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 |
|---|---|---|---|---|---|---|---|---|---|---|
OptimizedResidualBlock | import torch
import torch.nn as nn
import torch.nn.utils as utils
from torchvision import utils
class CustomConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=None, bias=True, spectral_norm=False, residual_init=True):
super(CustomConv2d, 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 ... | takuhirok/rGAN | OptimizedResidualBlock | false | 16,602 | [
"MIT"
] | 103 | 6f7a092de5814c662fd17224b3d48bebe7e03c2f | https://github.com/takuhirok/rGAN/tree/6f7a092de5814c662fd17224b3d48bebe7e03c2f |
WassersteinGeneratorLoss | import torch
import torch.nn as nn
def reduce(x, reduction=None):
"""Applies reduction on a torch.Tensor.
Args:
x (torch.Tensor): The tensor on which reduction is to be applied.
reduction (str, optional): The reduction to be applied. If ``mean`` the mean value of the
Tensor is re... | 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... | torchgan/torchgan | WassersteinGeneratorLoss | false | 16,603 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
Buck | import torch
import torch.nn
class Buck(torch.nn.Module):
def __init__(self, A=1.0, B=1.0, C=1.0):
super(Buck, self).__init__()
self.A = torch.nn.Parameter(torch.Tensor([A]))
self.B = torch.nn.Parameter(torch.Tensor([B]))
self.C = torch.nn.Parameter(torch.Tensor([C]))
def Buc... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_... | torchmd/mdgrad | Buck | false | 16,604 | [
"MIT"
] | 54 | 77bd7685b74b41acf54a9483546e1e8cb545eb01 | https://github.com/torchmd/mdgrad/tree/77bd7685b74b41acf54a9483546e1e8cb545eb01 |
ParityPonderGRU | from torch.nn import Module
import torch
from torch import nn
from typing import Tuple
import torch.utils.data
import torch.nn.functional
import torch.autograd
class ParityPonderGRU(Module):
"""
## PonderNet with GRU for Parity Task
This is a simple model that uses a [GRU Cell](https://pytorch.org/docs/s... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import nn
import... | techthiyanes/annotated_deep_learning_paper_implementations | ParityPonderGRU | false | 16,605 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
WassersteinDiscriminatorLoss | import torch
import torch.nn as nn
def reduce(x, reduction=None):
"""Applies reduction on a torch.Tensor.
Args:
x (torch.Tensor): The tensor on which reduction is to be applied.
reduction (str, optional): The reduction to be applied. If ``mean`` the mean value of the
Tensor is re... | 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... | torchgan/torchgan | WassersteinDiscriminatorLoss | false | 16,606 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
NormalizedLinear | import math
import torch
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt
import torch.nn
class NormalizedLinear(torch.nn.Module):
"""
A advanced Linear layer which supports weight normalization or cosine normalization.
"""
def __init__(self, in_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._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | tonysy/cvpods | NormalizedLinear | false | 16,607 | [
"Apache-2.0"
] | 548 | e322d7842ca0e34b1ef6237ea6d350633efc793a | https://github.com/tonysy/cvpods/tree/e322d7842ca0e34b1ef6237ea6d350633efc793a |
Value | import torch
import torch.nn as nn
import torch.nn.functional as F
class Value(nn.Module):
def __init__(self, num_inputs):
super(Value, self).__init__()
self.affine1 = nn.Linear(num_inputs, 64)
self.affine2 = nn.Linear(64, 64)
self.value_head = nn.Linear(64, 1)
self.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 ... | tpbarron/pytorch-ppo | Value | false | 16,608 | [
"MIT"
] | 47 | f73226865e34443f93dbec58939329c9278828e8 | https://github.com/tpbarron/pytorch-ppo/tree/f73226865e34443f93dbec58939329c9278828e8 |
MinimaxDiscriminatorLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def minimax_discriminator_loss(dx, dgz, label_smoothing=0.0, reduction='mean'):
target_ones = torch.ones_like(dgz) * (1.0 - label_smoothing)
target_zeros = torch.zeros_like(dx)
loss = F.binary_cross_entropy_with_logits(dx, target_ones, red... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | torchgan/torchgan | MinimaxDiscriminatorLoss | false | 16,609 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
VirtualBatchNorm | import torch
import torch.nn as nn
class VirtualBatchNorm(nn.Module):
"""Virtual Batch Normalization Module as proposed in the paper
`"Improved Techniques for Training GANs by Salimans et. al." <https://arxiv.org/abs/1805.08318>`_
Performs Normalizes the features of a batch based on the statistics collec... | 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_... | torchgan/torchgan | VirtualBatchNorm | false | 16,610 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
CosineFastRCNNOutputLayers | import math
import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt
import torch.nn
class NormalizedLinear(torch.nn.Module):
"""
A advanced Linear layer which supports weight normalization or cosine normalization.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tonysy/cvpods | CosineFastRCNNOutputLayers | false | 16,611 | [
"Apache-2.0"
] | 548 | e322d7842ca0e34b1ef6237ea6d350633efc793a | https://github.com/tonysy/cvpods/tree/e322d7842ca0e34b1ef6237ea6d350633efc793a |
MinibatchDiscrimination1d | import torch
import torch.nn as nn
class MinibatchDiscrimination1d(nn.Module):
"""1D Minibatch Discrimination Module as proposed in the paper `"Improved Techniques for
Training GANs by Salimans et. al." <https://arxiv.org/abs/1805.08318>`_
Allows the Discriminator to easily detect mode collapse by augmen... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | torchgan/torchgan | MinibatchDiscrimination1d | false | 16,612 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
GaussMembFunc | import torch
def _mk_param(val):
"""Make a torch parameter from a scalar value"""
if isinstance(val, torch.Tensor):
val = val.item()
return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))
class GaussMembFunc(torch.nn.Module):
"""
Gaussian membership functions, defined by two... | 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... | trituenhantaoio/anfis-pytorch | GaussMembFunc | false | 16,613 | [
"MIT"
] | 66 | 7a6bf123d69b550e46abeddd5b4a776243d43aa6 | https://github.com/trituenhantaoio/anfis-pytorch/tree/7a6bf123d69b550e46abeddd5b4a776243d43aa6 |
DisAlignCosineFastRCNNOutputLayers | import math
import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt
import torch.nn
def cat(tensors, dim=0):
"""
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tonysy/cvpods | DisAlignCosineFastRCNNOutputLayers | false | 16,615 | [
"Apache-2.0"
] | 548 | e322d7842ca0e34b1ef6237ea6d350633efc793a | https://github.com/tonysy/cvpods/tree/e322d7842ca0e34b1ef6237ea6d350633efc793a |
Policy | import copy
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def square(a):
return torch.pow(a, 2.0)
class Policy(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(Policy, self).__init__()
self.affine1 = nn.Linear(num_inputs, 64)
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.triton_helpers import libdevice, math as tl_math
im... | tpbarron/pytorch-ppo | Policy | false | 16,616 | [
"MIT"
] | 47 | f73226865e34443f93dbec58939329c9278828e8 | https://github.com/tpbarron/pytorch-ppo/tree/f73226865e34443f93dbec58939329c9278828e8 |
ActorCritic | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
class ActorCritic(nn.Module):
def __init__(self, num_inputs, num_outputs, hidden=64):
super(ActorCritic, self).__init__()
self.affine1 = nn.Linear(num_inputs, hidden)
self.affine2 = nn.Linear(hidden, hidden)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | tpbarron/pytorch-ppo | ActorCritic | false | 16,617 | [
