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 |
|---|---|---|---|---|---|---|---|---|---|---|
LsqQuan | import torch
import torch as t
import torch.utils.data
class GradScale(t.nn.Module):
def forward(self, x, scale):
y = x
y_grad = x * scale
return (y - y_grad).detach() + y_grad
class RoundPass(t.nn.Module):
def forward(self, x):
y = x.round()
y_grad = x
retu... | 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 as t
import tor... | HumberMe/lsq-net | LsqQuan | false | 556 | [
"MIT"
] | 0 | 7dcd75bff4aa7ff2d9c8a7902198fe411a38eb4c | https://github.com/HumberMe/lsq-net/tree/7dcd75bff4aa7ff2d9c8a7902198fe411a38eb4c |
ChamferLoss | import torch
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from typing import *
class ChamferLoss(nn.Module):
def __init__(self):
super(ChamferLoss, self).__init__()
self.use_cuda = torch.cuda.is_available()
def 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
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | DeVriesMatt/pointMLP-pytorch | ChamferLoss | false | 557 | [
"Apache-2.0"
] | 0 | e9c09a2038551e83b072353f3fd7e3294463e892 | https://github.com/DeVriesMatt/pointMLP-pytorch/tree/e9c09a2038551e83b072353f3fd7e3294463e892 |
Actor | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, 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 import triton_helpers
from torch._inductor.runtime.... | HuXiao-THU/Crane-Group-Control | Actor | false | 558 | [
"MIT"
] | 0 | ea71bc9b1e3957fd755312ceb52bda1be8244f5a | https://github.com/HuXiao-THU/Crane-Group-Control/tree/ea71bc9b1e3957fd755312ceb52bda1be8244f5a |
GCNAutoencoder | from torch.nn import Module
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.modules.loss
class GraphConvolutionDecoder(Module):
"""
Simple GCN layer, similar to https://arxiv.org/... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | HongyiZhu/EHI | GCNAutoencoder | false | 559 | [
"MIT"
] | 0 | 9fbbc6046546dd7fc6de5d831b4c941bc4404e02 | https://github.com/HongyiZhu/EHI/tree/9fbbc6046546dd7fc6de5d831b4c941bc4404e02 |
Pad_Pool | import torch
from torch import nn
class Pad_Pool(nn.Module):
"""
Implements a padding layer in front of pool1d layers used in our architectures to achieve padding=same output shape
Pads 0 to the left and 1 to the right side of x
"""
def __init__(self, left=0, right=1, value=0):
super().... | 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... | Hullimulli/EEGEyeNet | Pad_Pool | false | 560 | [
"MIT"
] | 0 | 677a791b39800f44dc254553b16ee2f92e62c423 | https://github.com/Hullimulli/EEGEyeNet/tree/677a791b39800f44dc254553b16ee2f92e62c423 |
LayerNorm2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNorm2d(nn.LayerNorm):
"""LayerNorm on channels for 2d images.
Args:
num_channels (int): The number of channels of the input tensor.
eps (float): a value added to the denominator for numerical stability.
