https://github.com/OBA9k/Test_dev/tree/bfdd337fb56ca160e1d09b6c310d1e6037d55fcd |
DownsampleA | import torch
import torch.nn as nn
import torch.nn.init
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
self.avg = nn.AvgPool2d(kernel_size=1, stride=stride)
def forward(self, x):
return torch.cat((self.avg(x), x.mul(0)), 1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | OBA9k/Test_dev | DownsampleA | false | 17,754 | [
"Apache-2.0"
] | 4 | bfdd337fb56ca160e1d09b6c310d1e6037d55fcd | https://github.com/OBA9k/Test_dev/tree/bfdd337fb56ca160e1d09b6c310d1e6037d55fcd |
AdaIN | import torch
import torch.nn as nn
class AdaIN(nn.Module):
def __init__(self, style_dim, num_features):
super().__init__()
self.norm = nn.InstanceNorm2d(num_features, affine=False)
self.fc = nn.Linear(style_dim, num_features * 2)
def forward(self, x, s):
h = self.fc(s)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ORANZINO/bouquet_server | AdaIN | false | 17,755 | [
"MIT"
] | 7 | 2ce1bb59df15297878c555dd97e0f27b5202ed02 | https://github.com/ORANZINO/bouquet_server/tree/2ce1bb59df15297878c555dd97e0f27b5202ed02 |
Linear_2L | import torch
import torch.nn as nn
import torch.utils.data
class Linear_2L(nn.Module):
def __init__(self, input_dim, output_dim, n_hid):
super(Linear_2L, self).__init__()
self.n_hid = n_hid
self.input_dim = input_dim
self.output_dim = output_dim
self.fc1 = nn.Linear(input_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Neronjust2017/Bayesian-neural-networks | Linear_2L | false | 17,756 | [
"MIT"
] | 4 | 9d7f781f5c2dfa8fadf26300b4b5b64366c939cd | https://github.com/Neronjust2017/Bayesian-neural-networks/tree/9d7f781f5c2dfa8fadf26300b4b5b64366c939cd |
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... | ORNL/AADL | ShuffleBlock | false | 17,757 | [
"BSD-3-Clause"
] | 6 | 8a509676d0a0a78f1f334a3dc93e92721cfcfe90 | https://github.com/ORNL/AADL/tree/8a509676d0a0a78f1f334a3dc93e92721cfcfe90 |
RotaryEmbedding | import torch
from typing import *
class RotaryEmbedding(torch.nn.Module):
"""`Rotary Position Embedding <https://arxiv.org/abs/2104.09864v2>
Args:
rotary_dim (int): rotary dimension
"""
def __init__(self, rotary_dim: 'int'):
super().__init__()
self.rotary_dim = rotary_dim
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from typing import *
assert_size_stride = torch._C._dynamo.gua... | OpenBMB/ModelCenter | RotaryEmbedding | false | 17,758 | [
"Apache-2.0"
] | 4 | 28073f24a67f6c0beb4fd5e2cd13284f9de2284a | https://github.com/OpenBMB/ModelCenter/tree/28073f24a67f6c0beb4fd5e2cd13284f9de2284a |
ResBlk | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def normalize(x, eps=1e-06):
"""Apply min-max normalization."""
x = x.contiguous()
N, C, H, W = x.size()
x_ = x.view(N * C, -1)
max_val = torch.max(x_, dim=1, keepdim=True)[0]
min_val = torch.min(x_, dim=1, keepdim=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ORANZINO/bouquet_server | ResBlk | false | 17,759 | [
"MIT"
] | 7 | 2ce1bb59df15297878c555dd97e0f27b5202ed02 | https://github.com/ORANZINO/bouquet_server/tree/2ce1bb59df15297878c555dd97e0f27b5202ed02 |
sum_squared_error | import torch
from torch.nn.modules.loss import _Loss
class sum_squared_error(_Loss):
"""
Definition: sum_squared_error = 1/2 * nn.MSELoss(reduction = 'sum')
The backward is defined as: input-target
"""
def __init__(self, size_average=None, reduce=None, reduction='sum'):
super(sum_squared_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.asse... | ORNL/AADL | sum_squared_error | false | 17,760 | [
"BSD-3-Clause"
] | 6 | 8a509676d0a0a78f1f334a3dc93e92721cfcfe90 | https://github.com/ORNL/AADL/tree/8a509676d0a0a78f1f334a3dc93e92721cfcfe90 |
HardAttn | import torch
import torch.nn as nn
from torch.nn import functional as F
from torchvision.transforms import *
class HardAttn(nn.Module):
"""Hard Attention (Sec. 3.1.II)"""
def __init__(self, in_channels):
super(HardAttn, self).__init__()