"MIT"
] | 47 | f73226865e34443f93dbec58939329c9278828e8 | https://github.com/tpbarron/pytorch-ppo/tree/f73226865e34443f93dbec58939329c9278828e8 |
BellMembFunc | import torch
def _mk_param(val):
"""Make a torch parameter from a scalar value"""
if isinstance(val, torch.Tensor):
val = val.item()
return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))
class BellMembFunc(torch.nn.Module):
"""
Generalised Bell membership function; defined ... | 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... | trituenhantaoio/anfis-pytorch | BellMembFunc | false | 16,618 | [
"MIT"
] | 66 | 7a6bf123d69b550e46abeddd5b4a776243d43aa6 | https://github.com/trituenhantaoio/anfis-pytorch/tree/7a6bf123d69b550e46abeddd5b4a776243d43aa6 |
DataEmbedding_wo_pos | import math
import torch
import torch.nn as nn
class PositionalEmbedding(nn.Module):
def __init__(self, d_model, max_len=5000):
super(PositionalEmbedding, self).__init__()
pe = torch.zeros(max_len, d_model).float()
pe.require_grad = False
position = torch.arange(0, max_len).float(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | thuml/Autoformer | DataEmbedding_wo_pos | false | 16,619 | [
"MIT"
] | 263 | 6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab | https://github.com/thuml/Autoformer/tree/6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab |
UpsampleConv | import torch
import torch.nn.functional as F
import torch.nn as nn
def l2normalize(v, esp=1e-08):
return v / (v.norm() + esp)
def sn_weight(weight, u, height, n_power_iterations):
weight.requires_grad_(False)
for _ in range(n_power_iterations):
v = l2normalize(torch.mv(weight.view(height, -1).t(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride = torch... | tsirif/cortex | UpsampleConv | false | 16,620 | [
"BSD-3-Clause"
] | 109 | 2837b220f9fb73279df3815bb18b274106412c08 | https://github.com/tsirif/cortex/tree/2837b220f9fb73279df3815bb18b274106412c08 |
DQN_RAM | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN_RAM(nn.Module):
def __init__(self, in_features=4, num_actions=18):
"""
Initialize a deep Q-learning network for testing algorithm
in_features: number of features of input.
num_actions: number of a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | transedward/pytoch-dqn | DQN_RAM | false | 16,621 | [
"MIT"
] | 358 | 1ffda6f3724b3bb37c3195b09b651b1682d4d4fd | https://github.com/transedward/pytoch-dqn/tree/1ffda6f3724b3bb37c3195b09b651b1682d4d4fd |
MySimpleNet | import torch
import torch.nn.functional as F
from torch import nn
class MySimpleNet(nn.Module):
"""
Very simple 2-layer net, slightly adapted from the docs:
https://skorch.readthedocs.io/en/stable/user/quickstart.html
"""
def __init__(self, num_in, num_feat, num_hidden=10, nonlin=F.re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | trituenhantaoio/anfis-pytorch | MySimpleNet | false | 16,622 | [
"MIT"
] | 66 | 7a6bf123d69b550e46abeddd5b4a776243d43aa6 | https://github.com/trituenhantaoio/anfis-pytorch/tree/7a6bf123d69b550e46abeddd5b4a776243d43aa6 |
MeanPoolConv | import torch
import torch.nn.functional as F
import torch.nn as nn
def l2normalize(v, esp=1e-08):
return v / (v.norm() + esp)
def sn_weight(weight, u, height, n_power_iterations):
weight.requires_grad_(False)
for _ in range(n_power_iterations):
v = l2normalize(torch.mv(weight.view(height, -1).t(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride = torch... | tsirif/cortex | MeanPoolConv | false | 16,623 | [
"BSD-3-Clause"
] | 109 | 2837b220f9fb73279df3815bb18b274106412c08 | https://github.com/tsirif/cortex/tree/2837b220f9fb73279df3815bb18b274106412c08 |
ECB | import torch
import torch.nn as nn
import torch.nn.functional as F
class SeqConv3x3(nn.Module):
def __init__(self, seq_type, inp_planes, out_planes, depth_multiplier):
super(SeqConv3x3, self).__init__()
self.type = seq_type
self.inp_planes = inp_planes
self.out_planes = out_planes... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | thinkreed/ECBSR | ECB | false | 16,624 | [
"Apache-2.0"
] | 162 | 152b9fef9b9fb61b6e5a93677229143652ef305d | https://github.com/thinkreed/ECBSR/tree/152b9fef9b9fb61b6e5a93677229143652ef305d |
XTanhLoss | import torch
class XTanhLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y_t, y_prime_t):
ey_t = y_t - y_prime_t
return torch.mean(ey_t * torch.tanh(ey_t))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_ini... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | tuantle/regression-losses-pytorch | XTanhLoss | false | 16,625 | [
"MIT"
] | 82 | 2893f4439ada5df239e3afd0ec7e781dd61403e9 | https://github.com/tuantle/regression-losses-pytorch/tree/2893f4439ada5df239e3afd0ec7e781dd61403e9 |
BondEnergyModule | import torch
import torch.nn
import torch.nn as nn
from itertools import repeat
def gen(src, index, dim=-1, out=None, dim_size=None, fill_value=0):
dim = range(src.dim())[dim]
if index.dim() == 1:
index_size = list(repeat(1, src.dim()))
index_size[dim] = src.size(dim)
index = index.vie... | 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
import torch.nn as nn
from itertools import repeat
assert_size_... | torchmd/mdgrad | BondEnergyModule | false | 16,626 | [
"MIT"
] | 54 | 77bd7685b74b41acf54a9483546e1e8cb545eb01 | https://github.com/torchmd/mdgrad/tree/77bd7685b74b41acf54a9483546e1e8cb545eb01 |
ConvMeanPool | import torch
import torch.nn.functional as F
import torch.nn as nn
def l2normalize(v, esp=1e-08):
return v / (v.norm() + esp)
def sn_weight(weight, u, height, n_power_iterations):
weight.requires_grad_(False)
for _ in range(n_power_iterations):
v = l2normalize(torch.mv(weight.view(height, -1).t(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride = torch... | tsirif/cortex | ConvMeanPool | false | 16,627 | [
"BSD-3-Clause"
] | 109 | 2837b220f9fb73279df3815bb18b274106412c08 | https://github.com/tsirif/cortex/tree/2837b220f9fb73279df3815bb18b274106412c08 |
XSigmoidLoss | import torch
class XSigmoidLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y_t, y_prime_t):
ey_t = y_t - y_prime_t
return torch.mean(2 * ey_t * torch.sigmoid(ey_t) - ey_t)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | tuantle/regression-losses-pytorch | XSigmoidLoss | false | 16,628 | [
"MIT"
] | 82 | 2893f4439ada5df239e3afd0ec7e781dd61403e9 | https://github.com/tuantle/regression-losses-pytorch/tree/2893f4439ada5df239e3afd0ec7e781dd61403e9 |
BiAttention | import torch
from typing import Optional
import torch.nn as nn
from torch.nn.parameter import Parameter
class BiAttention(nn.Module):
def __init__(self, input_size_encoder: 'int', input_size_decoder: 'int',
num_labels: 'int', biaffine: 'bool'=True, **kwargs) ->None:
super(BiAttention, 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
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_strid... | tucan9389/KLUE-baseline | BiAttention | false | 16,629 | [
"Apache-2.0"
] | 71 | add61158e61f86adfca65087237443828b650090 | https://github.com/tucan9389/KLUE-baseline/tree/add61158e61f86adfca65087237443828b650090 |
AlgebraicLoss | import torch
class AlgebraicLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y_t, y_prime_t):
ey_t = y_t - y_prime_t
return torch.mean(ey_t * ey_t / torch.sqrt(1 + ey_t * ey_t))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | tuantle/regression-losses-pytorch | AlgebraicLoss | false | 16,630 | [
"MIT"
] | 82 | 2893f4439ada5df239e3afd0ec7e781dd61403e9 | https://github.com/tuantle/regression-losses-pytorch/tree/2893f4439ada5df239e3afd0ec7e781dd61403e9 |
GPT2Postprocessing | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class GPT2Postprocessing(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_f = nn.LayerNorm(config.hidden_size, eps=config.
