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 |
|---|---|---|---|---|---|---|---|---|---|---|
InverseDepthSmoothnessLoss | import torch
import torch.nn as nn
def _gradient_x(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :, :-1] - img[:, :, :, 1:]
def _gradient_y(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :-1, :] - img[:, :, 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.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | JoanFM/kornia | InverseDepthSmoothnessLoss | false | 11,551 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
RgbaToBgr | import torch
import torch.nn as nn
def bgr_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
"""Convert a BGR image to RGB.
Args:
image: BGR Image to be converted to BGR of shape :math:`(*,3,H,W)`.
Returns:
RGB version of the image with shape of shape :math:`(*,3,H,W)`.
Example:
... | 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... | JoanFM/kornia | RgbaToBgr | false | 11,552 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
Encoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self, out_dim=64):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=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_... | JanSoltysik/SimCLR | Encoder | false | 11,553 | [
"MIT"
] | 0 | 34ea6d17a630382b65a00aa445d82876754ee679 | https://github.com/JanSoltysik/SimCLR/tree/34ea6d17a630382b65a00aa445d82876754ee679 |
InvDepth | import torch
import torch.nn as nn
class InvDepth(nn.Module):
def __init__(self, height, width, min_depth=0.5, max_depth=25.0):
super(InvDepth, self).__init__()
self._min_range = 1.0 / max_depth
self._max_range = 1.0 / min_depth
self.w = nn.Parameter(self._init_weights(height, wid... | 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... | JoanFM/kornia | InvDepth | false | 11,554 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
PoseNetFeat | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
class PoseNetFeat(nn.Module):
def __init__(self, num_points):
super(PoseNetFeat, self).__init__()
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.conv2 = torch.nn.Conv1d(64, 128... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | JiazeWang/6-PACK | PoseNetFeat | false | 11,555 | [
"MIT"
] | 0 | bce910213cfbf89b4ed7b59ff6c70a59a7c19b99 | https://github.com/JiazeWang/6-PACK/tree/bce910213cfbf89b4ed7b59ff6c70a59a7c19b99 |
Hflip | import torch
import torch.nn as nn
def hflip(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-1])
class Hflip(nn.Module):
"""Horizontally flip a tensor image or a batch of tensor images. Input must
be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
... | 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... | JoanFM/kornia | Hflip | false | 11,556 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
BinaryFocalLossWithLogits | import torch
import torch.nn as nn
def binary_focal_loss_with_logits(input: 'torch.Tensor', target:
'torch.Tensor', alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
'str'='none', eps: 'float'=1e-08) ->torch.Tensor:
"""Function that computes Binary Focal loss.
.. math::
\\text{FL}(p_t) = -... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | JoanFM/kornia | BinaryFocalLossWithLogits | false | 11,557 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
TotalVariation | import torch
import torch.nn as nn
def total_variation(img: 'torch.Tensor') ->torch.Tensor:
"""Function that computes Total Variation according to [1].
Args:
img (torch.Tensor): the input image with shape :math:`(N, C, H, W)` or :math:`(C, H, W)`.
Return:
torch.Tensor: a scalar with the ... | 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... | JoanFM/kornia | TotalVariation | false | 11,558 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
Vflip | import torch
import torch.nn as nn
def vflip(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-2])
class Vflip(nn.Module):
"""Vertically flip a tensor image or a batch of tensor images. Input must
be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
... | 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... | JoanFM/kornia | Vflip | false | 11,559 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
LinearSum | import torch
import torch.nn as nn
import torch.nn.functional as F
class LinearSum(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, activ_input=
'relu', activ_output='relu', normalize=False, dropout_input=0.0,
dropout_pre_lin=0.0, dropout_output=0.0):
super(LinearSum, 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
assert_... | JoannaLXY/block.bootstrap.pytorch | LinearSum | false | 11,560 | [
"BSD-3-Clause"
] | 0 | 42c3e7616b704e05c6ff2376ff68b5b18044fe77 | https://github.com/JoannaLXY/block.bootstrap.pytorch/tree/42c3e7616b704e05c6ff2376ff68b5b18044fe77 |
MFB | import torch
import torch.nn as nn
import torch.nn.functional as F
class MFB(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
activ_input='relu', activ_output='relu', normalize=False,
dropout_input=0.0, dropout_pre_norm=0.0, dropout_output=0.0):
super(MFB, 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.nn as nn
assert_... | JoannaLXY/block.bootstrap.pytorch | MFB | false | 11,561 | [
"BSD-3-Clause"
] | 0 | 42c3e7616b704e05c6ff2376ff68b5b18044fe77 | https://github.com/JoannaLXY/block.bootstrap.pytorch/tree/42c3e7616b704e05c6ff2376ff68b5b18044fe77 |
MFH | import torch
import torch.nn as nn
import torch.nn.functional as F
class MFH(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
activ_input='relu', activ_output='relu', normalize=False,
dropout_input=0.0, dropout_pre_lin=0.0, dropout_output=0.0):
super(MFH, 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
import torch.nn as nn
assert_... | JoannaLXY/block.bootstrap.pytorch | MFH | false | 11,562 | [
"BSD-3-Clause"
