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 |
|---|---|---|---|---|---|---|---|---|---|---|
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 Tuple
from typing import Optional
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 ... | EXAPPAI/opacus | DPLSTMCell | false | 2,195 | [
"Apache-2.0"
] | 0 | 11e188a2f03a8a08be51fdf2367cc1387879312a | https://github.com/EXAPPAI/opacus/tree/11e188a2f03a8a08be51fdf2367cc1387879312a |
MaxMarginRankingLoss | import torch
import numpy as np
import torch as th
import torch.nn.functional as F
class MaxMarginRankingLoss(th.nn.Module):
def __init__(self, margin=1.0, negative_weighting=False, batch_size=1,
n_pair=1, hard_negative_rate=0.5):
super(MaxMarginRankingLoss, self).__init__()
self.margin =... | 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 numpy as np
import torch as th
assert_size_stride = torch._C._dynamo.guards.assert... | ErinZhang1998/howto100m-erin | MaxMarginRankingLoss | false | 2,196 | [
"Apache-2.0"
] | 0 | 1152ea0fe328d20fcf2218a1d548644881632656 | https://github.com/ErinZhang1998/howto100m-erin/tree/1152ea0fe328d20fcf2218a1d548644881632656 |
Encoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class Scaled_Dot_Product_Attention(nn.Module):
"""Scaled Dot-Product Attention """
def __init__(self):
super(Scaled_Dot_Product_Attention, self).__init__()
def forward(self, Q, K, V, scale=None):
"""
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Ergtou/TextWord | Encoder | false | 2,197 | [
"MIT"
] | 0 | f05cc5a630fc8d05357b8a9bc0da3ec5cc255a30 | https://github.com/Ergtou/TextWord/tree/f05cc5a630fc8d05357b8a9bc0da3ec5cc255a30 |
MLP | import torch
from torch import Tensor
from torch import nn
class MLP(nn.Module):
def __init__(self, dim, embed_dim):
super().__init__()
self.proj = nn.Linear(dim, embed_dim)
def forward(self, x: 'Tensor') ->Tensor:
x = x.flatten(2).transpose(1, 2)
x = self.proj(x)
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | EmanuelNk/semantic-segmentation | MLP | false | 2,198 | [
"MIT"
] | 0 | 20ff16da49691fb407724909d9c7e84b47e2fee0 | https://github.com/EmanuelNk/semantic-segmentation/tree/20ff16da49691fb407724909d9c7e84b47e2fee0 |
Flatten | import torch
import torch.nn as nn
class Flatten(nn.Module):
def __init__(self):
super(Flatten, self).__init__()
def forward(self, x):
"""
Arguments:
x: a float tensor with shape [batch_size, c, h, w].
Returns:
a float tensor with shape [batch_size, c*... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Escaton615/mtcnn-pytorch | Flatten | false | 2,199 | [
"MIT"
] | 0 | 4a645c1bf8dca0b5410cc0454ee0a538ada2d241 | https://github.com/Escaton615/mtcnn-pytorch/tree/4a645c1bf8dca0b5410cc0454ee0a538ada2d241 |
SigmoidAbsoluteRelativeErrorLoss | import torch
from torch import nn
class SigmoidAbsoluteRelativeErrorLoss(nn.Module):
def __init__(self, epsilon=0.0001):
super().__init__()
self.epsilon = epsilon
def forward(self, pred, target):
error = (pred - target) / (target + self.epsilon)
return torch.sigmoid(torch.abs... | 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_... | ElectronicElephant/openpilot-reimplementation | SigmoidAbsoluteRelativeErrorLoss | false | 2,200 | [
"MIT"
] | 0 | 063a9f5c6bbbf02c03dadc59e236e8f7c253a350 | https://github.com/ElectronicElephant/openpilot-reimplementation/tree/063a9f5c6bbbf02c03dadc59e236e8f7c253a350 |
Position_wise_Feed_Forward | import torch
import torch.nn as nn
import torch.nn.functional as F
class Position_wise_Feed_Forward(nn.Module):
def __init__(self, dim_model, hidden, dropout=0.0):
super(Position_wise_Feed_Forward, self).__init__()
self.fc1 = nn.Linear(dim_model, hidden)
self.fc2 = nn.Linear(hidden, dim_m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Ergtou/TextWord | Position_wise_Feed_Forward | false | 2,201 | [
"MIT"
] | 0 | f05cc5a630fc8d05357b8a9bc0da3ec5cc255a30 | https://github.com/Ergtou/TextWord/tree/f05cc5a630fc8d05357b8a9bc0da3ec5cc255a30 |
TOP1_max | import torch
import torch.nn as nn
import torch.nn.functional as F
class TOP1_max(nn.Module):
def __init__(self):
super(TOP1_max, self).__init__()
def forward(self, logit):
logit_softmax = F.softmax(logit, dim=1)
diff = -(logit.diag().view(-1, 1).expand_as(logit) - logit)
los... | 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
... | Ethan-Yys/GRU4REC-pytorch-master | TOP1_max | false | 2,202 | [
"Apache-2.0"
] | 0 | 175ccb851f881d3506680c459491e76f50aa9898 | https://github.com/Ethan-Yys/GRU4REC-pytorch-master/tree/175ccb851f881d3506680c459491e76f50aa9898 |
Sentence_Maxpool | import torch
import torch as th
import torch.nn.functional as F
import torch.nn as nn
class Sentence_Maxpool(nn.Module):
def __init__(self, word_dimension, output_dim, relu=True):
super(Sentence_Maxpool, self).__init__()
self.fc = nn.Linear(word_dimension, output_dim)
self.out_dim = outpu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | ErinZhang1998/howto100m-erin | Sentence_Maxpool | false | 2,203 | [
"Apache-2.0"
] | 0 | 1152ea0fe328d20fcf2218a1d548644881632656 | https://github.com/ErinZhang1998/howto100m-erin/tree/1152ea0fe328d20fcf2218a1d548644881632656 |
Attention | import math
import torch
import torch.nn.functional as F
import torch.utils.data
def restricted_softmax(src, dim: 'int'=-1, margin: 'float'=0.0):
src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0.0)
out = (src - src_max).exp()
out = out / (out.sum(dim=dim, keepdim=True) + (margin - src_max).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.... | EricAlcaide/pytorch_geometric | Attention | false | 2,204 | [
"MIT"
] | 0 | 31cef566cfe22602459155fdf91e9b6ce398bfe7 | https://github.com/EricAlcaide/pytorch_geometric/tree/31cef566cfe22602459155fdf91e9b6ce398bfe7 |
AdaptiveConcatPool2d | import torch
from typing import Type
from typing import Optional
import torch.nn as nn
class AdaptiveConcatPool2d(nn.Module):
"""Layer that concats `AdaptiveAvgPool2d` and `AdaptiveMaxPool2d`."""
def __init__(self, size: 'Optional[int]'=None):
"""Output will be 2*size or 2 if size is 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 typing import Optional
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | Erlemar/kekas | AdaptiveConcatPool2d | false | 2,205 | [
"MIT"
] | 0 | 6fd8413f15390bf079bdb57a38a7094a5c53ab0f | https://github.com/Erlemar/kekas/tree/6fd8413f15390bf079bdb57a38a7094a5c53ab0f |
CustomNet | import torch
import torch.nn as nn
class CustomNet(nn.Module):
"""
A network with a fully connected layer followed by a sigmoid layer. This is
used for testing customized operation handles.
