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 |
|---|---|---|---|---|---|---|---|---|---|---|
DiceLoss | import torch
import torch.nn as nn
def IoU(logit, truth, smooth=1):
prob = torch.sigmoid(logit)
intersection = torch.sum(prob * truth)
union = torch.sum(prob + truth)
iou = (2 * intersection + smooth) / (union + smooth)
return iou
class DiceLoss(nn.Module):
def __init__(self, smooth=1):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | evilidol/kaggle-Steel-Defect-Detection | DiceLoss | false | 6,666 | [
"MIT"
] | 1 | 41e3e360f49d706c8c79bcd442342c529648a736 | https://github.com/evilidol/kaggle-Steel-Defect-Detection/tree/41e3e360f49d706c8c79bcd442342c529648a736 |
HorizontalMaxPool2d | import torch
import torch.nn as nn
class HorizontalMaxPool2d(nn.Module):
def __init__(self):
super(HorizontalMaxPool2d, self).__init__()
def forward(self, x):
inp_size = x.size()
return nn.functional.max_pool2d(input=x, kernel_size=(1, inp_size[3]))
def get_inputs():
return [to... | 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... | existentmember7/TEMP_monitor | HorizontalMaxPool2d | false | 6,667 | [
"MIT"
] | 1 | b8116f4c134793c4caa22eda78f90dd24d0cad30 | https://github.com/existentmember7/TEMP_monitor/tree/b8116f4c134793c4caa22eda78f90dd24d0cad30 |
SpatialGate2d | import torch
import torch.nn as nn
class SpatialGate2d(nn.Module):
def __init__(self, in_channels):
super(SpatialGate2d, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 1, kernel_size=1, stride=1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
cal = self.conv1(x)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | evilidol/kaggle-Steel-Defect-Detection | SpatialGate2d | false | 6,668 | [
"MIT"
] | 1 | 41e3e360f49d706c8c79bcd442342c529648a736 | https://github.com/evilidol/kaggle-Steel-Defect-Detection/tree/41e3e360f49d706c8c79bcd442342c529648a736 |
DiceCELoss | import torch
import warnings
from typing import Callable
from typing import Union
from typing import Optional
from enum import Enum
import torch.nn as nn
from torch.nn.modules.loss import _Loss
import torch.multiprocessing
class LossReduction(Enum):
"""
See also:
- :py:class:`monai.losses.dice.DiceLos... | 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 warnings
from t... | elitap/classimbalance | DiceCELoss | false | 6,669 | [
"Apache-2.0"
] | 1 | ae807ec533da5eef18f4180b29383399bc57696a | https://github.com/elitap/classimbalance/tree/ae807ec533da5eef18f4180b29383399bc57696a |
BiasConvFc2Net | import torch
import torch.nn as nn
class BiasConvFc2Net(nn.Module):
def __init__(self, in_channels, groups, n_segment, kernel_size=3, padding=1
):
super(BiasConvFc2Net, self).__init__()
self.conv = nn.Conv1d(in_channels, 1, kernel_size, padding=padding)
self.fc = nn.Linear(n_segme... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | eynaij/X-Temporal_catdim | BiasConvFc2Net | false | 6,670 | [
"MIT"
] | 1 | 6a2efba407c09c83ca061c8467c1373b6ed0c7eb | https://github.com/eynaij/X-Temporal_catdim/tree/6a2efba407c09c83ca061c8467c1373b6ed0c7eb |
SCse | import torch
import torch.nn as nn
class GAB(nn.Module):
def __init__(self, input_dim, reduction=4):
super(GAB, self).__init__()
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(input_dim, input_dim // reduction,
kernel_size=1, stride=1)
self.conv2 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | evilidol/kaggle-Steel-Defect-Detection | SCse | false | 6,671 | [
"MIT"
] | 1 | 41e3e360f49d706c8c79bcd442342c529648a736 | https://github.com/evilidol/kaggle-Steel-Defect-Detection/tree/41e3e360f49d706c8c79bcd442342c529648a736 |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertLayerNorm(nn.Module):
def __init__(self, config, variance_epsilon=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BLimmie/pytorch-pretrained-BERT | BertAttention | false | 6,672 | [
"Apache-2.0"
] | 1 | 2ac4b29641e569020ed2acc28016f481f617052b | https://github.com/BLimmie/pytorch-pretrained-BERT/tree/2ac4b29641e569020ed2acc28016f481f617052b |
WeightConvNet | import torch
import torch.nn as nn
class WeightConvNet(nn.Module):
def __init__(self, in_channels, groups, n_segment):
super(WeightConvNet, self).__init__()
self.lastlayer = nn.Conv1d(in_channels, groups, 3, padding=1)
self.groups = groups
def forward(self, x):
N, _C, T = x.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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | eynaij/X-Temporal_catdim | WeightConvNet | false | 6,673 | [
"MIT"
] | 1 | 6a2efba407c09c83ca061c8467c1373b6ed0c7eb | https://github.com/eynaij/X-Temporal_catdim/tree/6a2efba407c09c83ca061c8467c1373b6ed0c7eb |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, smooth=1e-06):
super(DiceLoss, self).__init__()
self.smooth = smooth
def forward(self, y_pred, y_true):
assert y_pred.size() == y_true.size()
y_pred = y_pred.contiguous().view(y_pred.shape[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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | fadamsyah/pytorch-brain-mri-segmentation | DiceLoss | false | 6,674 | [
"MIT"
] | 1 | bdb310ecacbddfce2cef20d50cf0b638dd1bc7b1 | https://github.com/fadamsyah/pytorch-brain-mri-segmentation/tree/bdb310ecacbddfce2cef20d50cf0b638dd1bc7b1 |
CircleLoss | import torch
from torch import Tensor
from torch import nn
class CircleLoss(nn.Module):
def __init__(self, m: 'float', gamma: 'float') ->None:
super(CircleLoss, self).__init__()
self.m = m
self.gamma = gamma
self.soft_plus = nn.Softplus()
def forward(self, sp: 'Tensor', sn: '... | 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 ... | fabiozappo/Person_reID_tensorrt | CircleLoss | false | 6,675 | [
"Apache-2.0"
] | 1 | 164441f35777698274e7664a9aefcc8d54467dc3 | https://github.com/fabiozappo/Person_reID_tensorrt/tree/164441f35777698274e7664a9aefcc8d54467dc3 |
Aggregation | import torch
from torch import nn
from torch.nn import *
class Aggregation(nn.Module):
"""
Aggregation layer for the Dueling architecture.
https://arxiv.org/abs/1511.06581
This layer computes a Q function by combining
an estimate of V with an estimate of the advantage.
