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 |
|---|---|---|---|---|---|---|---|---|---|---|
ThreeLayerCNN | import torch
import torch.utils.data
class ThreeLayerCNN(torch.nn.Module):
"""
Input: 128x128 face image (eye aligned).
Output: 1-D tensor with 2 elements. Used for binary classification.
Parameters:
Number of conv layers: 3
Number of fully connected layers: 2
"""
def __init__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
asser... | Bhaskers-Blu-Org1/Trusted-ML-Pipelines | ThreeLayerCNN | false | 7,783 | [
"Apache-2.0"
] | 13 | 3805a2e72f73cef318e1992eee70aeb319b06d1a | https://github.com/Bhaskers-Blu-Org1/Trusted-ML-Pipelines/tree/3805a2e72f73cef318e1992eee70aeb319b06d1a |
AdjDecoder | import torch
from torch import nn
import torch.utils.data
class AdjDecoder(nn.Module):
def __init__(self, featureSize, hiddenSize):
super(AdjDecoder, self).__init__()
self.decode = nn.Linear(featureSize, hiddenSize)
self.second = nn.Linear(hiddenSize, hiddenSize)
self.left = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | BigkoalaZhu/SCORES | AdjDecoder | false | 7,784 | [
"MIT"
] | 16 | 8332733c375ee85c02bd34c2adce6a3213aad3c4 | https://github.com/BigkoalaZhu/SCORES/tree/8332733c375ee85c02bd34c2adce6a3213aad3c4 |
HardSwish | import torch
from torch import nn
class HardSwish(nn.Module):
def __init__(self, inplace=True):
super(HardSwish, self).__init__()
self.relu6 = nn.ReLU6(inplace=inplace)
def forward(self, x):
return x * self.relu6(x + 3) / 6
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 import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Bo396543018/Picodet_Pytorch | HardSwish | false | 7,785 | [
"Apache-2.0"
] | 16 | 276ecbf6f4f7eefbf046d1bccc25293acf28ba25 | https://github.com/Bo396543018/Picodet_Pytorch/tree/276ecbf6f4f7eefbf046d1bccc25293acf28ba25 |
NormedLinear | import torch
import torch.nn.functional as F
from torch import nn
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Bo396543018/mmdetection | NormedLinear | false | 7,786 | [
"Apache-2.0"
] | 16 | eb337336d3c239dc1d20534496f69df41ae9a300 | https://github.com/Bo396543018/mmdetection/tree/eb337336d3c239dc1d20534496f69df41ae9a300 |
NodeClassifier | import torch
from torch import nn
import torch.utils.data
class NodeClassifier(nn.Module):
def __init__(self, featureSize, hiddenSize):
super(NodeClassifier, self).__init__()
self.first = nn.Linear(featureSize, hiddenSize)
self.tanh = nn.Tanh()
self.second = nn.Linear(hiddenSize, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | BigkoalaZhu/SCORES | NodeClassifier | false | 7,787 | [
"MIT"
] | 16 | 8332733c375ee85c02bd34c2adce6a3213aad3c4 | https://github.com/BigkoalaZhu/SCORES/tree/8332733c375ee85c02bd34c2adce6a3213aad3c4 |
CNNLayerNorm | import torch
import torch.nn as nn
class CNNLayerNorm(nn.Module):
"""Layer normalization built for cnns input"""
def __init__(self, n_feats):
super(CNNLayerNorm, self).__init__()
self.layer_norm = nn.LayerNorm(n_feats)
def forward(self, x):
x = x.transpose(2, 3).contiguous()
... | 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_... | BlackyYen/Speech_Recognition-PyTorch | CNNLayerNorm | false | 7,788 | [
"MIT"
] | 16 | 0a986f467c540c2be88f65064ebf5ce0f6bcf70a | https://github.com/BlackyYen/Speech_Recognition-PyTorch/tree/0a986f467c540c2be88f65064ebf5ce0f6bcf70a |
SymEncoder | import torch
from torch import nn
import torch.utils.data
class SymEncoder(nn.Module):
def __init__(self, featureSize, symmetrySize, hiddenSize):
super(SymEncoder, self).__init__()
self.left = nn.Linear(featureSize, hiddenSize)
self.right = nn.Linear(symmetrySize, hiddenSize)
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.triton_helpers import libdevice
from torch import n... | BigkoalaZhu/SCORES | SymEncoder | false | 7,789 | [
"MIT"
] | 16 | 8332733c375ee85c02bd34c2adce6a3213aad3c4 | https://github.com/BigkoalaZhu/SCORES/tree/8332733c375ee85c02bd34c2adce6a3213aad3c4 |
D_concat | import torch
import torch.utils.data
import torch.nn as nn
def add_layer(seq, ix, n_inputs, n_outputs, nonlin, normalization):
seq.add_module('L' + str(ix), nn.Linear(n_inputs, n_outputs))
if ix > 0 and normalization:
if normalization == 'LN':
seq.main.add_module('A' + str(ix), nn.LayerNor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | Bhaskers-Blu-Org1/SIC | D_concat | false | 7,790 | [
"Apache-2.0"
] | 12 | c4e45d7736da6e6faabdc56bfc1336445df99204 | https://github.com/Bhaskers-Blu-Org1/SIC/tree/c4e45d7736da6e6faabdc56bfc1336445df99204 |
RSoftmax | import torch
import torch.nn.functional as F
from torch import nn
class RSoftmax(nn.Module):
"""Radix Softmax module in ``SplitAttentionConv2d``.
Args:
radix (int): Radix of input.
groups (int): Groups of input.
"""
def __init__(self, radix, groups):
super().__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
from torch import nn
a... | Bo396543018/Picodet_Pytorch | RSoftmax | false | 7,791 | [
"Apache-2.0"
] | 16 | 276ecbf6f4f7eefbf046d1bccc25293acf28ba25 | https://github.com/Bo396543018/Picodet_Pytorch/tree/276ecbf6f4f7eefbf046d1bccc25293acf28ba25 |
PerceptronTanh | import torch
import torch.nn as nn
import torch.nn.functional as F
class PerceptronTanh(nn.Module):
"""Implements a 1-layer perceptron with Tanh activaton."""