layer_norm_epsilon)
self.lm_head = nn.Linear(config.hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | tunib-ai/large-scale-lm-tutorials | GPT2Postprocessing | false | 16,631 | [
"Apache-2.0"
] | 128 | ca29ff9f945a59abcc3e3f1000c4d83de97973d4 | https://github.com/tunib-ai/large-scale-lm-tutorials/tree/ca29ff9f945a59abcc3e3f1000c4d83de97973d4 |
KLDivergenceLoss | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional
import torch.autograd
class KLDivergenceLoss(Module):
"""
<a id="KLDivergenceLoss"></a>
## KL Divergence Regularization Loss
This tries to shrink the total evidence to zero if the sample cannot be correctly 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.triton_helpers import libdevice
from torch.nn import Module
import torch.utils.data
import torch.nn.functional
... | techthiyanes/annotated_deep_learning_paper_implementations | KLDivergenceLoss | false | 16,632 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
Conv2dZeroInit | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn
class Conv2dZeroInit(nn.Conv2d):
def __init__(self, channels_in, channels_out, filter_size, stride=1,
padding=0, logscale=3.0):
super().__init__(channels_in, channels_out, filter_size, stride=
stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | tychovdo/RevGAN | Conv2dZeroInit | false | 16,633 | [
"BSD-3-Clause"
] | 79 | 2af25e6a8176eaab3d424db45fb6ee2cfc5dc9a3 | https://github.com/tychovdo/RevGAN/tree/2af25e6a8176eaab3d424db45fb6ee2cfc5dc9a3 |
netmodel | import torch
import numpy as np
from torch.nn import Parameter
class netmodel(torch.nn.Module):
def __init__(self):
super(netmodel, self).__init__()
self.w0 = Parameter(torch.Tensor(1))
self.w1 = Parameter(torch.Tensor(1))
self.w0.data.uniform_(-1, 1)
self.w1.data.uniform_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
from torch.nn import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch... | uber-common/safemutations | netmodel | false | 16,634 | [
"MIT"
] | 91 | 40e5fd03a244f89bf157d4bedf79201e706aedc1 | https://github.com/uber-common/safemutations/tree/40e5fd03a244f89bf157d4bedf79201e706aedc1 |
DSC_loss | import torch
import torch.nn as nn
class DSC_loss(nn.Module):
def __init__(self):
super(DSC_loss, self).__init__()
self.epsilon = 1e-06
return
def forward(self, pred, target):
batch_num = pred.shape[0]
pred = pred.contiguous().view(batch_num, -1)
target = 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | twni2016/OrganSegRSTN_PyTorch | DSC_loss | false | 16,635 | [
"MIT"
] | 100 | bf571320e718c8f138e04d48645e3b4dfe75801d | https://github.com/twni2016/OrganSegRSTN_PyTorch/tree/bf571320e718c8f138e04d48645e3b4dfe75801d |
ConformerConvBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.cuda
class ConformerConvBlock(nn.Module):
def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
super(ConformerConvBlock, self).__init__()
assert (kernel_size - 1) % 2 == ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | tuannamnguyen93/NMTGMinor | ConformerConvBlock | false | 16,636 | [
"MIT"
] | 75 | acde3454343bda7060fae541c110d0ad1a8ac4f4 | https://github.com/tuannamnguyen93/NMTGMinor/tree/acde3454343bda7060fae541c110d0ad1a8ac4f4 |
N2 | import torch
from typing import Tuple
from abc import ABC
from abc import abstractmethod
from torch import nn
class Regularizer(nn.Module, ABC):
@abstractmethod
def forward(self, factors: 'Tuple[torch.Tensor]'):
pass
class N2(Regularizer):
def __init__(self, weight: 'float'):
super(N2,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from typing import Tuple
from abc import ABC
from abc import abstractmethod
fro... | uclnlp/cqd | N2 | false | 16,637 | [
"MIT"
] | 59 | 36148c110f336415250c98873fc27ca847741a78 | https://github.com/uclnlp/cqd/tree/36148c110f336415250c98873fc27ca847741a78 |
L2LossWithLogit | import torch
import torch.utils.data
import torch
from torch import nn
class L2LossWithLogit(nn.Module):
def __init__(self):
super(L2LossWithLogit, self).__init__()
self.mse = nn.MSELoss(reduction='sum')
def forward(self, logits, targets):
p = torch.sigmoid(logits)
return sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch
from torch import nn
assert_size_stride = torch._C._... | ucas-vg/TinyBenchmark | L2LossWithLogit | false | 16,638 | [
"MIT"
] | 495 | 36436df3716d842b6148fb6f6bc7715a2fbdfd92 | https://github.com/ucas-vg/TinyBenchmark/tree/36436df3716d842b6148fb6f6bc7715a2fbdfd92 |
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(1))
self.beta = nn.Parameter(torch.zeros(1))
self.eps = eps
def forward(self, x):
mean = x.me... | 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_... | uber-common/safemutations | LayerNorm | false | 16,639 | [
"MIT"
] | 91 | 40e5fd03a244f89bf157d4bedf79201e706aedc1 | https://github.com/uber-common/safemutations/tree/40e5fd03a244f89bf157d4bedf79201e706aedc1 |
HammingLoss | import torch
class HammingLoss(torch.nn.Module):
def forward(self, suggested, target):
errors = suggested * (1.0 - target) + (1.0 - suggested) * target
return errors.mean(dim=0).sum()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | uclnlp/torch-imle | HammingLoss | false | 16,640 | [
"MIT"
] | 205 | f595cd8d527466f6b5db79276f6ceee01d100a1c | https://github.com/uclnlp/torch-imle/tree/f595cd8d527466f6b5db79276f6ceee01d100a1c |
FCGenerator | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class FCGenerator(nn.Module):
def __init__(self, options):
"""
The fully connected generator is initialized by creating a chain of
fully connected layers that perform transform... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | unicredit/ganzo | FCGenerator | false | 16,641 | [
"Apache-2.0"
] | 73 | fb1d270f5091073e8f27da76ab508ab24e5d40e9 | https://github.com/unicredit/ganzo/tree/fb1d270f5091073e8f27da76ab508ab24e5d40e9 |
FusedDownsample | import torch
import torch.nn.functional as F
from torch import nn
from math import sqrt
class FusedDownsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(out_channel, in_channel, kernel_size, kernel_size)
bias = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | uzielroy/StyleGan_FewShot | FusedDownsample | false | 16,642 | [
"MIT"
] | 76 | 94e4c49dbf39d1c6299f33787afb3e471ece11e3 | https://github.com/uzielroy/StyleGan_FewShot/tree/94e4c49dbf39d1c6299f33787afb3e471ece11e3 |
L1Loss | import torch
import torch.nn.functional as F
import torch.onnx
class L1Loss(torch.nn.Module):
"""
L1 loss
"""
def __init__(self, **kwargs):
super(L1Loss, self).__init__()
self.loss_w = kwargs.get('loss_weight', 1)
def forward(self, preds, gts):
return F.l1_loss(preds.view... | 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.onnx
asse... | usutdzxych/CenseoQoE | L1Loss | false | 16,643 | [
"BSD-3-Clause"
] | 75 | 3f653296b223da6190e1e1781e7b9b54ff877102 | https://github.com/usutdzxych/CenseoQoE/tree/3f653296b223da6190e1e1781e7b9b54ff877102 |