The advantage is normal... | 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
from torch.nn import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._d... | ezelikman/autonomous-learning-library | Aggregation | false | 6,676 | [
"MIT"
] | 1 | b32d059ca8b191afe0b310102d0754796f391aff | https://github.com/ezelikman/autonomous-learning-library/tree/b32d059ca8b191afe0b310102d0754796f391aff |
Entropy | import torch
from torch import nn
class Entropy(nn.Module):
def __init__(self):
super(Entropy, self).__init__()
def forward(self, x):
plogp = x * torch.log(x)
plogp[plogp != plogp] = 0
return -torch.sum(plogp, dim=-1)
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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | fallcat/synst | Entropy | false | 6,677 | [
"BSD-3-Clause"
] | 1 | 0fa4adffa825af4a62b6e739b59c4125a7b6698e | https://github.com/fallcat/synst/tree/0fa4adffa825af4a62b6e739b59c4125a7b6698e |
QuaternionLinear | from torch.nn import Module
import torch
import numpy as np
from numpy.random import RandomState
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from scipy.stats import chi
import torch.fx
def quaternion_init(in_features, out_features, rng, kernel_size=None,
criterion='glorot'):
i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import numpy as np
from numpy.random import RandomSt... | eleGAN23/HI2I | QuaternionLinear | false | 6,678 | [
"MIT"
] | 1 | 7730ee0963614290099b011c113048ef6d1b149c | https://github.com/eleGAN23/HI2I/tree/7730ee0963614290099b011c113048ef6d1b149c |
MultiHeadedAttention | import torch
from torch import nn
from torch.nn import functional as F
def same_tensor(tensor, *args):
""" Do the input tensors all point to the same underlying data """
for other in args:
if not torch.is_tensor(other):
return False
if tensor.device != other.device:
ret... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | fallcat/synst | MultiHeadedAttention | false | 6,679 | [
"BSD-3-Clause"
] | 1 | 0fa4adffa825af4a62b6e739b59c4125a7b6698e | https://github.com/fallcat/synst/tree/0fa4adffa825af4a62b6e739b59c4125a7b6698e |
MultiLayeredConv1d | import torch
import torch.nn
class MultiLayeredConv1d(torch.nn.Module):
"""Multi-layered conv1d for Transformer block.
This is a module of multi-leyered conv1d designed to replace positionwise feed-forward network
in Transforner block, which is introduced in `FastSpeech: Fast, Robust and Controllable Tex... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
assert_size_s... | fancyliumeng/asv-subtools | MultiLayeredConv1d | false | 6,680 | [
"Apache-2.0"
] | 1 | 56a13484472e7ae6eb00d762c00d57e581e78eb4 | https://github.com/fancyliumeng/asv-subtools/tree/56a13484472e7ae6eb00d762c00d57e581e78eb4 |
LDEPooling | import torch
import torch.nn
class LDEPooling(torch.nn.Module):
"""A novel learnable dictionary encoding layer according to [Weicheng Cai, etc., "A NOVEL LEARNABLE
DICTIONARY ENCODING LAYER FOR END-TO-END LANGUAGE IDENTIFICATION", icassp, 2018]"""
def __init__(self, input_dim, c_num=64):
super(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 math as tl_math
import torch.nn
assert... | fancyliumeng/asv-subtools | LDEPooling | false | 6,681 | [
"Apache-2.0"
] | 1 | 56a13484472e7ae6eb00d762c00d57e581e78eb4 | https://github.com/fancyliumeng/asv-subtools/tree/56a13484472e7ae6eb00d762c00d57e581e78eb4 |
Conv1dLinear | import torch
import torch.nn
class Conv1dLinear(torch.nn.Module):
"""Conv1D + Linear for Transformer block.
A variant of MultiLayeredConv1d, which replaces second conv-layer to linear.
"""
def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
"""Initialize Conv1dLinear modu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
assert_size_s... | fancyliumeng/asv-subtools | Conv1dLinear | false | 6,682 | [
"Apache-2.0"
] | 1 | 56a13484472e7ae6eb00d762c00d57e581e78eb4 | https://github.com/fancyliumeng/asv-subtools/tree/56a13484472e7ae6eb00d762c00d57e581e78eb4 |
TdnnAffine | import torch
import torch.nn.functional as F
import torch.nn
def to_device(device_object, tensor):
"""
Select device for non-parameters tensor w.r.t model or tensor which has been specified a device.
"""
if isinstance(device_object, torch.nn.Module):
next(device_object.parameters()).device
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | fancyliumeng/asv-subtools | TdnnAffine | false | 6,683 | [
"Apache-2.0"
] | 1 | 56a13484472e7ae6eb00d762c00d57e581e78eb4 | https://github.com/fancyliumeng/asv-subtools/tree/56a13484472e7ae6eb00d762c00d57e581e78eb4 |
UpBlok | import torch
import torch.nn as nn
import torch.nn.functional as F
class UpBlok(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv1x1 = nn.Conv2d(in_channels, in_channels, kernel_size=1,
stride=1, padding=0)
self.conv3x3 = nn.Conv2d(in_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | fendaq/TextRSN | UpBlok | false | 6,684 | [
"MIT"
] | 1 | 02a6bc06cd64b581414ed5065a8c93e0c68a807a | https://github.com/fendaq/TextRSN/tree/02a6bc06cd64b581414ed5065a8c93e0c68a807a |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim):
super(Critic, self).__init__()
self.fc1 = nn.Linear(state_dim, 256)
self.fc2 = nn.Linear(256, 256)
self.fc3 = nn.Linear(256, 1)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | fengzhengyong-github/Deep-reinforcement-learning-with-pytorch | Critic | false | 6,685 | [
"MIT"
] | 1 | 3c56b601d14b0b0c8cde4b6bc6df5c1e8f298c7b | https://github.com/fengzhengyong-github/Deep-reinforcement-learning-with-pytorch/tree/3c56b601d14b0b0c8cde4b6bc6df5c1e8f298c7b |
ScaledDotProductAttention | import torch
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
def __init__(self, dropout: 'float'=0.0) ->None:
super(ScaledDotProductAttention, self).__init__()
self._dropout = nn.Dropout(dropout)
self._softmax = nn.Softmax(dim=2)
def forward(self, query: 'torch.Tens... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | fengtaoo/opmft | ScaledDotProductAttention | false | 6,686 | [
"MIT"
] | 1 | 64f2a12c724295cd913eda02502f2e2a20f2dd55 | https://github.com/fengtaoo/opmft/tree/64f2a12c724295cd913eda02502f2e2a20f2dd55 |
L_TV | import torch
import torch.nn as nn
import torch.optim
class L_TV(nn.Module):
def __init__(self, TVLoss_weight=1):
super(L_TV, self).__init__()
self.TVLoss_weight = TVLoss_weight
def forward(self, x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | farhantandia/Applied-CV-Zero-DCE-master | L_TV | false | 6,687 | [
"MIT"
] | 1 | 56a0f8aec799eb5d125f5d9f44f692b9a9a3c990 | https://github.com/farhantandia/Applied-CV-Zero-DCE-master/tree/56a0f8aec799eb5d125f5d9f44f692b9a9a3c990 |
PixelNormLayer | import torch
import torch.nn as nn
class PixelNormLayer(nn.Module):
"""
Pixelwise feature vector normalization.