... | 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_... | HumberMe/mmclassification | LayerNorm2d | false | 561 | [
"Apache-2.0"
] | 0 | 68f1542068d3af4db932c97e6a728181432fff0c | https://github.com/HumberMe/mmclassification/tree/68f1542068d3af4db932c97e6a728181432fff0c |
GeneralizedMeanPooling | import torch
from torch import Tensor
import torch.nn as nn
from torch.functional import Tensor
import torch.nn.functional as F
from torch import Tensor
from torch.nn.parameter import Parameter
def gem(x: 'Tensor', p: 'Parameter', eps: 'float'=1e-06, clamp=True) ->Tensor:
if clamp:
x = x.clamp(min=eps)
... | 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 ... | HumberMe/mmclassification | GeneralizedMeanPooling | false | 562 | [
"Apache-2.0"
] | 0 | 68f1542068d3af4db932c97e6a728181432fff0c | https://github.com/HumberMe/mmclassification/tree/68f1542068d3af4db932c97e6a728181432fff0c |
LN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class LN(nn.Module):
def forward(self, x):
return F.layer_norm(x, x.size()[1:], weight=None, bias=None, eps=1e-05)
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
import torch.utils.data
assert_size_stride = torch._C._dy... | ID56/OrigamiNet | LN | false | 563 | [
"Apache-2.0"
] | 0 | a71ec4984e3d5da7d635d68260026b749ec44fa9 | https://github.com/ID56/OrigamiNet/tree/a71ec4984e3d5da7d635d68260026b749ec44fa9 |
ResidualBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, input_channel, output_channel, upsample=True):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(input_channel, output_channel, kernel_size=3,
padding=0)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Holmes-Alan/Photo2Sketch | ResidualBlock | false | 564 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
GradScale | import torch
import torch as t
import torch.utils.data
class GradScale(t.nn.Module):
def forward(self, x, scale):
y = x
y_grad = x * scale
return (y - y_grad).detach() + y_grad
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch as t
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyn... | HumberMe/lsq-net | GradScale | false | 565 | [
"MIT"
] | 0 | 7dcd75bff4aa7ff2d9c8a7902198fe411a38eb4c | https://github.com/HumberMe/lsq-net/tree/7dcd75bff4aa7ff2d9c8a7902198fe411a38eb4c |
Pad_Conv | import math
import torch
from torch import nn
class Pad_Conv(nn.Module):
"""
Implements a padding layer in front of conv1d layers used in our architectures to achieve padding=same output shape
Pads 0 to the left and 1 to the right side of x
"""
def __init__(self, kernel_size, value=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
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guard... | Hullimulli/EEGEyeNet | Pad_Conv | false | 566 | [
"MIT"
] | 0 | 677a791b39800f44dc254553b16ee2f92e62c423 | https://github.com/Hullimulli/EEGEyeNet/tree/677a791b39800f44dc254553b16ee2f92e62c423 |
ZeroConv2d | import torch
from torch import nn
from torch.nn import functional as F
class ZeroConv2d(nn.Module):
def __init__(self, in_channel, out_channel, padding=1):
super().__init__()
self.conv = nn.Conv2d(in_channel, out_channel, 3, padding=0)
self.conv.weight.data.zero_()
self.conv.bias.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | HugoSenetaire/glow-pytorch | ZeroConv2d | false | 567 | [
"MIT"
] | 0 | 7f11be87cac9770df63867910c34738dedee6f56 | https://github.com/HugoSenetaire/glow-pytorch/tree/7f11be87cac9770df63867910c34738dedee6f56 |
ArctanLayer | import torch
import torch.nn as nn
import torch.nn
from abc import ABCMeta
from abc import abstractmethod
class Layer(nn.Module, metaclass=ABCMeta):
def __init__(self):
super(Layer, self).__init__()
@abstractmethod
def forward(self, x):
"""
>>> do forward pass with a given 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.triton_helpers import libdevice
import torch.nn as nn
import torch.nn
from abc import ABCMeta
from abc import a... | Isaac-Li-cn/certify_robustness | ArctanLayer | false | 568 | [
"BSD-3-Clause"
] | 0 | f904dc923afc6354e406c57a1c923d13fc39d315 | https://github.com/Isaac-Li-cn/certify_robustness/tree/f904dc923afc6354e406c57a1c923d13fc39d315 |
InstanceNormLayer | import torch
import torch.utils.data
import torch
from torch import nn
class InstanceNormLayer(nn.Module):
"""Implements instance normalization layer."""