def __init__(self, input_dimension, hidden_dimension, output_dimension):
super(PerceptronTanh, self).__init__()
self._layer1 = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Bhaskers-Blu-Org2/PDP-Solver | PerceptronTanh | false | 7,792 | [
"MIT"
] | 28 | 1fca34d81f36268288f46416fb6956e5b36df69e | https://github.com/Bhaskers-Blu-Org2/PDP-Solver/tree/1fca34d81f36268288f46416fb6956e5b36df69e |
SymDecoder | import torch
from torch import nn
import torch.utils.data
class SymDecoder(nn.Module):
def __init__(self, featureSize, symmetrySize, hiddenSize):
super(SymDecoder, self).__init__()
self.decode = nn.Linear(featureSize, hiddenSize)
self.second = nn.Linear(hiddenSize, hiddenSize)
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
from torch import n... | BigkoalaZhu/SCORES | SymDecoder | false | 7,793 | [
"MIT"
] | 16 | 8332733c375ee85c02bd34c2adce6a3213aad3c4 | https://github.com/BigkoalaZhu/SCORES/tree/8332733c375ee85c02bd34c2adce6a3213aad3c4 |
NormedConv2d | import torch
from torch import nn
class NormedConv2d(nn.Conv2d):
"""Normalized Conv2d Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value of divisor to
keep ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Bo396543018/mmdetection | NormedConv2d | false | 7,794 | [
"Apache-2.0"
] | 16 | eb337336d3c239dc1d20534496f69df41ae9a300 | https://github.com/Bo396543018/mmdetection/tree/eb337336d3c239dc1d20534496f69df41ae9a300 |
GAT | import torch
import torch.nn.functional as F
import torch.nn as nn
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLayer, self).__init__(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Anou9531/GUA | GAT | false | 7,795 | [
"MIT"
] | 20 | 354acceb69656e76fb4ee296c66ae42c18cd939f | https://github.com/Anou9531/GUA/tree/354acceb69656e76fb4ee296c66ae42c18cd939f |
PairwiseRankingLoss | import torch
import torch.nn as nn
class PairwiseRankingLoss(nn.Module):
"""
Pairwise ranking loss
"""
def __init__(self, margin):
super(PairwiseRankingLoss, self).__init__()
self.margin = margin
def forward(self, anchor1, anchor2, img_sentc, sent_imgc):
cost_sent = torch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | BinWang28/EvalRank-Embedding-Evaluation | PairwiseRankingLoss | false | 7,796 | [
"BSD-3-Clause"
] | 15 | 454dac5c7345f01993688f33375f637129c285e3 | https://github.com/BinWang28/EvalRank-Embedding-Evaluation/tree/454dac5c7345f01993688f33375f637129c285e3 |
ZeroConv2d | import torch
import torch.nn as nn
class ZeroConv2d(nn.Module):
def __init__(self, in_channel, out_channel, padding=1):
super().__init__()
self.in_channel = in_channel
self.conv = nn.Conv2d(in_channel, out_channel, [1, 1], padding=0)
self.conv.weight.data.zero_()
self.conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | BinWang28/EvalRank-Embedding-Evaluation | ZeroConv2d | false | 7,797 | [
"BSD-3-Clause"
] | 15 | 454dac5c7345f01993688f33375f637129c285e3 | https://github.com/BinWang28/EvalRank-Embedding-Evaluation/tree/454dac5c7345f01993688f33375f637129c285e3 |
DiceLoss | import torch
import torch.nn as nn
import torch.optim
class DiceLoss(nn.Module):
def __init__(self, smooth=1.0):
super(DiceLoss, self).__init__()
self.smooth = smooth
def _dice_coeff(self, pred, target):
"""
Args:
pred: [N, 1] within [0, 1]
target: [N,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.guards.ass... | Bobholamovic/SimpleCV | DiceLoss | false | 7,798 | [
"MIT"
] | 44 | f4edacf088d0155725a469e227de847820bdfa53 | https://github.com/Bobholamovic/SimpleCV/tree/f4edacf088d0155725a469e227de847820bdfa53 |
ResidualBlock | import torch
import torch.nn as nn
class ResidualBlock(nn.Sequential):
def __init__(self, *args):
super(ResidualBlock, self).__init__(*args)
def forward(self, x):
identity = x
x = super(ResidualBlock, self).forward(x)
x += identity
return x
def get_inputs():
ret... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_add_0(in_ptr0, out_... | Bobholamovic/ever | ResidualBlock | false | 7,799 | [
"Apache-2.0"
] | 22 | f38060674a40ed53072b9d9be99cc656a830398f | https://github.com/Bobholamovic/ever/tree/f38060674a40ed53072b9d9be99cc656a830398f |
GlobalAvgPool2DBaseline | import torch
import torch.nn as nn
import torch.optim
class GlobalAvgPool2DBaseline(nn.Module):
def __init__(self):
super(GlobalAvgPool2DBaseline, self).__init__()
def forward(self, x):
x_pool = torch.mean(x.view(x.size(0), x.size(1), x.size(2) * x.size
(3)), dim=2)
x_poo... | 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... | Bobholamovic/SimpleCV | GlobalAvgPool2DBaseline | false | 7,800 | [
"MIT"
] | 44 | f4edacf088d0155725a469e227de847820bdfa53 | https://github.com/Bobholamovic/SimpleCV/tree/f4edacf088d0155725a469e227de847820bdfa53 |
LinkClassifier | import torch
import torch.nn as nn
import torch.nn.functional as F
class LinkClassifier(nn.Module):
def __init__(self, in_features, dropout=0.2):
super(LinkClassifier, self).__init__()
self.input = nn.Linear(in_features, 32)
self.hidden1 = nn.Linear(32, 16)
self.hidden2 = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BlackReap-er/Sia | LinkClassifier | false | 7,801 | [
"MIT"
] | 13 | 70654d55caa3315187282c88a59cf9b6e0b7c52b | https://github.com/BlackReap-er/Sia/tree/70654d55caa3315187282c88a59cf9b6e0b7c52b |
MultiHeadAttention | import math
import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
"""
Multi-head Self-attention layers, a attention score dropout layer is introduced.