"""
def __init__(self, eps=1e-08):
super(PixelNormLayer, self).__init__()
self.eps = eps
def forward(self, x):
return x / torch.sqrt(torch.mean(x ** 2, dim=1, k... | 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_... | ferngonzalezp/turbulence-GAN | PixelNormLayer | false | 6,688 | [
"MIT"
] | 1 | a215a3c5af2dc9a723f95c344e295ecc08954f26 | https://github.com/ferngonzalezp/turbulence-GAN/tree/a215a3c5af2dc9a723f95c344e295ecc08954f26 |
CNN | import torch
from torch.nn import functional as F
from torch import nn
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 8, 6, 1)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(8, 16, 6, 1)
self.conv3 = nn.Conv2d(16, 24,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | eosay/robotCNN | CNN | false | 6,689 | [
"MIT"
] | 1 | 9eaefcc223e868c01f6d1a49a28d2a9f392857e5 | https://github.com/eosay/robotCNN/tree/9eaefcc223e868c01f6d1a49a28d2a9f392857e5 |
SequenceBias | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from torch.nn.parameter import Parameter
class SequenceBias(nn.Module):
"""
Adds one bias element to the end of the sequence.
so if the input has a shape ``(L, N, E)``, where
``L`` i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from torch.nn.parameter import Pa... | ffuuugor/opacus | SequenceBias | false | 6,690 | [
"Apache-2.0"
] | 1 | 2048a6e92902685c2a735e9fb7c0d48b4846b494 | https://github.com/ffuuugor/opacus/tree/2048a6e92902685c2a735e9fb7c0d48b4846b494 |
SoftmaxAffineLayer | import torch
import torch.nn.functional as F
import torch.nn
def to_device(device_object, tensor):
"""
Select device for non-parameters tensor w.r.t model or tensor which has been specified a device.
"""
if isinstance(device_object, torch.nn.Module):
next(device_object.parameters()).device
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | fancyliumeng/asv-subtools | SoftmaxAffineLayer | false | 6,691 | [
"Apache-2.0"
] | 1 | 56a13484472e7ae6eb00d762c00d57e581e78eb4 | https://github.com/fancyliumeng/asv-subtools/tree/56a13484472e7ae6eb00d762c00d57e581e78eb4 |
MLPLayer | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class MLPLayer(nn.Module):
"""
Head for getting sentence representations over RoBERTa/BERT's CLS representation.
"""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | BDBC-KG-NLP/MixCSE_AAAI2022 | MLPLayer | false | 6,692 | [
"MIT"
] | 1 | 884145e24a5258c044fedb658df9999f012df875 | https://github.com/BDBC-KG-NLP/MixCSE_AAAI2022/tree/884145e24a5258c044fedb658df9999f012df875 |
SEBlock | import torch
import torch.nn.functional as F
import torch.nn
def to_device(device_object, tensor):
"""
Select device for non-parameters tensor w.r.t model or tensor which has been specified a device.
"""
if isinstance(device_object, torch.nn.Module):
next(device_object.parameters()).device
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | fancyliumeng/asv-subtools | SEBlock | false | 6,693 | [
"Apache-2.0"
] | 1 | 56a13484472e7ae6eb00d762c00d57e581e78eb4 | https://github.com/fancyliumeng/asv-subtools/tree/56a13484472e7ae6eb00d762c00d57e581e78eb4 |
MultiheadAttention | import torch
import torch.nn.functional as F
import torch.utils.data
import torch.distributed
import torch.nn as nn
from torch.nn import Parameter
import torch.optim
import torch.optim.lr_scheduler
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more detail... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | farhanazareen/Fairseq | MultiheadAttention | false | 6,694 | [
"BSD-3-Clause"
] | 1 | 39c7b6804b4a3426ef23b30f3ca8a3c0a9948079 | https://github.com/farhanazareen/Fairseq/tree/39c7b6804b4a3426ef23b30f3ca8a3c0a9948079 |
DPLSTMCell | import math
import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Optional
from typing import Tuple
class LSTMLinear(nn.Linear):
"""
This function is the same as a nn.Linear layer, except that in the backward pass
the gra... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 math
import ... | ffuuugor/opacus | DPLSTMCell | false | 6,695 | [
"Apache-2.0"
] | 1 | 2048a6e92902685c2a735e9fb7c0d48b4846b494 | https://github.com/ffuuugor/opacus/tree/2048a6e92902685c2a735e9fb7c0d48b4846b494 |
ResidualBlock | import torch
import torch.nn as nn
def conv3x3(in_ch, out_ch, stride=1):
"""3x3 convolution with padding."""
return nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=stride, padding=1)
class ResidualBlock(nn.Module):
"""Simple residual block with two 3x3 convolutions.
Args:
in_ch (int): number... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | fqhank/HESIC | ResidualBlock | false | 6,696 | [
"Apache-2.0"
] | 1 | f15cb8e6822af45f0022ea4887fce915e250ed75 | https://github.com/fqhank/HESIC/tree/f15cb8e6822af45f0022ea4887fce915e250ed75 |
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 ... | fpaupier/stargan-v2 | AdaIN | false | 6,697 | [
"MIT"
] | 1 | 18d2e04ed6e6df963b84345e798d94383757aaa2 | https://github.com/fpaupier/stargan-v2/tree/18d2e04ed6e6df963b84345e798d94383757aaa2 |
SelfAttention | import math
import torch
import torch.nn as nn
from torch.nn import init
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasattr(m, 'weight'):
if init_type == '... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | fanyix/flownet2 | SelfAttention | false | 6,698 | [
"Apache-2.0"
] | 1 | 0643beef59eeaf4cf4907d0d51f486ffd713363f | https://github.com/fanyix/flownet2/tree/0643beef59eeaf4cf4907d0d51f486ffd713363f |
dense_warp | import torch
import torch.nn as nn
class dense_warp(nn.Module):
def __init__(self):
super().__init__()
def forward(self, h1, cost):
g2 = torch.zeros_like(h1)
clone_h1 = h1.detach()
if h1.device.type == 'cuda':
g2 = g2
clone_h1 = clone_h1
for d ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | fqhank/HESIC | dense_warp | false | 6,699 | [
"Apache-2.0"
] | 1 | f15cb8e6822af45f0022ea4887fce915e250ed75 | https://github.com/fqhank/HESIC/tree/f15cb8e6822af45f0022ea4887fce915e250ed75 |
KernelMatcher | import torch
from typing import Dict
import torch.nn as nn
import torch.nn.functional as F
class KernelMatcher(nn.Module):
def __init__(self, embed_dim: 'int', kernel_num: 'int'=21) ->None:
super(KernelMatcher, self).__init__()
self._embed_dim = embed_dim
self._kernel_num = kernel_num
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | fengtaoo/opmft | KernelMatcher | false | 6,700 | [
"MIT"
] | 1 | 64f2a12c724295cd913eda02502f2e2a20f2dd55 | https://github.com/fengtaoo/opmft/tree/64f2a12c724295cd913eda02502f2e2a20f2dd55 |
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... | fpaupier/stargan-v2 | ResBlk | false | 6,701 | [
"MIT"
] | 1 | 18d2e04ed6e6df963b84345e798d94383757aaa2 | https://github.com/fpaupier/stargan-v2/tree/18d2e04ed6e6df963b84345e798d94383757aaa2 |
Enhancement_Block | import torch
import torch.nn as nn
def conv3x3(in_ch, out_ch, stride=1):
"""3x3 convolution with padding."""
return nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=stride, padding=1)
class ResidualBlock(nn.Module):
"""Simple residual block with two 3x3 convolutions.