"""
def __init__(self, input_dim: 'int', output_dim: 'int') ->None:
super(CustomNet, self).__init__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DenXX/fvcore | CustomNet | false | 2,206 | [
"Apache-2.0"
] | 0 | 4b91cf092f4f5d379b2c93398780a3b5755e7179 | https://github.com/DenXX/fvcore/tree/4b91cf092f4f5d379b2c93398780a3b5755e7179 |
BPRLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BPRLoss(nn.Module):
def __init__(self):
super(BPRLoss, self).__init__()
def forward(self, logit):
"""
Args:
logit (BxB): Variable that stores the logits for the items in the mini-batch
... | 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... | Ethan-Yys/GRU4REC-pytorch-master | BPRLoss | false | 2,207 | [
"Apache-2.0"
] | 0 | 175ccb851f881d3506680c459491e76f50aa9898 | https://github.com/Ethan-Yys/GRU4REC-pytorch-master/tree/175ccb851f881d3506680c459491e76f50aa9898 |
ResBlock | import torch
import torch.nn as nn
def get_same_padding(kernel_size, dilation):
kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
padding = (kernel_size - 1) // 2
return padding
class ResBlock(nn.Module):
def __init__(self, inplanes, planes, kernel_size=3, stride=1, dilation=1):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Etienne66/CDVD-TSP | ResBlock | false | 2,208 | [
"MIT"
] | 0 | fccde88ff75832286612262613808eef7b1c3255 | https://github.com/Etienne66/CDVD-TSP/tree/fccde88ff75832286612262613808eef7b1c3255 |
Copy | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.init
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, de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ChrisGeishauser/ConvLab-2 | Copy | false | 2,209 | [
"Apache-2.0"
] | 0 | 8f55d033c6e2453fdc092c4f504be3973a55e7ea | https://github.com/ChrisGeishauser/ConvLab-2/tree/8f55d033c6e2453fdc092c4f504be3973a55e7ea |
BinaryNLLEntropy | import torch
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
from torch.nn.modules.loss import _Loss
class BinaryNLLEntropy(_Loss):
def __init__(self, size_average=True):
super(BinaryNLLEntropy, self).__init__()
self.size_average = size_average
def forward(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ChrisGeishauser/ConvLab-2 | BinaryNLLEntropy | false | 2,210 | [
"Apache-2.0"
] | 0 | 8f55d033c6e2453fdc092c4f504be3973a55e7ea | https://github.com/ChrisGeishauser/ConvLab-2/tree/8f55d033c6e2453fdc092c4f504be3973a55e7ea |
ClassHead | import torch
from itertools import product as product
import torch.nn as nn
class ClassHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(ClassHead, self).__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from itertools import product as product
import torch.nn as nn
assert_size_strid... | Danil328/Pytorch_Retinaface | ClassHead | false | 2,211 | [
"MIT"
] | 0 | 048a1d68217b2a99fbf83e2537ecc7e281ed6bd6 | https://github.com/Danil328/Pytorch_Retinaface/tree/048a1d68217b2a99fbf83e2537ecc7e281ed6bd6 |
SmallConvNet | import torch
import torch.nn as nn
from numpy import prod
class SmallConvNet(nn.Module):
"""
A network with three conv layers. This is used for testing convolution
layers for activation count.
"""
def __init__(self, input_dim: 'int') ->None:
super(SmallConvNet, self).__init__()
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
import torch.nn as nn
from numpy import prod
assert_size_stride = torch._C._dyna... | DenXX/fvcore | SmallConvNet | false | 2,212 | [
"Apache-2.0"
] | 0 | 4b91cf092f4f5d379b2c93398780a3b5755e7179 | https://github.com/DenXX/fvcore/tree/4b91cf092f4f5d379b2c93398780a3b5755e7179 |
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 Union
from typing import List
from typing import Tuple
from typing import Optional
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... | EXAPPAI/opacus | DPSLTMAdapter | false | 2,213 | [
"Apache-2.0"
] | 0 | 11e188a2f03a8a08be51fdf2367cc1387879312a | https://github.com/EXAPPAI/opacus/tree/11e188a2f03a8a08be51fdf2367cc1387879312a |
LandmarkHead | import torch
from itertools import product as product
import torch.nn as nn
class LandmarkHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(LandmarkHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=
(1, 1), stride=1, padd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from itertools import product as product
import torch.nn as nn
assert_size_strid... | Danil328/Pytorch_Retinaface | LandmarkHead | false | 2,214 | [
"MIT"
] | 0 | 048a1d68217b2a99fbf83e2537ecc7e281ed6bd6 | https://github.com/Danil328/Pytorch_Retinaface/tree/048a1d68217b2a99fbf83e2537ecc7e281ed6bd6 |
Normalize | import torch
import torch.nn as nn
from itertools import product as product
class Normalize(nn.Module):
def __init__(self, n_channels, scale=1.0):
super(Normalize, self).__init__()
self.n_channels = n_channels
self.scale = scale
self.eps = 1e-10
self.weight = nn.Parameter(... | 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
from itertools import product as product
assert_size_stri... | DongChengdongHangZhou/caffe-to-pytorch | Normalize | false | 2,215 | [
"Apache-2.0"
] | 0 | 5e3104f3aa77d35bad5d2de235b067460c136fd5 | https://github.com/DongChengdongHangZhou/caffe-to-pytorch/tree/5e3104f3aa77d35bad5d2de235b067460c136fd5 |
BboxHead | import torch
from itertools import product as product
import torch.nn as nn
class BboxHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(BboxHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(
1, 1), stride=1, padding=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 itertools import product as product
import torch.nn as nn
assert_size_strid... | Danil328/Pytorch_Retinaface | BboxHead | false | 2,216 | [
"MIT"
] | 0 | 048a1d68217b2a99fbf83e2537ecc7e281ed6bd6 | https://github.com/Danil328/Pytorch_Retinaface/tree/048a1d68217b2a99fbf83e2537ecc7e281ed6bd6 |
ConvNet | import torch
import torch.nn as nn
class ConvNet(nn.Module):
"""
A network with a single convolution layer. This is used for testing flop
count for convolution layers.