self.fc = nn.Linear(in_channels, 4 * 2)
self.ini... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | KevinDocel/deep-person-reid | HardAttn | false | 17,761 | [
"MIT"
] | 8 | fafcb5e39837b8e441e7b6f57d5355f50d28c81d | https://github.com/KevinDocel/deep-person-reid/tree/fafcb5e39837b8e441e7b6f57d5355f50d28c81d |
Linear_2L_KFRA | import torch
import torch.nn as nn
import torch.utils.data
def sample_K_laplace_MN(MAP, upper_Qinv, lower_HHinv):
Z = MAP.data.new(MAP.size()).normal_(mean=0, std=1)
all_mtx_sample = MAP + torch.matmul(torch.matmul(lower_HHinv, Z),
upper_Qinv)
weight_mtx_sample = all_mtx_sample[:, :-1]
bias_mt... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Neronjust2017/Bayesian-neural-networks | Linear_2L_KFRA | false | 17,762 | [
"MIT"
] | 4 | 9d7f781f5c2dfa8fadf26300b4b5b64366c939cd | https://github.com/Neronjust2017/Bayesian-neural-networks/tree/9d7f781f5c2dfa8fadf26300b4b5b64366c939cd |
VdLinear | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def calculate_kl(log_alpha):
return 0.5 * torch.sum(torch.log1p(torch.exp(-log_alpha)))
class VdLinear(nn.Module):
"""
variational dropout
"""
def __init__(self, n_in, n_out, alpha_shape=(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.triton_helpers import libdevice, math as tl_math
im... | Neronjust2017/Bayesian-neural-networks | VdLinear | false | 17,763 | [
"MIT"
] | 4 | 9d7f781f5c2dfa8fadf26300b4b5b64366c939cd | https://github.com/Neronjust2017/Bayesian-neural-networks/tree/9d7f781f5c2dfa8fadf26300b4b5b64366c939cd |
KLLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class KLLoss(nn.Module):
"""
KL-Divergence symmetric loss between two distributions
Used in here for knowledge distillation
"""
def __init__(self):
super(KLLoss, self).__init__()
self.similarity_f = nn.CosineSimila... | 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... | NeurAI-Lab/DoGo | KLLoss | false | 17,764 | [
"MIT"
] | 3 | e3038204f15a40a2d5caca20bb171c87a40d95ba | https://github.com/NeurAI-Lab/DoGo/tree/e3038204f15a40a2d5caca20bb171c87a40d95ba |
Linear_1L | import torch
import torch.nn as nn
import torch.utils.data
class Linear_1L(nn.Module):
def __init__(self, input_dim, output_dim, n_hid):
super(Linear_1L, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.fc1 = nn.Linear(input_dim, n_hid)
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 ... | Neronjust2017/Bayesian-neural-networks | Linear_1L | false | 17,765 | [
"MIT"
] | 4 | 9d7f781f5c2dfa8fadf26300b4b5b64366c939cd | https://github.com/Neronjust2017/Bayesian-neural-networks/tree/9d7f781f5c2dfa8fadf26300b4b5b64366c939cd |
Loss | import math
import torch
import torch.nn as nn
import torch.utils.data
class Loss(nn.Module):
def __init__(self, type_in='pred_intervals', alpha=0.1, loss_type=
'qd_soft', censor_R=False, soften=100.0, lambda_in=10.0, sigma_in=
0.5, use_cuda=True):
super().__init__()
self.alpha = ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Neronjust2017/Bayesian-neural-networks | Loss | false | 17,766 | [
"MIT"
] | 4 | 9d7f781f5c2dfa8fadf26300b4b5b64366c939cd | https://github.com/Neronjust2017/Bayesian-neural-networks/tree/9d7f781f5c2dfa8fadf26300b4b5b64366c939cd |
PSLoss | import torch
import torch.nn as nn
import torch.fft
class PSLoss(nn.Module):
def __init__(self):
super().__init__()
self.l1_loss = torch.nn.L1Loss()
def forward(self, x, y):
x_power = torch.abs(torch.fft.fftn(x, dim=[2, 3]))
y_power = torch.abs(torch.fft.fftn(y, dim=[2, 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | NejcHirci/material-addon | PSLoss | false | 17,767 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
ResolutionScalingLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.fft
class ResolutionScalingLayer(nn.Module):
"""Implements the resolution scaling layer.
Basically, this layer can be used to upsample feature maps from spatial domain
with nearest neighbor interpolation.