def __init__(self, epsilon=1e-08):
super().__init__()
self.eps = epsilon
def forward(self, x):
if len(x.shape) != 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
from torch import nn
assert_size_stride = ... | IVRL/BIGPrior | InstanceNormLayer | false | 569 | [
"MIT"
] | 0 | 6bf3b18fcbbd3c58bad7a792a8d28b017abb2411 | https://github.com/IVRL/BIGPrior/tree/6bf3b18fcbbd3c58bad7a792a8d28b017abb2411 |
Critic | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | HuXiao-THU/Crane-Group-Control | Critic | false | 570 | [
"MIT"
] | 0 | ea71bc9b1e3957fd755312ceb52bda1be8244f5a | https://github.com/HuXiao-THU/Crane-Group-Control/tree/ea71bc9b1e3957fd755312ceb52bda1be8244f5a |
AsymmetricLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
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 tensor.
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | HumberMe/mmclassification | AsymmetricLoss | false | 571 | [
"Apache-2.0"
] | 0 | 68f1542068d3af4db932c97e6a728181432fff0c | https://github.com/HumberMe/mmclassification/tree/68f1542068d3af4db932c97e6a728181432fff0c |
ResolutionScalingLayer | import torch
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F
class ResolutionScalingLayer(nn.Module):
"""Implements the resolution scaling layer.
Basically, this layer can be used to upsample feature maps from spatial domain
with nearest neighbor interpolation.
"""... | 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda... | IVRL/BIGPrior | ResolutionScalingLayer | false | 572 | [
"MIT"
] | 0 | 6bf3b18fcbbd3c58bad7a792a8d28b017abb2411 | https://github.com/IVRL/BIGPrior/tree/6bf3b18fcbbd3c58bad7a792a8d28b017abb2411 |
ReLULayer | import torch
import torch.nn as nn
import torch.nn
import torch.nn.functional as F
from abc import ABCMeta
from abc import abstractmethod
class Layer(nn.Module, metaclass=ABCMeta):
def __init__(self):
super(Layer, self).__init__()
@abstractmethod
def forward(self, x):
"""
>>> do ... | 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
import torch.nn.functional as F
from abc import ABC... | Isaac-Li-cn/certify_robustness | ReLULayer | false | 573 | [
"BSD-3-Clause"
] | 0 | f904dc923afc6354e406c57a1c923d13fc39d315 | https://github.com/Isaac-Li-cn/certify_robustness/tree/f904dc923afc6354e406c57a1c923d13fc39d315 |
EqualConv2d | import torch
import torch.nn as 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')
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from math import sqrt
assert_size_stride = torch._C._dynam... | IW276/IW276SS20-P3 | EqualConv2d | false | 574 | [
"MIT"
] | 0 | 7970bd332cc021cf1879f326c444eff3cf8593a1 | https://github.com/IW276/IW276SS20-P3/tree/7970bd332cc021cf1879f326c444eff3cf8593a1 |
ResidualBlock | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, channel_num, dilation=1, group=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(channel_num, channel_num, 3, 1, pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Hwihuni/Deep-Model-Watermarking | ResidualBlock | false | 575 | [
"MIT"
] | 0 | 73ea2286ace0aac3d55f6056da38ea2bc38ed00d | https://github.com/Hwihuni/Deep-Model-Watermarking/tree/73ea2286ace0aac3d55f6056da38ea2bc38ed00d |
BiasLayer | import torch
import torch.nn as nn
class BiasLayer(nn.Module):
def __init__(self, channels, skip_dims=2):
super().__init__()
self.bias = nn.Parameter(torch.zeros(channels, *([1] * skip_dims)))
def forward(self, net):
return net + self.bias
def extra_repr(self):
return f'... | 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... | JGU-VC/activation-pattern-analysis | BiasLayer | false | 576 | [
"MIT"
] | 0 | 14da42ad541ee4faf35d360a6e871fd44decd33d | https://github.com/JGU-VC/activation-pattern-analysis/tree/14da42ad541ee4faf35d360a6e871fd44decd33d |
InvConv2d | import torch
from torch import nn
from torch.nn import functional as F
class InvConv2d(nn.Module):
def __init__(self, in_channel):
super().__init__()
weight = torch.randn(in_channel, in_channel)
q, _ = torch.qr(weight)
weight = q.unsqueeze(2).unsqueeze(3)
self.weight = 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 import nn
from torch.nn import functional as F
assert_size_stride = t... | HugoSenetaire/glow-pytorch | InvConv2d | false | 577 | [
"MIT"
] | 0 | 7f11be87cac9770df63867910c34738dedee6f56 | https://github.com/HugoSenetaire/glow-pytorch/tree/7f11be87cac9770df63867910c34738dedee6f56 |
PixelNormLayer | import torch
import torch.utils.data
import torch
from torch import nn
class PixelNormLayer(nn.Module):
"""Implements pixel-wise feature vector normalization layer."""