] | 0 | 42c3e7616b704e05c6ff2376ff68b5b18044fe77 | https://github.com/JoannaLXY/block.bootstrap.pytorch/tree/42c3e7616b704e05c6ff2376ff68b5b18044fe77 |
BinaryExpAbs | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import abc
import inspect
import warnings
import torch.nn as nn
import to... | Johnsonms/NNI_master | BinaryExpAbs | false | 11,563 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
BinaryMul | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryMul | false | 11,564 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
NetVLAD | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from sklearn.neighbors import NearestNeighbors
class NetVLAD(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, num_clusters=64, dim=128, normalize_input=True,
vladv2=False):
"""
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.... | AlessandroRigoli/project_vg | NetVLAD | false | 11,565 | [
"MIT"
] | 0 | cb1323bee60cdb4108fe0aab68791321c7974832 | https://github.com/AlessandroRigoli/project_vg/tree/cb1323bee60cdb4108fe0aab68791321c7974832 |
Block | import torch
from torch import nn
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,
the original name is ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | JetRunner/PaSST-EE | Block | false | 11,566 | [
"Apache-2.0"
] | 0 | 2ff8f4fd0e9c1868856d08147e6e3cf1c1bed68c | https://github.com/JetRunner/PaSST-EE/tree/2ff8f4fd0e9c1868856d08147e6e3cf1c1bed68c |
BinarySigmoid | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinarySigmoid | false | 11,567 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
MLB | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLB(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, activ_input=
'relu', activ_output='relu', normalize=False, dropout_input=0.0,
dropout_pre_lin=0.0, dropout_output=0.0):
super(MLB, self).__init_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | JoannaLXY/block.bootstrap.pytorch | MLB | false | 11,568 | [
"BSD-3-Clause"
] | 0 | 42c3e7616b704e05c6ff2376ff68b5b18044fe77 | https://github.com/JoannaLXY/block.bootstrap.pytorch/tree/42c3e7616b704e05c6ff2376ff68b5b18044fe77 |
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 ... | JerryYLi/SlowFast | PatchEmbed | false | 11,569 | [
"Apache-2.0"
] | 0 | 70bbd8d917c49f86b41fdd7c2de5c1231e6d950c | https://github.com/JerryYLi/SlowFast/tree/70bbd8d917c49f86b41fdd7c2de5c1231e6d950c |
BinaryDivide | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryDivide | false | 11,570 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
BinaryMinus | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryMinus | false | 11,571 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
BinaryMin | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
... | Johnsonms/NNI_master | BinaryMin | false | 11,572 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
BinaryParamAdd | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryParamAdd | false | 11,573 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
BinaryMax | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
... | Johnsonms/NNI_master | BinaryMax | false | 11,574 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
DistillKL | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class DistillKL(nn.Module):
"""Distilling the Knowledge in a Neural Network"""
def __init__(self, T):
super(DistillKL, self).__init__()
s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Johnsonms/NNI_master | DistillKL | false | 11,575 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
PatchSequential | import math
import torch
import warnings
from typing import Dict
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import cast
from typing import List
from typing import Union
from torch.distributions import Bernoulli
from itertools import zip_longest... | 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
import warnings
from typing import Dict
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.... | JoanFM/kornia | PatchSequential | false | 11,576 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
LinearCombine | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class LinearCombine(nn.Module):
def __init__(self, layers_num, trainable=True, input_aware=False,
word_level=False):
super(LinearCombine, 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 math as tl_math
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import ... | Johnsonms/NNI_master | LinearCombine | false | 11,577 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
ResidualConvUnit | import torch
import torch.fft
import torch.nn as nn
import torch.utils.cpp_extension
class ResidualConvUnit(nn.Module):
def __init__(self, cin, activation, bn):
super().__init__()
self.conv = nn.Conv2d(cin, cin, kernel_size=3, stride=1, padding=1,
bias=True)
self.skip_add = 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
import torch.fft
import torch.nn as nn
import torch.utils.cpp_extension
assert_s... | CeciLyu/projected_gan | ResidualConvUnit | false | 11,578 | [
"MIT"
] | 0 | 5e86ee0c88d47164c30ede37448e7ba7f010fa7b | https://github.com/CeciLyu/projected_gan/tree/5e86ee0c88d47164c30ede37448e7ba7f010fa7b |
Pooling | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class ReLUConvBN(nn.Module):
"""
Parameters
---
C_in: int
the number of input channels
C_out: int
the number of output channels
stride: int
stride... | 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 typing import *
assert_size_stride = torch._C... | Johnsonms/NNI_master | Pooling | false | 11,579 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
Interpolate | import torch
import torch.fft
import torch.nn as nn
import torch.utils.cpp_extension
class Interpolate(nn.Module):
"""Interpolation module."""
def __init__(self, size, mode='bilinear', align_corners=False):
"""Init.
