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 |
|---|---|---|---|---|---|---|---|---|---|---|
SmoothL1Loss | import functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | 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... | CityU-AIM-Group/HTD | SmoothL1Loss | false | 17,115 | [
"MIT"
] | 5 | 0be9fd844118c275abc6053b3cbd5ffb589e62ee | https://github.com/CityU-AIM-Group/HTD/tree/0be9fd844118c275abc6053b3cbd5ffb589e62ee |
AppendDim | import torch
from torch import nn
class AppendDim(nn.Module):
"""
Append a new dim to states with size out_dim
"""
def __init__(self, out_dim=1):
super().__init__()
self.out_dim = out_dim
def forward(self, states, **kwargs):
x = states.unsqueeze(len(states.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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | AppendDim | false | 17,116 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
ConvWS2d | import torch
import torch.nn.functional as F
import torch.nn as nn
def conv_ws_2d(input, weight, bias=None, stride=1, padding=0, dilation=1,
groups=1, eps=1e-05):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1)
mean = weight_flat.mean(dim=1, keepdim=True).view(c_in, 1, 1, 1)
std = weight... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.fun... | CityU-AIM-Group/HTD | ConvWS2d | false | 17,117 | [
"MIT"
] | 5 | 0be9fd844118c275abc6053b3cbd5ffb589e62ee | https://github.com/CityU-AIM-Group/HTD/tree/0be9fd844118c275abc6053b3cbd5ffb589e62ee |
GHMR | import torch
import torch.nn as nn
class GHMR(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
`Gradient Harmonized Single-stage Detector
<https://arxiv.org/abs/1811.05181>`_.
Args:
mu (float): The parameter for the Authentic Smooth L1 loss.
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | CityU-AIM-Group/HTD | GHMR | false | 17,118 | [
"MIT"
] | 5 | 0be9fd844118c275abc6053b3cbd5ffb589e62ee | https://github.com/CityU-AIM-Group/HTD/tree/0be9fd844118c275abc6053b3cbd5ffb589e62ee |
AlignDifferential | import torch
from torch import nn
class AlignDifferential(nn.Module):
def __init__(self):
super().__init__()
def new_length(self, length):
return length
def forward(self, states):
"""
:param states: [batch, length, *]
"""
padded_states = torch.cat([states... | 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... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | AlignDifferential | false | 17,119 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
C1 | import torch
import torch.nn as nn
from collections import OrderedDict
class C1(nn.Module):
def __init__(self):
super(C1, self).__init__()
self.c1 = nn.Sequential(OrderedDict([('c1', nn.Conv2d(1, 6,
kernel_size=(5, 5))), ('relu1', nn.ReLU()), ('s1', nn.MaxPool2d
(kernel_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 co... | ConstantinSeibold/SGL | C1 | false | 17,120 | [
"MIT"
] | 7 | fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a | https://github.com/ConstantinSeibold/SGL/tree/fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a |
GaussianFocalLoss | import functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | 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... | CityU-AIM-Group/HTD | GaussianFocalLoss | false | 17,121 | [
"MIT"
] | 5 | 0be9fd844118c275abc6053b3cbd5ffb589e62ee | https://github.com/CityU-AIM-Group/HTD/tree/0be9fd844118c275abc6053b3cbd5ffb589e62ee |
Differential | import torch
from torch import nn
class Differential(nn.Module):
def __init__(self, kernel_size=3, stride=1, padding=0):
super().__init__()
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
def new_length(self, length):
new_length = (length + ... | 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... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | Differential | false | 17,122 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
MultiheadAttention | import torch
import torch.nn as nn
class MultiheadAttention(nn.Module):
"""A warpper for torch.nn.MultiheadAttention.
This module implements MultiheadAttention with residual connection,
and positional encoding used in DETR is also passed as input.
Args:
embed_dims (int): The embedding dimens... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | CityU-AIM-Group/HTD | MultiheadAttention | false | 17,123 | [
"MIT"
] | 5 | 0be9fd844118c275abc6053b3cbd5ffb589e62ee | https://github.com/CityU-AIM-Group/HTD/tree/0be9fd844118c275abc6053b3cbd5ffb589e62ee |
C2 | import torch
import torch.nn as nn
from collections import OrderedDict
class C2(nn.Module):
def __init__(self):
super(C2, self).__init__()
self.c2 = nn.Sequential(OrderedDict([('c2', nn.Conv2d(6, 16,
kernel_size=(5, 5))), ('relu2', nn.ReLU()), ('s2', nn.MaxPool2d
(kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from co... | ConstantinSeibold/SGL | C2 | false | 17,124 | [
"MIT"
] | 7 | fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a | https://github.com/ConstantinSeibold/SGL/tree/fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a |
Distance | import torch
from torch import nn
def apply_last_dim(model, x):
size = list(x.size())
y = model(x.contiguous().view(-1, size[-1]))
size[-1] = y.size(-1)
y = y.view(torch.Size(size))
return y
def get_int_dim_index(name):
if isinstance(name, int):
return name
name_list = 'axyz'
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | Distance | false | 17,125 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
SIMSE | import torch
import torch.nn as nn