def __init__(self, epsilon=1e-08):
super().__init__()
self.eps = epsilon
def forward(self, x):
return x / 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.utils.data
import torch
from torch import nn
assert_size_stride = ... | IVRL/BIGPrior | PixelNormLayer | false | 578 | [
"MIT"
] | 0 | 6bf3b18fcbbd3c58bad7a792a8d28b017abb2411 | https://github.com/IVRL/BIGPrior/tree/6bf3b18fcbbd3c58bad7a792a8d28b017abb2411 |
PixelNorm | import torch
from torch import nn
import torch.nn.parallel
import torch.utils.data
import torch.utils
class PixelNorm(nn.Module):
def __init__(self, epsilon=1e-08):
"""
@notice: avoid in-place ops.
https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-com... | 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.nn.parallel
import torch.utils.data
import to... | IdanAzuri/MixMatch-pytorch | PixelNorm | false | 579 | [
"MIT"
] | 0 | b8de2bc30c09e1256b92e0394403487fc4f90135 | https://github.com/IdanAzuri/MixMatch-pytorch/tree/b8de2bc30c09e1256b92e0394403487fc4f90135 |
BinaryCrossEntropy2D | import torch
import torch.nn as nn
from torch.nn.modules.loss import _WeightedLoss
class BinaryCrossEntropy2D(_WeightedLoss):
"""
Standard pytorch weighted nn.CrossEntropyLoss
"""
def __init__(self):
super(BinaryCrossEntropy2D, self).__init__()
self.nll_loss = nn.BCELoss(reduction='no... | 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... | JHalili/KidneySegQuicknat | BinaryCrossEntropy2D | false | 580 | [
"MIT"
] | 0 | 4ddc30f2cf935045bf6482a73a3e86e2d8da3696 | https://github.com/JHalili/KidneySegQuicknat/tree/4ddc30f2cf935045bf6482a73a3e86e2d8da3696 |
IIDIsotropicGaussianUVLoss | import math
import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class IIDIsotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of iid residuals with isotropic covariance:
$Sigma_i = sigma_i^2 I$
The loss (negative log likelihood) is then:
$1/2 sum_{i=1}^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 libdevice, math as tl_math
import math... | JHMeusener/detectron2-ResNeSt | IIDIsotropicGaussianUVLoss | false | 581 | [
"Apache-2.0"
] | 0 | 6abab6fb9496a528f6aa2d4e1e27f3e7ceb42685 | https://github.com/JHMeusener/detectron2-ResNeSt/tree/6abab6fb9496a528f6aa2d4e1e27f3e7ceb42685 |
IndepAnisotropicGaussianUVLoss | import math
import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class IndepAnisotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of independent residuals with anisotropic covariances:
$Sigma_i = sigma_i^2 I + r_i r_i^T$
The loss (negative log likelihood) is ... | 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 math... | JHMeusener/detectron2-ResNeSt | IndepAnisotropicGaussianUVLoss | false | 582 | [
"Apache-2.0"
] | 0 | 6abab6fb9496a528f6aa2d4e1e27f3e7ceb42685 | https://github.com/JHMeusener/detectron2-ResNeSt/tree/6abab6fb9496a528f6aa2d4e1e27f3e7ceb42685 |
L0Loss | import torch
from typing import *
from torch import nn
class L0Loss(nn.Module):
"""L0loss from
"Noise2Noise: Learning Image Restoration without Clean Data"
<https://arxiv.org/pdf/1803.04189>`_ paper.