Linear | import torch
import torch.nn.functional as F
import torch.nn as nn
class Linear(nn.Linear):
def forward(self, x):
weight = self.weight
weight_mean = weight.mean(dim=1, keepdim=True)
weight = weight - weight_mean
std = weight.std(dim=1, keepdim=True) + 1e-05
weight = weight... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | untitled-ai/self_supervised | Linear | false | 16,644 | [
"MIT"
] | 370 | 6d14ca0402ecc13feda9b3a9fdc056fd1ac24473 | https://github.com/untitled-ai/self_supervised/tree/6d14ca0402ecc13feda9b3a9fdc056fd1ac24473 |
FusedUpsample | import torch
import torch.nn.functional as F
from torch import nn
from math import sqrt
class FusedUpsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(in_channel, out_channel, kernel_size, kernel_size)
bias = to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 math import sqrt
assert_size_stride = torch._C._dynamo... | uzielroy/StyleGan_FewShot | FusedUpsample | false | 16,645 | [
"MIT"
] | 76 | 94e4c49dbf39d1c6299f33787afb3e471ece11e3 | https://github.com/uzielroy/StyleGan_FewShot/tree/94e4c49dbf39d1c6299f33787afb3e471ece11e3 |
AlbertAttentionWithoutSkipConnection | from _paritybench_helpers import _mock_config
import math
import torch
import torch.utils.checkpoint
from torch import nn
class AlbertAttentionWithoutSkipConnection(nn.Module):
def __init__(self, config):
super().__init__()
if (config.hidden_size % config.num_attention_heads != 0 and not
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | twistedcubic/attention-rank-collapse | AlbertAttentionWithoutSkipConnection | false | 16,646 | [
"Apache-2.0"
] | 118 | 38b5df6dc2add25f6d945e48a6baf96862368c20 | https://github.com/twistedcubic/attention-rank-collapse/tree/38b5df6dc2add25f6d945e48a6baf96862368c20 |
FCDiscriminator | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class FCDiscriminator(nn.Module):
def __init__(self, options):
"""
The fully connected generator is initialized by creating a chain of
fully connected layers that perform trans... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | unicredit/ganzo | FCDiscriminator | false | 16,647 | [
"Apache-2.0"
] | 73 | fb1d270f5091073e8f27da76ab508ab24e5d40e9 | https://github.com/unicredit/ganzo/tree/fb1d270f5091073e8f27da76ab508ab24e5d40e9 |
Highway | import torch
from torch import nn
import torch.utils.data
class Highway(nn.Module):
def __init__(self, input_dim, dropout):
super(Highway, self).__init__()
self.input_linear = nn.Linear(input_dim, input_dim)
self.relu = nn.ReLU()
self.gate_linear = nn.Linear(input_dim, input_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | uwnlp/piqa | Highway | false | 16,648 | [
"Apache-2.0"
] | 89 | e18f2189c93965c94655d5cc943dcecdc2c1ea57 | https://github.com/uwnlp/piqa/tree/e18f2189c93965c94655d5cc943dcecdc2c1ea57 |
Router | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class Squash(Module):
'\n ## Squash\n\n This is **squashing** function from paper, given by equation $(1)$.\n\n $$\\mathbf{v}_j = \x0crac{{\\lVert \\mathbf{s}_j \rVert}^2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | techthiyanes/annotated_deep_learning_paper_implementations | Router | false | 16,649 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
NoiseInjection | import torch
from torch import nn
class NoiseInjection(nn.Module):
def __init__(self, channel):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
def forward(self, image, noise):
return image + self.weight * noise
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | uzielroy/StyleGan_FewShot | NoiseInjection | false | 16,650 | [
"MIT"
] | 76 | 94e4c49dbf39d1c6299f33787afb3e471ece11e3 | https://github.com/uzielroy/StyleGan_FewShot/tree/94e4c49dbf39d1c6299f33787afb3e471ece11e3 |
L2Normalize | import torch
import torch.nn
import torch.nn.parallel
import torch.backends.cudnn
import torch.distributed
import torch.multiprocessing
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class L2Normalize(nn.Module):
def __init__(self, dim):
super(L2Normalize, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
import torch... | valeoai/obow | L2Normalize | false | 16,651 | [
"Apache-2.0"
] | 84 | 3758504f5e058275725c35ca7faca3731572b911 | https://github.com/valeoai/obow/tree/3758504f5e058275725c35ca7faca3731572b911 |
LDS | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class LDS(nn.Module):
def __init__(self):
super(LDS, self).__init__()
self.pool1 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)
self.pool2 = nn.MaxPool2d(kernel_size=(2, 2), ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as prod... | vaesl/LRF-Net | LDS | false | 16,653 | [
"MIT"
] | 180 | e44b120dd55288c02852f8e58cda31313525d748 | https://github.com/vaesl/LRF-Net/tree/e44b120dd55288c02852f8e58cda31313525d748 |
conv2d | import torch
import torch.nn as nn
from torch.autograd import Variable
def spectral_norm(module, name='weight'):
SpectralNorm.apply(module, name)
return module
class SpectralNorm:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import Variable
assert_size_stride = t... | vandit15/Self-Supervised-Gans-Pytorch | conv2d | false | 16,654 | [
"MIT"
] | 66 | 01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 | https://github.com/vandit15/Self-Supervised-Gans-Pytorch/tree/01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 |
EntropyLossEncap | import torch
from torch import nn
def feature_map_permute(input):
s = input.data.shape
l = len(s)
if l == 2:
x = input
elif l == 3:
x = input.permute(0, 2, 1)
elif l == 4:
x = input.permute(0, 2, 3, 1)
elif l == 5:
x = input.permute(0, 2, 3, 4, 1)
else:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | vartikagpt10/memae-anomaly-detection | EntropyLossEncap | false | 16,655 | [
"MIT"
] | 297 | ceece7714fb241e82ef3f3785d3d1ed86c28113e | https://github.com/vartikagpt10/memae-anomaly-detection/tree/ceece7714fb241e82ef3f3785d3d1ed86c28113e |
deconv2d | import torch
import torch.nn as nn
from torch.autograd import Variable
def spectral_norm(module, name='weight'):
SpectralNorm.apply(module, name)
return module
class SpectralNorm:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import Variable
assert_size_stride = t... | vandit15/Self-Supervised-Gans-Pytorch | deconv2d | false | 16,656 | [
"MIT"
] | 66 | 01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 | https://github.com/vandit15/Self-Supervised-Gans-Pytorch/tree/01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 |
L1RankLoss | import torch
import torch.nn.functional as F
import torch.onnx
class L1RankLoss(torch.nn.Module):
"""
L1 loss + Rank loss
"""
def __init__(self, **kwargs):
super(L1RankLoss, self).__init__()
self.l1_w = kwargs.get('l1_w', 1)
self.rank_w = kwargs.get('rank_w', 1)
self.h... | 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.onnx