Args:
input_tensor (torch.Tensor): the input of the multi-head self-attention layer
attention_mask (torch.Tensor): the 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
from torch._inductor.runtime.... | BELIEVEfxy/LightSANs | MultiHeadAttention | false | 7,802 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
DiceWithLogitsLoss | import torch
import torch.nn as nn
import torch.optim
class DiceWithLogitsLoss(nn.Module):
def __init__(self, smooth=1.0):
super(DiceWithLogitsLoss, self).__init__()
self.smooth = smooth
def _dice_coeff(self, pred, target):
"""
Args:
pred: [N, 1] within [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
import torch.optim
assert_size_stride = torch._C._dynamo.guards.ass... | Bobholamovic/SimpleCV | DiceWithLogitsLoss | false | 7,803 | [
"MIT"
] | 44 | f4edacf088d0155725a469e227de847820bdfa53 | https://github.com/Bobholamovic/SimpleCV/tree/f4edacf088d0155725a469e227de847820bdfa53 |
SigmoidRange | import torch
def sigmoid_range(x, low, high):
"""Sigmoid function with range `(low, high)`"""
return torch.sigmoid(x) * (high - low) + low
class SigmoidRange(torch.nn.Module):
"""Sigmoid module with range `(low, x_max)`"""
def __init__(self, low, high):
super(SigmoidRange, 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | BojarLab/glycowork | SigmoidRange | false | 7,804 | [
"MIT"
] | 22 | 72d37d406ad70bb9def4a5632a6605778e295fbb | https://github.com/BojarLab/glycowork/tree/72d37d406ad70bb9def4a5632a6605778e295fbb |
SCS_Cell | import random
import torch
import torch.nn.init
from torch import nn
from torch.autograd import Variable
import torch.utils.data
class SCS_Cell(nn.Module):
def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias,
p_TD):
super(SCS_Cell, self).__init__()
self.height, self.wi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.ini... | BoPang1996/Semi-Coupled-Structure-for-visual-sequental-tasks | SCS_Cell | false | 7,805 | [
"Apache-2.0"
] | 13 | c6fe7c77d08928bb30cc8683123f978b0e877394 | https://github.com/BoPang1996/Semi-Coupled-Structure-for-visual-sequental-tasks/tree/c6fe7c77d08928bb30cc8683123f978b0e877394 |
RelativeL1 | import torch
import torch.utils.data
from torch import nn
import torch.jit
class RelativeL1(nn.Module):
def __init__(self):
super().__init__()
self.criterion = torch.nn.L1Loss()
def forward(self, input, target):
base = target + 0.01
return self.criterion(input / base, target ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | BlueAmulet/BasicSR | RelativeL1 | false | 7,806 | [
"Apache-2.0"
] | 12 | 7040913d8659a05af4c2428feb71c260efbf1e9c | https://github.com/BlueAmulet/BasicSR/tree/7040913d8659a05af4c2428feb71c260efbf1e9c |
ScaledDotProductAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | BlackNoodle/TUCORE-GCN | ScaledDotProductAttention | false | 7,807 | [
"MIT"
] | 27 | 16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 | https://github.com/BlackNoodle/TUCORE-GCN/tree/16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 |
MultiHeadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | BlackNoodle/TUCORE-GCN | MultiHeadAttention | false | 7,808 | [
"MIT"
] | 27 | 16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 | https://github.com/BlackNoodle/TUCORE-GCN/tree/16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 |
Classifier | import torch
import torch.distributed
import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, hidden_size):
super(Classifier, self).__init__()
self.linear1 = nn.Linear(hidden_size, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x, mask_cls):
h = 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.distributed
import torch
import torch.nn as nn
assert_size_stride =... | BoonthichaSaejia/ThaiSum | Classifier | false | 7,809 | [
"Apache-2.0"
] | 23 | fdb99eab23e60a933acf4e84836f53ddf05b7c8b | https://github.com/BoonthichaSaejia/ThaiSum/tree/fdb99eab23e60a933acf4e84836f53ddf05b7c8b |
Get_gradient_nopadding | import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
import torch.jit
class Get_gradient_nopadding(nn.Module):
def __init__(self):
super(Get_gradient_nopadding, self).__init__()
kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
kernel_h = [[0, 0, 0], [-1, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | BlueAmulet/BasicSR | Get_gradient_nopadding | false | 7,811 | [
"Apache-2.0"
] | 12 | 7040913d8659a05af4c2428feb71c260efbf1e9c | https://github.com/BlueAmulet/BasicSR/tree/7040913d8659a05af4c2428feb71c260efbf1e9c |
Quantinizer | import torch
class Quantinizer(torch.nn.Module):
def __init__(self, size):
super(Quantinizer, self).__init__()
self.size = size
def forward(self, x):
x = (x * self.size * 0.999).long()
return torch.nn.functional.one_hot(x, num_classes=self.size).float()
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | CODEJIN/SPEECHSPLIT | Quantinizer | false | 7,812 | [
"MIT"
] | 13 | b4201ca9822b2e73f98f60c160c00db3b49a0050 | https://github.com/CODEJIN/SPEECHSPLIT/tree/b4201ca9822b2e73f98f60c160c00db3b49a0050 |
CharbonnierLoss | import torch
import torch.utils.data
from torch import nn
import torch.jit
class CharbonnierLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=1e-06):
super(CharbonnierLoss, self).__init__()
self.eps = eps
def forward(self, x, y):
b, c, h, w = y.size()
di... | 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
from... | BlueAmulet/BasicSR | CharbonnierLoss | false | 7,813 | [
"Apache-2.0"
] | 12 | 7040913d8659a05af4c2428feb71c260efbf1e9c | https://github.com/BlueAmulet/BasicSR/tree/7040913d8659a05af4c2428feb71c260efbf1e9c |
SpatialCrossMapLRN | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.utils.data
import torch.backends.cudnn
import torch.autograd
import torch.nn
class SpatialCrossMapLRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1,
ACROSS_CHANNELS=True):
super(SpatialCros... | 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.utils.data.dataloader
import torch.utils.dat... | CASIA-IVA-Lab/DCFST | SpatialCrossMapLRN | false | 7,814 | [
"Apache-2.0"
] | 22 | ca881ba3aae1ce00e4a7a6db01d99e5f6efff68b | https://github.com/CASIA-IVA-Lab/DCFST/tree/ca881ba3aae1ce00e4a7a6db01d99e5f6efff68b |
PositionwiseFeedForward | import math
import torch
import torch.distributed
import torch
import torch.nn as nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | BoonthichaSaejia/ThaiSum | PositionwiseFeedForward | false | 7,815 | [
"Apache-2.0"
] | 23 | fdb99eab23e60a933acf4e84836f53ddf05b7c8b | https://github.com/BoonthichaSaejia/ThaiSum/tree/fdb99eab23e60a933acf4e84836f53ddf05b7c8b |
L1CosineSim | import torch
import torch.utils.data
from torch import nn
import torch.jit
class L1CosineSim(nn.Module):
def __init__(self, loss_lambda=5):
super(L1CosineSim, self).__init__()
self.similarity = torch.nn.CosineSimilarity(dim=1, eps=1e-20)
self.l1_loss = nn.L1Loss()
self.loss_lambda... | 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... | BlueAmulet/BasicSR | L1CosineSim | false | 7,816 | [
"Apache-2.0"
] | 12 | 7040913d8659a05af4c2428feb71c260efbf1e9c | https://github.com/BlueAmulet/BasicSR/tree/7040913d8659a05af4c2428feb71c260efbf1e9c |
statm_loss | import torch
import torch.nn as nn
class statm_loss(nn.Module):
def __init__(self, eps=2):
super(statm_loss, self).__init__()
self.eps = eps
def forward(self, x, y):
x = x.view(x.size(0), x.size(1), -1)
y = y.view(y.size(0), y.size(1), -1)