class SIMSE(nn.Module):
def __init__(self):
super(SIMSE, self).__init__()
def forward(self, pred, real):
diffs = torch.add(real, -pred)
n = torch.numel(diffs.data)
simse = torch.sum(diffs).pow(2) / n ** 2
return simse
def get_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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Columbine21/TFR-Net | SIMSE | false | 17,126 | [
"MIT"
] | 7 | 1da01577542e7f477fdf7323ec0696aebc632357 | https://github.com/Columbine21/TFR-Net/tree/1da01577542e7f477fdf7323ec0696aebc632357 |
SoftSmall | import math
import torch
from torch import nn
class SoftCompare(nn.Module):
def __init__(self, alpha=None, beta=None):
super().__init__()
self.alpha = nn.Parameter(torch.ones(1) * (0 if alpha is None else
alpha), requires_grad=True)
self.beta = nn.Parameter(torch.ones(1) * (0 ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
from torch import nn
assert_size_stride = torch._C._dynamo.gu... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | SoftSmall | false | 17,127 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
GHMC | import torch
import torch.nn.functional as F
import torch.nn as nn
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero((labels >= 0) & (labels < label_channels),
as_tuple=False).squeeze()
if inds.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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | CityU-AIM-Group/HTD | GHMC | false | 17,128 | [
"MIT"
] | 5 | 0be9fd844118c275abc6053b3cbd5ffb589e62ee | https://github.com/CityU-AIM-Group/HTD/tree/0be9fd844118c275abc6053b3cbd5ffb589e62ee |
Inequality | import math
import torch
from torch import nn
class Normalize(nn.Module):
def __init__(self, distribution=None, **kwargs):
super().__init__()
self.distribution = distribution
self.data_ = []
if distribution is None:
pass
elif distribution == 'normal':
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guard... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | Inequality | false | 17,129 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
MinPoolTrinary | import torch
from torch import nn
class MinPoolTrinary(nn.Module):
def __init__(self):
super().__init__()
def new_length(self, length):
return length
def forward(self, states):
"""
:param states: [batch, length, *]
"""
assert states.size(1) >= 3
s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | MinPoolTrinary | false | 17,130 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
MaxPoolTrinary | import torch
from torch import nn
class MaxPoolTrinary(nn.Module):
def __init__(self):
super().__init__()
def new_length(self, length):
return length
def forward(self, states):
"""
:param states: [batch, length, *]
"""
assert states.size(1) >= 3
s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | MaxPoolTrinary | false | 17,131 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
BalancedL1Loss | import functools
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tenso... | 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... | CityU-AIM-Group/HTD | BalancedL1Loss | false | 17,132 | [
"MIT"
] | 5 | 0be9fd844118c275abc6053b3cbd5ffb589e62ee | https://github.com/CityU-AIM-Group/HTD/tree/0be9fd844118c275abc6053b3cbd5ffb589e62ee |
C3 | import torch
import torch.nn as nn
from collections import OrderedDict
class C3(nn.Module):
def __init__(self):
super(C3, self).__init__()
self.c3 = nn.Sequential(OrderedDict([('c3', nn.Conv2d(16, 120,
kernel_size=(5, 5))), ('relu3', nn.ReLU())]))
def forward(self, img):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 co... | ConstantinSeibold/SGL | C3 | false | 17,133 | [
"MIT"
] | 7 | fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a | https://github.com/ConstantinSeibold/SGL/tree/fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a |
Subsample | import torch
import torch.utils.data
import torch.nn as nn
class Subsample(nn.Module):
def __init__(self):
super().__init__()
def forward(self, feats, lengths):
out = feats[:, ::2]
lengths = lengths // 2
return out, lengths
def get_inputs():
return [torch.rand([4, 4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | CoraJung/flexible-input-slu | Subsample | false | 17,134 | [
"Apache-2.0"
] | 7 | 6a1a6bf105f1a0c07e8d483aa6da1df7a554392d | https://github.com/CoraJung/flexible-input-slu/tree/6a1a6bf105f1a0c07e8d483aa6da1df7a554392d |
MultiheadAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import Parameter
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(self, embed_dim, num_heads, att... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Columbine21/TFR-Net | MultiheadAttention | false | 17,135 | [
"MIT"
] | 7 | 1da01577542e7f477fdf7323ec0696aebc632357 | https://github.com/Columbine21/TFR-Net/tree/1da01577542e7f477fdf7323ec0696aebc632357 |
L2Norm | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
import torch.nn.init as init
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.gamma = scale or 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.triton_helpers import libdevice
import torch.nn as nn
from math import sqrt as sqrt
from itertools import produ... | Coral-SH/TextBoxes_PyTorch | L2Norm | false | 17,136 | [
"MIT"
] | 8 | fb1636139d69e762b567a234c3a4b69e3dd43071 | https://github.com/Coral-SH/TextBoxes_PyTorch/tree/fb1636139d69e762b567a234c3a4b69e3dd43071 |
Ternary | import torch
from torch import nn
class Ternary(nn.Module):
"""
Ternarize the input activations to -1, 0, 1.