asse... | usutdzxych/CenseoQoE | L1RankLoss | false | 16,658 | [
"BSD-3-Clause"
] | 75 | 3f653296b223da6190e1e1781e7b9b54ff877102 | https://github.com/usutdzxych/CenseoQoE/tree/3f653296b223da6190e1e1781e7b9b54ff877102 |
Iter_Downsample | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class Iter_Downsample(nn.Module):
def __init__(self):
super(Iter_Downsample, self).__init__()
self.init_ds = nn.Sequential(nn.MaxPool2d(kernel_size=2, stride=2,
padding=0), nn.Max... | 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 math import sqrt as sqrt
from itertools import product as prod... | vaesl/LFIP | Iter_Downsample | false | 16,659 | [
"MIT"
] | 59 | eb9d934616c508c9a9032f170baa1d97fa792822 | https://github.com/vaesl/LFIP/tree/eb9d934616c508c9a9032f170baa1d97fa792822 |
_Residual_Block | import torch
import torch.nn as nn
class _Residual_Block(nn.Module):
def __init__(self):
super(_Residual_Block, self).__init__()
self.conv1 = nn.Conv2d(in_channels=256, out_channels=256,
kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | twtygqyy/pytorch-EDSR | _Residual_Block | false | 16,661 | [
"MIT"
] | 59 | 001031b6563fcc45d4e7edb7e14c41fb9982ce64 | https://github.com/twtygqyy/pytorch-EDSR/tree/001031b6563fcc45d4e7edb7e14c41fb9982ce64 |
Residual_D | import torch
import torch.nn as nn
from torch.autograd import Variable
def spectral_norm(module, name='weight'):
SpectralNorm.apply(module, name)
return module
class SpectralNorm:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(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 torch.nn as nn
from to... | vandit15/Self-Supervised-Gans-Pytorch | Residual_D | false | 16,663 | [
"MIT"
] | 66 | 01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 | https://github.com/vandit15/Self-Supervised-Gans-Pytorch/tree/01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 |
GumbelSoftmaxLayer | import torch
import torch.nn as nn
from torch.distributions import RelaxedOneHotCategorical
import torch.nn.parallel
import torch.utils.data
import torch.distributions
def gumbel_softmax_sample(logits: 'torch.Tensor', temperature: 'float'=1.0,
training: 'bool'=True, straight_through: 'bool'=False):
size = log... | 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.distributions import RelaxedOneHotCategorical
import torch.nn.parallel
import torch.utils.data
import torch... | vengalraoguttha/EGG | GumbelSoftmaxLayer | false | 16,664 | [
"MIT"
] | 254 | e4f8412f197543ec7f1f00cf89b5a364b038dc57 | https://github.com/vengalraoguttha/EGG/tree/e4f8412f197543ec7f1f00cf89b5a364b038dc57 |
ReinforcedReceiver | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
from torch.distributions import Bernoulli
import torch.distributions
class ReinforcedReceiver(nn.Module):
def __init__(self, n_bits, n_hidden):
super(ReinforcedReceiver, 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
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import to... | vengalraoguttha/EGG | ReinforcedReceiver | false | 16,665 | [
"MIT"
] | 254 | e4f8412f197543ec7f1f00cf89b5a364b038dc57 | https://github.com/vengalraoguttha/EGG/tree/e4f8412f197543ec7f1f00cf89b5a364b038dc57 |
EntropyLoss | import torch
from torch import nn
class EntropyLoss(nn.Module):
def __init__(self, eps=1e-12):
super(EntropyLoss, self).__init__()
self.eps = eps
def forward(self, x):
b = x * torch.log(x + self.eps)
b = -1.0 * b.sum(dim=1)
b = b.mean()
return b
def get_inpu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | vartikagpt10/memae-anomaly-detection | EntropyLoss | false | 16,666 | [
"MIT"
] | 297 | ceece7714fb241e82ef3f3785d3d1ed86c28113e | https://github.com/vartikagpt10/memae-anomaly-detection/tree/ceece7714fb241e82ef3f3785d3d1ed86c28113e |
BahdanauAttention | import torch
import torch.nn as nn
class BahdanauAttention(nn.Module):
def __init__(self, annot_dim, query_dim, attn_dim):
super(BahdanauAttention, self).__init__()
self.query_layer = nn.Linear(query_dim, attn_dim, bias=True)
self.annot_layer = nn.Linear(annot_dim, attn_dim, bias=True)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | vigilancetrent/chatbot-advanced | BahdanauAttention | false | 16,667 | [
"Apache-2.0"
] | 52 | 2e0c72c4df2e1434da995b7105f8f0414aba6248 | https://github.com/vigilancetrent/chatbot-advanced/tree/2e0c72c4df2e1434da995b7105f8f0414aba6248 |
Interpolate | import torch
import torch.nn as nn
import torch.nn.functional as F
class Interpolate(nn.Module):
def __init__(self, scale_factor, mode='bilinear', align_corners=True):
super(Interpolate, self).__init__()
self.scale_factor = scale_factor
self.mode = mode
self.align_corners = align_... | 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... | vietnhatthai/3d-vehicle-tracking | Interpolate | false | 16,668 | [
"BSD-3-Clause"
] | 603 | 8ee189f6792897651bb56bb2950ce07c9629a89d | https://github.com/vietnhatthai/3d-vehicle-tracking/tree/8ee189f6792897651bb56bb2950ce07c9629a89d |
UpSample | import torch
import torch.nn as nn
import torch.nn.functional as F
class UpSample(nn.Sequential):
def __init__(self, skip_input, output_features):
super(UpSample, self).__init__()
self.convA = nn.Conv2d(skip_input, output_features, kernel_size=3,
stride=1, padding=1)
self.leak... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | varun-affinsys/Monocular-Depth-Estimation-with-Transfer-Learning-pretrained-MobileNetV2 | UpSample | false | 16,669 | [
"MIT"
] | 70 | 9b20c5b3d7a9f90e1dc6f40e17ee31d9b3dee684 | https://github.com/varun-affinsys/Monocular-Depth-Estimation-with-Transfer-Learning-pretrained-MobileNetV2/tree/9b20c5b3d7a9f90e1dc6f40e17ee31d9b3dee684 |
GTConv_2 | import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class GTConv_2(nn.Module):
def __init__(self, in_channels, out_channels):
super(GTConv_2, self).__init__()
self.in_channels = 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 math as tl_math
from torch import nn
i... | verashira/TSPNet | GTConv_2 | false | 16,670 | [
"MIT"
] | 83 | ee454165dcc61cdbbff19565364e2221727ed2b8 | https://github.com/verashira/TSPNet/tree/ee454165dcc61cdbbff19565364e2221727ed2b8 |
TemporalBlock | import torch
from torch import nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
def TemporalConvLayer(input_channels, output_channels, kernel_size):
m = nn.Conv1d(in_channels=input_channels, out_channels=output_channels,
kernel_size=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 import nn
import t... | verashira/TSPNet | TemporalBlock | false | 16,671 | [
"MIT"
] | 83 | ee454165dcc61cdbbff19565364e2221727ed2b8 | https://github.com/verashira/TSPNet/tree/ee454165dcc61cdbbff19565364e2221727ed2b8 |
SoftmaxAllocator | import torch
class SoftmaxAllocator(torch.nn.Module):
"""Portfolio creation by computing a softmax over the asset dimension with temperature.