"""
def __init__(self, conv_dim: 'int', input_dim: 'int', output_dim: 'int',
kernel_size: 'int', spatial_dim: 'int', stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DenXX/fvcore | ConvNet | false | 2,217 | [
"Apache-2.0"
] | 0 | 4b91cf092f4f5d379b2c93398780a3b5755e7179 | https://github.com/DenXX/fvcore/tree/4b91cf092f4f5d379b2c93398780a3b5755e7179 |
TOP1Loss | import torch
import torch.nn as nn
class TOP1Loss(nn.Module):
def __init__(self):
super(TOP1Loss, self).__init__()
def forward(self, logit):
"""
Args:
logit (BxB): Variable that stores the logits for the items in the mini-batch
The first dimension... | 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... | Ethan-Yys/GRU4REC-pytorch-master | TOP1Loss | false | 2,218 | [
"Apache-2.0"
] | 0 | 175ccb851f881d3506680c459491e76f50aa9898 | https://github.com/Ethan-Yys/GRU4REC-pytorch-master/tree/175ccb851f881d3506680c459491e76f50aa9898 |
PNet | import torch
import torch.nn.functional as F
import torch.nn as nn
from collections import OrderedDict
class PNet(nn.Module):
def __init__(self):
super(PNet, self).__init__()
self.features = nn.Sequential(OrderedDict([('conv1', nn.Conv2d(3,
10, 3, 1)), ('prelu1', nn.PReLU(10)), ('poo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Escaton615/mtcnn-pytorch | PNet | false | 2,219 | [
"MIT"
] | 0 | 4a645c1bf8dca0b5410cc0454ee0a538ada2d241 | https://github.com/Escaton615/mtcnn-pytorch/tree/4a645c1bf8dca0b5410cc0454ee0a538ada2d241 |
Conv2d_GN_ReLU | import torch
import torch.nn as nn
class Conv2d_GN_ReLU(nn.Module):
""" Implements a module that performs
conv2d + groupnorm + ReLU +
Assumes kernel size is odd
"""
def __init__(self, in_channels, out_channels, num_groups, ksize=3, stride=1
):
super(Conv2d_GN_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
from torch._inductor.runtime.... | FANG-Xiaolin/uois | Conv2d_GN_ReLU | false | 2,220 | [
"MIT"
] | 0 | 7489e69d1513faf2f3f030a441abdd33ca22304c | https://github.com/FANG-Xiaolin/uois/tree/7489e69d1513faf2f3f030a441abdd33ca22304c |
mlp | import torch
from torch import nn
class mlp(nn.Module):
def __init__(self, in_feature, **kwargs):
super().__init__()
self.in_feature = in_feature
self.relu = nn.ReLU()
self.linear1 = nn.Linear(in_feature, in_feature)
self.dropout1 = nn.Dropout(p=0.3)
self.linear2 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | EuphoriaYan/sales_pred | mlp | false | 2,221 | [
"MIT"
] | 0 | cc39c32a3387285f3561aeeea7a133810069dc98 | https://github.com/EuphoriaYan/sales_pred/tree/cc39c32a3387285f3561aeeea7a133810069dc98 |
ScaledDotProductAttention | import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
import torch.nn.init
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super(ScaledDotProductAttention, self).__init__()
self.tempera... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ChrisGeishauser/ConvLab-2 | ScaledDotProductAttention | false | 2,222 | [
"Apache-2.0"
] | 0 | 8f55d033c6e2453fdc092c4f504be3973a55e7ea | https://github.com/ChrisGeishauser/ConvLab-2/tree/8f55d033c6e2453fdc092c4f504be3973a55e7ea |
CELossWeighted | import torch
import torch.nn as nn
class WeightedLoss(nn.Module):
def __init__(self):
super(WeightedLoss, self).__init__()
self.weighted = False
def generate_weight_mask(self, mask, to_ignore=None):
""" Generates a weight mask where pixel weights are inversely proportional 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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | FANG-Xiaolin/uois | CELossWeighted | false | 2,223 | [
"MIT"
] | 0 | 7489e69d1513faf2f3f030a441abdd33ca22304c | https://github.com/FANG-Xiaolin/uois/tree/7489e69d1513faf2f3f030a441abdd33ca22304c |
SelfAttn | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
import torch as th
class SelfAttn(nn.Module):
def __init__(self, hidden_size):
super(SelfAttn, self).__init__()
self.query = nn.Linear(hidden_size, 1)
def forward(self, keys, 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 import triton_helpers
from torch._inductor.runtime.... | ChrisGeishauser/ConvLab-2 | SelfAttn | false | 2,224 | [
"Apache-2.0"
] | 0 | 8f55d033c6e2453fdc092c4f504be3973a55e7ea | https://github.com/ChrisGeishauser/ConvLab-2/tree/8f55d033c6e2453fdc092c4f504be3973a55e7ea |
BCEWithLogitsLossWeighted | import torch
import torch.nn as nn
class WeightedLoss(nn.Module):
def __init__(self):
super(WeightedLoss, self).__init__()
self.weighted = False
def generate_weight_mask(self, mask, to_ignore=None):
""" Generates a weight mask where pixel weights are inversely proportional 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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | FANG-Xiaolin/uois | BCEWithLogitsLossWeighted | false | 2,225 | [
"MIT"
] | 0 | 7489e69d1513faf2f3f030a441abdd33ca22304c | https://github.com/FANG-Xiaolin/uois/tree/7489e69d1513faf2f3f030a441abdd33ca22304c |
Attn | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
class Attn(nn.Module):
def __init__(self, method, hidden_size):
super(Attn, self).__init__()
self.method = method
self.hidden_size = hidden_size
self.attn = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ChrisGeishauser/ConvLab-2 | Attn | false | 2,226 | [
"Apache-2.0"
] | 0 | 8f55d033c6e2453fdc092c4f504be3973a55e7ea | https://github.com/ChrisGeishauser/ConvLab-2/tree/8f55d033c6e2453fdc092c4f504be3973a55e7ea |
Scale | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from itertools import product as product
class Scale(nn.Module):
def __init__(self, channels):
super(Scale, self).__init__()
self.weight = Parameter(torch.Tensor(channels))
self.bias = Parameter(torch.Tensor(chann... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
from itertools import product as product
assert_size_stride = torch._C._dynam... | DongChengdongHangZhou/caffe-to-pytorch | Scale | false | 2,227 | [
"Apache-2.0"
] | 0 | 5e3104f3aa77d35bad5d2de235b067460c136fd5 | https://github.com/DongChengdongHangZhou/caffe-to-pytorch/tree/5e3104f3aa77d35bad5d2de235b067460c136fd5 |
PositionwiseFeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
class PositionwiseFeedForward(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super(PositionwiseFeedForward, self).__init__()
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.... | ChrisGeishauser/ConvLab-2 | PositionwiseFeedForward | false | 2,228 | [
"Apache-2.0"
] | 0 | 8f55d033c6e2453fdc092c4f504be3973a55e7ea | https://github.com/ChrisGeishauser/ConvLab-2/tree/8f55d033c6e2453fdc092c4f504be3973a55e7ea |
BPR_max | import torch
import torch.nn as nn
import torch.nn.functional as F
class BPR_max(nn.Module):
def __init__(self):
super(BPR_max, self).__init__()
def forward(self, logit):
logit_softmax = F.softmax(logit, dim=1)
diff = logit.diag().view(-1, 1).expand_as(logit) - logit
loss = -... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Ethan-Yys/GRU4REC-pytorch-master | BPR_max | false | 2,229 | [
"Apache-2.0"
] | 0 | 175ccb851f881d3506680c459491e76f50aa9898 | https://github.com/Ethan-Yys/GRU4REC-pytorch-master/tree/175ccb851f881d3506680c459491e76f50aa9898 |
SilogLoss | import torch
import torch.nn as nn
class SilogLoss(nn.Module):
def __init__(self, ratio=10, ratio2=0.85):
super().__init__()
self.ratio = ratio
self.ratio2 = ratio2
def forward(self, pred, gt):
log_diff = torch.log(pred * self.ratio) - torch.log(gt * self.ratio)
silog... | 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... | Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING- | SilogLoss | false | 2,230 | [
"MIT"
] | 0 | 13fac05601efed16ae8b29989aad487e04cd90a7 | https://github.com/Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING-/tree/13fac05601efed16ae8b29989aad487e04cd90a7 |
PatchEmbed | import torch
from itertools import chain as chain
import torch.utils.data
import torch.nn as nn
class PatchEmbed(nn.Module):
"""
PatchEmbed.