x_mean = x.mean(dim=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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | COMP6248-Reproducability-Challenge/KD_SRRL | statm_loss | false | 7,817 | [
"MIT"
] | 27 | 958c8f9fbeb7893f9bd866aff5b065b2bde87f23 | https://github.com/COMP6248-Reproducability-Challenge/KD_SRRL/tree/958c8f9fbeb7893f9bd866aff5b065b2bde87f23 |
resblock | import torch
import torch.nn as nn
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, type=1):
super(mfm, self).__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv2d(in_channels, 2 * out_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
import torch.nn as nn
assert_... | BradyFU/DVG-Face | resblock | false | 7,818 | [
"MIT"
] | 33 | 16d51fe7da6e4a52d144e938afb3072eb8e4e8de | https://github.com/BradyFU/DVG-Face/tree/16d51fe7da6e4a52d144e938afb3072eb8e4e8de |
group | import torch
import torch.nn as nn
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, type=1):
super(mfm, self).__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv2d(in_channels, 2 * out_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
import torch.nn as nn
assert_... | BradyFU/DVG-Face | group | false | 7,819 | [
"MIT"
] | 33 | 16d51fe7da6e4a52d144e938afb3072eb8e4e8de | https://github.com/BradyFU/DVG-Face/tree/16d51fe7da6e4a52d144e938afb3072eb8e4e8de |
biLinearModel | import torch
import torch.distributed
import torch
import torch.nn as nn
class biLinearModel(nn.Module):
"""Currently just for a pair"""
def __init__(self, hidden_size):
super(biLinearModel, self).__init__()
self.bilinear = nn.Bilinear(hidden_size, hidden_size, 1)
def forward(self, doc_e... | 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.distributed
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | BoonthichaSaejia/ThaiSum | biLinearModel | false | 7,820 | [
"Apache-2.0"
] | 23 | fdb99eab23e60a933acf4e84836f53ddf05b7c8b | https://github.com/BoonthichaSaejia/ThaiSum/tree/fdb99eab23e60a933acf4e84836f53ddf05b7c8b |
FiLM | import torch
import torch.nn as nn
class FiLM(nn.Module):
def __init__(self, zdim, maskdim):
super(FiLM, self).__init__()
self.gamma = nn.Linear(zdim, maskdim)
self.beta = nn.Linear(zdim, maskdim)
def forward(self, x, z):
gamma = self.gamma(z).unsqueeze(-1).unsqueeze(-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... | CPJKU/audio_conditioned_unet | FiLM | false | 7,821 | [
"MIT"
] | 20 | 68f20f5280079e99be260f9fe9933c0064eb2d7f | https://github.com/CPJKU/audio_conditioned_unet/tree/68f20f5280079e99be260f9fe9933c0064eb2d7f |
Swish | import torch
import torch.utils.data
from torch import nn
import torch.jit
def swish_func(x, beta=1.0):
"""
"Swish: a Self-Gated Activation Function"
Searching for Activation Functions (https://arxiv.org/abs/1710.05941)
If beta=1 applies the Sigmoid Linear Unit (SiLU) function element-wise
If... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
import torch.jit
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_... | BlueAmulet/BasicSR | Swish | false | 7,822 | [
"Apache-2.0"
] | 12 | 7040913d8659a05af4c2428feb71c260efbf1e9c | https://github.com/BlueAmulet/BasicSR/tree/7040913d8659a05af4c2428feb71c260efbf1e9c |
ResBlock | import torch
from torch import 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):
e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | BoyanJIANG/4D-Compositional-Representation | ResBlock | false | 7,823 | [
"Apache-2.0"
] | 12 | 64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c | https://github.com/BoyanJIANG/4D-Compositional-Representation/tree/64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c |
convblock | import torch
import torch.nn as nn
import torch.nn.functional as F
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = mom... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | BradyFU/DVG-Face | convblock | false | 7,824 | [
"MIT"
] | 33 | 16d51fe7da6e4a52d144e938afb3072eb8e4e8de | https://github.com/BradyFU/DVG-Face/tree/16d51fe7da6e4a52d144e938afb3072eb8e4e8de |
SirenLayer | import torch
import numpy as np
import torch.nn as nn
class SirenLayer(nn.Module):
def __init__(self, in_f, out_f, w0=30, is_first=False, is_last=False):
super().__init__()
self.in_f = in_f
self.w0 = w0
self.linear = nn.Linear(in_f, out_f)
self.is_first = is_first
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 numpy ... | BoyuanChen/neural-state-variables | SirenLayer | false | 7,825 | [
"MIT"
] | 17 | 10483d93ac8c006f3786c434fb57d70d9ab465ec | https://github.com/BoyuanChen/neural-state-variables/tree/10483d93ac8c006f3786c434fb57d70d9ab465ec |
ConstantODE | import torch
class ConstantODE(torch.nn.Module):
def __init__(self, device):
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)) ... | 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... | BoyanJIANG/4D-Compositional-Representation | ConstantODE | false | 7,826 | [
"Apache-2.0"
] | 12 | 64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c | https://github.com/BoyanJIANG/4D-Compositional-Representation/tree/64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c |
LatentPredModel | import torch
import torch.nn as nn
class LatentPredModel(torch.nn.Module):
def __init__(self, in_channels):
super(LatentPredModel, self).__init__()
self.layer1 = nn.Linear(in_channels, 32)
self.relu1 = nn.ReLU()
self.layer2 = nn.Linear(32, 64)
self.relu2 = nn.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_... | BoyuanChen/neural-state-variables | LatentPredModel | false | 7,827 | [
"MIT"
] | 17 | 10483d93ac8c006f3786c434fb57d70d9ab465ec | https://github.com/BoyuanChen/neural-state-variables/tree/10483d93ac8c006f3786c434fb57d70d9ab465ec |
GatedConv2d | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils
import torch.distributions
class GatedConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation=1):
super(GatedConv2d, self).__init__()
self.conv = nn.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 import nn
import torch.utils
import torch.distributions
assert_size_s... | Butters-cloud/denoising-normalizing-flow | GatedConv2d | false | 7,828 | [
"MIT"
] | 12 | 12d56a0d069e10a744acabf5e78fdbfba8df54ee | https://github.com/Butters-cloud/denoising-normalizing-flow/tree/12d56a0d069e10a744acabf5e78fdbfba8df54ee |
GlobalAttention | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional as F
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_le... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | BoonthichaSaejia/ThaiSum | GlobalAttention | false | 7,829 | [
"Apache-2.0"
] | 23 | fdb99eab23e60a933acf4e84836f53ddf05b7c8b | https://github.com/BoonthichaSaejia/ThaiSum/tree/fdb99eab23e60a933acf4e84836f53ddf05b7c8b |
Swish | import torch
import torch.nn as nn
class Swish(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.sigmoid(x) * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | CW-Huang/sdeflow-light | Swish | false | 7,830 | [
"MIT"
] | 35 | 524650bc5ad69522b3e0905672deef0650374512 | https://github.com/CW-Huang/sdeflow-light/tree/524650bc5ad69522b3e0905672deef0650374512 |
mfm | import torch
import torch.nn as nn
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, type=1):
super(mfm, self).__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv2d(in_channels, 2 * out_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
import torch.nn as nn
assert_... | BradyFU/DVG-Face | mfm | false | 7,831 | [
"MIT"
] | 33 | 16d51fe7da6e4a52d144e938afb3072eb8e4e8de | https://github.com/BradyFU/DVG-Face/tree/16d51fe7da6e4a52d144e938afb3072eb8e4e8de |
LinearDiag | import torch