Args:
scale_factor (float): scaling
mode (str): inter... | 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.fft
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_strid... | CeciLyu/projected_gan | Interpolate | false | 11,580 | [
"MIT"
] | 0 | 5e86ee0c88d47164c30ede37448e7ba7f010fa7b | https://github.com/CeciLyu/projected_gan/tree/5e86ee0c88d47164c30ede37448e7ba7f010fa7b |
Mask | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class Mask(nn.Module):
def forward(self, seq, mask):
seq_mask = torch.unsqueeze(mask, 2)
seq_mask = torch.transpose(seq_mask.repeat(1, 1, seq.size()[1]), 1, 2)
retur... | 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 typing import *
assert_size_stride = torch._C... | Johnsonms/NNI_master | Mask | false | 11,581 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
MLP | import torch
import torch.nn as nn
class FC(nn.Module):
def __init__(self, in_size, out_size, dropout_r=0.0, use_relu=True):
super(FC, self).__init__()
self.dropout_r = dropout_r
self.use_relu = use_relu
self.linear = nn.Linear(in_size, out_size)
if use_relu:
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
assert_... | JoonseoKang/mcan-cap | MLP | false | 11,582 | [
"Apache-2.0"
] | 0 | 788e21fc1bc712018166aa44cc3298264f493f3b | https://github.com/JoonseoKang/mcan-cap/tree/788e21fc1bc712018166aa44cc3298264f493f3b |
InformedSender | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class InformedSender(nn.Module):
def __init__(self, game_size, feat_size, embedding_size, hidden_size,
vocab_size=100, temp=1.0):
super(InformedSender, se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | IA3005/NLP_ens | InformedSender | false | 11,583 | [
"MIT"
] | 0 | 794ebbff46d5e6d5476f29b577b40bbb52991246 | https://github.com/IA3005/NLP_ens/tree/794ebbff46d5e6d5476f29b577b40bbb52991246 |
BinaryExpSquare | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import abc
import inspect
import warnings
import torch.nn as nn
import to... | Johnsonms/NNI_master | BinaryExpSquare | false | 11,584 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
Hsigmoid | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class Hsigmoid(nn.Module):
"""Hsigmoid activation function."""
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
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
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data... | Johnsonms/NNI_master | Hsigmoid | false | 11,585 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
SymmSoftplus | import torch
from torch.utils.data import Dataset as Dataset
import torch.utils.data
def symm_softplus(x, softplus_=torch.nn.functional.softplus):
return softplus_(x) - 0.5 * x
class SymmSoftplus(torch.nn.Module):
def forward(self, x):
return symm_softplus(x)
def get_inputs():
return [torch.r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.utils.data import Dataset as Dataset
import torch.u... | JunLi-Galios/CP-Flow | SymmSoftplus | false | 11,586 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff |
InteractiveKLLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class InteractiveKLLoss(nn.Module):
def __init__(self, temperature):
super().__init__()
self.temperature = temperature
self.kl_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 libdevice, math as tl_math
import torc... | Johnsonms/NNI_master | InteractiveKLLoss | false | 11,587 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
GlobalAvgPool1d | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from abc import abstractmethod
from torch.nn import functional
from typing import *
class AvgPool(nn.Module):
"""
AvgPool Module.
"""
def __init__(self):
super().__init__()
@abstractmet... | 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 abc import abstractmethod
from typing import ... | Johnsonms/NNI_master | GlobalAvgPool1d | false | 11,588 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
BinaryAdd | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryAdd | false | 11,589 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
BackboneModel1 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class BackboneModel1(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 1, 1, 1)
def forward(self, x):
return self.conv1(x)
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
import torch.nn.parallel
import torch.optim
import torch.u... | Johnsonms/NNI_master | BackboneModel1 | false | 11,590 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
MultiHeadAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
def attention(query, key, value, mask=None, dropout=None):
d_k = query.size(-1)
logits = torch.matmul(query, key.transpose(-2, -1)) / math.sqr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Johnsonms/NNI_master | MultiHeadAttention | false | 11,591 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
PosLinear2 | import torch
from torch import Tensor
from torch.utils.data import Dataset as Dataset
import torch.nn as nn
import torch.utils.data
class PosLinear2(torch.nn.Linear):
def forward(self, x: 'Tensor') ->Tensor:
return nn.functional.linear(x, torch.nn.functional.softmax(self.
weight, 1), self.bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | JunLi-Galios/CP-Flow | PosLinear2 | false | 11,592 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff |
ActorCritic | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class ActorCritic(nn.Module):
def __init__(self, num_states, num_actions, hidden_size):
super(ActorCritic, self).__init__()
self.num_actions ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Johnsonms/NNI_master | ActorCritic | false | 11,593 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
MSELoss | import torch
import torch.nn as nn
class MSELoss(nn.Module):
""" Mean-squared error loss """
def __init__(self, reduction='mean', eps=1e-08):
super().__init__()
if reduction not in ('mean', 'sum'):
raise ValueError(
'`reduction` not recognized. must be "mean" or "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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | KAGRA-TW-ML/deepclean-prod | MSELoss | false | 11,594 | [
"MIT"
] | 0 | 9fb834cb4027fd3b377bc0e763c237235c98eabd | https://github.com/KAGRA-TW-ML/deepclean-prod/tree/9fb834cb4027fd3b377bc0e763c237235c98eabd |
PosLinear | import torch
from torch import Tensor
from torch.utils.data import Dataset as Dataset
import torch.nn as nn
import torch.utils.data
class PosLinear(torch.nn.Linear):
def forward(self, x: 'Tensor') ->Tensor:
gain = 1 / x.size(1)
return nn.functional.linear(x, torch.nn.functional.softplus(self.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | JunLi-Galios/CP-Flow | PosLinear | false | 11,595 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff |
PFLDLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class PFLDLoss(nn.Module):
"""Weighted loss of L2 distance with the pose angle for PFLD."""