"""
def __init__(self, gamma=2, eps=1e-08):
super(L0Loss, self).__init__()
self.gamma = g... | 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 typing import *
f... | JacobARose/image-utils | L0Loss | false | 583 | [
"MIT"
] | 0 | aa0e005c0b4df5198d188b074f4e21f8d8f97962 | https://github.com/JacobARose/image-utils/tree/aa0e005c0b4df5198d188b074f4e21f8d8f97962 |
OHEM_CrossEntroy_Loss | import torch
from torch import nn
class OHEM_CrossEntroy_Loss(nn.Module):
def __init__(self, threshold, keep_num):
super(OHEM_CrossEntroy_Loss, self).__init__()
self.threshold = threshold
self.keep_num = keep_num
self.loss_function = nn.CrossEntropyLoss(reduction='none')
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | HaowenWeiJohn/CV_Project | OHEM_CrossEntroy_Loss | false | 584 | [
"MIT"
] | 0 | 8e2414796f60a8c3fe452f3721e4a6ef7edfdb11 | https://github.com/HaowenWeiJohn/CV_Project/tree/8e2414796f60a8c3fe452f3721e4a6ef7edfdb11 |
TwoLayerFCBodyWithAction | import torch
import torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class TwoLayerFCBodyWithAction(nn.Module):
def __init__(self, state_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Fieps1/p3-tennis | TwoLayerFCBodyWithAction | false | 585 | [
"MIT"
] | 0 | 29f3dab5810d7cd7f84120416a615956d266c256 | https://github.com/Fieps1/p3-tennis/tree/29f3dab5810d7cd7f84120416a615956d266c256 |
Conv2dSame | import torch
from torch import nn
import torch.nn
class Conv2dSame(torch.nn.Module):
"""2D convolution that pads to keep spatial dimensions equal.
Cannot deal with stride. Only quadratic kernels (=scalar kernel_size).
"""
def __init__(self, in_channels, out_channels, kernel_size, bias=True,
p... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 im... | Jack12xl/scene-representation-networks | Conv2dSame | false | 586 | [
"MIT"
] | 0 | 2691b23c956cf188a1fe4c84a888b19871cac8f4 | https://github.com/Jack12xl/scene-representation-networks/tree/2691b23c956cf188a1fe4c84a888b19871cac8f4 |
CharbonnierLoss | import torch
import torch.nn as nn
import torch.utils.data
class CharbonnierLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=1e-06):
super(CharbonnierLoss, self).__init__()
self.eps = eps
def forward(self, x, y):
diff = x - y
loss = torch.sum(torch.sqrt... | 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
import... | JaguAroo/SRResCGAN | CharbonnierLoss | false | 587 | [
"MIT"
] | 0 | 9aac612aff631f7fb9142e0a36de9559cfc1a62d | https://github.com/JaguAroo/SRResCGAN/tree/9aac612aff631f7fb9142e0a36de9559cfc1a62d |
Classifier | import torch
from torch import nn
import torch.nn.parallel
import torch.utils.data
import torch.utils
class Classifier(nn.Module):
def __init__(self, num_classes, dim=128):
super(Classifier, self).__init__()
self.num_classes = num_classes
self.dim = dim
self.fc1 = nn.Linear(self.d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.parallel
import torch.utils.data
import tor... | IdanAzuri/MixMatch-pytorch | Classifier | false | 588 | [
"MIT"
] | 0 | b8de2bc30c09e1256b92e0394403487fc4f90135 | https://github.com/IdanAzuri/MixMatch-pytorch/tree/b8de2bc30c09e1256b92e0394403487fc4f90135 |
ScaleLayer | import torch