import torch.nn as nn
class LinearDiag(nn.Module):
def __init__(self, num_features, bias=False):
super(LinearDiag, self).__init__()
weight = torch.FloatTensor(num_features).fill_(1)
self.weight = nn.Parameter(weight, requires_grad=True)
if bias:
bias = 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | CSer-Tang-hao/FS-KTN | LinearDiag | false | 7,832 | [
"MIT"
] | 19 | 8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 | https://github.com/CSer-Tang-hao/FS-KTN/tree/8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 |
GaussianFilter | import torch
import torch.utils.data
from torch import nn
import torch.jit
class GaussianFilter(nn.Module):
def __init__(self, kernel_size=13, stride=1, padding=6):
super(GaussianFilter, self).__init__()
mean = (kernel_size - 1) / 2.0
variance = ((kernel_size - 1) / 6.0) ** 2.0
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.utils.data
from torch import nn
import torch.jit
assert_size_stride... | BlueAmulet/BasicSR | GaussianFilter | false | 7,833 | [
"Apache-2.0"
] | 12 | 7040913d8659a05af4c2428feb71c260efbf1e9c | https://github.com/BlueAmulet/BasicSR/tree/7040913d8659a05af4c2428feb71c260efbf1e9c |
Get_gradient | import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
import torch.jit
class Get_gradient(nn.Module):
def __init__(self):
super(Get_gradient, self).__init__()
kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 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.triton_helpers import libdevice
import torch.utils.... | BlueAmulet/BasicSR | Get_gradient | false | 7,834 | [
"Apache-2.0"
] | 12 | 7040913d8659a05af4c2428feb71c260efbf1e9c | https://github.com/BlueAmulet/BasicSR/tree/7040913d8659a05af4c2428feb71c260efbf1e9c |
GAP | import torch
import torch.nn as nn
import torch.utils.data
class GAP(nn.Module):
def __init__(self, dimension=1):
"""
:param dimension:
"""
super(GAP, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
def forward(self, x):
"""
:param 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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | CaptainEven/MCMOT-ByteTrack | GAP | false | 7,835 | [
"MIT"
] | 20 | e014275cfb25147dfa6f49cdbed24e91e5d6c41e | https://github.com/CaptainEven/MCMOT-ByteTrack/tree/e014275cfb25147dfa6f49cdbed24e91e5d6c41e |
ODEfunc | import torch
from torch import 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 = 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.... | BoyanJIANG/4D-Compositional-Representation | ODEfunc | false | 7,836 | [
"Apache-2.0"
] | 12 | 64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c | https://github.com/BoyanJIANG/4D-Compositional-Representation/tree/64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c |
WeightedFeatureFusion | import torch
import torch.nn as nn
import torch.utils.data
class WeightedFeatureFusion(nn.Module):
def __init__(self, layers, weight=False):
"""
:param layers:
:param weight:
"""
super(WeightedFeatureFusion, self).__init__()
self.layers = layers
self.weight... | 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.... | CaptainEven/MCMOT-ByteTrack | WeightedFeatureFusion | false | 7,837 | [
"MIT"
] | 20 | e014275cfb25147dfa6f49cdbed24e91e5d6c41e | https://github.com/CaptainEven/MCMOT-ByteTrack/tree/e014275cfb25147dfa6f49cdbed24e91e5d6c41e |
ResnetBlockFC | import torch
from torch import nn
class ResnetBlockFC(nn.Module):
""" Fully connected ResNet Block class.
Args:
size_in (int): input dimension
size_out (int): output dimension
size_h (int): hidden dimension
"""
def __init__(self, size_in, size_out=None, size_h=None):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | BoyanJIANG/4D-Compositional-Representation | ResnetBlockFC | false | 7,838 | [
"Apache-2.0"
] | 12 | 64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c | https://github.com/BoyanJIANG/4D-Compositional-Representation/tree/64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c |
simple_decoder | import torch
from torch import nn
import torch.utils
import torch.distributions
class simple_decoder(nn.Module):
def __init__(self, channels, width, height, dropout):
super(simple_decoder, self).__init__()
self.width = width
self.height = height
self.channels = channels
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 import nn
import torch.utils
import torch.distributions
assert_size_s... | Butters-cloud/denoising-normalizing-flow | simple_decoder | false | 7,839 | [
"MIT"
] | 12 | 12d56a0d069e10a744acabf5e78fdbfba8df54ee | https://github.com/Butters-cloud/denoising-normalizing-flow/tree/12d56a0d069e10a744acabf5e78fdbfba8df54ee |
Reorg | import torch
import torch.nn as nn
class Reorg(nn.Module):
def __init__(self, stride=2):
super(Reorg, self).__init__()
self.stride = stride
def forward(self, x):
assert x.data.dim() == 4
B = x.data.size(0)
C = x.data.size(1)
H = x.data.size(2)
W = x.da... | 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... | CharlesPikachu/CharlesFace | Reorg | false | 7,840 | [
"MIT"
] | 13 | 90bfe38c58068228d0069dce43b55b2570acaa16 | https://github.com/CharlesPikachu/CharlesFace/tree/90bfe38c58068228d0069dce43b55b2570acaa16 |
ContrastiveLoss | import torch
from torch import nn
from torch.nn import functional as F
class ContrastiveLoss(nn.Module):
"""
Contrastive loss function. ref: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin=2.0):
super(ContrastiveLoss, self).__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._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | CV-ZMH/human-action-recognition | ContrastiveLoss | false | 7,841 | [
"MIT"
] | 36 | 009bd1da71c087c3071173b325e34ed342599581 | https://github.com/CV-ZMH/human-action-recognition/tree/009bd1da71c087c3071173b325e34ed342599581 |
Upsample | import torch
import torch.nn as nn
class Upsample(nn.Module):
def __init__(self, stride=2):
super(Upsample, self).__init__()
self.stride = stride
def forward(self, x):
assert x.data.dim() == 4
B = x.data.size(0)
C = x.data.size(1)
H = x.data.size(2)
W ... | 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... | CharlesPikachu/CharlesFace | Upsample | false | 7,842 | [
"MIT"
] | 13 | 90bfe38c58068228d0069dce43b55b2570acaa16 | https://github.com/CharlesPikachu/CharlesFace/tree/90bfe38c58068228d0069dce43b55b2570acaa16 |
softmax_SR | import torch
import torch.nn as nn
import torch.nn.functional as F
class softmax_SR(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
sr = F.softmax(x.reshape(x.size(0), x.size(1), -1), dim=2)
sr = sr.transpose(1, 2)
return sr
def get_inputs():
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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | CILAB-MA/Machine_ToM | softmax_SR | false | 7,843 | [
"MIT"
] | 13 | 8c168ee31cc95a7f57998e8907273799533fe04f | https://github.com/CILAB-MA/Machine_ToM/tree/8c168ee31cc95a7f57998e8907273799533fe04f |
Attn | import torch
import torch.nn.functional as F
from torch import nn
class Attn(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.hidden_size = hidden_size
self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
self.v = nn.Linear(hidden_size, 1, bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ChansongJo/DAMD | Attn | false | 7,844 | [
"Apache-2.0"
] | 39 | 9b0456d7e590fb5de77ec81e967e8010487eeb56 | https://github.com/ChansongJo/DAMD/tree/9b0456d7e590fb5de77ec81e967e8010487eeb56 |
InputInjection | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class InputInjection(nn.Module):
"""Downsampling module for CGNet."""