"""
def __init__(self, left=-0.25, right=0.25):
super().__init__()
self.left = left
self.right = right
def forward(self, input):
input = input.clone()
... | 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... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | Ternary | false | 17,137 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
NonLocalLayer | import torch
from torch import nn
class NonLocalLayer(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim=None,
t_kernel_size=1, t_stride=1, t_padding=None, t_dilation=1, bias=
True, residual=True):
super().__init__()
if t_padding is None:
t_padding = (t_k... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | NonLocalLayer | false | 17,138 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
Abs | import torch
import torch.utils.data
class Abs(torch.nn.Module):
def __init__(self):
super(Abs, self).__init__()
def forward(self, input):
return torch.abs(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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asse... | CoraJung/flexible-input-slu | Abs | false | 17,139 | [
"Apache-2.0"
] | 7 | 6a1a6bf105f1a0c07e8d483aa6da1df7a554392d | https://github.com/CoraJung/flexible-input-slu/tree/6a1a6bf105f1a0c07e8d483aa6da1df7a554392d |
AvgPoolPad | import torch
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
class AvgPoolPad(nn.Module):
def __init__(self, stride=2, padding=1):
super(AvgPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.AvgPool2d(3, stride=stride, padding=padding,... | 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
import torch.backends.cudnn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
em... | CalebEverett/fastai-dl2 | AvgPoolPad | false | 17,140 | [
"Apache-2.0"
] | 4 | 64d23592eddca6ca1f3647e73c319e97c8eb392b | https://github.com/CalebEverett/fastai-dl2/tree/64d23592eddca6ca1f3647e73c319e97c8eb392b |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch.utils.data
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
return nn.functional.adaptive_avg_pool2... | 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.... | BigFishMaster/tnt | GlobalAvgPool2d | false | 17,141 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 |
LayerNorm | import torch
import torch.utils.data
import torch.nn as nn
class LayerNorm(torch.nn.Module):
def __init__(self, dim, eps=1e-06):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(dim))
self.beta = nn.Parameter(torch.zeros(dim))
self.eps = eps
def forward(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | CoraJung/flexible-input-slu | LayerNorm | false | 17,142 | [
"Apache-2.0"
] | 7 | 6a1a6bf105f1a0c07e8d483aa6da1df7a554392d | https://github.com/CoraJung/flexible-input-slu/tree/6a1a6bf105f1a0c07e8d483aa6da1df7a554392d |
WithBall | import math
import torch
from torch import nn
def apply_last_dim(model, x):
size = list(x.size())
y = model(x.contiguous().view(-1, size[-1]))
size[-1] = y.size(-1)
y = y.view(torch.Size(size))
return y
def get_int_dim_index(name):
if isinstance(name, int):
return name
name_list ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math
from torch import nn
assert_size_stride = torch._C... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | WithBall | false | 17,143 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
SoftLarge | import math
import torch
from torch import nn
class SoftCompare(nn.Module):
def __init__(self, alpha=None, beta=None):
super().__init__()
self.alpha = nn.Parameter(torch.ones(1) * (0 if alpha is None else
alpha), requires_grad=True)
self.beta = nn.Parameter(torch.ones(1) * (0 ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
from torch import nn
assert_size_stride = torch._C._dynamo.gu... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | SoftLarge | false | 17,144 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
TernaryLinear | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import init
class Ternary(nn.Module):
"""
Ternarize the input activations to -1, 0, 1.
"""
def __init__(self, left=-0.25, right=0.25):
super().__init__()
self.left = left
self.right = right
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 import nn
from torch.nn import init
assert_size_stride = torch._C._dy... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | TernaryLinear | false | 17,145 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
FinalPool | import torch
import torch.utils.data
class FinalPool(torch.nn.Module):
def __init__(self):
super(FinalPool, self).__init__()
def forward(self, input):
"""
input : Tensor of shape (batch size, T, Cin)
Outputs a Tensor of shape (batch size, Cin).
"""
return input.max(dim=1)[0]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | CoraJung/flexible-input-slu | FinalPool | false | 17,146 | [
"Apache-2.0"
] | 7 | 6a1a6bf105f1a0c07e8d483aa6da1df7a554392d | https://github.com/CoraJung/flexible-input-slu/tree/6a1a6bf105f1a0c07e8d483aa6da1df7a554392d |
BinaryPrimitivesPredefined_v2 | import math
import torch
from torch import nn
def apply_last_dim(model, x):
size = list(x.size())
y = model(x.contiguous().view(-1, size[-1]))
size[-1] = y.size(-1)
y = y.view(torch.Size(size))
return y
def get_int_dim_index(name):
if isinstance(name, int):
return name
name_list ... | 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 math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.a... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | BinaryPrimitivesPredefined_v2 | false | 17,147 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
BinaryPrimitivesPredefined | import math
import torch
from torch import nn
def apply_last_dim(model, x):
size = list(x.size())
y = model(x.contiguous().view(-1, size[-1]))
size[-1] = y.size(-1)
y = y.view(torch.Size(size))
return y
def get_int_dim_index(name):
if isinstance(name, int):
return name
name_list ... | 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 math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.a... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | BinaryPrimitivesPredefined | false | 17,148 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
Conv3BN | import torch
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
def conv3x3(in_, out):
return nn.Conv2d(in_, out, 3, padding=1)
class Conv3BN(nn.Module):
def __init__(self, in_: 'int', out: 'int', bn=False):
super().__init__()
self.conv = conv3x3(in_, out)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | CalebEverett/fastai-dl2 | Conv3BN | false | 17,149 | [
"Apache-2.0"
] | 4 | 64d23592eddca6ca1f3647e73c319e97c8eb392b | https://github.com/CalebEverett/fastai-dl2/tree/64d23592eddca6ca1f3647e73c319e97c8eb392b |
MatrixTree | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class MatrixTree(nn.Module):
"""Implementation of the matrix-tree theorem for computing marginals
of non-projective dependency parsing. This attention layer is used
in the paper "Learning Structured Text Representations"
:ci... | 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
import torch.cuda
import torch.distributed
assert_s... | ChenRocks/Distill-BERT-Textgen-ONMT | MatrixTree | false | 17,150 | [
"MIT"
] | 7 | d83dd1a95af7513cbfae4a2768f6effc2f3a589f | https://github.com/ChenRocks/Distill-BERT-Textgen-ONMT/tree/d83dd1a95af7513cbfae4a2768f6effc2f3a589f |