import torch.nn as nn
class ScaleLayer(nn.Module):
def __init__(self, channels, skip_dims=2):
super().__init__()
self.scale = nn.Parameter(torch.ones(channels, *([1] * skip_dims)))
def forward(self, net):
return net * self.scale
def extra_repr(self):
return ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | JGU-VC/activation-pattern-analysis | ScaleLayer | false | 589 | [
"MIT"
] | 0 | 14da42ad541ee4faf35d360a6e871fd44decd33d | https://github.com/JGU-VC/activation-pattern-analysis/tree/14da42ad541ee4faf35d360a6e871fd44decd33d |
L1GradLoss | import torch
import torch.nn as nn
import torch.utils.data
class L1GradLoss(nn.Module):
def __init__(self, grad=False):
super(L1GradLoss, self).__init__()
self.grad = grad
def forward(self, input, target):
err = input - target
loss = err.norm(p=1).div(err.numel())
if ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | JaguAroo/SRResCGAN | L1GradLoss | false | 590 | [
"MIT"
] | 0 | 9aac612aff631f7fb9142e0a36de9559cfc1a62d | https://github.com/JaguAroo/SRResCGAN/tree/9aac612aff631f7fb9142e0a36de9559cfc1a62d |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.optim
import torch.autograd
class Policy(nn.Module):
def __init__(self, learning_rate, gamma, in_dim, out_dim):
super(Policy, self).__init__()
self.learning_rate = learning_rate
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
from torch._inductor.runtime.... | ChangQingAAS/Deep-Reinforcement-Learning | Policy | false | 591 | [
"MIT"
] | 0 | 3bc1381c632b1730a48e63e972aea62086c4287c | https://github.com/ChangQingAAS/Deep-Reinforcement-Learning/tree/3bc1381c632b1730a48e63e972aea62086c4287c |
AdaptiveConcatPool2d | import torch
from typing import *
from typing import Optional
from torch import nn
class AdaptiveConcatPool2d(nn.Module):
"""Layer that concats `AdaptiveAvgPool2d` and `AdaptiveMaxPool2d`."""
def __init__(self, sz: 'Optional[int]'=None):
super(AdaptiveConcatPool2d, self).__init__()
"""Output ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from typing import *
from typing import Optional
from torch import nn
assert_size_stride ... | JacobARose/image-utils | AdaptiveConcatPool2d | false | 592 | [
"MIT"
] | 0 | aa0e005c0b4df5198d188b074f4e21f8d8f97962 | https://github.com/JacobARose/image-utils/tree/aa0e005c0b4df5198d188b074f4e21f8d8f97962 |
ILN | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.utils.data
class ILN(nn.Module):
def __init__(self, num_features, eps=1e-05):
super(ILN, self).__init__()
self.eps = eps
self.rho = Parameter(torch.Tensor(1, num_features, 1, 1))
self.gamma = ... | 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 torch.nn.parameter import Parameter
import torch.uti... | JW9MsjwjnpdRLFw/RMT | ILN | false | 593 | [
"MIT"
] | 0 | a877fd78639a8d4c534d0373b9d0ad023e0fa2dd | https://github.com/JW9MsjwjnpdRLFw/RMT/tree/a877fd78639a8d4c534d0373b9d0ad023e0fa2dd |
CircleLoss | import torch
from typing import *
from torch import nn
import torch.nn.functional as F
from torch import functional as F
from torch.nn import functional as F
class CircleLoss(nn.Module):
"""CircleLoss from
`"Circle Loss: A Unified Perspective of Pair Similarity Optimization"