"""
def __init__(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.fft
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | NejcHirci/material-addon | ResolutionScalingLayer | false | 17,768 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
PositionalEncoding | import math
import torch
from torch import nn
class PositionalEncoding(nn.Module):
"""Implement the PE function."""
def __init__(self, d_model, dropout, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guard... | OpenNLPhub/MRC_NER | PositionalEncoding | false | 17,769 | [
"MIT"
] | 4 | 27ca063764aed9eb5f2ac672bb10052acbf374a5 | https://github.com/OpenNLPhub/MRC_NER/tree/27ca063764aed9eb5f2ac672bb10052acbf374a5 |
InstanceNormLayer | import torch
import torch.nn as nn
import torch.fft
class InstanceNormLayer(nn.Module):
"""Implements instance normalization layer."""
def __init__(self, epsilon=1e-08):
super().__init__()
self.eps = epsilon
def forward(self, x):
if len(x.shape) != 4:
raise ValueError... | 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.fft
assert_size_stride = torch._C._dynamo.gu... | NejcHirci/material-addon | InstanceNormLayer | false | 17,770 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
AdaptiveInstanceNormalization | import torch
import torch.nn as nn
import torch.fft
class AdaptiveInstanceNormalization(nn.Module):
def and__init__(self):
super(AdaptiveInstanceNormalization, self).__init__()
def forward(self, x, mean, std):
whitened_x = torch.nn.functional.instance_norm(x)
return whitened_x * std ... | 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.fft
assert_size_stride = torch._C._dynamo.gu... | NejcHirci/material-addon | AdaptiveInstanceNormalization | false | 17,771 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
VGGLoss | import torch
import torch.nn as nn
import torch.fft
class VGGLoss(nn.Module):
def __init__(self):
super().__init__()
self.mse_loss = torch.nn.MSELoss()
def forward(self, x, y):
loss = torch.tensor(0.0, device=x[0].device)
input_features = x
output_features = y
... | 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.fft
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | NejcHirci/material-addon | VGGLoss | false | 17,772 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
NegPearson | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class NegPearson(nn.Module):
def __init__(self):
super(NegPearson, self).__init__()
return
def forward(self, preds, labels):
loss = 0
for i in range(preds.shape[0]):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | Oichii/resnet3D_pulse | NegPearson | false | 17,773 | [
"MIT"
] | 4 | d123abfdb14eedc972ab1e0c4c3026fe8c4074af | https://github.com/Oichii/resnet3D_pulse/tree/d123abfdb14eedc972ab1e0c4c3026fe8c4074af |
FocalLoss1 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
class FocalLoss1(nn.Module):
def __init__(self, gamma):
super(FocalLoss1, self).__init__()
self.gamma = gamma
def forward(self, input, target):
if not target.size() == input.size():
raise... | 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... | OnurUner/DeepSide | FocalLoss1 | false | 17,774 | [
"MIT"
] | 4 | dffb7ddc1d1bde36bbf5abb6eac107d39985c57a | https://github.com/OnurUner/DeepSide/tree/dffb7ddc1d1bde36bbf5abb6eac107d39985c57a |
GramMatrix | import torch
import torch.fft
class GramMatrix(torch.nn.Module):
def forward(self, input):
b, c, h, w = input.size()
features = input.view(b, c, h * w)
gram_matrix = torch.bmm(features, features.transpose(1, 2))
gram_matrix.div_(h * w)
return gram_matrix
def get_inputs()... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.fft
assert_size_stride = torch._C._dynamo.guards.assert_size_stride... | NejcHirci/material-addon | GramMatrix | false | 17,775 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
Mapping | import torch
import torch.nn as nn
import torch.fft
class Mapping(nn.Module):
def __init__(self, z_size, out_size):
super(Mapping, self).__init__()
self.out_size = out_size
self.mapping_layers = nn.ModuleList()
self.linear = nn.Linear(z_size, z_size)
self.relu = nn.ReLU(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
import ... | NejcHirci/material-addon | Mapping | false | 17,776 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
FocusLayer | import torch
import torch.nn as nn
class FocusLayer(nn.Module):
def __init__(self, c1, c2, k=1):
super(FocusLayer, self).__init__()
def forward(self, x):
return torch.cat([x[..., ::2], x[..., 1::2]], dim=1)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | OrigamiSL/TCCT2021-Neurocomputing- | FocusLayer | false | 17,777 | [
"Apache-2.0"
] | 4 | c98c7add5d68510db61f49038970d145393d42a5 | https://github.com/OrigamiSL/TCCT2021-Neurocomputing-/tree/c98c7add5d68510db61f49038970d145393d42a5 |
vd_linear_1L | import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def calculate_kl(log_alpha):
return 0.5 * torch.sum(torch.log1p(torch.exp(-log_alpha)))
class VdLinear(nn.Module):
"""
variational dropout
"""