NullaryPrimitivesPredefined_v2 | import math
import torch
from torch import nn
class Normalize(nn.Module):
def __init__(self, distribution=None, **kwargs):
super().__init__()
self.distribution = distribution
self.data_ = []
if distribution is None:
pass
elif distribution == 'normal':
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.a... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | NullaryPrimitivesPredefined_v2 | false | 17,151 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
MaxPoolPad | import torch
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
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)
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
assert_size_str... | CalebEverett/fastai-dl2 | MaxPoolPad | false | 17,152 | [
"Apache-2.0"
] | 4 | 64d23592eddca6ca1f3647e73c319e97c8eb392b | https://github.com/CalebEverett/fastai-dl2/tree/64d23592eddca6ca1f3647e73c319e97c8eb392b |
MaxPool | import torch
import torch.nn as nn
import torch.utils.data
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_pad else None
self.pool = nn.MaxPool2d(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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | BigFishMaster/tnt | MaxPool | false | 17,153 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 |
UnaryPrimitivesToyotaJoint | import math
import torch
from torch import nn
class Normalize(nn.Module):
def __init__(self, distribution=None, **kwargs):
super().__init__()
self.distribution = distribution
self.data_ = []
if distribution is None:
pass
elif distribution == 'normal':
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guard... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | UnaryPrimitivesToyotaJoint | false | 17,154 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
MaxPoolPad | import torch
import torch.nn as nn
import torch.utils.data
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)
def forward(self, x):
x = self.pad(x)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | BigFishMaster/tnt | MaxPoolPad | false | 17,155 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 |
SpaceTimeRegionalConv | import torch
from torch import nn
class SpaceTimeRegionalConv(nn.Module):
"""
Space Time Region Graph
"""
def __init__(self, input_dim, output_dim, t_kernel_size=1, t_stride=1,
t_padding=None, t_dilation=1, bias=True, residual=True):
super().__init__()
self.input_dim = input_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._inductor.runtime import triton_helpers
from torch import nn
assert_s... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | SpaceTimeRegionalConv | false | 17,156 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
Sigmoid | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import Sigmoid
class Sigmoid(nn.Module):
def __init__(self, inplace: 'bool'=False):
super(Sigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return x.sigmoid_() if self.inplace else x.sigmoid... | 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.... | BigFishMaster/tnt | Sigmoid | false | 17,157 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 |
AvgPoolPad | import torch
import torch.nn as nn
import torch.utils.data
class AvgPoolPad(nn.Module):
def __init__(self, stride=2, padding=1):
super(AvgPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.AvgPool2d(3, stride=stride, padding=padding,
count_include_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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | BigFishMaster/tnt | AvgPoolPad | false | 17,158 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 |
Tanh | import torch
import torch.nn as nn
import torch.utils.data
class Tanh(nn.Module):
def __init__(self, inplace: 'bool'=False):
super(Tanh, self).__init__()
self.inplace = inplace
def forward(self, x):
return x.tanh_() if self.inplace else x.tanh()
def get_inputs():
return [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.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | BigFishMaster/tnt | Tanh | false | 17,159 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 |
ValueNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
def mish(x):
"""
Mish: A Self Regularized Non-Monotonic Neural Activation Function
https://arxiv.org/abs/1908.08681v1
implemented for PyTorch / FastAI by lessw2020
https://github.com/lessw2020/mish
param:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Crawford-fang/ROS_pytorch_RL | ValueNetwork | false | 17,160 | [
"Apache-2.0"
] | 10 | 2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f | https://github.com/Crawford-fang/ROS_pytorch_RL/tree/2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f |
AdaptiveConcatPool2d | import torch
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
class AdaptiveConcatPool2d(nn.Module):
def __init__(self, sz=None):
super().__init__()
sz = sz or (1, 1)
self.ap = nn.AdaptiveAvgPool2d(sz)
self.mp = nn.AdaptiveMaxPool2d(sz)
def forward(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
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
assert_size_str... | CalebEverett/fastai-dl2 | AdaptiveConcatPool2d | false | 17,161 | [
"Apache-2.0"
] | 4 | 64d23592eddca6ca1f3647e73c319e97c8eb392b | https://github.com/CalebEverett/fastai-dl2/tree/64d23592eddca6ca1f3647e73c319e97c8eb392b |
SpatialCrossMapLRN | import torch
import torch.nn as nn
import torch.utils.data
class SpatialCrossMapLRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1,
ACROSS_CHANNELS=True):
super(SpatialCrossMapLRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | BigFishMaster/tnt | SpatialCrossMapLRN | false | 17,162 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 |
MLP | import torch
from torch import nn
from torch.nn import functional as F
class MLP(nn.Module):
def __init__(self, input_dim, output_dim, dropout=0.5):
super(MLP, self).__init__()
self.input_fc = nn.Linear(input_dim, 250)
self.hidden_fc = nn.Linear(250, 100)
self.output_fc = 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 import nn
assert_s... | CrispenGari/pneumonia-infection | MLP | false | 17,163 | [
"MIT"
] | 4 | 8d1fc5f61aa8c4eb06d640e6da5abbbe23ccb85e | https://github.com/CrispenGari/pneumonia-infection/tree/8d1fc5f61aa8c4eb06d640e6da5abbbe23ccb85e |
OutConv_Sigmoid | import torch
import torch.nn as nn
class OutConv_Sigmoid(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutConv_Sigmoid, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
return... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Curli-quan/oneshot-medical-landmark | OutConv_Sigmoid | false | 17,164 | [
"Apache-2.0"
] | 7 | 572926077fffbe9832aa16baa98bd046ec326700 | https://github.com/Curli-quan/oneshot-medical-landmark/tree/572926077fffbe9832aa16baa98bd046ec326700 |
QNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class QNetwork(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Crawford-fang/ROS_pytorch_RL | QNetwork | false | 17,165 | [
"Apache-2.0"
] | 10 | 2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f | https://github.com/Crawford-fang/ROS_pytorch_RL/tree/2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f |
LayerNormalization | import torch
import torch.nn as nn
class LayerNormalization(nn.Module):
def __init__(self, normal_shape, gamma=True, beta=True, epsilon=1e-10):