Parameters
----------
temperature : None or float
If None, then needs to be provided per sample during forward pass. If ``float`` then assumed
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | vishalbelsare/deepdow | SoftmaxAllocator | false | 16,672 | [
"Apache-2.0"
] | 511 | cbb99347fba9a447d4fcae64fe5137c203643e44 | https://github.com/vishalbelsare/deepdow/tree/cbb99347fba9a447d4fcae64fe5137c203643e44 |
TransformerEncoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class TransformerEncoderLayer(nn.Module):
def __init__(self, embed_dim, num_heads, hidden_size, dropout=0.0,
attention_dropout=0.0, activation_dropout=0.0):
... | 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.... | vengalraoguttha/EGG | TransformerEncoderLayer | false | 16,673 | [
"MIT"
] | 254 | e4f8412f197543ec7f1f00cf89b5a364b038dc57 | https://github.com/vengalraoguttha/EGG/tree/e4f8412f197543ec7f1f00cf89b5a364b038dc57 |
SpatialCrossMapLRN | import torch
import torch.nn as nn
import torch.nn.parallel
class SpatialCrossMapLRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1,
ACROSS_CHANNELS=True):
super(SpatialCrossMapLRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._d... | vfdev-5/models-comparison.pytorch | SpatialCrossMapLRN | false | 16,674 | [
"BSD-3-Clause"
] | 174 | 6a09c41c1ed6160af0734924700a9150249c3df6 | https://github.com/vfdev-5/models-comparison.pytorch/tree/6a09c41c1ed6160af0734924700a9150249c3df6 |
Symmetric | import torch
from torch import nn
class NonSquareError(ValueError):
def __init__(self, name, size):
super().__init__(
'The {} parametrization can just be applied to square matrices. Got a tensor of size {}'
.format(name, size))
class VectorError(ValueError):
def __init__(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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | vishalbelsare/geotorch | Symmetric | false | 16,675 | [
"MIT"
] | 422 | ba38d406c245d609fee4b4dac3f6427bf6d73a8e | https://github.com/vishalbelsare/geotorch/tree/ba38d406c245d609fee4b4dac3f6427bf6d73a8e |
Cov2Corr | import torch
import torch.nn as nn
class Cov2Corr(nn.Module):
"""Conversion from covariance matrix to correlation matrix."""
def forward(self, covmat):
"""Convert.
Parameters
----------
covmat : torch.Tensor
Covariance matrix of shape (n_samples, n_assets, n_asset... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | vishalbelsare/deepdow | Cov2Corr | false | 16,676 | [
"Apache-2.0"
] | 511 | cbb99347fba9a447d4fcae64fe5137c203643e44 | https://github.com/vishalbelsare/deepdow/tree/cbb99347fba9a447d4fcae64fe5137c203643e44 |
InformedSender | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class InformedSender(nn.Module):
def __init__(self, game_size, feat_size, embedding_size, hidden_size,
vocab_size=100, temp=1.0):
super(InformedSender, se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | vengalraoguttha/EGG | InformedSender | false | 16,677 | [
"MIT"
] | 254 | e4f8412f197543ec7f1f00cf89b5a364b038dc57 | https://github.com/vengalraoguttha/EGG/tree/e4f8412f197543ec7f1f00cf89b5a364b038dc57 |
Skew | import torch
from torch import nn
class NonSquareError(ValueError):
def __init__(self, name, size):
super().__init__(
'The {} parametrization can just be applied to square matrices. Got a tensor of size {}'
.format(name, size))
class VectorError(ValueError):
def __init__(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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | vishalbelsare/geotorch | Skew | false | 16,678 | [
"MIT"
] | 422 | ba38d406c245d609fee4b4dac3f6427bf6d73a8e | https://github.com/vishalbelsare/geotorch/tree/ba38d406c245d609fee4b4dac3f6427bf6d73a8e |
Naked | from torch.nn import Module
import torch
from torch import Tensor
class Naked(Module):
"""Returns a tensor filled with the scalar value zero.
Args:
out_features (int, default=1): Size of each output sample.
Shape:
- Input: :math:`(N, *, H_{\\text{in}})` where
:math:`*` means an... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | vishalbelsare/pfhedge | Naked | false | 16,679 | [
"MIT"
] | 81 | 4d7ff173995e0795942bc6ec55f3fdc5bfb7a5f1 | https://github.com/vishalbelsare/pfhedge/tree/4d7ff173995e0795942bc6ec55f3fdc5bfb7a5f1 |
GatingMechanism | import torch
class GatingMechanism(torch.nn.Module):
def __init__(self, d_input, bg=0.1):
super(GatingMechanism, self).__init__()
self.Wr = torch.nn.Linear(d_input, d_input)
self.Ur = torch.nn.Linear(d_input, d_input)
self.Wz = torch.nn.Linear(d_input, d_input)
self.Uz = t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | victor-psiori/Transformers-RL | GatingMechanism | false | 16,680 | [
"MIT"
] | 50 | 85b3f2376ba473a45ca18c969aebb1ae82cf8475 | https://github.com/victor-psiori/Transformers-RL/tree/85b3f2376ba473a45ca18c969aebb1ae82cf8475 |
EntropicLoss | from torch.nn import Module
import torch
from torch import Tensor
from typing import Callable
from typing import Union
from abc import ABC
def _format_float(value: 'float') ->str:
"""
>>> _format_float(1)
'1'
>>> _format_float(1.0)
'1.'
>>> _format_float(1e-4)
'1.0000e-04'
"""
tens... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
from torch import Tensor
from typing import C... | vishalbelsare/pfhedge | EntropicLoss | false | 16,681 | [
"MIT"
] | 81 | 4d7ff173995e0795942bc6ec55f3fdc5bfb7a5f1 | https://github.com/vishalbelsare/pfhedge/tree/4d7ff173995e0795942bc6ec55f3fdc5bfb7a5f1 |
ActorNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class ActorNetwork(nn.Module):
def __init__(self, state_size, action_size, hidden_size, seed=1412):
super(ActorNetwork, self).__init__()
self.seed = torch.manual_seed(seed)
self.fc1 = nn.Linear(state_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | vlgiitr/Workshop-Spring-2022 | ActorNetwork | false | 16,682 | [
"MIT"
] | 69 | 003ed62c75a876e946eaa481c27224dd38914015 | https://github.com/vlgiitr/Workshop-Spring-2022/tree/003ed62c75a876e946eaa481c27224dd38914015 |
SphereEmbedded | import torch
from torch import nn
def _extra_repr(**kwargs):
if 'n' in kwargs:
ret = 'n={}'.format(kwargs['n'])
elif 'dim' in kwargs:
ret = 'dim={}'.format(kwargs['dim'])
else:
ret = ''
if 'k' in kwargs:
ret += ', k={}'.format(kwargs['k'])
if 'rank' in kwargs:
... | 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... | vishalbelsare/geotorch | SphereEmbedded | false | 16,683 | [
"MIT"
] | 422 | ba38d406c245d609fee4b4dac3f6427bf6d73a8e | https://github.com/vishalbelsare/geotorch/tree/ba38d406c245d609fee4b4dac3f6427bf6d73a8e |
Decoder | import torch
import torch.nn as nn
from collections import OrderedDict
class Decoder(nn.Module):
def __init__(self, style_dim, class_dim):
super(Decoder, self).__init__()
self.linear_model = nn.Sequential(OrderedDict([('linear_1', nn.