def __init__(self, n_in, n_ou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Neronjust2017/Bayesian-neural-networks | vd_linear_1L | false | 17,778 | [
"MIT"
] | 4 | 9d7f781f5c2dfa8fadf26300b4b5b64366c939cd | https://github.com/Neronjust2017/Bayesian-neural-networks/tree/9d7f781f5c2dfa8fadf26300b4b5b64366c939cd |
GramLoss | import torch
import torch.nn as nn
import torch.fft
class GramMatrix(torch.nn.Module):
def forward(self, input):
b, c, h, w = input.size()
features = input.view(b, c, h * w)
gram_matrix = torch.bmm(features, features.transpose(1, 2))
gram_matrix.div_(h * w)
return gram_mat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | NejcHirci/material-addon | GramLoss | false | 17,779 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
vd_linear_1L_hetero | import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def calculate_kl(log_alpha):
return 0.5 * torch.sum(torch.log1p(torch.exp(-log_alpha)))
class VdLinear(nn.Module):
"""
variational dropout
"""
def __init__(self, n_in, n_ou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Neronjust2017/Bayesian-neural-networks | vd_linear_1L_hetero | false | 17,780 | [
"MIT"
] | 4 | 9d7f781f5c2dfa8fadf26300b4b5b64366c939cd | https://github.com/Neronjust2017/Bayesian-neural-networks/tree/9d7f781f5c2dfa8fadf26300b4b5b64366c939cd |
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(28 * 28, 1024)
self.fc2 = nn.Linear(1024, 10)
def forward(self, x):
x = f.relu(self.fc1(x.view(-1, 28 * 28)))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | PacktPublishing/Hands-On-Computer-Vision-with-PyTorch-1.x | Net | false | 17,781 | [
"MIT"
] | 6 | bad073f7489792d3c4bc860a2d56fa133ba63617 | https://github.com/PacktPublishing/Hands-On-Computer-Vision-with-PyTorch-1.x/tree/bad073f7489792d3c4bc860a2d56fa133ba63617 |
ThreeLayerNet_tanh | import torch
class ThreeLayerNet_tanh(torch.nn.Module):
def __init__(self, D_in, H_1, H_2, D_out):
super(ThreeLayerNet_tanh, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H_1)
self.tanh = torch.nn.Tanh()
self.linear2 = torch.nn.Linear(H_1, H_2)
self.linear3 = torch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | PanosAntoniadis/pattern_recognition-ntua | ThreeLayerNet_tanh | false | 17,782 | [
"MIT"
] | 6 | 6dca44de77f0ca94221980fc789446a2e10410a4 | https://github.com/PanosAntoniadis/pattern_recognition-ntua/tree/6dca44de77f0ca94221980fc789446a2e10410a4 |
BertLMPredictionHead | import torch
import torch.nn as nn
class BertPredictionHeadTransform(nn.Module):
def __init__(self, hidden_size, hidden_act=nn.GELU()):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.transform_act_fn = hidden_act
self.LayerNorm = nn.LayerNorm(hidden_size)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | PKU-DAIR/2021_CCF_BDCI_LargeBERT_Rank1st | BertLMPredictionHead | false | 17,783 | [
"Apache-2.0"
] | 4 | 6382433cda69c655f03c3cc284dc076407f18dc9 | https://github.com/PKU-DAIR/2021_CCF_BDCI_LargeBERT_Rank1st/tree/6382433cda69c655f03c3cc284dc076407f18dc9 |
BertPredictionHeadTransform | import torch
import torch.nn as nn
class BertPredictionHeadTransform(nn.Module):
def __init__(self, hidden_size, hidden_act=nn.GELU()):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.transform_act_fn = hidden_act
self.LayerNorm = nn.LayerNorm(hidden_size)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | PKU-DAIR/2021_CCF_BDCI_LargeBERT_Rank1st | BertPredictionHeadTransform | false | 17,784 | [
"Apache-2.0"
] | 4 | 6382433cda69c655f03c3cc284dc076407f18dc9 | https://github.com/PKU-DAIR/2021_CCF_BDCI_LargeBERT_Rank1st/tree/6382433cda69c655f03c3cc284dc076407f18dc9 |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self, input_size, hidden_size, dropout_rate, out_size):
super(Net, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear(hidden_size, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | PatWalters/yamc | Net | false | 17,785 | [
"MIT"
] | 7 | 8fcde09305d6600fdea6211d0941977bb2cff65b | https://github.com/PatWalters/yamc/tree/8fcde09305d6600fdea6211d0941977bb2cff65b |
StyleBlock | import torch
import torch.nn as nn
import torch.fft
class AdaptiveInstanceNormalization(nn.Module):
def and__init__(self):
super(AdaptiveInstanceNormalization, self).__init__()
def forward(self, x, mean, std):
whitened_x = torch.nn.functional.instance_norm(x)
return whitened_x * std ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | NejcHirci/material-addon | StyleBlock | false | 17,786 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
SDRLoss | import torch
import torch.nn as nn
class SDRLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, deg, clean):
loss_sdr = -1.0 * torch.mean(deg * clean) ** 2 / (torch.mean(deg **
2) + 2e-07)
return loss_sdr
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._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | PandoraLS/SpeechEnhancement | SDRLoss | false | 17,787 | [
"MIT"
] | 6 | f548eaafbe524a40c8cfd2221f7adf3a444b7a7d | https://github.com/PandoraLS/SpeechEnhancement/tree/f548eaafbe524a40c8cfd2221f7adf3a444b7a7d |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, smooth=1):
"""Dice Loss.