def __init__(self, num_downsampling):
super(InputInjection, self).__init__()
self.pool = nn.ModuleList()
for i in range(num_downsampling)... | 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._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | CarnoZhao/mmsegmentation | InputInjection | false | 7,845 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
MultiHeadAttentionWithPooling | import math
import torch
import torch.nn as nn
class kAttentionPooling(nn.Module):
def __init__(self, seq_len, hidden_size, k_heads=5):
super().__init__()
self.k_heads = k_heads
self.theta_k = nn.Parameter(torch.randn([hidden_size, k_heads]))
def forward(self, input_tensor):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | BELIEVEfxy/LightSANs | MultiHeadAttentionWithPooling | false | 7,846 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
ExampleBackbone | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class ExampleBackbone(nn.Module):
def __init__(self):
super(ExampleBackbone, self).__init__()
self.conv = nn.Conv2d(3, 3, 3)
def init_weights(self, pretrained=None):
pass
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
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_str... | CarnoZhao/mmsegmentation | ExampleBackbone | false | 7,847 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
WScaleLayer | import torch
import torch.nn as nn
class WScaleLayer(nn.Module):
def __init__(self, size):
super(WScaleLayer, self).__init__()
self.scale = nn.Parameter(torch.randn([1]))
self.b = nn.Parameter(torch.randn(size))
self.size = size
def forward(self, x):
x_size = x.size()... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors | WScaleLayer | false | 7,848 | [
"MIT"
] | 24 | 4198bd2d325a32ffc4e714c486540e63440ab110 | https://github.com/ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors/tree/4198bd2d325a32ffc4e714c486540e63440ab110 |
SpatialGatherModule | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
class SpatialGatherModule(nn.Module):
"""Aggregate the context features according to the initial predicted
probability distribution.
Employ the soft-weighted method to aggregate the context.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CarnoZhao/mmsegmentation | SpatialGatherModule | false | 7,849 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
SineODE | import math
import torch
class SineODE(torch.nn.Module):
def __init__(self, device):
super(SineODE, self).__init__()
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 * ... | 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... | BoyanJIANG/4D-Compositional-Representation | SineODE | false | 7,850 | [
"Apache-2.0"
] | 12 | 64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c | https://github.com/BoyanJIANG/4D-Compositional-Representation/tree/64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c |
PPMConcat | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class PPMConcat(nn.ModuleList):
"""Pyramid Pooling Module that only concat the features of each layer.
Args:
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module.
"""
def __init__(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
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | CarnoZhao/mmsegmentation | PPMConcat | false | 7,851 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
JaccardLoss | import torch
from torch.nn import functional as F
from torch.nn.modules.loss import _Loss
class JaccardLoss(_Loss):
def __init__(self):
super(JaccardLoss, self).__init__()
def forward(self, output, target):
output = F.sigmoid(output)
intersection = torch.sum(output * target)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.asse... | BloodAxe/segmentation-networks-benchmark | JaccardLoss | false | 7,852 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
AsymmetricLossMultiLabel | import torch
import torch.nn as nn
import torch.multiprocessing
import torch.utils.data
import torch.nn.parallel
from torch import optim as optim
class AsymmetricLossMultiLabel(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08,
disable_torch_grad_focal_loss=False):
sup... | 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... | ChenMnZ/CF-ViT | AsymmetricLossMultiLabel | false | 7,853 | [
"Apache-2.0"
] | 18 | afc7ba54510cfbd410921a8b5eb5d6f0243718e7 | https://github.com/ChenMnZ/CF-ViT/tree/afc7ba54510cfbd410921a8b5eb5d6f0243718e7 |
RefineModelReLU | import torch
import torch.nn as nn
class RefineModelReLU(torch.nn.Module):
def __init__(self, in_channels):
super(RefineModelReLU, self).__init__()
self.layer1 = nn.Linear(in_channels, 128)
self.relu1 = nn.ReLU()
self.layer2 = nn.Linear(128, 64)
self.relu2 = nn.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_... | BoyuanChen/neural-state-variables | RefineModelReLU | false | 7,854 | [
"MIT"
] | 17 | 10483d93ac8c006f3786c434fb57d70d9ab465ec | https://github.com/BoyuanChen/neural-state-variables/tree/10483d93ac8c006f3786c434fb57d70d9ab465ec |
Block | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
class LayerNorm(nn.Module):
""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | CarnoZhao/mmsegmentation | Block | false | 7,855 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
ConvRelu | import torch
from torch import nn
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=True)
class ConvRelu(nn.Module):
def __init__(self, in_: 'int', out: 'int'):
super().__init__()
self.conv = conv3x3(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._inductor.runtime import triton_helpers
from torch import nn
assert_s... | BloodAxe/segmentation-networks-benchmark | ConvRelu | false | 7,856 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
ConvBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel, stride, padding=0):
super(ConvBlock, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel,
stride=stride... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | CPJKU/audio_conditioned_unet | ConvBlock | false | 7,857 | [
"MIT"
] | 20 | 68f20f5280079e99be260f9fe9933c0064eb2d7f | https://github.com/CPJKU/audio_conditioned_unet/tree/68f20f5280079e99be260f9fe9933c0064eb2d7f |
JaccardScore | import torch
from torch.nn import functional as F
from torch.nn.modules.loss import _Loss
class JaccardScore(_Loss):
def __init__(self):
super(JaccardScore, self).__init__()
def forward(self, output, target):
output = F.sigmoid(output)
target = target.float()
intersection = (... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.asse... | BloodAxe/segmentation-networks-benchmark | JaccardScore | false | 7,858 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