Linear(in_features=style_dim + class_dim, 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | vicissitude1999/multi-level-vae | Decoder | false | 16,684 | [
"MIT"
] | 68 | 83bc98fbe5046c61941298d4fd49b08fd868ee89 | https://github.com/vicissitude1999/multi-level-vae/tree/83bc98fbe5046c61941298d4fd49b08fd868ee89 |
MixtureDensityHead | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.distributions import Categorical
class MixtureDensityHead(nn.Module):
def __init__(self, config: 'DictConfig', **kwargs):
self.hparams = config
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | robburdon/pytorch_tabular | MixtureDensityHead | false | 16,685 | [
"MIT"
] | 560 | 9bf75f22c6e1b3033ad699713e77c283d55f3555 | https://github.com/robburdon/pytorch_tabular/tree/9bf75f22c6e1b3033ad699713e77c283d55f3555 |
EntropicRiskMeasure | from torch.nn import Module
import torch
from torch import Tensor
from typing import Callable
from typing import Union
from abc import ABC
def _format_float(value: 'float') ->str:
"""
>>> _format_float(1)
'1'
>>> _format_float(1.0)
'1.'
>>> _format_float(1e-4)
'1.0000e-04'
"""
tens... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
from torch import Tensor
from typing import C... | vishalbelsare/pfhedge | EntropicRiskMeasure | false | 16,686 | [
"MIT"
] | 81 | 4d7ff173995e0795942bc6ec55f3fdc5bfb7a5f1 | https://github.com/vishalbelsare/pfhedge/tree/4d7ff173995e0795942bc6ec55f3fdc5bfb7a5f1 |
VectorQuantizer | import torch
from torch import Tensor
from torch import nn
import torch.nn.functional as F
class VectorQuantizer(nn.Module):
"""
Reference:
[1] https://github.com/deepmind/sonnet/blob/v2/sonnet/src/nets/vqvae.py
"""
def __init__(self, num_embeddings: 'int', embedding_dim: 'int', beta:
'fl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 Tensor
from... | vipavlovic/pyprobml | VectorQuantizer | false | 16,687 | [
"MIT"
] | 4,895 | 59a2edc682d0163955db5e2f27491ad772b60141 | https://github.com/vipavlovic/pyprobml/tree/59a2edc682d0163955db5e2f27491ad772b60141 |
Warp | import torch
import torch.nn as nn
class Warp(torch.nn.Module):
"""Custom warping layer."""
def __init__(self, mode='bilinear', padding_mode='reflection'):
super().__init__()
self.mode = mode
self.padding_mode = padding_mode
def forward(self, x, tform):
"""Warp the tensor... | 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
assert_size... | vishalbelsare/deepdow | Warp | false | 16,688 | [
"Apache-2.0"
] | 511 | cbb99347fba9a447d4fcae64fe5137c203643e44 | https://github.com/vishalbelsare/deepdow/tree/cbb99347fba9a447d4fcae64fe5137c203643e44 |
BertLayerNormNoVar | import torch
import torch.nn as nn
class BertLayerNormNoVar(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
super(BertLayerNormNoVar, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsil... | 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... | vtu81/auto_LiRPA | BertLayerNormNoVar | false | 16,689 | [
"BSD-3-Clause"
] | 161 | 294152077c0abfafb5d62fee39335e60eff087b4 | https://github.com/vtu81/auto_LiRPA/tree/294152077c0abfafb5d62fee39335e60eff087b4 |
ExpectedShortfall | from torch.nn import Module
import torch
from torch import Tensor
from typing import Callable
from typing import Union
from typing import Optional
from abc import ABC
from math import ceil
def bisect(fn: 'Callable[[Tensor], Tensor]', target: 'Tensor', lower:
'Union[float, Tensor]', upper: 'Union[float, Tensor]', ... | 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.nn import Module
from torch import Tensor
from typing import Callable
from typing import Union
from typing import Optional
from a... | vishalbelsare/pfhedge | ExpectedShortfall | false | 16,690 | [
"MIT"
] | 81 | 4d7ff173995e0795942bc6ec55f3fdc5bfb7a5f1 | https://github.com/vishalbelsare/pfhedge/tree/4d7ff173995e0795942bc6ec55f3fdc5bfb7a5f1 |
MultiHeadDenseLayer | import torch
import tensorflow as tf
import torch.nn as nn
import torch.nn.functional as F
def get_activation(activ):
if callable(activ):
return activ
if activ is None:
return lambda x: x
if activ == 'tanh':
return F.tanh
elif activ == 'relu':
return F.relu
elif act... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 tensorflow as tf
import torch.nn as nn
import torch.nn.functional as F
as... | ishine/neurst | MultiHeadDenseLayer | false | 16,691 | [
"Apache-2.0"
] | 208 | 2ba322393fcfed4261b33f4a657e12bbe321baaa | https://github.com/ishine/neurst/tree/2ba322393fcfed4261b33f4a657e12bbe321baaa |
FocalLoss | import torch
import torch.nn as nn
from matplotlib.font_manager import *
class FocalLoss(nn.Module):
"""
Focal loss: focus more on hard samples
"""
def __init__(self, gamma=0, eps=1e-07):
"""
:param gamma:
:param eps:
"""
super(FocalLoss, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | wang-tf/RepNet-MDNet-VehicleReID | FocalLoss | false | 16,692 | [
"MIT"
] | 226 | d3d184331206ca4bdb5ea399e5b90a9ccc53b400 | https://github.com/wang-tf/RepNet-MDNet-VehicleReID/tree/d3d184331206ca4bdb5ea399e5b90a9ccc53b400 |
L1DepthLoss | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class L1DepthLoss(nn.Module):
"""Custom L1 loss for depth sequences."""
def __init__(self, args):
super(L1DepthLoss, self).__init__()
self.args = args
self.word_dim = 1
def forward(self, predictions,... | 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... | wanyao1992/structural-probes | L1DepthLoss | false | 16,693 | [
"Apache-2.0"
] | 357 | 3071c93b23601d834628d79a74e46e8ab5e5a66b | https://github.com/wanyao1992/structural-probes/tree/3071c93b23601d834628d79a74e46e8ab5e5a66b |
HLoss | import torch
import torch.nn.functional as F
from torch import nn
class HLoss(nn.Module):
"""
returning the negative entropy of an input tensor
"""
def __init__(self, is_maximization=False):
super(HLoss, self).__init__()
self.is_neg = is_maximization
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | vt-vl-lab/SDN | HLoss | false | 16,694 | [
"MIT"
] | 88 | d1f0a448acf720b9b86527f808cb17d30ed2f4e9 | https://github.com/vt-vl-lab/SDN/tree/d1f0a448acf720b9b86527f808cb17d30ed2f4e9 |
Align | import torch
import torch.nn.functional as F
class Align(torch.nn.Module):
def __init__(self, p):
super(Align, self).__init__()
self.p = p
def forward(self, e1, e2):
pred = -torch.norm(e1 - e2, p=self.p, dim=1)
return pred
def only_pos_loss(self, e1, r, e2):
retu... | 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.functional as F
assert_size_stride = torch._C._dynamo.guards.as... | weihangzhang/EAkit | Align | false | 16,695 | [
"MIT"
] | 102 | dde8e914480cd1a3585271f70db11d567d9c2a04 | https://github.com/weihangzhang/EAkit/tree/dde8e914480cd1a3585271f70db11d567d9c2a04 |
N_TransE | import torch
import torch.nn.functional as F
class N_TransE(torch.nn.Module):
def __init__(self, p, params):
super(N_TransE, self).__init__()
self.p = p
self.params = params
def forward(self, e1, r, e2):
pred = -torch.norm(e1 + r - e2, p=self.p, dim=1)
return pred
... | 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.functional as F
assert_size_stride = torch._C._dynamo.guards.as... | weihangzhang/EAkit | N_TransE | false | 16,696 | [
"MIT"
] | 102 | dde8e914480cd1a3585271f70db11d567d9c2a04 | https://github.com/weihangzhang/EAkit/tree/dde8e914480cd1a3585271f70db11d567d9c2a04 |
L1DistanceLoss | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class L1DistanceLoss(nn.Module):
"""Custom L1 loss for distance matrices."""