Args:
smooth (float, optional): Smoothing value. A larger
smooth value (also known as Laplace smooth, or
Additive smooth) can be used to avoid ove... | 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... | Pandinosaurus/Depth-Estimation-Segmentation | DiceLoss | false | 17,788 | [
"MIT"
] | 4 | 2eea883c96bf106774ea94464fc16c6baea86a95 | https://github.com/Pandinosaurus/Depth-Estimation-Segmentation/tree/2eea883c96bf106774ea94464fc16c6baea86a95 |
BertPreTrainingHeads | import torch
import torch.nn as nn
class BertPredictionHeadTransform(nn.Module):
def __init__(self, hidden_size, hidden_act=nn.GELU()):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.transform_act_fn = hidden_act
self.LayerNorm = nn.LayerNorm(hidden_size)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | PKU-DAIR/2021_CCF_BDCI_LargeBERT_Rank1st | BertPreTrainingHeads | false | 17,789 | [
"Apache-2.0"
] | 4 | 6382433cda69c655f03c3cc284dc076407f18dc9 | https://github.com/PKU-DAIR/2021_CCF_BDCI_LargeBERT_Rank1st/tree/6382433cda69c655f03c3cc284dc076407f18dc9 |
ThreeLayerNet | import torch
class ThreeLayerNet(torch.nn.Module):
def __init__(self, D_in, H_1, H_2, D_out):
super(ThreeLayerNet, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H_1)
self.relu = torch.nn.ReLU()
self.linear2 = torch.nn.Linear(H_1, H_2)
self.linear3 = torch.nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | PanosAntoniadis/pattern_recognition-ntua | ThreeLayerNet | false | 17,790 | [
"MIT"
] | 6 | 6dca44de77f0ca94221980fc789446a2e10410a4 | https://github.com/PanosAntoniadis/pattern_recognition-ntua/tree/6dca44de77f0ca94221980fc789446a2e10410a4 |
Sine | import torch
import torch.nn as nn
class Sine(nn.Module):
def __init__(self, w0: 'float'=30.0):
super(Sine, self).__init__()
self.w0 = w0
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
return torch.sin(self.w0 * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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 math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | Open-Catalyst-Project/baselines | Sine | false | 17,791 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
NavigatorUnit | import torch
import torch.utils.data
import torch.nn as nn
def conv1x1(in_channels, out_channels, stride=1, groups=1, bias=False):
"""
Convolution 1x1 layer.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
st... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | HyperGAN/imgclsmob | NavigatorUnit | false | 17,792 | [
"MIT"
] | 9 | 88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 | https://github.com/HyperGAN/imgclsmob/tree/88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 |
RMSELoss | import torch
import torch.nn as nn
class RMSELoss(nn.Module):
def __init__(self, smooth=1e-06):
"""RMSE Loss.
Args:
smooth (float, optional): Smoothing value.
"""
super().__init__()
self.mse = nn.MSELoss()
self.smooth = smooth
def forward(self, in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Pandinosaurus/Depth-Estimation-Segmentation | RMSELoss | false | 17,793 | [
"MIT"
] | 4 | 2eea883c96bf106774ea94464fc16c6baea86a95 | https://github.com/Pandinosaurus/Depth-Estimation-Segmentation/tree/2eea883c96bf106774ea94464fc16c6baea86a95 |
BaseCNN | import torch
import torch.nn as nn
class BaseCNN(nn.Module):
def __init__(self):
super(BaseCNN, self).__init__()
self.conv = nn.Conv1d(in_channels=1, out_channels=512, kernel_size=
64, stride=32, padding=16)
self.deconv = nn.ConvTranspose1d(in_channels=512, out_channels=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... | PandoraLS/SpeechEnhancement | BaseCNN | false | 17,794 | [
"MIT"
] | 6 | f548eaafbe524a40c8cfd2221f7adf3a444b7a7d | https://github.com/PandoraLS/SpeechEnhancement/tree/f548eaafbe524a40c8cfd2221f7adf3a444b7a7d |
RmseBceDiceLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def dice_loss(smooth=1):
"""Create Dice Loss.
Args:
smooth (float, optional): Smoothing value. A larger
smooth value (also known as Laplace smooth, or
Additive smooth) can be used to avoid overfitting.