def __init__(self):
super(PFLDLoss, self).__init__()
def forward(self, landmark_... | 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.nn.parallel
import torch.optim
import ... | Johnsonms/NNI_master | PFLDLoss | false | 11,596 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
NoiseInjection | import torch
from torch import nn
class NoiseInjection(nn.Module):
def __init__(self, channel):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
def forward(self, image, noise):
return image + self.weight * noise
def get_inputs():
return [torch.rand(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | KUMartin77/AAA738_StyleGAN_pytorch | NoiseInjection | false | 11,597 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd |
wide_basic | import torch
import torch.nn as nn
def get_norm(n_filters, norm):
if norm is None:
return Identity()
elif norm == 'batch':
return nn.BatchNorm2d(n_filters, momentum=0.9)
elif norm == 'instance':
return nn.InstanceNorm2d(n_filters, affine=True)
elif norm == 'layer':
retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | JunLi-Galios/JEM | wide_basic | false | 11,598 | [
"Apache-2.0"
] | 0 | dd4d33f64269d3999458f129ac83a3043ad7e63f | https://github.com/JunLi-Galios/JEM/tree/dd4d33f64269d3999458f129ac83a3043ad7e63f |
Softplus | import torch
import numpy as np
from torch.utils.data import Dataset as Dataset
import torch.nn as nn
import torch.utils.data
def activation_shifting(activation):
def shifted_activation(x):
return activation(x) - activation(torch.zeros_like(x))
return shifted_activation
def cauchy_softplus(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, math as tl_math
import numpy as np
from torch.utils.data import Dataset as Dat... | JunLi-Galios/CP-Flow | Softplus | false | 11,599 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff |
PosConv2d | import torch
from torch import Tensor
from torch.utils.data import Dataset as Dataset
import torch.nn.init as init
import torch.utils.data
class PosConv2d(torch.nn.Conv2d):
def reset_parameters(self) ->None:
super().reset_parameters()
self.fan_in, _ = init._calculate_fan_in_and_fan_out(self.weigh... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | JunLi-Galios/CP-Flow | PosConv2d | false | 11,600 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff |
EqualConv2d | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | KUMartin77/AAA738_StyleGAN_pytorch | EqualConv2d | false | 11,601 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd |
SoftCrossEntropyLoss | import torch
import torch.utils.data
class SoftCrossEntropyLoss(torch.nn.Module):
"""SoftCrossEntropyLoss (useful for label smoothing and mixup).
Identical to torch.nn.CrossEntropyLoss if used with one-hot labels."""
def __init__(self):
super(SoftCrossEntropyLoss, self).__init__()
def forwar... | 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... | KateHaeun/pycls | SoftCrossEntropyLoss | false | 11,602 | [
"MIT"
] | 0 | f3d87a36cb0a8adead31c7ad98f43facf7fe4c47 | https://github.com/KateHaeun/pycls/tree/f3d87a36cb0a8adead31c7ad98f43facf7fe4c47 |
FusedUpsample | import torch
from torch import nn
from torch.nn import functional as F
from math import sqrt
class FusedUpsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(in_channel, out_channel, kernel_size, kernel_size)
bias... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | KUMartin77/AAA738_StyleGAN_pytorch | FusedUpsample | false | 11,603 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd |
AdaptiveInstanceNorm | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | KUMartin77/AAA738_StyleGAN_pytorch | AdaptiveInstanceNorm | false | 11,604 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd |
ResHead | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn as nn
def gap2d(_w_in):
"""Helper for building a gap2d layer."""
return nn.AdaptiveAvgPool2d((1, 1))
def gap2d_cx(cx, _w_in):
"""Accumulates complexity of gap2d into cx = (h, w, flops, params, acts)."""
flops, params, 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.nn import Module
import torch.utils.data
import torch.nn as nn
assert... | KateHaeun/pycls | ResHead | false | 11,605 | [
"MIT"
] | 0 | f3d87a36cb0a8adead31c7ad98f43facf7fe4c47 | https://github.com/KateHaeun/pycls/tree/f3d87a36cb0a8adead31c7ad98f43facf7fe4c47 |
EqualLinear | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | KUMartin77/AAA738_StyleGAN_pytorch | EqualLinear | false | 11,606 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd |
TransformerEncoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class TransformerEncoderLayer(nn.Module):
def __init__(self, embed_dim, num_heads, hidden_size, dropout=0.0,
attention_dropout=0.0, activation_dropout=0.0):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | IA3005/NLP_ens | TransformerEncoderLayer | false | 11,607 | [
"MIT"
] | 0 | 794ebbff46d5e6d5476f29b577b40bbb52991246 | https://github.com/IA3005/NLP_ens/tree/794ebbff46d5e6d5476f29b577b40bbb52991246 |
ConvInRelu | import torch
import numpy as np
from torch import nn
import torch.onnx
class ConvInRelu(nn.Module):