"""
def __init__(self, dim_in=3, dim_out=768, kernel=(1, 16, 16), stride=(1,
4, 4), padding=(1, 7, 7), conv_2d=False):
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from itertools import chain as chain
import torch.utils.data
import torch.nn as ... | Drill-D/SlowFast | PatchEmbed | false | 2,231 | [
"Apache-2.0"
] | 0 | d55ae1cf30a9415858a9bd5da983790a2b418653 | https://github.com/Drill-D/SlowFast/tree/d55ae1cf30a9415858a9bd5da983790a2b418653 |
ThreeNet | import torch
import torch.nn as nn
class ThreeNet(nn.Module):
"""
A network with three layers. This is used for testing a network with more
than one operation. The network has a convolution layer followed by two
fully connected layers.
"""
def __init__(self, input_dim: 'int', conv_dim: 'int',... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | DenXX/fvcore | ThreeNet | false | 2,232 | [
"Apache-2.0"
] | 0 | 4b91cf092f4f5d379b2c93398780a3b5755e7179 | https://github.com/DenXX/fvcore/tree/4b91cf092f4f5d379b2c93398780a3b5755e7179 |
Hidden2Discrete | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
class Hidden2Discrete(nn.Module):
def __init__(self, input_size, y_size, k_size, is_lstm=False, has_bias=True
):
super(Hidden2Discrete, self).__init__()
self.y_size = y_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ChrisGeishauser/ConvLab-2 | Hidden2Discrete | false | 2,233 | [
"Apache-2.0"
] | 0 | 8f55d033c6e2453fdc092c4f504be3973a55e7ea | https://github.com/ChrisGeishauser/ConvLab-2/tree/8f55d033c6e2453fdc092c4f504be3973a55e7ea |
NormKLLoss | import torch
import torch.utils.data
import torch.nn.init
import torch as th
from torch.nn.modules.loss import _Loss
class NormKLLoss(_Loss):
def __init__(self, unit_average=False):
super(NormKLLoss, self).__init__()
self.unit_average = unit_average
def forward(self, recog_mu, recog_logvar, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.data
import torch.nn.init
from torch.nn.modules.loss i... | ChrisGeishauser/ConvLab-2 | NormKLLoss | false | 2,234 | [
"Apache-2.0"
] | 0 | 8f55d033c6e2453fdc092c4f504be3973a55e7ea | https://github.com/ChrisGeishauser/ConvLab-2/tree/8f55d033c6e2453fdc092c4f504be3973a55e7ea |
tofp16 | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
import torch.optim
class tofp16(nn.Module):
"""
Model wrapper that implements::
def forward(self, input):
return input.half()
"""
def __init__(self):
su... | 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
import torch.optim
assert_size_st... | FDecaYed/apex | tofp16 | false | 2,235 | [
"BSD-3-Clause"
] | 0 | 789afd89fe2c5a3e772f557055a9cf0f5e9d1241 | https://github.com/FDecaYed/apex/tree/789afd89fe2c5a3e772f557055a9cf0f5e9d1241 |
ConvBlock | import torch
import torch.nn as nn
class Conv3x3(nn.Module):
"""Layer to pad and convolve input
"""
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING- | ConvBlock | false | 2,236 | [
"MIT"
] | 0 | 13fac05601efed16ae8b29989aad487e04cd90a7 | https://github.com/Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING-/tree/13fac05601efed16ae8b29989aad487e04cd90a7 |
PairwiseBCELoss | import torch
from abc import abstractmethod
import torch.utils.data.dataloader
import torch.nn.functional as F
import torch.nn as nn
import torch.nn
class SimilarityLoss(nn.Module):
def __init__(self):
super(SimilarityLoss, self).__init__()
@abstractmethod
def forward(self, inputs, targets):
... | 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 abc im... | FranziskaKuhls/flair | PairwiseBCELoss | false | 2,237 | [
"MIT"
] | 0 | 2bd9e72c961651c7c020076cb8fd80cbbb36da7c | https://github.com/FranziskaKuhls/flair/tree/2bd9e72c961651c7c020076cb8fd80cbbb36da7c |
Sine | import torch
import torch.nn as nn
class Sine(nn.Module):
"""
A wrapper for PyTorch sine function.
"""
def __init__(self, w0=1.0):
super().__init__()
self.w0 = w0
@staticmethod
def forward(x):
return torch.sin(x)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | FinbarArgus/phynn | Sine | false | 2,238 | [
"Apache-2.0"
] | 0 | 436bfd6fa4ad86692bf12b4f76c92bc177626c40 | https://github.com/FinbarArgus/phynn/tree/436bfd6fa4ad86692bf12b4f76c92bc177626c40 |
InvDepth | import torch
import torch.nn as nn
class InvDepth(nn.Module):
"""Inverse depth layer"""
def __init__(self, in_channels, out_channels=1, min_depth=0.5):
"""
Initializes an InvDepth object.
Parameters
----------
in_channels : int
Number of input channels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING- | InvDepth | false | 2,239 | [
"MIT"
] | 0 | 13fac05601efed16ae8b29989aad487e04cd90a7 | https://github.com/Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING-/tree/13fac05601efed16ae8b29989aad487e04cd90a7 |
FeatNet | import torch
import torch.nn as nn
from itertools import product as product
class FeatNet(nn.Module):
def __init__(self):
super(FeatNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=
(3, 7), stride=1, padding=(1, 3), bias=False)
self.tanh... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | DongChengdongHangZhou/caffe-to-pytorch | FeatNet | false | 2,240 | [
"Apache-2.0"
] | 0 | 5e3104f3aa77d35bad5d2de235b067460c136fd5 | https://github.com/DongChengdongHangZhou/caffe-to-pytorch/tree/5e3104f3aa77d35bad5d2de235b067460c136fd5 |
PaddedMaxPool2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class PaddedMaxPool2d(nn.Module):
""" Maxpool layer with a replicating padding.
Args:
kernel_size (int or tuple): Kernel size for maxpooling
stride (int or tuple, optional): The stride of the window; Default ``kernel_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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | FenryrMKIII/objectDetection-lightnet | PaddedMaxPool2d | false | 2,241 | [
"MIT"
] | 0 | 3a1fa7b77227210060714a9e22d7d241888b36b4 | https://github.com/FenryrMKIII/objectDetection-lightnet/tree/3a1fa7b77227210060714a9e22d7d241888b36b4 |
Residual | import torch
import torch.nn as nn
class Residual(nn.Sequential):
""" Residual block that runs like a Sequential, but then adds the original input to the output tensor.
See :class:`torch.nn.Sequential` for more information.