Args:
in_ch (int): number... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | fqhank/HESIC | Enhancement_Block | false | 6,702 | [
"Apache-2.0"
] | 1 | f15cb8e6822af45f0022ea4887fce915e250ed75 | https://github.com/fqhank/HESIC/tree/f15cb8e6822af45f0022ea4887fce915e250ed75 |
PolicyNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class PolicyNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size, init_w=3e-05):
super(PolicyNetwork, self).__init__()
self.linear1 = nn.Linear(num_inputs, hidden_size)
self.linear2 = nn.Linear(hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | frknayk/Reinforcement-Learning-In-Control | PolicyNetwork | false | 6,703 | [
"MIT"
] | 1 | 24c7eb6fa6b6390ee2dd04f25036c37896ecd944 | https://github.com/frknayk/Reinforcement-Learning-In-Control/tree/24c7eb6fa6b6390ee2dd04f25036c37896ecd944 |
enhance_net_nopool | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class enhance_net_nopool(nn.Module):
def __init__(self):
super(enhance_net_nopool, self).__init__()
self.relu = nn.ReLU(inplace=True)
number_f = 32
self.e_conv1 = nn.Conv2d(3, number_f, 3, 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 import triton_helpers
from torch._inductor.runtime.... | farhantandia/Applied-CV-Zero-DCE-master | enhance_net_nopool | false | 6,704 | [
"MIT"
] | 1 | 56a0f8aec799eb5d125f5d9f44f692b9a9a3c990 | https://github.com/farhantandia/Applied-CV-Zero-DCE-master/tree/56a0f8aec799eb5d125f5d9f44f692b9a9a3c990 |
ActorCriticContinuous | import torch
from torch import nn
import torch.nn.functional as F
class ActorCriticContinuous(nn.Module):
"""
Actor-Critic for continuous action spaces. The network returns a state_value (critic) and
action mean and action standarddeviation (actor). The action is the sampled from a normal
distribution... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | fschur/Advantage-Actor-Critic-for-OpenAi-gym | ActorCriticContinuous | false | 6,705 | [
"MIT"
] | 1 | c130038789425301684825e09e77f17e89d21859 | https://github.com/fschur/Advantage-Actor-Critic-for-OpenAi-gym/tree/c130038789425301684825e09e77f17e89d21859 |
Policy | import torch
import torch.nn.functional as F
from torch import nn
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.conv1 = nn.Conv2d(2, 4, kernel_size=6, stride=2, bias=False)
self.conv2 = nn.Conv2d(4, 16, kernel_size=6, stride=4)
self.size = 9 * 9 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | francescotorregrossa/deep-reinforcement-learning-nanodegree | Policy | false | 6,706 | [
"MIT"
] | 1 | 396648570aa53c9e727a8de69175e4a139d4ded5 | https://github.com/francescotorregrossa/deep-reinforcement-learning-nanodegree/tree/396648570aa53c9e727a8de69175e4a139d4ded5 |
EqualConv2d | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | g33sean/RTIL | EqualConv2d | false | 6,707 | [
"BSD-2-Clause",
"MIT"
] | 1 | 5325f6d5e3ddf7579b6bd8199898e00eff3da631 | https://github.com/g33sean/RTIL/tree/5325f6d5e3ddf7579b6bd8199898e00eff3da631 |
Feedforward | import torch
class Feedforward(torch.nn.Module):
def __init__(self, input_size, output_size, hidden_size):
super(Feedforward, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.hidden_size = hidden_size
self.fc1 = torch.nn.Linear(self.input_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | fywu85/eecs206a_project | Feedforward | false | 6,708 | [
"MIT"
] | 1 | 73ea518779da4d187df8bbe4cbe46bca6d1a0714 | https://github.com/fywu85/eecs206a_project/tree/73ea518779da4d187df8bbe4cbe46bca6d1a0714 |
ActorCriticDiscrete | import torch
from torch import nn
import torch.nn.functional as F
class ActorCriticDiscrete(nn.Module):
"""
Actor-Critic for discrete action spaces. The network returns a state_value (critic)and action probabilities (actor).
"""
def __init__(self, action_dim, state_dim, hidden_dim):
super(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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | fschur/Advantage-Actor-Critic-for-OpenAi-gym | ActorCriticDiscrete | false | 6,709 | [
"MIT"
] | 1 | c130038789425301684825e09e77f17e89d21859 | https://github.com/fschur/Advantage-Actor-Critic-for-OpenAi-gym/tree/c130038789425301684825e09e77f17e89d21859 |
EqualLinear | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | g33sean/RTIL | EqualLinear | false | 6,710 | [
"BSD-2-Clause",
"MIT"
] | 1 | 5325f6d5e3ddf7579b6bd8199898e00eff3da631 | https://github.com/g33sean/RTIL/tree/5325f6d5e3ddf7579b6bd8199898e00eff3da631 |
UNet | import torch
from torch.functional import F
import torch.nn as nn
import torch.nn.functional as F
class down(nn.Module):
def __init__(self, inChannels, outChannels, filterSize):
"""
Parameters
----------
inChannels : int
number of input channels for the first c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.functional import ... | delldu/VideoSlow | UNet | false | 6,711 | [
"MIT"
] | 1 | 2badbbfa2187ea15ea37ef35e70a103ef98c1e33 | https://github.com/delldu/VideoSlow/tree/2badbbfa2187ea15ea37ef35e70a103ef98c1e33 |
ConstantODE | import torch
class ConstantODE(torch.nn.Module):
def __init__(self):
super(ConstantODE, self).__init__()
self.a = torch.nn.Parameter(torch.tensor(0.2))
self.b = torch.nn.Parameter(torch.tensor(3.0))
def forward(self, t, y):
return self.a + (y - (self.a * t + self.b)) ** 5
... | 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... | gaozhihan/torchdiffeq | ConstantODE | false | 6,712 | [
"MIT"
] | 1 | 414781617d595ba01cc3f23382e25ab890f4ca66 | https://github.com/gaozhihan/torchdiffeq/tree/414781617d595ba01cc3f23382e25ab890f4ca66 |
SineODE | import math
import torch
class SineODE(torch.nn.Module):
def forward(self, t, y):
return 2 * y / t + t ** 4 * torch.sin(2 * t) - t ** 2 + 4 * t ** 3
def y_exact(self, t):
return -0.5 * t ** 4 * torch.cos(2 * t) + 0.5 * t ** 3 * torch.sin(
2 * t) + 0.25 * t ** 2 * torch.cos(2 * t)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | gaozhihan/torchdiffeq | SineODE | false | 6,713 | [
"MIT"
] | 1 | 414781617d595ba01cc3f23382e25ab890f4ca66 | https://github.com/gaozhihan/torchdiffeq/tree/414781617d595ba01cc3f23382e25ab890f4ca66 |
ResidualBlock | import torch
from torch import nn
class ResidualBlock(nn.Module):
def __init__(self, filter_size, dilation, residual_channels,
dilated_channels, skip_channels):
super().__init__()
self.conv = nn.Conv1d(residual_channels, dilated_channels,
kernel_size=filter_size, padding=dilat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | fukuroder/pytorch_lightning_wavenet | ResidualBlock | false | 6,714 | [
"MIT"
] | 1 | 440ef4092397998edf0df4625f1f10157db2243e | https://github.com/fukuroder/pytorch_lightning_wavenet/tree/440ef4092397998edf0df4625f1f10157db2243e |
EqualConvTranspose2d | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | g33sean/RTIL | EqualConvTranspose2d | false | 6,715 | [
"BSD-2-Clause",
"MIT"
] | 1 | 5325f6d5e3ddf7579b6bd8199898e00eff3da631 | https://github.com/g33sean/RTIL/tree/5325f6d5e3ddf7579b6bd8199898e00eff3da631 |
CombineSlices | import torch
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
class CombineSlices(nn.Module):
def __init__(self, slice_dim=2):
super().__init__()
self.slice_dim = slice_dim
def forward(self, x):
return torch.index_select(x, dim=self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
assert_size_stride = torch._C._dynamo.gu... | gbosdet/fastMRI | CombineSlices | false | 6,716 | [
"MIT"
] | 1 | 7f94f8006f8919d98fb87788b6dadec9a58d1a3a | https://github.com/gbosdet/fastMRI/tree/7f94f8006f8919d98fb87788b6dadec9a58d1a3a |
ResBlock | import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def norm(dim):
return nn.GroupNorm(min(32, dim), dim)