def __init__(self, channels_in, channels_out, kernel_size, stride=1):
super(ConvInRelu, self).__init__()
self.n_params = 0
self.channels = channels_out
self.reflection_pad = nn.Refl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | JuanFuriaz/donkey_share | ConvInRelu | false | 11,608 | [
"MIT"
] | 0 | caad831ca21094f05f9084f881ca3bbfa4168e4c | https://github.com/JuanFuriaz/donkey_share/tree/caad831ca21094f05f9084f881ca3bbfa4168e4c |
FCNet | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class FCNet(nn.Module):
def __init__(self, input_size, output_size):
super().__init__()
self.l1 = nn.Linear(input_size, 5)
self.relu = nn.ReLU()
self.l2 = 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 ... | Johnsonms/NNI_master | FCNet | false | 11,609 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
Classifier | import torch
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.linear(x)
return to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | KathleenQ/context-aware-doc-analysis | Classifier | false | 11,610 | [
"MIT"
] | 0 | 93af994b2dee09f5fe6bfcc2e76e47e74708d3fe | https://github.com/KathleenQ/context-aware-doc-analysis/tree/93af994b2dee09f5fe6bfcc2e76e47e74708d3fe |
AdaptiveCatAvgMaxPool2d | import torch
import torch.nn as nn
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
def adaptive_catavgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_m... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
import torchvision.transforms.functional as... | DifferentSC/pytorch-image-models | AdaptiveCatAvgMaxPool2d | false | 11,611 | [
"Apache-2.0"
] | 0 | ccfb5751abc70d80add4f197464190c4a2637c6c | https://github.com/DifferentSC/pytorch-image-models/tree/ccfb5751abc70d80add4f197464190c4a2637c6c |
GlobalAttention | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional as F
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_le... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Katarina11/PreSumm | GlobalAttention | false | 11,612 | [
"MIT"
] | 0 | 616e72f038d512e9e9112af375d66a0b2e3db6cd | https://github.com/Katarina11/PreSumm/tree/616e72f038d512e9e9112af375d66a0b2e3db6cd |
UnbalancedLoss | import torch
import torch.nn as nn
import torch.utils.data
class UnbalancedLoss(nn.Module):
NUM_LABELS = 2
def __init__(self):
super().__init__()
self.crit = nn.BCEWithLogitsLoss()
def forward(self, logits, label):
return self.crit(logits, label)
def get_inputs():
return [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, math as tl_math
import torc... | Kwonyoung-Ryu/DeepGlobalRegistration | UnbalancedLoss | false | 11,613 | [
"MIT"
] | 0 | 0045118d96182047f4c09c4c4fe2a1b2b527cc5f | https://github.com/Kwonyoung-Ryu/DeepGlobalRegistration/tree/0045118d96182047f4c09c4c4fe2a1b2b527cc5f |
Network | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
class Network(nn.Module):
def __init__(self):
super(Network, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Karansutradhar/Convolution-Neural-Network-Objection-Recognition-Dogs-Cats | Network | false | 11,614 | [
"MIT"
] | 0 | 85dfab2e8758a5cf49368938b03720f197a06b18 | https://github.com/Karansutradhar/Convolution-Neural-Network-Objection-Recognition-Dogs-Cats/tree/85dfab2e8758a5cf49368938b03720f197a06b18 |
ConformerFeedForward | import torch
from torch import nn
import torch.utils.data
import torch.optim
class Swish(nn.Module):
"""
Swish activation function introduced in 'https://arxiv.org/abs/1710.05941'
"""
def forward(self, x):
return x * torch.sigmoid(x)
class ConformerFeedForward(nn.Module):
"""
feed-f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.optim
assert_size_stri... | JINHXu/NeMo | ConformerFeedForward | false | 11,615 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 |
AngleSimpleLinear | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Parameter
import torch.utils.data
class AngleSimpleLinear(nn.Module):
"""Computes cos of angles between input vectors and weights vectors"""
def __init__(self, in_features, out_features):
super(AngleSimpleLinear, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | KhurramPirov/Twins-recognition | AngleSimpleLinear | false | 11,616 | [
"MIT"
] | 0 | f99ba1128afb3674a49db6a4b19afd5108c3fdf9 | https://github.com/KhurramPirov/Twins-recognition/tree/f99ba1128afb3674a49db6a4b19afd5108c3fdf9 |
ScalarMix | import torch
import torch.nn as nn
class ScalarMix(nn.Module):
"""
Computes a parameterised scalar mixture of :math:`N` tensors, :math:`mixture = \\gamma * \\sum_{k}(s_k * tensor_k)`
where :math:`s = \\mathrm{softmax}(w)`, with :math:`w` and :math:`\\gamma` scalar parameters.
Args:
n_layers (... | 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... | KoichiYasuoka/SuPar | ScalarMix | false | 11,617 | [
"MIT"
] | 0 | 9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | https://github.com/KoichiYasuoka/SuPar/tree/9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 |
ConvGLU | import torch
from torch import nn
import torch.utils.data
import torch.optim
def str2act(txt):
"""Translates text to neural network activation"""
return {'sigmoid': nn.Sigmoid(), 'relu': nn.ReLU(), 'none': nn.