RefineFireModel | import torch
import numpy as np
import torch.nn as nn
class SirenLayer(nn.Module):
def __init__(self, in_f, out_f, w0=30, is_first=False, is_last=False):
super().__init__()
self.in_f = in_f
self.w0 = w0
self.linear = nn.Linear(in_f, out_f)
self.is_first = is_first
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 numpy ... | BoyuanChen/neural-state-variables | RefineFireModel | false | 7,859 | [
"MIT"
] | 17 | 10483d93ac8c006f3786c434fb57d70d9ab465ec | https://github.com/BoyuanChen/neural-state-variables/tree/10483d93ac8c006f3786c434fb57d70d9ab465ec |
DiceLoss | import functools
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | CarnoZhao/mmsegmentation | DiceLoss | false | 7,860 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
RefineElasticPendulumModel | import torch
import numpy as np
import torch.nn as nn
class SirenLayer(nn.Module):
def __init__(self, in_f, out_f, w0=30, is_first=False, is_last=False):
super().__init__()
self.in_f = in_f
self.w0 = w0
self.linear = nn.Linear(in_f, out_f)
self.is_first = is_first
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 numpy ... | BoyuanChen/neural-state-variables | RefineElasticPendulumModel | false | 7,861 | [
"MIT"
] | 17 | 10483d93ac8c006f3786c434fb57d70d9ab465ec | https://github.com/BoyuanChen/neural-state-variables/tree/10483d93ac8c006f3786c434fb57d70d9ab465ec |
outconv | import torch
from torch import nn
class outconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(outconv, self).__init__()
self.conv = nn.Conv2d(in_ch, out_ch, 1)
def forward(self, x):
x = self.conv(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | BloodAxe/segmentation-networks-benchmark | outconv | false | 7,862 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
Copy | import torch
from torch import nn
class Copy(nn.Module):
def __init__(self, hidden_size, copy_weight=1.0):
super().__init__()
self.Wcopy = nn.Linear(hidden_size, hidden_size)
self.copy_weight = copy_weight
def forward(self, enc_out_hs, dec_hs):
"""
get unnormalized 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
from torch import n... | ChansongJo/DAMD | Copy | false | 7,863 | [
"Apache-2.0"
] | 39 | 9b0456d7e590fb5de77ec81e967e8010487eeb56 | https://github.com/ChansongJo/DAMD/tree/9b0456d7e590fb5de77ec81e967e8010487eeb56 |
ConvEncoder3D | import torch
from torch import nn
class ConvEncoder3D(nn.Module):
""" Simple convolutional conditioning network.
It consists of 6 convolutional layers, each downsampling the input by a
factor of 2, and a final fully-connected layer projecting the output to
c_dim dimensions.
"""
def __init__(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | BoyanJIANG/4D-Compositional-Representation | ConvEncoder3D | false | 7,864 | [
"Apache-2.0"
] | 12 | 64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c | https://github.com/BoyanJIANG/4D-Compositional-Representation/tree/64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c |
RefineCircularMotionModel | import torch
import numpy as np
import torch.nn as nn
class SirenLayer(nn.Module):
def __init__(self, in_f, out_f, w0=30, is_first=False, is_last=False):
super().__init__()
self.in_f = in_f
self.w0 = w0
self.linear = nn.Linear(in_f, out_f)
self.is_first = is_first
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 numpy ... | BoyuanChen/neural-state-variables | RefineCircularMotionModel | false | 7,865 | [
"MIT"
] | 17 | 10483d93ac8c006f3786c434fb57d70d9ab465ec | https://github.com/BoyuanChen/neural-state-variables/tree/10483d93ac8c006f3786c434fb57d70d9ab465ec |
RefineLavaLampModel | import torch
import numpy as np
import torch.nn as nn
class SirenLayer(nn.Module):
def __init__(self, in_f, out_f, w0=30, is_first=False, is_last=False):
super().__init__()
self.in_f = in_f
self.w0 = w0
self.linear = nn.Linear(in_f, out_f)
self.is_first = is_first
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 numpy ... | BoyuanChen/neural-state-variables | RefineLavaLampModel | false | 7,866 | [
"MIT"
] | 17 | 10483d93ac8c006f3786c434fb57d70d9ab465ec | https://github.com/BoyuanChen/neural-state-variables/tree/10483d93ac8c006f3786c434fb57d70d9ab465ec |
ConcatConv2d | import torch
from torch import 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | BoyanJIANG/4D-Compositional-Representation | ConcatConv2d | false | 7,867 | [
"Apache-2.0"
] | 12 | 64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c | https://github.com/BoyanJIANG/4D-Compositional-Representation/tree/64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c |
Decoder | import torch
from torch import 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
from torch import nn
assert_s... | BoyanJIANG/4D-Compositional-Representation | Decoder | false | 7,868 | [
"Apache-2.0"
] | 12 | 64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c | https://github.com/BoyanJIANG/4D-Compositional-Representation/tree/64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c |
FeatExemplarAvgBlock | import torch
import torch.nn as nn
class FeatExemplarAvgBlock(nn.Module):
def __init__(self, nFeat):
super(FeatExemplarAvgBlock, self).__init__()
def forward(self, features_train, labels_train):
labels_train_transposed = labels_train.transpose(1, 2)
weight_novel = torch.bmm(labels_tr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | CSer-Tang-hao/FS-KTN | FeatExemplarAvgBlock | false | 7,869 | [
"MIT"
] | 19 | 8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 | https://github.com/CSer-Tang-hao/FS-KTN/tree/8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 |
SmoothJaccardLoss | import torch
from torch.nn import functional as F
from torch.nn.modules.loss import _Loss
class SmoothJaccardLoss(_Loss):
def __init__(self, smooth=100):
super(SmoothJaccardLoss, self).__init__()
self.smooth = smooth
def forward(self, output, target):
output = F.sigmoid(output)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.asse... | BloodAxe/segmentation-networks-benchmark | SmoothJaccardLoss | false | 7,870 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
RefineDoublePendulumModel | import torch
import numpy as np
import torch.nn as nn
class SirenLayer(nn.Module):
def __init__(self, in_f, out_f, w0=30, is_first=False, is_last=False):
super().__init__()
self.in_f = in_f
self.w0 = w0
self.linear = nn.Linear(in_f, out_f)
self.is_first = is_first
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 numpy ... | BoyuanChen/neural-state-variables | RefineDoublePendulumModel | false | 7,871 | [
"MIT"
] | 17 | 10483d93ac8c006f3786c434fb57d70d9ab465ec | https://github.com/BoyuanChen/neural-state-variables/tree/10483d93ac8c006f3786c434fb57d70d9ab465ec |