"""Layer normalization layer
See: [Layer Normalization](https://arxiv.org/pdf/1607.06450.pdf)
:param normal_shape: The shape of the input tenso... | 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_... | CyberZHG/torch-layer-normalization | LayerNormalization | false | 17,166 | [
"MIT"
] | 9 | 89f405b60f53f85da6f03fe685c190ef394ce50c | https://github.com/CyberZHG/torch-layer-normalization/tree/89f405b60f53f85da6f03fe685c190ef394ce50c |
DQN_hot2 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_hot2(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self, m, n, num_actions):
super(DQN_hot2, self).__init__()
self.fc1 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | CoAxLab/azad | DQN_hot2 | false | 17,167 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
PolicyNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.distributions import Normal
def mish(x):
"""
Mish: A Self Regularized Non-Monotonic Neural Activation Function
https://arxiv.org/abs/1908.08681v1
implemented for PyTorch / FastAI by lessw2020
https://gith... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Crawford-fang/ROS_pytorch_RL | PolicyNetwork | false | 17,168 | [
"Apache-2.0"
] | 10 | 2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f | https://github.com/Crawford-fang/ROS_pytorch_RL/tree/2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f |
UnaryPrimitivesPredefined_v2 | import math
import torch
from torch import nn
def apply_last_dim(model, x):
size = list(x.size())
y = model(x.contiguous().view(-1, size[-1]))
size[-1] = y.size(-1)
y = y.view(torch.Size(size))
return y
def get_int_dim_index(name):
if isinstance(name, int):
return name
name_list ... | 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 math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.a... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | UnaryPrimitivesPredefined_v2 | false | 17,169 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
SELayer | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch.backends.cudnn
class SELayer(nn.Module):
def __init__(self, in_channels, reduction):
super(SELayer, self).__init__()
mid_channels = in_channels // reduction
self.fc1 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | CrazyStoneonRoad/pytorch_image_classification | SELayer | false | 17,170 | [
"MIT"
] | 4 | 1dcf6d0ee8f4a102ca93cc6e5e325a2e9153918b | https://github.com/CrazyStoneonRoad/pytorch_image_classification/tree/1dcf6d0ee8f4a102ca93cc6e5e325a2e9153918b |
MultiHeadAttention | import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
"""
input:
query --- [N, T_q, query_dim]
key --- [N, T_k, key_dim]
output:
out --- [N, T_q, num_units]
"""
def __init__(self, query_dim, key_dim, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | CookiePPP/pag-tacotron2 | MultiHeadAttention | false | 17,171 | [
"BSD-3-Clause"
] | 10 | 503e7e9e892c5c0795f6278e70e72b627ed1cfb7 | https://github.com/CookiePPP/pag-tacotron2/tree/503e7e9e892c5c0795f6278e70e72b627ed1cfb7 |
GCN | import torch
import torch.nn as nn
class GCN(nn.Module):
def __init__(self, in_ft, out_ft, act, bias=True):
super(GCN, self).__init__()
self.fc = nn.Linear(in_ft, out_ft, bias=False)
self.act = nn.PReLU() if act == 'prelu' else act
if bias:
self.bias = nn.Parameter(tor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | CrowdDynamicsLab/InfoMotif | GCN | false | 17,172 | [
"BSD-3-Clause"
] | 7 | cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b | https://github.com/CrowdDynamicsLab/InfoMotif/tree/cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b |
DQN_hot4 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_hot4(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self, m, n, num_actions):
super(DQN_hot4, self).__init__()
self.fc1 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | CoAxLab/azad | DQN_hot4 | false | 17,173 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
DQN_hot1 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_hot1(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self, m, n, num_actions):
super(DQN_hot1, self).__init__()
self.fc1 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | CoAxLab/azad | DQN_hot1 | false | 17,174 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
SoftQNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
def mish(x):
"""
Mish: A Self Regularized Non-Monotonic Neural Activation Function
https://arxiv.org/abs/1908.08681v1
implemented for PyTorch / FastAI by lessw2020
https://github.com/lessw2020/mish
param:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Crawford-fang/ROS_pytorch_RL | SoftQNetwork | false | 17,175 | [
"Apache-2.0"
] | 10 | 2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f | https://github.com/Crawford-fang/ROS_pytorch_RL/tree/2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f |
DQN_hot3 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_hot3(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self, m, n, num_actions):
super(DQN_hot3, self).__init__()
self.fc1 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | CoAxLab/azad | DQN_hot3 | false | 17,176 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
Encoder_attention | import torch
import torch.nn as nn
class Encoder_attention(nn.Module):
def __init__(self, n_h):
super(Encoder_attention, self).__init__()
self.linear = nn.Linear(n_h, 1)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
"""Output: X """
x1 = self.linear(x).squeez... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | CrowdDynamicsLab/InfoMotif | Encoder_attention | false | 17,177 | [
"BSD-3-Clause"
] | 7 | cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b | https://github.com/CrowdDynamicsLab/InfoMotif/tree/cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b |
DQN_xy3 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_xy3(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self):
super(DQN_xy3, self).__init__()
self.fc1 = nn.Linear(4, 10)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | CoAxLab/azad | DQN_xy3 | false | 17,178 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
BPR | import torch
class BPR(torch.nn.Module):
def __init__(self):
super(BPR, self).__init__()
self._sigmoid = torch.nn.Sigmoid()
def forward(self, pos, neg):
loss = torch.log(self._sigmoid(pos.double() - neg.double()))
return -loss.mean()
def get_inputs():
return [torch.rand... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | DanielMorales9/FactorizationPyTorch | BPR | false | 17,179 | [
"MIT"
] | 4 | 50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 | https://github.com/DanielMorales9/FactorizationPyTorch/tree/50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 |
UpsampleConvLayer | import torch
from torch import nn
import torch.nn.functional as f
import torch.nn.parallel
class UpsampleConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, activation='relu', norm=None):
super(UpsampleConvLayer, self).__init__()
bias = Fa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | DA4EVENT/home | UpsampleConvLayer | false | 17,180 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 |
DeepTable3 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DeepTable3(nn.Module):
"""A deep differentialable 'Table' for learning one-hot input and output.