Sequential(), 'lrelu': nn.LeakyReLU(0.2), 'selu': nn.SELU()}[txt.
lower()]
class C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.optim
assert_size_stri... | JINHXu/NeMo | ConvGLU | false | 11,618 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 |
AdaptiveAvgMaxPool2d | import torch
import torch.nn as nn
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
def adaptive_avgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
import torchvision.transforms.functional as... | DifferentSC/pytorch-image-models | AdaptiveAvgMaxPool2d | false | 11,619 | [
"Apache-2.0"
] | 0 | ccfb5751abc70d80add4f197464190c4a2637c6c | https://github.com/DifferentSC/pytorch-image-models/tree/ccfb5751abc70d80add4f197464190c4a2637c6c |
DuelingQNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class DuelingQNetwork(nn.Module):
"""Dueling Q-network (https://arxiv.org/abs/1511.06581)"""
def __init__(self, state_size, action_size, hidsize1=128, hidsize2=128):
super(DuelingQNetwork, self).__init__()
self.fc1_val = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | KantiCodes/flatland-rl | DuelingQNetwork | false | 11,620 | [
"MIT"
] | 0 | fcc10e83d2548470ebaa5540b967db0940eb30dd | https://github.com/KantiCodes/flatland-rl/tree/fcc10e83d2548470ebaa5540b967db0940eb30dd |
AsymmetricLossOptimized | import torch
import torch.nn as nn
class AsymmetricLossOptimized(nn.Module):
""" Notice - optimized version, minimizes memory allocation and gpu uploading,
favors inplace operations
https://github.com/Alibaba-MIIL/ASL/blob/main/src/loss_functions/losses.py
"""
def __init__(self, gamma_neg=4, gamm... | 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... | LanXiangExcavator/python-classifier-2021 | AsymmetricLossOptimized | false | 11,621 | [
"BSD-2-Clause"
] | 0 | 851079e76db8e5070132d1120dba941967e1245b | https://github.com/LanXiangExcavator/python-classifier-2021/tree/851079e76db8e5070132d1120dba941967e1245b |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, input, target, logits=True):
if logits:
input = nn.Sigmoid()(input)
N = target.size(0)
smooth = 1
input_flat = input.view(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | LanXiangExcavator/python-classifier-2021 | DiceLoss | false | 11,622 | [
"BSD-2-Clause"
] | 0 | 851079e76db8e5070132d1120dba941967e1245b | https://github.com/LanXiangExcavator/python-classifier-2021/tree/851079e76db8e5070132d1120dba941967e1245b |
MultiLayerPerceptron | import torch
import torch.utils.data
import torch.optim
class MultiLayerPerceptron(torch.nn.Module):
"""
A simple MLP that can either be used independently or put on top
of pretrained models (such as BERT) and act as a classifier.
Args:
hidden_size (int): the size of each layer
num_cla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | JINHXu/NeMo | MultiLayerPerceptron | false | 11,623 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 |
TransformerDecoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class TransformerDecoderLayer(nn.Module):
"""Decoder layer block. Follows an implementation in fairseq with args.decoder_normalize_before=True,
i.e. order of operation... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | IA3005/NLP_ens | TransformerDecoderLayer | false | 11,624 | [
"MIT"
] | 0 | 794ebbff46d5e6d5476f29b577b40bbb52991246 | https://github.com/IA3005/NLP_ens/tree/794ebbff46d5e6d5476f29b577b40bbb52991246 |
FixedSubnetConv | import math
import torch
import torch.multiprocessing
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class FixedSubnetConv(nn.Conv2d):
def __init__(self, *args, **kwargs):
super().__init__(*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
import math
import torch.multiprocessing
import torch.nn as nn
import torch.nn.p... | Lasitha-93/CRTIDL_2021 | FixedSubnetConv | false | 11,625 | [
"Apache-2.0"
] | 0 | d6bc6fbe08161c3574511623230a7aa4895f65e1 | https://github.com/Lasitha-93/CRTIDL_2021/tree/d6bc6fbe08161c3574511623230a7aa4895f65e1 |
AttentionBlock | import math
import torch
from torch.nn import functional as F
from torch import nn
import torch.utils.data
import torch.optim
def convert_pad_shape(pad_shape):
"""
Used to get arguments for F.pad
"""
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | JINHXu/NeMo | AttentionBlock | false | 11,626 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 |
LayerNorm | import torch
from torch import nn
from torch.nn import LayerNorm
import torch.utils.data
import torch.optim
class LayerNorm(nn.Module):
def __init__(self, channels, eps=0.0001):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channel... | 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
import torch.utils.data
import torch.optim
assert_size_str... | JINHXu/NeMo | LayerNorm | false | 11,627 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 |
FusedDownsample | import torch
from torch import nn
from torch.nn import functional as F
from math import sqrt
class FusedDownsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(out_channel, in_channel, kernel_size, kernel_size)
bi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | KUMartin77/AAA738_StyleGAN_pytorch | FusedDownsample | false | 11,628 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd |
Biaffine | import torch
import torch.nn as nn
class Biaffine(nn.Module):
"""
Biaffine layer for first-order scoring.
This function has a tensor of weights :math:`W` and bias terms if needed.
The score :math:`s(x, y)` of the vector pair :math:`(x, y)` is computed as :math:`x^T W y`,
in which :math:`x` and :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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | KoichiYasuoka/SuPar | Biaffine | false | 11,629 | [
"MIT"
] | 0 | 9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | https://github.com/KoichiYasuoka/SuPar/tree/9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 |
MulticlassDiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, input, target, logits=True):
if logits:
input = nn.Sigmoid()(input)
N = target.size(0)
smooth = 1
input_flat = input.view(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | LanXiangExcavator/python-classifier-2021 | MulticlassDiceLoss | false | 11,630 | [
"BSD-2-Clause"
] | 0 | 851079e76db8e5070132d1120dba941967e1245b | https://github.com/LanXiangExcavator/python-classifier-2021/tree/851079e76db8e5070132d1120dba941967e1245b |
Triaffine | import torch
import torch.nn as nn
class Triaffine(nn.Module):
"""
Triaffine layer for second-order scoring.