Warning:
The dimension between the input and output of the mo... | 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... | FenryrMKIII/objectDetection-lightnet | Residual | false | 2,242 | [
"MIT"
] | 0 | 3a1fa7b77227210060714a9e22d7d241888b36b4 | https://github.com/FenryrMKIII/objectDetection-lightnet/tree/3a1fa7b77227210060714a9e22d7d241888b36b4 |
CELossWeightedMasked | import torch
import torch.nn as nn
class WeightedLoss(nn.Module):
def __init__(self):
super(WeightedLoss, self).__init__()
self.weighted = False
def generate_weight_mask(self, mask, to_ignore=None):
""" Generates a weight mask where pixel weights are inversely proportional 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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | FANG-Xiaolin/uois | CELossWeightedMasked | false | 2,243 | [
"MIT"
] | 0 | 7489e69d1513faf2f3f030a441abdd33ca22304c | https://github.com/FANG-Xiaolin/uois/tree/7489e69d1513faf2f3f030a441abdd33ca22304c |
upsample | import torch
import torch.nn as nn
class upsample(nn.Module):
def __init__(self, scale_factor):
super(upsample, self).__init__()
self.scale_factor = scale_factor
def forward(self, x):
return nn.functional.interpolate(x, scale_factor=self.scale_factor)
def get_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... | FMsunyh/CornerNet-Lite | upsample | false | 2,244 | [
"BSD-3-Clause"
] | 0 | 85770fa6682646d572a5bd2277a0075d6dd22b93 | https://github.com/FMsunyh/CornerNet-Lite/tree/85770fa6682646d572a5bd2277a0075d6dd22b93 |
Conv3x3 | import torch
import torch.nn as nn
class Conv3x3(nn.Module):
"""Layer to pad and convolve input
"""
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING- | Conv3x3 | false | 2,245 | [
"MIT"
] | 0 | 13fac05601efed16ae8b29989aad487e04cd90a7 | https://github.com/Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING-/tree/13fac05601efed16ae8b29989aad487e04cd90a7 |
Conv2d_GN_ReLUx2 | import torch
import torch.nn as nn
class Conv2d_GN_ReLU(nn.Module):
""" Implements a module that performs
conv2d + groupnorm + ReLU +
Assumes kernel size is odd
"""
def __init__(self, in_channels, out_channels, num_groups, ksize=3, stride=1
):
super(Conv2d_GN_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
from torch._inductor.runtime.... | FANG-Xiaolin/uois | Conv2d_GN_ReLUx2 | false | 2,246 | [
"MIT"
] | 0 | 7489e69d1513faf2f3f030a441abdd33ca22304c | https://github.com/FANG-Xiaolin/uois/tree/7489e69d1513faf2f3f030a441abdd33ca22304c |
Reorg | import torch
import torch.nn as nn
class Reorg(nn.Module):
""" This layer reorganizes a tensor according to a stride.
The dimensions 2,3 will be sliced by the stride and then stacked in dimension 1. (input must have 4 dimensions)
Args:
stride (int): stride to divide the input tensor
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | FenryrMKIII/objectDetection-lightnet | Reorg | false | 2,247 | [
"MIT"
] | 0 | 3a1fa7b77227210060714a9e22d7d241888b36b4 | https://github.com/FenryrMKIII/objectDetection-lightnet/tree/3a1fa7b77227210060714a9e22d7d241888b36b4 |
LayerNorm | import torch
import torch.nn as nn
import torch.nn.parallel
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.affine:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._d... | FUTUREEEEEE/S2R-DepthNet | LayerNorm | false | 2,248 | [
"MIT"
] | 0 | 415cc40aef10f9540026ff435d14a9ba9e30ad74 | https://github.com/FUTUREEEEEE/S2R-DepthNet/tree/415cc40aef10f9540026ff435d14a9ba9e30ad74 |
Conv2dBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
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
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | FUTUREEEEEE/S2R-DepthNet | Conv2dBlock | false | 2,249 | [
"MIT"
] | 0 | 415cc40aef10f9540026ff435d14a9ba9e30ad74 | https://github.com/FUTUREEEEEE/S2R-DepthNet/tree/415cc40aef10f9540026ff435d14a9ba9e30ad74 |
Siren | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Sine(nn.Module):
"""
A wrapper for PyTorch sine function.
"""
def __init__(self, w0=1.0):
super().__init__()
self.w0 = w0
@staticmethod
def forward(x):
return torch.sin(x)
class Sir... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
i... | FinbarArgus/phynn | Siren | false | 2,250 | [
"Apache-2.0"
] | 0 | 436bfd6fa4ad86692bf12b4f76c92bc177626c40 | https://github.com/FinbarArgus/phynn/tree/436bfd6fa4ad86692bf12b4f76c92bc177626c40 |
ResBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
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
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | FUTUREEEEEE/S2R-DepthNet | ResBlock | false | 2,251 | [
"MIT"
] | 0 | 415cc40aef10f9540026ff435d14a9ba9e30ad74 | https://github.com/FUTUREEEEEE/S2R-DepthNet/tree/415cc40aef10f9540026ff435d14a9ba9e30ad74 |
UnpackLayerConv2d | import torch
import torch.nn as nn
class Conv2D(nn.Module):
"""
2D convolution with GroupNorm and ELU
Parameters
----------
in_channels : int
Number of input channels
out_channels : int
Number of output channels
kernel_size : int
Kernel size
stride : int
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING- | UnpackLayerConv2d | false | 2,252 | [
"MIT"
] | 0 | 13fac05601efed16ae8b29989aad487e04cd90a7 | https://github.com/Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING-/tree/13fac05601efed16ae8b29989aad487e04cd90a7 |
DAInsHead | import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn
class DAInsHead(nn.Module):
"""
Adds a simple Instance-level Domain Classifier head
"""
def __init__(self, in_channels):
"""
Arguments:
in_channels (int): number of channels of the in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | Feobi1999/unbiased-teacher | DAInsHead | false | 2,254 | [
"MIT"
] | 0 | 9baacec16833bdff0dc089057e50903a92c700cb | https://github.com/Feobi1999/unbiased-teacher/tree/9baacec16833bdff0dc089057e50903a92c700cb |
Network | import torch
import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self, input_size, nb_action):
super(Network, self).__init__()
self.input_size = input_size
self.nb_action = nb_action
self.fc1 = nn.Linear(input_size, 30)
self.fc2 = 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
import torch.nn as nn
assert_... | Flames-LLC/GX-V1NLPModule | Network | false | 2,255 | [
"MIT"
] | 0 | 85e656c02269e57384b6e67ab4e4bceef4feb92e | https://github.com/Flames-LLC/GX-V1NLPModule/tree/85e656c02269e57384b6e67ab4e4bceef4feb92e |
FocalLoss | import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn
class FocalLoss(nn.Module):
def __init__(self, weight=None, gamma=1.0, num_classes=80):
super(FocalLoss, self).__init__()
assert gamma >= 0
self.gamma = gamma
self.weight = weight
sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | Feobi1999/unbiased-teacher | FocalLoss | false | 2,256 | [
"MIT"
] | 0 | 9baacec16833bdff0dc089057e50903a92c700cb | https://github.com/Feobi1999/unbiased-teacher/tree/9baacec16833bdff0dc089057e50903a92c700cb |
RankingLoss | import torch
from abc import abstractmethod
import torch.utils.data.dataloader
import torch.nn.functional as F
import torch.nn as nn
import torch.nn
class SimilarityLoss(nn.Module):
def __init__(self):
super(SimilarityLoss, self).__init__()
@abstractmethod
def forward(self, inputs, targets):
... | 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 abc import abstractmethod
import torch.utils.data.dataloader
import torch.nn as nn
i... | FranziskaKuhls/flair | RankingLoss | false | 2,257 | [
"MIT"
] | 0 | 2bd9e72c961651c7c020076cb8fd80cbbb36da7c | https://github.com/FranziskaKuhls/flair/tree/2bd9e72c961651c7c020076cb8fd80cbbb36da7c |
KeyValueAttention | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
class KeyValueAttention(nn.Module):
def __init__(self, query_size, key_size, value_size, hid_size, init_range):
super(KeyValueAttention, 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.... | ChrisGeishauser/ConvLab-2 | KeyValueAttention | false | 2,258 | [
"Apache-2.0"
] | 0 | 8f55d033c6e2453fdc092c4f504be3973a55e7ea | https://github.com/ChrisGeishauser/ConvLab-2/tree/8f55d033c6e2453fdc092c4f504be3973a55e7ea |
NodeAdaptiveEncoder | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class NodeAdaptiveEncoder(nn.Module):
def __init__(self, num_features, dropout=0.5):
super(NodeAdaptiveEncoder, self).__init__()
self.fc = nn.Parameter(torch.zeros(size=(num_features, 1)))
nn.init.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
import torch.nn as nn
assert_size_stride = torch._C._dyn... | Brickser/cogdl | NodeAdaptiveEncoder | false | 2,259 | [
"MIT"
] | 0 | 3952dd11075634cc0f3b669996cfc780635ce026 | https://github.com/Brickser/cogdl/tree/3952dd11075634cc0f3b669996cfc780635ce026 |
Wide | import math
import torch
from torch import Tensor
from torch import nn
class Wide(nn.Module):
"""Wide component
Linear model implemented via an Embedding layer connected to the output
neuron(s).