"""
def __init__(self, in_channels, out_channels, num_hidden1=200,
num_hidden2=100):
super(Deep... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | CoAxLab/azad | DeepTable3 | false | 17,181 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
BinaryPrimitivesSomethingElse | import math
import torch
from torch import nn
def apply_last_dim(model, x):
size = list(x.size())
y = model(x.contiguous().view(-1, size[-1]))
size[-1] = y.size(-1)
y = y.view(torch.Size(size))
return y
def get_int_dim_index(name):
if isinstance(name, int):
return name
name_list ... | 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 math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.a... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | BinaryPrimitivesSomethingElse | false | 17,182 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
DQN_xy2 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_xy2(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self):
super(DQN_xy2, self).__init__()
self.fc1 = nn.Linear(4, 100)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | CoAxLab/azad | DQN_xy2 | false | 17,183 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
MMDLoss | import torch
from torch import nn
import torch.nn.parallel
def gaussian_kernel(source, target, kernel_mul=2.0, kernel_num=5, fix_sigma
=None):
n_samples = int(source.size()[0]) + int(target.size()[0])
total = torch.cat([source, target], dim=0)
total0 = total.unsqueeze(0).expand(int(total.size(0)), int... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.nn.parallel
assert_size_stride = torch.... | DA4EVENT/home | MMDLoss | false | 17,184 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 |
TransposedConvLayer | import torch
from torch import nn
import torch.nn.parallel
class TransposedConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, activation='relu', norm=None):
super(TransposedConvLayer, self).__init__()
bias = False if norm == 'BN' else Tru... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | DA4EVENT/home | TransposedConvLayer | false | 17,185 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 |
ConvLayer | import torch
from torch import nn
import torch.nn.parallel
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, activation='relu', norm=None):
super(ConvLayer, self).__init__()
bias = False if norm == 'BN' else True
self.conv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | DA4EVENT/home | ConvLayer | false | 17,186 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 |
DQN_xy4 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_xy4(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self):
super(DQN_xy4, self).__init__()
self.fc1 = nn.Linear(4, 100)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | CoAxLab/azad | DQN_xy4 | false | 17,187 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
DQN_hot5 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_hot5(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
Params
------
m, n: int
Board size
num_actions: int
Number of action-valu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | CoAxLab/azad | DQN_hot5 | false | 17,188 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
DQN_xy5 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_xy5(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
Params
------
m, n: int
Board size
num_actions: int
Number of action-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
import torch.nn as nn
import ... | CoAxLab/azad | DQN_xy5 | false | 17,189 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
DQN_xy1 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_xy1(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a (x,y) coordinate board/action representation.
"""
def __init__(self):
super(DQN_xy1, self).__init__()
self.fc1 = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | CoAxLab/azad | DQN_xy1 | false | 17,190 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
down | import torch
from torch import nn
from torch.nn import functional as F
import torch.nn.parallel
class down(nn.Module):
"""
A class for creating neural network blocks containing layers:
Average Pooling --> Convlution + Leaky ReLU --> Convolution + Leaky ReLU
This is used in the UNet Class to create 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 import nn
import torch.nn.parallel
assert_size_stride = torch._C._dyn... | DA4EVENT/home | down | false | 17,191 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 |
MaskUpdate | import torch
from torch import nn
from torch.nn.parameter import Parameter
class MaskUpdate(nn.Module):
def __init__(self, alpha):
super(MaskUpdate, self).__init__()
self.updateFunc = nn.ReLU(False)
self.alpha = Parameter(torch.tensor(alpha, dtype=torch.float32))
def forward(self, in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | DLwbm123/LBAM_inpainting | MaskUpdate | false | 17,192 | [
"MIT"
] | 7 | c809c3cedf09cda7c175e930c7834ac39d8f526f | https://github.com/DLwbm123/LBAM_inpainting/tree/c809c3cedf09cda7c175e930c7834ac39d8f526f |
ResidualBlock | import torch
from torch import nn
import torch.nn.parallel
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, downsample=None,
norm=None):
super(ResidualBlock, self).__init__()
bias = False if norm == 'BN' else True
self.conv1 = nn.Conv2d(in_ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | DA4EVENT/home | ResidualBlock | false | 17,193 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 |
DQN_mlp | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_mlp(nn.Module):
"""Layers for a Deep Q Network, based on a simple MLP."""