class ResBlock(nn.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
from torch._inductor.runtime.... | gaozhihan/torchdiffeq | ResBlock | false | 6,717 | [
"MIT"
] | 1 | 414781617d595ba01cc3f23382e25ab890f4ca66 | https://github.com/gaozhihan/torchdiffeq/tree/414781617d595ba01cc3f23382e25ab890f4ca66 |
ConcatConv2d | import torch
import torch.nn as nn
class ConcatConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatConv2d, self).__init__()
module = nn.ConvTranspose2d if transpose else nn.Conv2d
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | gaozhihan/torchdiffeq | ConcatConv2d | false | 6,718 | [
"MIT"
] | 1 | 414781617d595ba01cc3f23382e25ab890f4ca66 | https://github.com/gaozhihan/torchdiffeq/tree/414781617d595ba01cc3f23382e25ab890f4ca66 |
Decoder | import torch
import torch.nn as nn
class Decoder(nn.Module):
def __init__(self, latent_dim=4, obs_dim=2, nhidden=20):
super(Decoder, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.fc1 = nn.Linear(latent_dim, nhidden)
self.fc2 = nn.Linear(nhidden, obs_dim)
def forward... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | gaozhihan/torchdiffeq | Decoder | false | 6,719 | [
"MIT"
] | 1 | 414781617d595ba01cc3f23382e25ab890f4ca66 | https://github.com/gaozhihan/torchdiffeq/tree/414781617d595ba01cc3f23382e25ab890f4ca66 |
MaskedHuberLoss | import torch
import torch.nn as nn
class MaskedHuberLoss(torch.nn.Module):
def __init__(self):
super(MaskedHuberLoss, self).__init__()
def forward(self, output, labels, mask):
lossHuber = nn.SmoothL1Loss(reduction='none')
l = lossHuber(output * mask, labels * mask)
l = l.sum(... | 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... | gabrieleangeletti/GndNet | MaskedHuberLoss | false | 6,720 | [
"MIT"
] | 1 | 323af65c9c16a725805f480ff799936b77b04d53 | https://github.com/gabrieleangeletti/GndNet/tree/323af65c9c16a725805f480ff799936b77b04d53 |
Netleaky | import torch
import torch.nn as nn
import torch.nn.functional as F
class Netleaky(nn.Module):
def __init__(self, input_dim, output_dim):
super(Netleaky, self).__init__()
self.linear1 = nn.Linear(input_dim, 32)
self.linear2 = nn.Linear(32, 32)
self.linear3 = nn.Linear(32, 64)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | gautam-sharma1/openRL | Netleaky | false | 6,721 | [
"MIT"
] | 1 | 14310a97a328fe5682a01ee85d83a6b5e1ae29ca | https://github.com/gautam-sharma1/openRL/tree/14310a97a328fe5682a01ee85d83a6b5e1ae29ca |
Cartesian | import torch
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
class Cartesian(nn.Module):
def forward(self, x):
r, phi = x[..., 0], x[..., 1]
return torch.stack((r * torch.cos(phi), r * torch.sin(phi)), dim=-1)
def get_inputs():
return [tor... | 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
import torch.utils.data
import torch.utils.data.dist... | gbosdet/fastMRI | Cartesian | false | 6,722 | [
"MIT"
] | 1 | 7f94f8006f8919d98fb87788b6dadec9a58d1a3a | https://github.com/gbosdet/fastMRI/tree/7f94f8006f8919d98fb87788b6dadec9a58d1a3a |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, input_dim, output_dim):
super(Net, self).__init__()
self.linear1 = nn.Linear(input_dim, 256)
self.linear2 = nn.Linear(256, output_dim)
def forward(self, x):
x = F.relu(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | gautam-sharma1/openRL | Net | false | 6,723 | [
"MIT"
] | 1 | 14310a97a328fe5682a01ee85d83a6b5e1ae29ca | https://github.com/gautam-sharma1/openRL/tree/14310a97a328fe5682a01ee85d83a6b5e1ae29ca |
NN_2layer_regression | import torch
from torch import nn
class NN_2layer_regression(nn.Module):
def __init__(self, input_dim, interm_dim1, interm_dim2):
super().__init__()
self.d = input_dim
self.interm_dim1 = interm_dim1
self.interm_dim2 = interm_dim2
self.fc1 = nn.Linear(input_dim, interm_dim1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | gaseln/FLIX_small_scale_experiments | NN_2layer_regression | false | 6,724 | [
"MIT"
] | 1 | af9ebd7f192fc0f67a6a94af7939fd3d6f548bd6 | https://github.com/gaseln/FLIX_small_scale_experiments/tree/af9ebd7f192fc0f67a6a94af7939fd3d6f548bd6 |
HighwayLayer | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.onnx.operators
class HighwayLayer(nn.Module):
def __init__(self, input_dim, transform_activation=F.relu,
gate_activation=F.softmax, gate_bias=-2):
super().__init__()
self.highway_transform_activation = transfo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gardenia22/translate | HighwayLayer | false | 6,725 | [
"BSD-3-Clause"
] | 1 | 0be57c8f55b52fc9d39197efa02e05d1c1cda024 | https://github.com/gardenia22/translate/tree/0be57c8f55b52fc9d39197efa02e05d1c1cda024 |
DPSLTMAdapter | import math
import torch
from torch import Tensor
import torch.nn as nn
from torch.nn.utils.rnn import pad_sequence
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Optional
from typing import Union
from typing import List
from typing import Tuple
from torch.nn.uti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 math
from to... | ffuuugor/opacus | DPSLTMAdapter | false | 6,726 | [
"Apache-2.0"
] | 1 | 2048a6e92902685c2a735e9fb7c0d48b4846b494 | https://github.com/ffuuugor/opacus/tree/2048a6e92902685c2a735e9fb7c0d48b4846b494 |
Net16 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net16(nn.Module):
def __init__(self, input_dim, output_dim):
super(Net16, self).__init__()
self.linear1 = nn.Linear(input_dim, 16)
self.linear2 = nn.Linear(16, output_dim)
def forward(self, x):
x = F.rel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | gautam-sharma1/openRL | Net16 | false | 6,727 | [
"MIT"
] | 1 | 14310a97a328fe5682a01ee85d83a6b5e1ae29ca | https://github.com/gautam-sharma1/openRL/tree/14310a97a328fe5682a01ee85d83a6b5e1ae29ca |
MLP | import torch
import torch.nn as nn
import torch.utils.data
class MLP(nn.Module):
def __init__(self, input_size, output_size, hidden_size=500,
weight_decay=0.0):
super(MLP, self).__init__()
self.i2h = nn.Linear(in_features=input_size, out_features=hidden_size)
self.Dropout = nn.Dro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | gchrupala/platalea | MLP | false | 6,728 | [
"Apache-2.0"
] | 1 | 65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 | https://github.com/gchrupala/platalea/tree/65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 |
MeanPool | import torch
import torch.nn as nn
import torch.utils.data
class MeanPool(nn.Module):
def __init__(self):
super(MeanPool, self).__init__()
def forward(self, input):
x = input.mean(dim=1)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
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 as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | gchrupala/platalea | MeanPool | false | 6,729 | [
"Apache-2.0"
] | 1 | 65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 | https://github.com/gchrupala/platalea/tree/65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 |
NNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class NNet(nn.Module):
def __init__(self, input_dim, output_dim):
super(NNet, self).__init__()
self.linear1 = nn.Linear(input_dim, 64)
self.linear2 = nn.Linear(64, 256)
self.linear3 = nn.Linear(256, output_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
import torch.nn as nn
assert_... | gautam-sharma1/openRL | NNet | false | 6,730 | [
"MIT"
] | 1 | 14310a97a328fe5682a01ee85d83a6b5e1ae29ca | https://github.com/gautam-sharma1/openRL/tree/14310a97a328fe5682a01ee85d83a6b5e1ae29ca |
ELBO | import torch
import torch.nn.functional
import torch.nn as nn
class ELBO(nn.Module):
def __init__(self, train_size, loss_function=nn.MSELoss()):
"""
Quantify the Evidence Lower Bound (ELBO) and provide the total loss.