... | 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... | Pandinosaurus/Depth-Estimation-Segmentation | RmseBceDiceLoss | false | 17,795 | [
"MIT"
] | 4 | 2eea883c96bf106774ea94464fc16c6baea86a95 | https://github.com/Pandinosaurus/Depth-Estimation-Segmentation/tree/2eea883c96bf106774ea94464fc16c6baea86a95 |
ExponentialEnvelope | import torch
class ExponentialEnvelope(torch.nn.Module):
"""
Exponential envelope function that ensures a smooth cutoff,
as proposed in Unke, Chmiela, Gastegger, Schütt, Sauceda, Müller 2021.
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom
and Nonlocal Effects
"""
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 math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | Open-Catalyst-Project/baselines | ExponentialEnvelope | false | 17,796 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
PolynomialEnvelope | import torch
class PolynomialEnvelope(torch.nn.Module):
"""
Polynomial envelope function that ensures a smooth cutoff.
Parameters
----------
exponent: int
Exponent of the envelope function.
"""
def __init__(self, exponent):
super().__init__()
assert expone... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Open-Catalyst-Project/baselines | PolynomialEnvelope | false | 17,797 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
ScaledSiLU | import torch
class ScaledSiLU(torch.nn.Module):
def __init__(self):
super().__init__()
self.scale_factor = 1 / 0.6
self._activation = torch.nn.SiLU()
def forward(self, x):
return self._activation(x) * self.scale_factor
def get_inputs():
return [torch.rand([4, 4, 4, 4])]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Open-Catalyst-Project/baselines | ScaledSiLU | false | 17,798 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
ScalingFactor | import logging
import torch
class ScalingFactor(torch.nn.Module):
"""
Scale the output y of the layer s.t. the (mean) variance wrt. to the reference input x_ref is preserved.
"""
def __init__(self):
super().__init__()
self.scale_factor = torch.nn.Parameter(torch.tensor(1.0),
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import logging
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_c... | Open-Catalyst-Project/baselines | ScalingFactor | false | 17,799 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
SiQU | import torch
class SiQU(torch.nn.Module):
def __init__(self):
super().__init__()
self._activation = torch.nn.SiLU()
def forward(self, x):
return x * self._activation(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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Open-Catalyst-Project/baselines | SiQU | false | 17,800 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
BCEDiceLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def dice_loss(smooth=1):
"""Create Dice Loss.
Args:
smooth (float, optional): Smoothing value. A larger
smooth value (also known as Laplace smooth, or
Additive smooth) can be used to avoid overfitting.
... | 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... | Pandinosaurus/Depth-Estimation-Segmentation | BCEDiceLoss | false | 17,801 | [
"MIT"
] | 4 | 2eea883c96bf106774ea94464fc16c6baea86a95 | https://github.com/Pandinosaurus/Depth-Estimation-Segmentation/tree/2eea883c96bf106774ea94464fc16c6baea86a95 |
GaussianSmearing | import torch
import torch.nn as nn
class GaussianSmearing(nn.Module):
def __init__(self, in_features, start=0, end=1, num_freqs=50):
super(GaussianSmearing, self).__init__()
self.num_freqs = num_freqs
offset = torch.linspace(start, end, num_freqs)
self.coeff = -0.5 / (offset[1] - ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | Open-Catalyst-Project/baselines | GaussianSmearing | false | 17,802 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
RmseBceLoss | import torch
import torch.nn as nn
def rmse_loss(smooth=1e-06):
"""Create Root Mean Squared Error Loss.
Returns:
Root mean squared error loss function
"""
return RMSELoss(smooth=1e-06)
def bce_loss():
"""Create Binary Cross Entropy Loss.
The loss automatically applies the sigmoid ac... | 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... | Pandinosaurus/Depth-Estimation-Segmentation | RmseBceLoss | false | 17,803 | [
"MIT"
] | 4 | 2eea883c96bf106774ea94464fc16c6baea86a95 | https://github.com/Pandinosaurus/Depth-Estimation-Segmentation/tree/2eea883c96bf106774ea94464fc16c6baea86a95 |
SphericalBesselBasis | import math
import torch
import numpy as np
class SphericalBesselBasis(torch.nn.Module):
"""
1D spherical Bessel basis
Parameters
----------
num_radial: int
Controls maximum frequency.
cutoff: float
Cutoff distance in Angstrom.