DiceLoss | import torch
from torch import nn
from torch.nn import functional as F
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, output, target):
prediction = F.sigmoid(output)
intersection = torch.sum(prediction * target)
union = torch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | BloodAxe/segmentation-networks-benchmark | DiceLoss | false | 7,872 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
GraphConv | import torch
import torch.nn as nn
from torch.nn.init import xavier_uniform_
class GraphConv(nn.Module):
def __init__(self, in_channels, out_channels, dropout=False, relu=True):
super(GraphConv, self).__init__()
if dropout:
self.dropout = nn.Dropout(p=0.5)
else:
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.init import xavier_uniform_
assert_size_stri... | CSer-Tang-hao/FS-KTN | GraphConv | false | 7,873 | [
"MIT"
] | 19 | 8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 | https://github.com/CSer-Tang-hao/FS-KTN/tree/8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 |
TransitionUp | import torch
from torch import nn
def center_crop(layer, max_height, max_width):
_, _, h, w = layer.size()
xy1 = (w - max_width) // 2
xy2 = (h - max_height) // 2
return layer[:, :, xy2:xy2 + max_height, xy1:xy1 + max_width]
class TransitionUp(nn.Module):
def __init__(self, in_channels, out_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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | BloodAxe/segmentation-networks-benchmark | TransitionUp | false | 7,874 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
GenNoise | import torch
import torch.optim
import torch.nn as nn
import torch.nn.init
class GenNoise(nn.Module):
def __init__(self, dim2):
super(GenNoise, self).__init__()
self.dim2 = dim2
def forward(self, input):
a = list(input.size())
a[1] = self.dim2
b = torch.zeros(a).type_... | 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.optim
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_... | ChongYou/robust-image-recovery | GenNoise | false | 7,875 | [
"MIT"
] | 13 | 5bb23142509f307d31fd435de12787a70ec3a5bc | https://github.com/ChongYou/robust-image-recovery/tree/5bb23142509f307d31fd435de12787a70ec3a5bc |
_BoundaryRefineModule | import torch
from torch import nn
class _BoundaryRefineModule(nn.Module):
def __init__(self, dim):
super(_BoundaryRefineModule, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(dim, dim, kernel_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 import triton_helpers
from torch import nn
assert_s... | BloodAxe/segmentation-networks-benchmark | _BoundaryRefineModule | false | 7,876 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
_GlobalConvModule | import torch
from torch import nn
class _GlobalConvModule(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size):
super(_GlobalConvModule, self).__init__()
pad0 = (kernel_size[0] - 1) // 2
pad1 = (kernel_size[1] - 1) // 2
super(_GlobalConvModule, self).__init__()
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | BloodAxe/segmentation-networks-benchmark | _GlobalConvModule | false | 7,877 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
NormUpscaleConvBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class PixelNormLayer(nn.Module):
def __init__(self):
super(PixelNormLayer, self).__init__()
def forward(self, x):
return x / torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) + 1e-08)
class WScaleLayer(nn.Module):
def... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors | NormUpscaleConvBlock | false | 7,878 | [
"MIT"
] | 24 | 4198bd2d325a32ffc4e714c486540e63440ab110 | https://github.com/ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors/tree/4198bd2d325a32ffc4e714c486540e63440ab110 |
DFire | import torch
from torch import nn
class DFire(nn.Module):
def __init__(self, inplanes, squeeze_planes, expand1x1_planes,
expand3x3_planes):
super(DFire, self).__init__()
self.inplanes = inplanes
self.expand1x1 = nn.Conv2d(inplanes, expand1x1_planes, kernel_size=1)
self.exp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | BloodAxe/segmentation-networks-benchmark | DFire | false | 7,879 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
ZeroPad1d | 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 ZeroPad1d(nn.Module):
def __init__(self, pad_left, pad_right):
super().__init__()
self.pad_left = pad_left
self.pa... | 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.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
assert_size_stri... | ChenDdon/AGBTcode | ZeroPad1d | false | 7,880 | [
"MIT"
] | 21 | 6c259d18b48dc8d6da1357c42a1ee088666fb7b4 | https://github.com/ChenDdon/AGBTcode/tree/6c259d18b48dc8d6da1357c42a1ee088666fb7b4 |
ResidualSequential | import torch
import torch.optim
import torch.nn as nn
import torch.nn.init
class ResidualSequential(nn.Sequential):
def __init__(self, *args):
super(ResidualSequential, self).__init__(*args)
def forward(self, x):
out = super(ResidualSequential, self).forward(x)
x_ = None
if o... | 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.optim
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_... | ChongYou/robust-image-recovery | ResidualSequential | false | 7,881 | [
"MIT"
] | 13 | 5bb23142509f307d31fd435de12787a70ec3a5bc | https://github.com/ChongYou/robust-image-recovery/tree/5bb23142509f307d31fd435de12787a70ec3a5bc |
NormConvBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class PixelNormLayer(nn.Module):
def __init__(self):
super(PixelNormLayer, self).__init__()
def forward(self, x):
return x / torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) + 1e-08)
class WScaleLayer(nn.Module):
def... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors | NormConvBlock | false | 7,882 | [
"MIT"
] | 24 | 4198bd2d325a32ffc4e714c486540e63440ab110 | https://github.com/ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors/tree/4198bd2d325a32ffc4e714c486540e63440ab110 |
RegularizationLoss | import torch
import torch.nn as nn
class RegularizationLoss(nn.Module):
def __init__(self, lambda_p: 'float', max_layers: 'int'):
super().__init__()
p_g = torch.zeros((max_layers,))
not_halted = 1.0
for k in range(max_layers):
p_g[k] = lambda_p * not_halted
... | 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... | ChenghaoMou/embeddings | RegularizationLoss | false | 7,883 | [
"MIT"
] | 12 | e63c2f2f4a688302de37bb8ccfd37a0170e2c374 | https://github.com/ChenghaoMou/embeddings/tree/e63c2f2f4a688302de37bb8ccfd37a0170e2c374 |
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