"""
super(ELBO, self).__init__()
self.train_size = trai... | 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.functional
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | geek-yang/NEmo | ELBO | false | 6,731 | [
"Apache-2.0"
] | 1 | 4f310535c4865f3816155b99b4a2bbb891672cc9 | https://github.com/geek-yang/NEmo/tree/4f310535c4865f3816155b99b4a2bbb891672cc9 |
SCNLayer | import torch
import torch.nn as nn
def chebyshev(L, X, k=3):
if k == 1:
return torch.sparse.mm(L, X)
dp = [X, torch.sparse.mm(L, X)]
for i in range(2, k):
nxt = 2 * torch.sparse.mm(L, dp[i - 1])
dp.append(torch.sparse.FloatTensor.add(nxt, -dp[i - 2]))
return torch.cat(dp, dim=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... | ggoh29/Simplicial-neural-network-benchmark | SCNLayer | false | 6,733 | [
"MIT"
] | 1 | 9a12bcd054251790d85e3971f5473dcffaa5664b | https://github.com/ggoh29/Simplicial-neural-network-benchmark/tree/9a12bcd054251790d85e3971f5473dcffaa5664b |
Temp | import torch
import torch.nn as nn
import torch.nn.functional as F
class Temp(nn.Module):
def __init__(self, input_dim, output_dim):
super(Temp, self).__init__()
self.linear1 = nn.Linear(input_dim, 256)
self.linear2 = nn.Linear(256, 256)
self.linear3 = nn.Linear(256, 256)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | gautam-sharma1/openRL | Temp | false | 6,734 | [
"MIT"
] | 1 | 14310a97a328fe5682a01ee85d83a6b5e1ae29ca | https://github.com/gautam-sharma1/openRL/tree/14310a97a328fe5682a01ee85d83a6b5e1ae29ca |
GroupNorm | import torch
import torch.nn as nn
class GroupNorm(nn.Module):
def __init__(self, c_num, group_num=16, eps=1e-10):
"""
The groupnorm layer from https://arxiv.org/abs/1803.08494
Args:
c_num (int): Number of input channels
group_num (int): Number of group by which to... | 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_... | generall/Torchlite | GroupNorm | false | 6,735 | [
"MIT"
] | 1 | 2eb3e2a20b7619bd58b0b0fca120e2aefca0e79a | https://github.com/generall/Torchlite/tree/2eb3e2a20b7619bd58b0b0fca120e2aefca0e79a |
LinearAttention | import torch
import torch.nn as nn
import torch.utils.data
class LinearAttention(nn.Module):
def __init__(self, in_size):
super(LinearAttention, self).__init__()
self.out = nn.Linear(in_size, 1)
nn.init.orthogonal_(self.out.weight.data)
self.softmax = nn.Softmax(dim=1)
def fo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gchrupala/platalea | LinearAttention | false | 6,736 | [
"Apache-2.0"
] | 1 | 65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 | https://github.com/gchrupala/platalea/tree/65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 |
GreedyCTCDecoder | import torch
import torch.nn as nn
class GreedyCTCDecoder(nn.Module):
""" Greedy CTC Decoder
"""
def __init__(self, **kwargs):
nn.Module.__init__(self)
def forward(self, log_probs):
with torch.no_grad():
argmx = log_probs.argmax(dim=-1, keepdim=False).int()
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ghdrl95/Naver-Speech-Hackathon | GreedyCTCDecoder | false | 6,737 | [
"Apache-2.0"
] | 1 | 10b4526d98ce535415cb91d24338790d9c175b63 | https://github.com/ghdrl95/Naver-Speech-Hackathon/tree/10b4526d98ce535415cb91d24338790d9c175b63 |
RegionPenaltyLoss | import torch
from torch import nn
class RegionPenaltyLoss(nn.Module):
def __init__(self, scale=1.0):
"""
Multiplicative penalty.
Penalizes "forbidden" regions instead of exact distribution matches.
Optionally used in tandem with MTCrossEntropyRegionAwareLoss.
`scale` para... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | geoffreyangus/pet-ct | RegionPenaltyLoss | false | 6,738 | [
"Apache-2.0"
] | 1 | fa96a07734afade475f6a1e1587ec14965fe2de3 | https://github.com/geoffreyangus/pet-ct/tree/fa96a07734afade475f6a1e1587ec14965fe2de3 |
Network | import torch
class Network(torch.nn.Module):
def __init__(self, input_dimension, output_dimension):
super(Network, self).__init__()
self.layer_1 = torch.nn.Linear(in_features=input_dimension,
out_features=100)
self.layer_2 = torch.nn.Linear(in_features=100, out_features=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 import triton_helpers
assert_size_stride = torch._C... | georgeyiasemis/Deep-Reinforcement-Learning-on-a-Grid-World- | Network | false | 6,739 | [
"MIT"
] | 1 | f32ceac5f4e29cba212d6fd1b8a25c08ac733666 | https://github.com/georgeyiasemis/Deep-Reinforcement-Learning-on-a-Grid-World-/tree/f32ceac5f4e29cba212d6fd1b8a25c08ac733666 |
ScalarAttention | import torch
import torch.nn as nn
import torch.utils.data
class ScalarAttention(nn.Module):
def __init__(self, in_size, hidden_size):
super(ScalarAttention, self).__init__()
self.hidden = nn.Linear(in_size, hidden_size)
nn.init.orthogonal_(self.hidden.weight.data)
self.out = nn.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.... | gchrupala/platalea | ScalarAttention | false | 6,740 | [
"Apache-2.0"
] | 1 | 65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 | https://github.com/gchrupala/platalea/tree/65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 |
ODEfunc | import torch
import torch.nn as nn
def norm(dim):
return nn.GroupNorm(min(32, dim), dim)
class ConcatConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatConv2d, self).__init__()
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
from torch._inductor.runtime.... | gaozhihan/torchdiffeq | ODEfunc | false | 6,741 | [
"MIT"
] | 1 | 414781617d595ba01cc3f23382e25ab890f4ca66 | https://github.com/gaozhihan/torchdiffeq/tree/414781617d595ba01cc3f23382e25ab890f4ca66 |
MSELoss2d | import torch
from torch import nn
class MSELoss2d(nn.Module):
def __init__(self, size_average=None, reduce=None, reduction='mean',
ignore_index=255):
super(MSELoss2d, self).__init__()
self.MSE = nn.MSELoss(size_average=size_average, reduce=reduce,
reduction=reduction)
def... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | giannifranchi/deeplabv3-superpixelmix | MSELoss2d | false | 6,742 | [
"MIT"
] | 1 | db52bf83b3b242af05bde5e39ee3de896e44c264 | https://github.com/giannifranchi/deeplabv3-superpixelmix/tree/db52bf83b3b242af05bde5e39ee3de896e44c264 |
ResidualBlock | import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.in1 = nn.InstanceNorm2d(channels)
self.prelu = nn.PReLU()
self.c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | generall/Torchlite | ResidualBlock | false | 6,743 | [
"MIT"
] | 1 | 2eb3e2a20b7619bd58b0b0fca120e2aefca0e79a | https://github.com/generall/Torchlite/tree/2eb3e2a20b7619bd58b0b0fca120e2aefca0e79a |
ConvBlock | import torch
import torch.nn as nn
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvBlock, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
stride, padding=1)
self.lr = nn.LeakyReLU()
d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | gle-bellier/DuelingNetwork | ConvBlock | false | 6,744 | [
"MIT"