"""
def __init__(self, num_radial: 'int'... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import numpy as np
assert_size_stride = torch._C._dynamo.guar... | Open-Catalyst-Project/baselines | SphericalBesselBasis | false | 17,804 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
GCNet | import torch
import torch.nn as nn
class GCN(nn.Module):
def __init__(self, in_ft, out_ft, act, bias=True):
super(GCN, self).__init__()
self.fc = nn.Linear(in_ft, out_ft, bias=False)
self.act = nn.PReLU() if act == 'prelu' else act
if bias:
self.bias = nn.Parameter(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
import torch.nn as nn
assert_... | PetarV-/telesign | GCNet | false | 17,805 | [
"MIT"
] | 4 | 05f58162b7c5fbc3993d320fdbc4d5465dd1c71e | https://github.com/PetarV-/telesign/tree/05f58162b7c5fbc3993d320fdbc4d5465dd1c71e |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, input_dim):
super(Critic, self).__init__()
self.fc1 = nn.Linear(input_dim, 128)
self.fc2 = nn.Linear(128, 1)
def forward(self, x):
x = F.relu(self.fc1(x))
x ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | PaulPan00/donkey_wrapper | Critic | false | 17,806 | [
"MIT"
] | 6 | a03cf0f42f65625fbce792b06c98acd153c5d6c8 | https://github.com/PaulPan00/donkey_wrapper/tree/a03cf0f42f65625fbce792b06c98acd153c5d6c8 |
Discriminator | import torch
import torch.nn as nn
import torch.nn.functional as F
class Discriminator(nn.Module):
def __init__(self, gen_out_dim):
super().__init__()
self.l1 = torch.nn.Linear(gen_out_dim, 256)
self.l2 = torch.nn.Linear(256, 256)
self.l3 = torch.nn.Linear(256, 256)
self.l... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Phutoast/Win-or-Learn-Fast | Discriminator | false | 17,807 | [
"MIT"
] | 7 | 5a6b4ee0dee3bce87a2b75c90269ef431e54c2d7 | https://github.com/Phutoast/Win-or-Learn-Fast/tree/5a6b4ee0dee3bce87a2b75c90269ef431e54c2d7 |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
class Policy(nn.Module):
def __init__(self, input_dim, hidden_size, output_dim):
super(Policy, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | PaulPan00/donkey_wrapper | Policy | false | 17,808 | [
"MIT"
] | 6 | a03cf0f42f65625fbce792b06c98acd153c5d6c8 | https://github.com/PaulPan00/donkey_wrapper/tree/a03cf0f42f65625fbce792b06c98acd153c5d6c8 |
Generator | import torch
import torch.nn as nn
import torch.nn.functional as F
class Generator(nn.Module):
def __init__(self, z_dim):
super().__init__()
self.l1 = torch.nn.Linear(z_dim, 256)
self.l2 = torch.nn.Linear(256, 256)
self.l3 = torch.nn.Linear(256, 256)
self.l4 = torch.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
import torch.nn as nn
assert_... | Phutoast/Win-or-Learn-Fast | Generator | false | 17,809 | [
"MIT"
] | 7 | 5a6b4ee0dee3bce87a2b75c90269ef431e54c2d7 | https://github.com/Phutoast/Win-or-Learn-Fast/tree/5a6b4ee0dee3bce87a2b75c90269ef431e54c2d7 |
TLU | import torch
from torch import nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
class TLU(nn.Module):
def __init__(self, num_features):
"""max(y, tau) = max(y - tau, 0) + tau = ReLU(y - tau) + tau"""
super(TLU, self).__init__()
self.num_features = num_features
... | 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
from torch.nn import Parameter
from torch.nn.parameter import Parame... | PangJian123/ISM-ReID | TLU | false | 17,810 | [
"Apache-2.0"
] | 8 | 4c8e4b4ae591add83e1e6ba0b4b7d2750eeb9ee9 | https://github.com/PangJian123/ISM-ReID/tree/4c8e4b4ae591add83e1e6ba0b4b7d2750eeb9ee9 |
FastBiliner | import math
import torch
import torch.nn as nn
class FastBiliner(nn.Module):
def __init__(self, in1_features, in2_features, out_features):
super(FastBiliner, self).__init__()
weight = torch.randn(out_features, in1_features, in2_features
) * math.sqrt(2 / (in1_features + in2_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
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | Perfec-Yu/Lifelong-ED | FastBiliner | false | 17,811 | [
"MIT"
] | 6 | f1af49129dd6ed4ff545f84e680565cccdb5b55a | https://github.com/Perfec-Yu/Lifelong-ED/tree/f1af49129dd6ed4ff545f84e680565cccdb5b55a |
ConvMlp | import torch
import torch.nn as nn
class ConvMlp(nn.Module):
""" MLP using 1x1 convs that keeps spatial dims
"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.ReLU, norm_layer=None, drop=0.0):
super().__init__()
out_features = out_features 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | RICE-EIC/Patch-Fool | ConvMlp | false | 17,812 | [