def __init__(self, m, n, num_actions, num_hidden1=1000, num_hidden2=2000):
super(DQN_mlp, self).__init__()
self.m = 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
import torch.nn as nn
import ... | CoAxLab/azad | DQN_mlp | false | 17,194 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
Attention | import torch
import torch.nn as nn
class Attention(nn.Module):
def __init__(self, n_h):
super(Attention, self).__init__()
self.linear = nn.Linear(n_h * 2, 1)
self.softmax = nn.Softmax(dim=2)
def forward(self, x):
curr_node = x[:, :, 0, :].unsqueeze(2).expand_as(x)
sta... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | CrowdDynamicsLab/InfoMotif | Attention | false | 17,195 | [
"BSD-3-Clause"
] | 7 | cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b | https://github.com/CrowdDynamicsLab/InfoMotif/tree/cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b |
MCDO | import torch
from torch import nn
class MCDO(nn.Module):
def __init__(self, in_dim, out_dim, n_layers=1, hid_dim=50, p=0.05):
super().__init__()
self.n_layers = n_layers
self.linear_in = nn.Linear(in_dim, hid_dim)
nn.init.normal_(self.linear_in.weight, std=1 / (4 * hid_dim) ** 0.5... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Daniil-Selikhanovych/bnn-vi | MCDO | false | 17,196 | [
"MIT"
] | 3 | 6788edc1438c66609abca249e33a81da7a0ff1a2 | https://github.com/Daniil-Selikhanovych/bnn-vi/tree/6788edc1438c66609abca249e33a81da7a0ff1a2 |
GaussActivation | import torch
from torch import nn
from torch.nn.parameter import Parameter
class GaussActivation(nn.Module):
def __init__(self, a, mu, sigma1, sigma2):
super(GaussActivation, self).__init__()
self.a = Parameter(torch.tensor(a, dtype=torch.float32))
self.mu = Parameter(torch.tensor(mu, dty... | 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
f... | DLwbm123/LBAM_inpainting | GaussActivation | false | 17,197 | [
"MIT"
] | 7 | c809c3cedf09cda7c175e930c7834ac39d8f526f | https://github.com/DLwbm123/LBAM_inpainting/tree/c809c3cedf09cda7c175e930c7834ac39d8f526f |
FactorizationMachine | from torch.nn import Module
import torch
from torch import FloatTensor
from torch.nn import Parameter
class SecondOrderInteraction(Module):
"""
Factorized parameters for the Second Order Interactions
Parameters
----------
n_features: int
Length of the input vector.
n_factors: int, opt... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import FloatTensor
from torch.nn import P... | DanielMorales9/FactorizationPyTorch | FactorizationMachine | false | 17,198 | [
"MIT"
] | 4 | 50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 | https://github.com/DanielMorales9/FactorizationPyTorch/tree/50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 |
GEGLU | import torch
import torch.nn as nn
import torch.nn.functional as F
class GEGLU(nn.Module):
def forward(self, x):
x, gate = x.chunk(2, dim=-1)
return F.gelu(gate) * 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | DannielSilva/MMBERT | GEGLU | false | 17,199 | [
"MIT"
] | 4 | 2c9069b59b66b8f3fec6de2e68ec42b489a3a437 | https://github.com/DannielSilva/MMBERT/tree/2c9069b59b66b8f3fec6de2e68ec42b489a3a437 |
SecondOrderInteraction | from torch.nn import Module
import torch
from torch.nn import Parameter
class SecondOrderInteraction(Module):
"""
Factorized parameters for the Second Order Interactions
Parameters
----------
n_features: int
Length of the input vector.
n_factors: int, optional
Number of factor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch.nn import Parameter
assert_size_stride = ... | DanielMorales9/FactorizationPyTorch | SecondOrderInteraction | false | 17,200 | [
"MIT"
] | 4 | 50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 | https://github.com/DanielMorales9/FactorizationPyTorch/tree/50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 |
GatedConv2d | import torch
import torch.nn as nn
import torch.utils.data
class GatedConv2d(nn.Module):
def __init__(self, input_channels, output_channels, kernel_size, stride,
padding, dilation=1, activation=None):
super(GatedConv2d, self).__init__()
self.activation = activation
self.sigmoid = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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._dyn... | Daulbaev/IRDM | GatedConv2d | false | 17,201 | [
"MIT"
] | 10 | 4bb60191ac0072e4349ca47092675d06b39a979a | https://github.com/Daulbaev/IRDM/tree/4bb60191ac0072e4349ca47092675d06b39a979a |
SERF | import torch
import torch.nn as nn
class SERF(nn.Module):
def __init__(self, thresh=50):
super().__init__()
self.thresh = thresh
None
def forward(self, x):
return self.serf_log1pexp(x)
def serf(self, x):
return x * torch.erf(torch.log(1 + torch.exp(x)))
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | DannielSilva/MMBERT | SERF | false | 17,202 | [
"MIT"
] | 4 | 2c9069b59b66b8f3fec6de2e68ec42b489a3a437 | https://github.com/DannielSilva/MMBERT/tree/2c9069b59b66b8f3fec6de2e68ec42b489a3a437 |
BCEWithLogitsWithClassWeightLoss | import torch
from torch import Tensor
from typing import NoReturn
from torch import nn
class BCEWithLogitsWithClassWeightLoss(nn.BCEWithLogitsLoss):
""" finished, checked,
"""
__name__ = 'BCEWithLogitsWithClassWeightsLoss'
def __init__(self, class_weight: 'Tensor') ->NoReturn:
""" finished, c... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | DeepPSP/torch_ecg | BCEWithLogitsWithClassWeightLoss | false | 17,203 | [
"MIT"
] | 9 | 6db5ffb063d0e8fb4ce97029a0d184a658f43a37 | https://github.com/DeepPSP/torch_ecg/tree/6db5ffb063d0e8fb4ce97029a0d184a658f43a37 |
MLP | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
def create_all_possible_moves(m, n):
"""Create all moves on a (m,n) board."""