def __init__(self, args):
super(L1DistanceLoss, self).__init__()
self.args = args
self.word_pair_dims = 1, 2
def forward(s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | wanyao1992/structural-probes | L1DistanceLoss | false | 16,697 | [
"Apache-2.0"
] | 357 | 3071c93b23601d834628d79a74e46e8ab5e5a66b | https://github.com/wanyao1992/structural-probes/tree/3071c93b23601d834628d79a74e46e8ab5e5a66b |
LayerNorm | import torch
class LayerNorm(torch.nn.Module):
def __init__(self, input_dim):
super(LayerNorm, self).__init__()
self.gamma = torch.nn.Parameter(torch.ones(input_dim))
self.beta = torch.nn.Parameter(torch.zeros(input_dim))
self.eps = 1e-06
def forward(self, x, mask):
m... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | watchernyu/MatchLSTM-Analyze-Adversarial-Training | LayerNorm | false | 16,698 | [
"MIT"
] | 50 | 00bd33d3dd22d5291dc2c1ec5feef5eb93b59b3a | https://github.com/watchernyu/MatchLSTM-Analyze-Adversarial-Training/tree/00bd33d3dd22d5291dc2c1ec5feef5eb93b59b3a |
StructuredAttention_bi | import torch
import torch.nn as nn
import torch.nn.functional as F
class StructuredAttention_bi(nn.Module):
def __init__(self, dropout=0.1, scale=100):
super(StructuredAttention_bi, self).__init__()
self.dropout = dropout
self.scale = scale
def forward(self, C, Q, c_mask, q_mask):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | vivekrajput566/03testing2022 | StructuredAttention_bi | false | 16,699 | [
"MIT"
] | 49 | f7e04f921c6607d383806ca2bbb85d2de84e0369 | https://github.com/vivekrajput566/03testing2022/tree/f7e04f921c6607d383806ca2bbb85d2de84e0369 |
AlignEA | import torch
import torch.nn.functional as F
class AlignEA(torch.nn.Module):
def __init__(self, p, feat_drop, params):
super(AlignEA, self).__init__()
self.params = params
def forward(self, e1, r, e2):
return torch.sum(torch.pow(e1 + r - e2, 2), 1)
def only_pos_loss(self, e1, r,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards... | weihangzhang/EAkit | AlignEA | false | 16,700 | [
"MIT"
] | 102 | dde8e914480cd1a3585271f70db11d567d9c2a04 | https://github.com/weihangzhang/EAkit/tree/dde8e914480cd1a3585271f70db11d567d9c2a04 |
N_R_Align | import torch
import torch.nn as nn
class N_R_Align(torch.nn.Module):
def __init__(self, params):
super(N_R_Align, self).__init__()
self.params = params
self.cos_sim = nn.CosineSimilarity(dim=1, eps=1e-06)
def forward(self, e1, e2, n1, n2):
return self.params * torch.sigmoid(s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | weihangzhang/EAkit | N_R_Align | false | 16,701 | [
"MIT"
] | 102 | dde8e914480cd1a3585271f70db11d567d9c2a04 | https://github.com/weihangzhang/EAkit/tree/dde8e914480cd1a3585271f70db11d567d9c2a04 |
HLoss | import torch
import torch.nn as nn
class HLoss(nn.Module):
def __init__(self):
super(HLoss, self).__init__()
def forward(self, x):
b = x * torch.log(x)
b[torch.isnan(b)] = 0
b = -1.0 * b.sum()
return b
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def ge... | 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... | wengong-jin/chemprop | HLoss | false | 16,702 | [
"MIT"
] | 77 | 3ad3577367d8a53f28aade0be41b56b1f25b6125 | https://github.com/wengong-jin/chemprop/tree/3ad3577367d8a53f28aade0be41b56b1f25b6125 |
depthwise_block | import torch
import torch.nn as nn
import torch.utils.data
class depthwise_conv(nn.Module):
def __init__(self, kernel_size=3, stride=1, padding=1):
super(depthwise_conv, self).__init__()
self.depthwise = nn.Conv2d(1, 1, kernel_size=kernel_size, stride=
stride, padding=padding)
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | whiteking64/lang-seg | depthwise_block | false | 16,703 | [
"MIT"
] | 202 | 9d063b126f1b64e38ddb20cc75fc74435bfdcbd3 | https://github.com/whiteking64/lang-seg/tree/9d063b126f1b64e38ddb20cc75fc74435bfdcbd3 |
AttentionCollapse | import torch
import torch.nn as nn
class AttentionCollapse(nn.Module):
"""Collapsing over the channels with attention.
Parameters
----------
n_channels : int
Number of input channels.
Attributes
----------
affine : nn.Module
Fully connected layer performing linear mapping... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | vishalbelsare/deepdow | AttentionCollapse | false | 16,704 | [
"Apache-2.0"
] | 511 | cbb99347fba9a447d4fcae64fe5137c203643e44 | https://github.com/vishalbelsare/deepdow/tree/cbb99347fba9a447d4fcae64fe5137c203643e44 |
Gating | import torch
import torch.nn as nn
import torch.nn.functional as F
class Gating(nn.Module):
"""
FCN architecture for large scale scene coordiante regression.
"""
def __init__(self, num_experts, capacity=1):
"""
Constructor.
"""
super(Gating, self).__init__()
self.capacity = cap... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | vislearn/esac | Gating | false | 16,705 | [
"BSD-3-Clause"
] | 62 | 4004b251525fa238a1cb6e1043fb41a4719a4ff2 | https://github.com/vislearn/esac/tree/4004b251525fa238a1cb6e1043fb41a4719a4ff2 |
LearnedKernel | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class LearnedKernel(nn.Module):
def __init__(self, args: 'Namespace'):
super(LearnedKernel, self).__init__()
self.A = nn.Linear(args.ffn_hidden_size, args.ffn_hidden_size)
def forward(self, encodings: 'torch.Ten... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | wengong-jin/chemprop | LearnedKernel | false | 16,706 | [
"MIT"
] | 77 | 3ad3577367d8a53f28aade0be41b56b1f25b6125 | https://github.com/wengong-jin/chemprop/tree/3ad3577367d8a53f28aade0be41b56b1f25b6125 |
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