This function has a tensor of weights :math:`W` and bias terms if needed.
The score :math:`s(x, y, z)` of the vector triple :math:`(x, y, z)` is computed as :math:`x^T z^T W y`.
Usually, :... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | KoichiYasuoka/SuPar | Triaffine | false | 11,631 | [
"MIT"
] | 0 | 9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | https://github.com/KoichiYasuoka/SuPar/tree/9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 |
InvConvNear | import torch
from torch.nn import functional as F
from torch import nn
import torch.utils.data
import torch.optim
class InvConvNear(nn.Module):
def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
super().__init__()
assert n_split % 2 == 0
self.channels = 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 import nn
import torch.utils.data
import torch.optim
assert_size_stri... | JINHXu/NeMo | InvConvNear | false | 11,632 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 |
TVLoss | import torch
from torch import nn
import torch.utils.data
from torchvision.transforms import *
class TVLoss(nn.Module):
def __init__(self, tv_loss_weight=1):
super(TVLoss, self).__init__()
self.tv_loss_weight = tv_loss_weight
def forward(self, x):
batch_size = x.size()[0]
h_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 import nn
import torch.utils.data
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | HamsterBiz/iSeeBetter | TVLoss | false | 11,633 | [
"MIT"
] | 0 | a71cee61583bdedab1f3b368e2cb7dc5ad969aed | https://github.com/HamsterBiz/iSeeBetter/tree/a71cee61583bdedab1f3b368e2cb7dc5ad969aed |
SpaceToDepth | import torch
import torch.optim
import torch.nn as nn
import torch.utils.data
class SpaceToDepth(nn.Module):
def __init__(self, block_size):
super(SpaceToDepth, self).__init__()
self.block_size = block_size
self.block_size_sq = block_size * block_size
def forward(self, 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.optim
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | LeikvollE/pytorch-superpoint | SpaceToDepth | false | 11,634 | [
"MIT"
] | 0 | 52144a760e0cc46259e57397a5a55f0585fe6d0b | https://github.com/LeikvollE/pytorch-superpoint/tree/52144a760e0cc46259e57397a5a55f0585fe6d0b |
GEGLU | import torch
import torch.nn as nn
import torch.nn.functional as F
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Lawliet19189/squad-1 | GEGLU | false | 11,635 | [
"MIT"
] | 0 | 75531054d74e20838d8acff81749f335973b9ae3 | https://github.com/Lawliet19189/squad-1/tree/75531054d74e20838d8acff81749f335973b9ae3 |
ScaleNorm | import torch
import torch.nn as nn
class ScaleNorm(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1))
def forward(self, x):
n = torch.norm(x, dim=-1, keepdim=True).clamp(min=self.eps)
return x / n * s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Lawliet19189/squad-1 | ScaleNorm | false | 11,636 | [
"MIT"
] | 0 | 75531054d74e20838d8acff81749f335973b9ae3 | https://github.com/Lawliet19189/squad-1/tree/75531054d74e20838d8acff81749f335973b9ae3 |
MultiHeadAttention | import math
import torch
from torch import nn
import torch.utils.data
import torch.optim
class MultiHeadAttention(nn.Module):
"""
Multi-head scaled dot-product attention layer.
Args:
hidden_size: size of the embeddings in the model, also known as d_model
num_attention_heads: number of hea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | JINHXu/NeMo | MultiHeadAttention | false | 11,637 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 |
Inception3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=True, **kwargs)
def forward(self, x):
x ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Galaxies99/inception-cuda | Inception3 | false | 11,638 | [
"MIT"
] | 0 | ed8fdbe3caef415e60b52e671273be90e9423e44 | https://github.com/Galaxies99/inception-cuda/tree/ed8fdbe3caef415e60b52e671273be90e9423e44 |
MLP | import torch
import torch.nn as nn
class SharedDropout(nn.Module):
"""
SharedDropout differs from the vanilla dropout strategy in that
the dropout mask is shared across one dimension.
Args:
p (float):
The probability of an element to be zeroed. Default: 0.5.
batch_first (b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | KoichiYasuoka/SuPar | MLP | false | 11,639 | [
"MIT"
] | 0 | 9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | https://github.com/KoichiYasuoka/SuPar/tree/9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 |
D_UpBlock | import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torchvision.transforms import *
assert_size_stride ... | HamsterBiz/iSeeBetter | D_UpBlock | false | 11,640 | [
"MIT"
] | 0 | a71cee61583bdedab1f3b368e2cb7dc5ad969aed | https://github.com/HamsterBiz/iSeeBetter/tree/a71cee61583bdedab1f3b368e2cb7dc5ad969aed |
LSTMClassCriterion | import torch
import torch.nn as nn
def to_contiguous(tensor):
if tensor.is_contiguous():
return tensor
else:
return tensor.contiguous()
class LSTMClassCriterion(nn.Module):
def __init__(self):
super(LSTMClassCriterion, self).__init__()
def forward(self, pred, target, mask):... | 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... | LeoZDong/shape2prog | LSTMClassCriterion | false | 11,641 | [
"BSD-2-Clause"
] | 0 | 2185d1d4eb7a1c4c55e644c6af477fd8e8e70241 | https://github.com/LeoZDong/shape2prog/tree/2185d1d4eb7a1c4c55e644c6af477fd8e8e70241 |
LSTMRegressCriterion | import torch
import torch.nn as nn
class LSTMRegressCriterion(nn.Module):
def __init__(self):
super(LSTMRegressCriterion, self).__init__()
def forward(self, pred, target, mask):
pred = pred.clone()
target = target.clone()
mask = mask.clone()
target = target[:, :pred.s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | LeoZDong/shape2prog | LSTMRegressCriterion | false | 11,642 | [
"BSD-2-Clause"
] | 0 | 2185d1d4eb7a1c4c55e644c6af477fd8e8e70241 | https://github.com/LeoZDong/shape2prog/tree/2185d1d4eb7a1c4c55e644c6af477fd8e8e70241 |
ViTStemPatchify | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn as nn
def patchify2d(w_in, w_out, k, *, bias=True):
"""Helper for building a patchify layer as used by ViT models."""