<https://arxiv.org/pdf/2002.10857>`_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | JacobARose/image-utils | CircleLoss | false | 594 | [
"MIT"
] | 0 | aa0e005c0b4df5198d188b074f4e21f8d8f97962 | https://github.com/JacobARose/image-utils/tree/aa0e005c0b4df5198d188b074f4e21f8d8f97962 |
BasicBlock | import torch
from torch import nn
import torch.nn
def conv3x3(in_planes, out_planes, stride=1, dilation=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Jack12xl/scene-representation-networks | BasicBlock | false | 595 | [
"MIT"
] | 0 | 2691b23c956cf188a1fe4c84a888b19871cac8f4 | https://github.com/Jack12xl/scene-representation-networks/tree/2691b23c956cf188a1fe4c84a888b19871cac8f4 |
ResnetBlock | import torch
from torch import nn
import torch.nn.parallel
import torch.utils.data
import torch.utils
def actvn(x):
out = nn.functional.leaky_relu(x, 0.2)
return out
class ResnetBlock(nn.Module):
def __init__(self, fin, fout, fhidden=None, is_bias=True):
super(ResnetBlock, 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 import nn
import torch.nn.parallel
import torch.utils.data
import tor... | IdanAzuri/MixMatch-pytorch | ResnetBlock | false | 596 | [
"MIT"
] | 0 | b8de2bc30c09e1256b92e0394403487fc4f90135 | https://github.com/IdanAzuri/MixMatch-pytorch/tree/b8de2bc30c09e1256b92e0394403487fc4f90135 |
GaussianFilter | import torch
import torch.nn as nn
import torch.utils.data
class GaussianFilter(nn.Module):
def __init__(self, kernel_size=13, stride=1, padding=6):
super(GaussianFilter, self).__init__()
mean = (kernel_size - 1) / 2.0
variance = ((kernel_size - 1) / 6.0) ** 2.0
x_coord = torch.ar... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | JaguAroo/SRResCGAN | GaussianFilter | false | 597 | [
"MIT"
] | 0 | 9aac612aff631f7fb9142e0a36de9559cfc1a62d | https://github.com/JaguAroo/SRResCGAN/tree/9aac612aff631f7fb9142e0a36de9559cfc1a62d |
RecurrentNeuralNetwork | import torch
from torch import Tensor
from torch.functional import Tensor
from torch import nn
from typing import Tuple
from typing import Any
class RecurrentNeuralNetwork(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super().__init__()
self.hidden_size = hidden_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JThissen/machine_learning | RecurrentNeuralNetwork | false | 598 | [
"MIT"
] | 0 | 82e2b003fb25111dc2d9ac1c1b2fd637e9f4fdbc | https://github.com/JThissen/machine_learning/tree/82e2b003fb25111dc2d9ac1c1b2fd637e9f4fdbc |
LayerNormConv2d | import torch
from torch import nn
import torch.nn
class LayerNormConv2d(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamma = nn... | 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.nn
assert_size_stride = torch._C._dynamo.guar... | Jack12xl/scene-representation-networks | LayerNormConv2d | false | 599 | [
"MIT"
] | 0 | 2691b23c956cf188a1fe4c84a888b19871cac8f4 | https://github.com/Jack12xl/scene-representation-networks/tree/2691b23c956cf188a1fe4c84a888b19871cac8f4 |
TransformerLayer | import torch
from typing import Optional
from torch import nn
import torch.nn.functional as nnf
class MlpTransformer(nn.Module):
def __init__(self, in_dim, h_dim, out_d: 'Optional[int]'=None, act=nnf.