"MIT"
] | 7 | 9638ec33a4d13b0c5ff0ec3ee5ce6b46ea7da5a6 | https://github.com/RICE-EIC/Patch-Fool/tree/9638ec33a4d13b0c5ff0ec3ee5ce6b46ea7da5a6 |
Generator | import torch
import torch.onnx
import torch.nn as nn
def outputActivation(x):
muX = x[:, :, 0:1]
muY = x[:, :, 1:2]
sigX = x[:, :, 2:3]
sigY = x[:, :, 3:4]
rho = x[:, :, 4:5]
sigX = torch.exp(sigX)
sigY = torch.exp(sigY)
rho = torch.tanh(rho)
out = torch.cat([muX, muY, sigX, sigY, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | PhilippeW83440/conv-social-pooling | Generator | false | 17,813 | [
"MIT"
] | 4 | 93d3a08af8678c3309d75a9bfb37df500da5cc46 | https://github.com/PhilippeW83440/conv-social-pooling/tree/93d3a08af8678c3309d75a9bfb37df500da5cc46 |
VectorQuantizer | import torch
import torch.nn as nn
class VectorQuantizer(nn.Module):
"""
Reference:
Taming Transformers for High-Resolution Image Synthesis
https://arxiv.org/pdf/2012.09841.pdf
"""
def __init__(self, n_e, e_dim, beta=1.0):
super().__init__()
self.n_e = n_e
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | PeikeLi/pytorch-vector-quantization | VectorQuantizer | false | 17,814 | [
"MIT"
] | 6 | 48ce6a74ec56b9d8c11dde2cd35b055a925c3070 | https://github.com/PeikeLi/pytorch-vector-quantization/tree/48ce6a74ec56b9d8c11dde2cd35b055a925c3070 |
GAT | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLayer, 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._inductor.runtime.... | OkYongChoi/smac | GAT | false | 17,815 | [
"Apache-2.0"
] | 8 | 5b2b59e42d17a124e97feeecf9154a3a0aa9d260 | https://github.com/OkYongChoi/smac/tree/5b2b59e42d17a124e97feeecf9154a3a0aa9d260 |
myDecoder | import torch
import torch.nn.functional as F
class myDecoder(torch.nn.Module):
def __init__(self, fomSize, romSize):
super(myDecoder, self).__init__()
self.romSize_ = romSize
self.fomSize_ = fomSize
self.fc1 = torch.nn.Linear(romSize, 64)
self.fc2 = torch.nn.Linear(64, 200... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Pressio/pressio4py | myDecoder | false | 17,816 | [
"Unlicense",
"BSD-3-Clause"
] | 4 | 36676dbd112a7c7960ccbf302ff14d4376c819ec | https://github.com/Pressio/pressio4py/tree/36676dbd112a7c7960ccbf302ff14d4376c819ec |
KDLoss_source_code | import torch
import torch.nn.functional as F
from torch import nn
class KDLoss_source_code(nn.Module):
def __init__(self, temp: 'float', reduction: 'str'):
super(KDLoss_source_code, self).__init__()
self.temp = temp
self.reduction = reduction
self.kl_loss = nn.KLDivLoss(reduction=... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | PangJian123/ISM-ReID | KDLoss_source_code | false | 17,817 | [
"Apache-2.0"
] | 8 | 4c8e4b4ae591add83e1e6ba0b4b7d2750eeb9ee9 | https://github.com/PangJian123/ISM-ReID/tree/4c8e4b4ae591add83e1e6ba0b4b7d2750eeb9ee9 |
ConvAttentionLayer | import math
import torch
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torch.optim
import torch.utils.data
import torch.onnx.operators
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | PeterouZh/SemiNAS | ConvAttentionLayer | false | 17,818 | [
"Apache-2.0"
] | 5 | 39731663271b994571160d43d796b2bb93386b3b | https://github.com/PeterouZh/SemiNAS/tree/39731663271b994571160d43d796b2bb93386b3b |
Attention | import torch
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torch.optim
import torch.utils.data
import torch.onnx.operators
class Attention(nn.Module):
def __init__(self, input_dim, source_dim=None, output_dim=None, bias=False
):
super(Attention, 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._inductor.runtime.... | PeterouZh/SemiNAS | Attention | false | 17,819 | [
"Apache-2.0"
] | 5 | 39731663271b994571160d43d796b2bb93386b3b | https://github.com/PeterouZh/SemiNAS/tree/39731663271b994571160d43d796b2bb93386b3b |
Normalize | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional as F
class Normalize(Module):
"""Performs :math:`L_p` normalization of inputs over specified dimension.
Does:
.. math::
v = \\frac{v}{\\max(\\lVert v \\rVert_p, \\epsilon)}
for each subtensor v over ... | 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.nn import Module
... | RL-WWW/ISST | Normalize | false | 17,820 | [
"BSD-3-Clause"
] | 5 | 42b656686fa9660794007a0bc00a7177937410e9 | https://github.com/RL-WWW/ISST/tree/42b656686fa9660794007a0bc00a7177937410e9 |
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