moves = []
for i in range(m):
for j in range(n):
moves.append((i, j))
return list(set(moves))
class MLP(nn.Modu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | CoAxLab/azad | MLP | false | 17,204 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
PatchEmbedding | import torch
import torch.nn as nn
class PatchEmbedding(nn.Module):
def __init__(self, image_size, patch_size, embed_dim, channels):
super().__init__()
self.image_size = image_size
if image_size[0] % patch_size != 0 or image_size[1] % patch_size != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Curli-quan/fewshot-select | PatchEmbedding | false | 17,205 | [
"Apache-2.0"
] | 7 | 34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe | https://github.com/Curli-quan/fewshot-select/tree/34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe |
h_swish | import torch
import torch.nn as nn
import torch.nn.functional as F
class h_swish(nn.Module):
def __init__(self, inplace=True):
super(h_swish, self).__init__()
self.inplace = inplace
def forward(self, x):
out = F.relu6(x + 3.0, inplace=self.inplace) / 6.0
return out * x
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | DandelionLau/NetworkCollections | h_swish | false | 17,206 | [
"Apache-2.0"
] | 8 | 29e5cd2091f7085b3241209ed9447f2baadbce41 | https://github.com/DandelionLau/NetworkCollections/tree/29e5cd2091f7085b3241209ed9447f2baadbce41 |
Net1 | import torch
import torch.nn.functional as F
from torch import nn
class Net1(nn.Module):
def __init__(self):
super(Net1, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=0)
self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
self.conv2 = nn.Conv2d(64,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | DPBayes/DP-cross-silo-federated-learning | Net1 | false | 17,207 | [
"Apache-2.0"
] | 8 | 6707db703de5fae48c06116ae8ceee0685c9615d | https://github.com/DPBayes/DP-cross-silo-federated-learning/tree/6707db703de5fae48c06116ae8ceee0685c9615d |
Upsample | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch._utils
class Upsample(nn.Module):
def __init__(self, stride=2):
super(Upsample, self).__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert ... | 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._utils
assert_size_stride = torch._C._dynamo.... | DatatangAILAB/SuanFaShiXun04 | Upsample | false | 17,209 | [
"Apache-2.0"
] | 5 | f478e40dd84240ac71cbb54e6bacf9ff556fbb3e | https://github.com/DatatangAILAB/SuanFaShiXun04/tree/f478e40dd84240ac71cbb54e6bacf9ff556fbb3e |
PatchEmbed | import torch
import torch.nn as nn
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
num_patches = img_size // patch_size * (img_size // patch_size)
self.img_size = img_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Curli-quan/fewshot-select | PatchEmbed | false | 17,210 | [
"Apache-2.0"
] | 7 | 34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe | https://github.com/Curli-quan/fewshot-select/tree/34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe |
ExtResNetBlock | import torch
from torch.nn import functional as F
import torch.nn as nn
def padding(im, patch_size, fill_value=0):
H, W = im.size(2), im.size(3)
pad_h, pad_w = 0, 0
if H % patch_size > 0:
pad_h = patch_size - H % patch_size
if W % patch_size > 0:
pad_w = patch_size - W % patch_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | Curli-quan/fewshot-select | ExtResNetBlock | false | 17,211 | [
"Apache-2.0"
] | 7 | 34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe | https://github.com/Curli-quan/fewshot-select/tree/34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe |
HardSwish | import torch
import torch.nn as nn
class HardSwish(nn.Module):
def __init__(self, inplace=False):
super(HardSwish, self).__init__()
self.act = nn.ReLU6(inplace)
"""forward"""
def forward(self, x):
return x * self.act(x + 3) / 6
def get_inputs():
return [torch.rand([4, 4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | DetectionBLWX/WSDDN.pytorch | HardSwish | false | 17,212 | [
"MIT"
] | 7 | 05020d9d0445af90ba0af3f095aa12b18e3da7d2 | https://github.com/DetectionBLWX/WSDDN.pytorch/tree/05020d9d0445af90ba0af3f095aa12b18e3da7d2 |
ReverseMaskConv | import math
import torch
from torch import nn
from torch.nn.parameter import Parameter
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasattr(m, 'weight'):
if ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | DLwbm123/LBAM_inpainting | ReverseMaskConv | false | 17,213 | [
"MIT"
] | 7 | c809c3cedf09cda7c175e930c7834ac39d8f526f | https://github.com/DLwbm123/LBAM_inpainting/tree/c809c3cedf09cda7c175e930c7834ac39d8f526f |
ReOrgLayer | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch._utils
class ReOrgLayer(nn.Module):
def __init__(self, stride=2):
super(ReOrgLayer, self).__init__()
self.stride = stride
def forward(self, x):
assert x.data.dim() == 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
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch._utils
assert_size_stride = torch._C._dynamo.... | DatatangAILAB/SuanFaShiXun04 | ReOrgLayer | false | 17,214 | [
"Apache-2.0"
] | 5 | f478e40dd84240ac71cbb54e6bacf9ff556fbb3e | https://github.com/DatatangAILAB/SuanFaShiXun04/tree/f478e40dd84240ac71cbb54e6bacf9ff556fbb3e |
Net | import torch
import torch.nn.functional as F
import torch.nn as nn
class Net(nn.Module):
def __init__(self, x_d, w_d, out_d, hidden_d1=256, hidden_d2=512,
hidden_d3=256, is_discrete_input=False, is_discrete_output=False,
embedding_dim=None):
super().__init__()
self._x_d = x_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._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | DataCanvasIO/YLearn | Net | false | 17,215 | [
"Apache-2.0"
] | 3 | d65b5afb83deed154c710de9096317165d95014a | https://github.com/DataCanvasIO/YLearn/tree/d65b5afb83deed154c710de9096317165d95014a |
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