Parameters
-----------
wide_dim: int
size of the Embedding layer. `wide_dim` is the summa... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guard... | FlyingWing/pytorch-widedeep | Wide | false | 2,260 | [
"MIT"
] | 0 | 91a255d08bc9bdd5a05669465b7cf0313849ec9c | https://github.com/FlyingWing/pytorch-widedeep/tree/91a255d08bc9bdd5a05669465b7cf0313849ec9c |
Classifier | import torch
import torch.utils.data
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, n_hid, n_out):
super(Classifier, self).__init__()
self.n_hid = n_hid
self.n_out = n_out
self.linear = nn.Linear(n_hid, n_out)
def forward(self, x):
tx = self.lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Brickser/cogdl | Classifier | false | 2,261 | [
"MIT"
] | 0 | 3952dd11075634cc0f3b669996cfc780635ce026 | https://github.com/Brickser/cogdl/tree/3952dd11075634cc0f3b669996cfc780635ce026 |
DistMultLayer | import torch
import torch.utils.data
import torch.nn as nn
class DistMultLayer(nn.Module):
def __init__(self):
super(DistMultLayer, self).__init__()
def forward(self, sub_emb, obj_emb, rel_emb):
return torch.sum(sub_emb * obj_emb * rel_emb, dim=-1)
def predict(self, sub_emb, obj_emb, 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.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | Brickser/cogdl | DistMultLayer | false | 2,262 | [
"MIT"
] | 0 | 3952dd11075634cc0f3b669996cfc780635ce026 | https://github.com/Brickser/cogdl/tree/3952dd11075634cc0f3b669996cfc780635ce026 |
StdConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class StdConv2d(nn.Conv2d):
def forward(self, x):
w = self.weight
v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
w = (w - m) / torch.sqrt(v + 1e-05)
return F.conv2d(x, w, self.bias, self.stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | GUOSHU-COOL/TransUNet | StdConv2d | false | 2,263 | [
"Apache-2.0"
] | 0 | 6cb2c2f35eb6a571b12edbd095de5dda16c25015 | https://github.com/GUOSHU-COOL/TransUNet/tree/6cb2c2f35eb6a571b12edbd095de5dda16c25015 |
clip_nonlinear | import torch
import torch.nn as nn
def quantize_a(x):
x = Q_A.apply(x)
return x
def fa(x, bitA):
if bitA == 32:
return x
return quantize_a(x)
class Q_A(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
out = x.new(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... | GakkiChen/TWB-Net | clip_nonlinear | false | 2,264 | [
"MIT"
] | 0 | bb4917c697c09585bb3fe163a8b429b6dd250f18 | https://github.com/GakkiChen/TWB-Net/tree/bb4917c697c09585bb3fe163a8b429b6dd250f18 |
GELU | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from itertools import chain as chain
import torch.hub
class GELU(nn.Module):
"""
Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
"""
def forward(self, x)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | EddieMG/LateTemporalModeling3DCNN | GELU | false | 2,265 | [
"MIT"
] | 0 | 94c87dc1d31d09bc310d0e735a2e55453976cb0d | https://github.com/EddieMG/LateTemporalModeling3DCNN/tree/94c87dc1d31d09bc310d0e735a2e55453976cb0d |
ChannelPool | import torch
import torch.nn as nn
import torch.onnx
import torch.nn.parallel
class ChannelPool(nn.Module):
def forward(self, x):
return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1)
.unsqueeze(1)), dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.onnx
import torch.nn.parallel
assert_size_stride = tor... | Ganzooo/soil_segmentation | ChannelPool | false | 2,266 | [
"MIT"
] | 0 | 56f410e3e184f24e52dd4b542ea309f0d203ca00 | https://github.com/Ganzooo/soil_segmentation/tree/56f410e3e184f24e52dd4b542ea309f0d203ca00 |
BERTEmbedding4 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from itertools import chain as chain
import torch.hub
class LearnedPositionalEmbedding3(nn.Module):
def __init__(self, d_model, max_len=512):
super().__init__()
pe = torch.zeros(max_len, d_model... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from itertools import chain as chain
import torch.... | EddieMG/LateTemporalModeling3DCNN | BERTEmbedding4 | false | 2,267 | [
"MIT"
] | 0 | 94c87dc1d31d09bc310d0e735a2e55453976cb0d | https://github.com/EddieMG/LateTemporalModeling3DCNN/tree/94c87dc1d31d09bc310d0e735a2e55453976cb0d |
BERTEmbedding3 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from itertools import chain as chain
import torch.hub
class LearnedPositionalEmbedding(nn.Module):
def __init__(self, d_model, max_len=512):
super().__init__()
pe = torch.zeros(max_len, d_model)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from itertools import chain as chain
import torch.... | EddieMG/LateTemporalModeling3DCNN | BERTEmbedding3 | false | 2,268 | [
"MIT"
] | 0 | 94c87dc1d31d09bc310d0e735a2e55453976cb0d | https://github.com/EddieMG/LateTemporalModeling3DCNN/tree/94c87dc1d31d09bc310d0e735a2e55453976cb0d |
Network | import torch
import torch.nn as nn
class Network(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv3d(in_channels=1, out_channels=3, kernel_size=3)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 1, 64, 64, 64])]
def get_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | GReguig/torchio | Network | false | 2,269 | [
"Apache-2.0"
] | 0 | 0cd4f3105408410adda4fddf4873eb8c12883ecc | https://github.com/GReguig/torchio/tree/0cd4f3105408410adda4fddf4873eb8c12883ecc |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, loss_weight=1.0):
super(DiceLoss, self).__init__()
self.loss_weight = loss_weight
def forward(self, input, target, mask, reduce=True):
batch_size = input.size(0)
input = torch.sigmoid(input)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | GaroneHuang/pan_pp.pytorch | DiceLoss | false | 2,270 | [
"Apache-2.0"
] | 0 | dde41ad652179433ad8a9650f671dc6742b783f9 | https://github.com/GaroneHuang/pan_pp.pytorch/tree/dde41ad652179433ad8a9650f671dc6742b783f9 |
CMVN | import torch
import torch.nn as nn
class CMVN(nn.Module):
__constants__ = ['mode', 'dim', 'eps']
def __init__(self, mode='global', dim=2, eps=1e-10):
super(CMVN, self).__init__()
if mode != 'global':
raise NotImplementedError(