return nn.Conv2d(w_in, w_out, k, stride=k, padding=0, bias=bias)
def patchify2d_cx(cx, w_in, w_out, k, *, bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.utils.data
import torch.nn as nn
assert... | LicharYuan/pycls | ViTStemPatchify | false | 11,643 | [
"MIT"
] | 0 | 633529425f2c9ffadd892c1a0418b37891ee2d44 | https://github.com/LicharYuan/pycls/tree/633529425f2c9ffadd892c1a0418b37891ee2d44 |
RegressionModel | import torch
import torch.nn as nn
class RegressionModel(nn.Module):
def __init__(self, num_features_in, num_anchors=9, feature_size=256):
super(RegressionModel, self).__init__()
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3,
padding=1)
self.act1 = nn.ReL... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Hyojin021/auto_labeling | RegressionModel | false | 11,644 | [
"Apache-2.0"
] | 0 | 1ccf0cd1c5adf34692751553a988aa0fcf4efefb | https://github.com/Hyojin021/auto_labeling/tree/1ccf0cd1c5adf34692751553a988aa0fcf4efefb |
NormedLinear | import torch
import torch.nn.functional as F
from torch import nn
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | LiuXiaoxuanPKU/mmdetection | NormedLinear | false | 11,645 | [
"Apache-2.0"
] | 0 | 05b46eccbe5c4953d5a406f545fe529ce4e146d3 | https://github.com/LiuXiaoxuanPKU/mmdetection/tree/05b46eccbe5c4953d5a406f545fe529ce4e146d3 |
MergeGate | import torch
import torch.nn as nn
import torch.nn.functional as F
class MergeGate(nn.Module):
def __init__(self, hidden_size):
super(MergeGate, self).__init__()
self.hidden_size = hidden_size
self.WSh = nn.Linear(hidden_size, hidden_size)
self.WSc = nn.Linear(hidden_size, hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | LeiShenVictoria/Static-Dynamic-Attention-CNNRNN | MergeGate | false | 11,646 | [
"MIT"
] | 0 | e2823717d22c9e543428d471ff19113bbb59ebfe | https://github.com/LeiShenVictoria/Static-Dynamic-Attention-CNNRNN/tree/e2823717d22c9e543428d471ff19113bbb59ebfe |
Actor | import torch
import torch.nn as nn
import torch as t
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, action_range):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, action_dim)
self.action_range ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | LeonLester/Machin-title-in-progress- | Actor | false | 11,647 | [
"MIT"
] | 0 | 777479d47b520dcdc6b09c247591b5fe1d6cbe8c | https://github.com/LeonLester/Machin-title-in-progress-/tree/777479d47b520dcdc6b09c247591b5fe1d6cbe8c |
Critic | import torch
import torch.nn as nn
import torch as t
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim + action_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, 1)
def forward(self, state, actio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | LeonLester/Machin-title-in-progress- | Critic | false | 11,648 | [
"MIT"
] | 0 | 777479d47b520dcdc6b09c247591b5fe1d6cbe8c | https://github.com/LeonLester/Machin-title-in-progress-/tree/777479d47b520dcdc6b09c247591b5fe1d6cbe8c |
NormedConv2d | import torch
from torch import nn
class NormedConv2d(nn.Conv2d):
"""Normalized Conv2d Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value of divisor to
keep ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | LiuXiaoxuanPKU/mmdetection | NormedConv2d | false | 11,649 | [
"Apache-2.0"
] | 0 | 05b46eccbe5c4953d5a406f545fe529ce4e146d3 | https://github.com/LiuXiaoxuanPKU/mmdetection/tree/05b46eccbe5c4953d5a406f545fe529ce4e146d3 |
conv_head_pooling | import torch
import torch.nn as nn
import torch.autograd
class conv_head_pooling(nn.Module):
def __init__(self, in_feature, out_feature, stride, padding_mode='zeros'):
super(conv_head_pooling, self).__init__()
self.maxpool = nn.MaxPool2d(3, 2, 1)
self.avgpool = nn.AvgPool2d(3, 2, 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
import ... | LeiZhang1998/TransReID | conv_head_pooling | false | 11,650 | [
"MIT"
] | 0 | 5a3f140633e3418c7cff2603ff2e814b9ab466ac | https://github.com/LeiZhang1998/TransReID/tree/5a3f140633e3418c7cff2603ff2e814b9ab466ac |
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