] | 1 | 8909fe1ba6aee08b6249cb6ca3287752039c6410 | https://github.com/gle-bellier/DuelingNetwork/tree/8909fe1ba6aee08b6249cb6ca3287752039c6410 |
WordPredictor | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.onnx.operators
class WordPredictor(nn.Module):
def __init__(self, encoder_output_dim, hidden_dim, output_dim):
super().__init__()
self.encoder_output_dim = encoder_output_dim
self.hidden_dim = hidden_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.... | gardenia22/translate | WordPredictor | false | 6,745 | [
"BSD-3-Clause"
] | 1 | 0be57c8f55b52fc9d39197efa02e05d1c1cda024 | https://github.com/gardenia22/translate/tree/0be57c8f55b52fc9d39197efa02e05d1c1cda024 |
VideoNormalizer | import torch
import torch.nn as nn
class VideoNormalizer(nn.Module):
def __init__(self):
super(VideoNormalizer, self).__init__()
self.scale = nn.Parameter(torch.Tensor([255.0]), requires_grad=False)
self.mean = nn.Parameter(torch.Tensor([0.485, 0.456, 0.406]),
requires_grad=Fa... | 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... | glee1228/segment_temporal_context_aggregation | VideoNormalizer | false | 6,746 | [
"Apache-2.0"
] | 1 | e5778f848f1cfd89bd1f77beb5e1b38a66a2f13d | https://github.com/glee1228/segment_temporal_context_aggregation/tree/e5778f848f1cfd89bd1f77beb5e1b38a66a2f13d |
LogSparsemax | from torch.autograd import Function
import torch
import torch.nn.init
import torch.nn as nn
def _make_ix_like(input, dim=0):
d = input.size(dim)
rho = torch.arange(1, d + 1, device=input.device, dtype=input.dtype)
view = [1] * input.dim()
view[0] = -1
return rho.view(view).transpose(0, dim)
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.autograd im... | gililior/qasrl-modeling | LogSparsemax | false | 6,747 | [
"MIT"
] | 1 | 2f9684536f6d5f0283b0e4b90a911ea12fa72f72 | https://github.com/gililior/qasrl-modeling/tree/2f9684536f6d5f0283b0e4b90a911ea12fa72f72 |
RegressionSubNet | import torch
import torch.nn as nn
class RegressionSubNet(nn.Module):
def __init__(self, in_channels, num_anchors=9):
super().__init__()
self.conv2d_1 = nn.Conv2d(in_channels, 256, 3, padding=1)
nn.init.normal_(self.conv2d_1.weight.data, std=0.01)
nn.init.zeros_(self.conv2d_1.bias... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | geez0219/ARC | RegressionSubNet | false | 6,748 | [
"Apache-2.0"
] | 1 | f2176f0d442d4a2d6028f0770b1efc1a9ae982b8 | https://github.com/geez0219/ARC/tree/f2176f0d442d4a2d6028f0770b1efc1a9ae982b8 |
Attention | import torch
import torch.nn as nn
class Attention(nn.Module):
def __init__(self, dims, norm=False):
super(Attention, self).__init__()
self.norm = norm
if self.norm:
self.constrain = L2Constrain()
else:
self.transform = nn.Linear(dims, dims)
self.co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | glee1228/segment_temporal_context_aggregation | Attention | false | 6,749 | [
"Apache-2.0"
] | 1 | e5778f848f1cfd89bd1f77beb5e1b38a66a2f13d | https://github.com/glee1228/segment_temporal_context_aggregation/tree/e5778f848f1cfd89bd1f77beb5e1b38a66a2f13d |
Sparsemax | from torch.autograd import Function
import torch
import torch.nn.init
import torch.nn as nn
def _make_ix_like(input, dim=0):
d = input.size(dim)
rho = torch.arange(1, d + 1, device=input.device, dtype=input.dtype)
view = [1] * input.dim()
view[0] = -1
return rho.view(view).transpose(0, dim)
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.autograd import Function
import torch.nn.init
import torch.nn as nn
assert_siz... | gililior/qasrl-modeling | Sparsemax | false | 6,750 | [
"MIT"
] | 1 | 2f9684536f6d5f0283b0e4b90a911ea12fa72f72 | https://github.com/gililior/qasrl-modeling/tree/2f9684536f6d5f0283b0e4b90a911ea12fa72f72 |
ActorNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class ActorNetwork(nn.Module):
def __init__(self, state_size, action_size, hidden_size, init_w=0.003,
log_std_min=-20, log_std_max=2):
super(ActorNetwork, self).__init__()
self.log_std_min = log_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 import triton_helpers
import torch.nn as nn
import ... | godnpeter/DMC_Clustering_PICA | ActorNetwork | false | 6,751 | [
"BSD-2-Clause"
] | 1 | 1b3e14dd4034f3941af1caa06c1d4b6f9d606408 | https://github.com/godnpeter/DMC_Clustering_PICA/tree/1b3e14dd4034f3941af1caa06c1d4b6f9d606408 |
Net | import torch
import torch.nn.functional as F
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | generall/Torchlite | Net | false | 6,752 | [
"MIT"
] | 1 | 2eb3e2a20b7619bd58b0b0fca120e2aefca0e79a | https://github.com/generall/Torchlite/tree/2eb3e2a20b7619bd58b0b0fca120e2aefca0e79a |
ClassificationSubNet | import torch
import numpy as np
import torch.nn as nn
class ClassificationSubNet(nn.Module):
def __init__(self, in_channels, num_classes, num_anchors=9):
super().__init__()
self.num_classes = num_classes
self.conv2d_1 = nn.Conv2d(in_channels, 256, 3, padding=1)
nn.init.normal_(sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | geez0219/ARC | ClassificationSubNet | false | 6,753 | [
"Apache-2.0"
] | 1 | f2176f0d442d4a2d6028f0770b1efc1a9ae982b8 | https://github.com/geez0219/ARC/tree/f2176f0d442d4a2d6028f0770b1efc1a9ae982b8 |
ZeroConv1d | import torch
import torch.nn as nn
class ZeroConv1d(nn.Module):
def __init__(self, in_channel, out_channel):
super().__init__()
self.conv = nn.Conv1d(in_channel, out_channel, 1, padding=0)
self.conv.weight.data.zero_()
self.conv.bias.data.zero_()
self.scale = nn.Parameter(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | gorinars/VQ-VAE-Speech | ZeroConv1d | false | 6,754 | [
"MIT"
] | 1 | 60398f03eb129195bce402a423ace8cca8995f3c | https://github.com/gorinars/VQ-VAE-Speech/tree/60398f03eb129195bce402a423ace8cca8995f3c |
Conv | import torch
import torch.nn as nn
class Conv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1,
causal=True):
super(Conv, self).__init__()
self.causal = causal
if self.causal:
self.padding = dilation * (kernel_size - 1)
else:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | gorinars/VQ-VAE-Speech | Conv | false | 6,755 | [
"MIT"
] | 1 | 60398f03eb129195bce402a423ace8cca8995f3c | https://github.com/gorinars/VQ-VAE-Speech/tree/60398f03eb129195bce402a423ace8cca8995f3c |
HingeLoss | import torch
import torch.utils.data
from torch import nn
import torch
import torch.nn.parallel
import torch.optim
class HingeLoss(nn.Module):
def __init__(self):
super(HingeLoss, self).__init__()
self.margin = 1.0
def hinge_loss(self, input, target):
output = self.margin - input.mul... | 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
from torch import nn
import torch
import torch.nn.parallel
import... | graphbuilder/BNN | HingeLoss | false | 6,756 | [
"MIT"
] | 1 | d99eb5c7ef19f8b0c14a135d40a489f154a3c894 | https://github.com/graphbuilder/BNN/tree/d99eb5c7ef19f8b0c14a135d40a489f154a3c894 |
ContrastiveLoss | import torch
import torch.utils.data
class ContrastiveLoss(torch.nn.Module):
"""
Contrastive loss function.