'Only support global mean variance nor... | 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_... | Ethan07902050/s3prl | CMVN | false | 2,271 | [
"MIT"
] | 0 | 854aff0b3062fc2cff531401923b8745f64701e7 | https://github.com/Ethan07902050/s3prl/tree/854aff0b3062fc2cff531401923b8745f64701e7 |
Block | import torch
import torch.nn as nn
import torch.autograd
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | FelixWUS/TransReID | Block | false | 2,272 | [
"MIT"
] | 0 | 9b0c6f30bd726677a4ce44450cb89427f05df9b1 | https://github.com/FelixWUS/TransReID/tree/9b0c6f30bd726677a4ce44450cb89427f05df9b1 |
StableBCELoss | import torch
class StableBCELoss(torch.nn.modules.Module):
def __init__(self):
super(StableBCELoss, self).__init__()
def forward(self, input, target):
neg_abs = -input.abs()
loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
return loss.mean()
def get_in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | GenoM87/hubmap | StableBCELoss | false | 2,273 | [
"MIT"
] | 0 | 4acd11c373c6bb136ea9c6627a174ff02afa5986 | https://github.com/GenoM87/hubmap/tree/4acd11c373c6bb136ea9c6627a174ff02afa5986 |
ConsinSimilarityLoss | import torch
import torch.nn as nn
class ConsinSimilarityLoss(nn.Module):
def __init__(self, dim: 'int'=1, eps: 'float'=1e-08, min_zero: 'bool'=True
):
super().__init__()
self.criterion = nn.CosineSimilarity(dim, eps)
self.min_zero = min_zero
def forward(self, output: '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._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Geson-anko/VQ_AutoEncoder | ConsinSimilarityLoss | false | 2,274 | [
"MIT"
] | 0 | 62e1694de38ea6f152891e19abc190ad4048e587 | https://github.com/Geson-anko/VQ_AutoEncoder/tree/62e1694de38ea6f152891e19abc190ad4048e587 |
SDNE_layer | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class SDNE_layer(nn.Module):
def __init__(self, num_node, hidden_size1, hidden_size2, droput, alpha,
beta, nu1, nu2):
super(SDNE_layer, self).__init__()
self.num_node = num_node
self.hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Brickser/cogdl | SDNE_layer | false | 2,275 | [
"MIT"
] | 0 | 3952dd11075634cc0f3b669996cfc780635ce026 | https://github.com/Brickser/cogdl/tree/3952dd11075634cc0f3b669996cfc780635ce026 |
MaxPool | import torch
import torch.utils.data
import torch.nn as nn
from torch import optim as optim
import torch.nn.parallel
class MaxPool(nn.Module):
def __init__(self, kernel_size, stride=1, padding=1, zero_pad=False):
super(MaxPool, self).__init__()
self.zero_pad = nn.ZeroPad2d((1, 0, 1, 0)) if zero_p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
from torch import optim as optim
import tor... | Exir-lxr/crldr-prune-pytorch | MaxPool | false | 2,276 | [
"Apache-2.0"
] | 0 | adeb5e0b24ce66ff9531d4d947f72412c1b5c033 | https://github.com/Exir-lxr/crldr-prune-pytorch/tree/adeb5e0b24ce66ff9531d4d947f72412c1b5c033 |
SelfAttentionPooling | import torch
import torch.nn as nn
class SelfAttentionPooling(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super(SelfAttentio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Ethan07902050/s3prl | SelfAttentionPooling | false | 2,277 | [
"MIT"
] | 0 | 854aff0b3062fc2cff531401923b8745f64701e7 | https://github.com/Ethan07902050/s3prl/tree/854aff0b3062fc2cff531401923b8745f64701e7 |
CossimLoss | import torch
import torch.nn as nn
class CossimLoss(nn.Module):
def __init__(self, dim: 'int'=1, eps: 'float'=1e-08):
super().__init__()
self.cos_sim = nn.CosineSimilarity(dim, eps)
def forward(self, output, target):
return -self.cos_sim(output, target).mean() + 1
def get_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Geson-anko/VQ_AutoEncoder | CossimLoss | false | 2,278 | [
"MIT"
] | 0 | 62e1694de38ea6f152891e19abc190ad4048e587 | https://github.com/Geson-anko/VQ_AutoEncoder/tree/62e1694de38ea6f152891e19abc190ad4048e587 |
MovingAverage | import torch
from torch import Tensor
import torch as pt
from torch import nn
class MovingAverage(nn.Module):
def __init__(self, cond_size: 'int', pred_size: 'int') ->None:
super().__init__()
self.left_window = cond_size // 2
self.right_window = cond_size - self.left_window
def forwa... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Gandor26/cacovid | MovingAverage | false | 2,279 | [
"MIT"
] | 0 | 2b966579dba1eac6e33f439515de6de9e802d08a | https://github.com/Gandor26/cacovid/tree/2b966579dba1eac6e33f439515de6de9e802d08a |
MaskNet | import torch
import torch.nn as nn
from itertools import product as product
class MaskNet(nn.Module):
def __init__(self):
super(MaskNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=
5, stride=1, padding=2)
self.relu1 = 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
from it... | DongChengdongHangZhou/caffe-to-pytorch | MaskNet | false | 2,280 | [
"Apache-2.0"
] | 0 | 5e3104f3aa77d35bad5d2de235b067460c136fd5 | https://github.com/DongChengdongHangZhou/caffe-to-pytorch/tree/5e3104f3aa77d35bad5d2de235b067460c136fd5 |
self_conv | import torch
import torch.nn as nn
import torch.nn.functional as F
def quantize_w(x):
x = Q_W.apply(x)
return x
def fw(x, bitW):
if bitW == 32:
return x
x = quantize_w(x)
return x
class Q_W(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return x.sign() * ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | GakkiChen/TWB-Net | self_conv | false | 2,281 | [
"MIT"
] | 0 | bb4917c697c09585bb3fe163a8b429b6dd250f18 | https://github.com/GakkiChen/TWB-Net/tree/bb4917c697c09585bb3fe163a8b429b6dd250f18 |
Fp32GroupNorm | import torch
import torch.nn as nn
import torch.nn.functional as F
class Fp32GroupNorm(nn.GroupNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input):
output = F.group_norm(input.float(), self.num_groups, self.weight.
float() if sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Ethan07902050/s3prl | Fp32GroupNorm | false | 2,282 | [
"MIT"
] | 0 | 854aff0b3062fc2cff531401923b8745f64701e7 | https://github.com/Ethan07902050/s3prl/tree/854aff0b3062fc2cff531401923b8745f64701e7 |
BiTemperedLogisticLoss | import torch
import torch.nn as nn
def log_t(u, t):
"""Compute log_t for `u'."""
if t == 1.0:
return u.log()
else:
return (u.pow(1.0 - t) - 1.0) / (1.0 - t)
def exp_t(u, t):
"""Compute exp_t for `u'."""
if t == 1:
return u.exp()
else:
return (1.0 + (1.0 - 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | GenoM87/cassava_leaf | BiTemperedLogisticLoss | false | 2,283 | [
"MIT"
] | 0 | 51cc78b99a687b2d38be1930c40fc8aef3105b42 | https://github.com/GenoM87/cassava_leaf/tree/51cc78b99a687b2d38be1930c40fc8aef3105b42 |
NextSentencePrediction | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from itertools import chain as chain
import torch.hub
class NextSentencePrediction(nn.Module):
"""
2-class classification model : is_next, is_not_next
"""
def __init__(self, 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.... | EddieMG/LateTemporalModeling3DCNN | NextSentencePrediction | false | 2,284 | [
"MIT"
] | 0 | 94c87dc1d31d09bc310d0e735a2e55453976cb0d | https://github.com/EddieMG/LateTemporalModeling3DCNN/tree/94c87dc1d31d09bc310d0e735a2e55453976cb0d |
PositionwiseFeedForward | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from itertools import chain as chain
import torch.hub
class GELU(nn.Module):
"""
Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
"""
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.triton_helpers import libdevice
import math
import ... | EddieMG/LateTemporalModeling3DCNN | PositionwiseFeedForward | false | 2,285 | [
"MIT"
] | 0 | 94c87dc1d31d09bc310d0e735a2e55453976cb0d | https://github.com/EddieMG/LateTemporalModeling3DCNN/tree/94c87dc1d31d09bc310d0e735a2e55453976cb0d |
MaxPoolPad | import torch
import torch.utils.data
import torch.nn as nn
from torch import optim as optim
import torch.nn.parallel
class MaxPoolPad(nn.Module):
def __init__(self):
super(MaxPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.MaxPool2d(3, stride=2, padding=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.utils.data
import torch.nn as nn
from torch import optim as optim
import tor... | Exir-lxr/crldr-prune-pytorch | MaxPoolPad | false | 2,286 | [
"Apache-2.0"
] | 0 | adeb5e0b24ce66ff9531d4d947f72412c1b5c033 | https://github.com/Exir-lxr/crldr-prune-pytorch/tree/adeb5e0b24ce66ff9531d4d947f72412c1b5c033 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, size_average=True, ignore_index=-100, reduce=True):
super(DiceLoss, self).__init__()
self.size_average = size_average
self.ignore_index = ignore_index
self.reduce = reduce
self.softMax = nn.So... | 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
... | GTreeSoftware/DB-Enhance | DiceLoss | false | 2,287 | [
"MIT"
] | 0 | 98332c62297db7756f5385c038089bb8736a27c0 | https://github.com/GTreeSoftware/DB-Enhance/tree/98332c62297db7756f5385c038089bb8736a27c0 |
ConvTransposeInstanceNorm2d | import torch
import torch.nn as nn
from typing import Tuple
from typing import Union
class ConvTransposeInstanceNorm2d(nn.Module):
def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
'Union[int, Tuple[int]]', stride: 'Union[int, Tuple[int]]'=1,
padding: 'Union[int, Tuple[int]... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Geson-anko/ReimplementCycleGAN | ConvTransposeInstanceNorm2d | false | 2,288 | [
"MIT"
] | 0 | 3bd40c519d53a7ebb284c718e935e3832326633f | https://github.com/Geson-anko/ReimplementCycleGAN/tree/3bd40c519d53a7ebb284c718e935e3832326633f |
Quantizing | import torch
import torch.nn as nn
from typing import Tuple
class Quantizing(nn.Module):
"""
This is quantizing layer.
"""
__initialized: 'bool' = True
def __init__(self, num_quantizing: 'int', quantizing_dim: 'int',
_weight: 'torch.Tensor'=None, initialize_by_dataset: 'bool'=True,
... | 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... | Geson-anko/VQ_AutoEncoder | Quantizing | false | 2,289 | [
"MIT"
] | 0 | 62e1694de38ea6f152891e19abc190ad4048e587 | https://github.com/Geson-anko/VQ_AutoEncoder/tree/62e1694de38ea6f152891e19abc190ad4048e587 |
Sine | import torch
from torch import nn
class Sine(nn.Module):
def __init__(self, w0=30):
super().__init__()
self.w0 = w0
def forward(self, input):
return torch.sin(self.w0 * input)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | Fred62879/ACORN | Sine | false | 2,290 | [
"MIT"
] | 0 | 2de0bf747d595dbdc4d67311fb8f46cf47f9b4cb | https://github.com/Fred62879/ACORN/tree/2de0bf747d595dbdc4d67311fb8f46cf47f9b4cb |
BasicConv | import torch
import torch.nn as nn
import torch.onnx
import torch.nn.parallel
class BasicConv(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1,
padding=0, dilation=1, groups=1, relu=True, bn=False, bias=False):
super(BasicConv, self).__init__()
self.out_channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Ganzooo/soil_segmentation | BasicConv | false | 2,291 | [
"MIT"
] | 0 | 56f410e3e184f24e52dd4b542ea309f0d203ca00 | https://github.com/Ganzooo/soil_segmentation/tree/56f410e3e184f24e52dd4b542ea309f0d203ca00 |
Hardswish | import torch
from torch.nn import functional as F
import torch.nn as nn
class Hardswish(nn.Module):
@staticmethod
def forward(x):
return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | GoalballAnalysis/GUI | Hardswish | false | 2,292 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
TransitionUp | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
import torch.nn.parallel
class TransitionUp(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
def forward(self, x, skip, concat=True):
out = F.interpolate(x, size=(skip.size(2), s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.onnx
import torch.nn.parallel
assert_size_stride = tor... | Ganzooo/soil_segmentation | TransitionUp | false | 2,293 | [
"MIT"
] | 0 | 56f410e3e184f24e52dd4b542ea309f0d203ca00 | https://github.com/Ganzooo/soil_segmentation/tree/56f410e3e184f24e52dd4b542ea309f0d203ca00 |
AddLayer | import torch
from torch import nn
import torch.utils.checkpoint
class AddLayer(nn.Module):
def __init__(self, t1, t2):
super(AddLayer, self).__init__()
self.t1 = t1
self.t2 = t2
def forward(self, x, y):
return x + y
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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 import nn
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | DeepPoolML/DeepPool | AddLayer | false | 2,294 | [
"MIT"
] | 0 | 7f823f26747c9399524e74f2d81c99a2bb677f7c | https://github.com/DeepPoolML/DeepPool/tree/7f823f26747c9399524e74f2d81c99a2bb677f7c |
MyModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class MyModel(nn.Module):
def __init__(self, state_size, action_size):
super(MyModel, self).__init__()
self.fc1 = nn.Linear(state_size, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, action_size)
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Ghazalmg/slimevolleygym | MyModel | false | 2,295 | [
"Apache-2.0"
] | 0 | d880a35625c22bbe0bc10fa0352495f0aea06364 | https://github.com/Ghazalmg/slimevolleygym/tree/d880a35625c22bbe0bc10fa0352495f0aea06364 |
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