"""
def __init__(self, margin=1.0):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, x0, x1, y):
diff = x0 - x1
dist_sq = torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
asse... | guruprasaad123/all_dl_projects | ContrastiveLoss | false | 6,757 | [
"Apache-2.0"
] | 1 | 04c869f7f001ef94c467740260663d91a34815e0 | https://github.com/guruprasaad123/all_dl_projects/tree/04c869f7f001ef94c467740260663d91a34815e0 |
AlignQuestionEmbedding | import torch
import torch.nn.functional as F
from torch import nn
class AlignQuestionEmbedding(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.linear = nn.Linear(input_dim, input_dim)
self.relu = nn.ReLU()
def forward(self, context, question, question_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.... | gustavhartz/legal-contract-elements | AlignQuestionEmbedding | false | 6,758 | [
"MIT"
] | 1 | 7a1e1f0024f9d336c7166f51b4325acf03db86a2 | https://github.com/gustavhartz/legal-contract-elements/tree/7a1e1f0024f9d336c7166f51b4325acf03db86a2 |
BasicBlock | import torch
import torch.utils.data
from torch import nn
import torch
import torch.nn.parallel
import torch.optim
def Binarize(tensor, quant_mode='det'):
if quant_mode == 'det':
tensor = tensor.sign()
zero = torch.zeros_like(tensor)
one = torch.ones_like(tensor)
zero - one
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | graphbuilder/BNN | BasicBlock | false | 6,759 | [
"MIT"
] | 1 | d99eb5c7ef19f8b0c14a135d40a489f154a3c894 | https://github.com/graphbuilder/BNN/tree/d99eb5c7ef19f8b0c14a135d40a489f154a3c894 |
NetVLAD | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class NetVLAD(nn.Module):
def __init__(self, dims, num_clusters, outdims=None):
super(NetVLAD, self).__init__()
self.num_clusters = num_clusters
self.dims = dims
self.centroids = nn.Parameter(torch.rand... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | glee1228/segment_temporal_context_aggregation | NetVLAD | false | 6,760 | [
"Apache-2.0"
] | 1 | e5778f848f1cfd89bd1f77beb5e1b38a66a2f13d | https://github.com/glee1228/segment_temporal_context_aggregation/tree/e5778f848f1cfd89bd1f77beb5e1b38a66a2f13d |
Attention | import torch
import torch.nn as nn
import torch.utils.data
class Attention(nn.Module):
def __init__(self, in_size, hidden_size):
super(Attention, self).__init__()
self.hidden = nn.Linear(in_size, hidden_size)
nn.init.orthogonal_(self.hidden.weight.data)
self.out = nn.Linear(hidden... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gchrupala/platalea | Attention | false | 6,761 | [
"Apache-2.0"
] | 1 | 65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 | https://github.com/gchrupala/platalea/tree/65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 |
Auxiliary | import torch
import torch.nn as nn
import torch.nn.functional as F
class Auxiliary(nn.Module):
def __init__(self, input_channels, n_classes):
super(Auxiliary, self).__init__()
self.Conv2 = nn.Conv2d(input_channels, 128, kernel_size=1)
self.FC1 = nn.Linear(2048, 1024)
self.FC2 = 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_... | gogo5911/PyTorch_Network | Auxiliary | false | 6,762 | [
"MIT"
] | 1 | 396e2ebfe2c7e23143e72972e2fd55613c0098a3 | https://github.com/gogo5911/PyTorch_Network/tree/396e2ebfe2c7e23143e72972e2fd55613c0098a3 |
Conv2dBlock | import torch
import torch.utils.data
import torch
from torch import nn
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | guyii54/Contrastive-I2I | Conv2dBlock | false | 6,763 | [
"BSD-3-Clause"
] | 1 | e73daa0f9d3770c2280a304c39678d5b22440647 | https://github.com/guyii54/Contrastive-I2I/tree/e73daa0f9d3770c2280a304c39678d5b22440647 |
GRU | import torch
import torch as tc
import torch.nn as nn
class Layer_Norm(nn.Module):
def __init__(self, d_hid, eps=0.001):
super(Layer_Norm, self).__init__()
self.eps = eps
self.g = nn.Parameter(tc.ones(d_hid), requires_grad=True)
self.b = nn.Parameter(tc.zeros(d_hid), requires_grad... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 as tc
... | gushu333/DA4NMT | GRU | false | 6,764 | [
"Apache-2.0"
] | 1 | dba52a3d3784cd795b6f9aaf655b63475a848798 | https://github.com/gushu333/DA4NMT/tree/dba52a3d3784cd795b6f9aaf655b63475a848798 |
Normalize | import torch
import torch.utils.data
import torch
from torch import nn
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
out =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch
from torch import nn
assert_size_stride = ... | guyii54/Contrastive-I2I | Normalize | false | 6,765 | [
"BSD-3-Clause"
] | 1 | e73daa0f9d3770c2280a304c39678d5b22440647 | https://github.com/guyii54/Contrastive-I2I/tree/e73daa0f9d3770c2280a304c39678d5b22440647 |
GroupedChannelNorm | import torch
import torch.utils.data
import torch
from torch import nn
class GroupedChannelNorm(nn.Module):
def __init__(self, num_groups):
super().__init__()
self.num_groups = num_groups
def forward(self, x):
shape = list(x.shape)
new_shape = [shape[0], self.num_groups, shap... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch
from torch import nn
assert_size_stride = ... | guyii54/Contrastive-I2I | GroupedChannelNorm | false | 6,766 | [
"BSD-3-Clause"
] | 1 | e73daa0f9d3770c2280a304c39678d5b22440647 | https://github.com/guyii54/Contrastive-I2I/tree/e73daa0f9d3770c2280a304c39678d5b22440647 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.