relu, dropout=0.0):
super().__init__()
out_d = out_d if out_d is not None else in_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.... | JAVI897/CLIP_prefix_caption | TransformerLayer | false | 600 | [
"MIT"
] | 0 | f4569891d01a5a790e9cdf850fb7feda3a0affc7 | https://github.com/JAVI897/CLIP_prefix_caption/tree/f4569891d01a5a790e9cdf850fb7feda3a0affc7 |
MyLinear | import torch
from torch import nn
from torch.nn import functional as F
import torchvision.transforms.functional as F
import torch.nn.functional as F
class MyLinear(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | AnimeshKoratana/blurryface | MyLinear | false | 601 | [
"Apache-2.0"
] | 0 | c6cb5feec02f6d5af3acb1678336800390715d65 | https://github.com/AnimeshKoratana/blurryface/tree/c6cb5feec02f6d5af3acb1678336800390715d65 |
KnowledgeDistillationLoss | import torch
from typing import *
from torch import nn
import torch.nn.functional as F
from torch import functional as F
from torch.nn import functional as F
class KnowledgeDistillationLoss(nn.Module):
def __init__(self, temperature=1):
super().__init__()
self.temperature = temperature
def f... | 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 typing import *
f... | JacobARose/image-utils | KnowledgeDistillationLoss | false | 602 | [
"MIT"
] | 0 | aa0e005c0b4df5198d188b074f4e21f8d8f97962 | https://github.com/JacobARose/image-utils/tree/aa0e005c0b4df5198d188b074f4e21f8d8f97962 |
AddPositionEmbs | import torch
from typing import *
from torch import nn
class AddPositionEmbs(nn.Module):
"""Adds (optionally learned) positional embeddings to the inputs."""
def __init__(self, num_patches: 'int', dim: 'int', dropout_rate:
'float'=0.0):
super(AddPositionEmbs, self).__init__()
self.pos... | 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 typing import *
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyn... | JacobARose/image-utils | AddPositionEmbs | false | 603 | [
"MIT"
] | 0 | aa0e005c0b4df5198d188b074f4e21f8d8f97962 | https://github.com/JacobARose/image-utils/tree/aa0e005c0b4df5198d188b074f4e21f8d8f97962 |
SplAtConv2d | from torch.autograd import Function
from torch.nn import Module
import logging
import torch
import torch.utils.data
import torch.distributed as dist
from torch import nn
import torch.nn.functional as F
from torch.autograd.function import Function
from torch.autograd import Function
from torch.nn.modules.utils import _p... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | JHMeusener/detectron2-ResNeSt | SplAtConv2d | false | 604 | [
"Apache-2.0"
] | 0 | 6abab6fb9496a528f6aa2d4e1e27f3e7ceb42685 | https://github.com/JHMeusener/detectron2-ResNeSt/tree/6abab6fb9496a528f6aa2d4e1e27f3e7ceb42685 |
cPReLU | import torch
import torch.nn as nn
class cPReLU(nn.Module):
def __init__(self, complex_axis=1):
super(cPReLU, self).__init__()
self.r_prelu = nn.PReLU()
self.i_prelu = nn.PReLU()
self.complex_axis = complex_axis
def forward(self, inputs):
real, imag = torch.chunk(inpu... | 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... | JamesLiao714/FullSubNet | cPReLU | false | 605 | [
"MIT"
] | 0 | dad740bac35b5d7544c97740ae59101455acdc40 | https://github.com/JamesLiao714/FullSubNet/tree/dad740bac35b5d7544c97740ae59101455acdc40 |
VAE | import torch
from torch.nn import functional as F
from torch import nn
import torch.nn
class VAE(nn.Module):
def __init__(self, in_ch, out_ch, hidden_ch=128):
super(VAE, self).__init__()
self.in_ch = in_ch
self.out_ch = out_ch
self.fc1 = nn.Linear(in_ch, hidden_ch)
self.fc... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Jack12xl/scene-representation-networks | VAE | false | 606 | [
"MIT"
] | 0 | 2691b23c956cf188a1fe4c84a888b19871cac8f4 | https://github.com/Jack12xl/scene-representation-networks/tree/2691b23c956cf188a1fe4c84a888b19871cac8f4 |
RealConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class RealConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=(1, 1),
stride=(1, 1), padding=(0, 0), dilation=1, groups=1, causal=True,
complex_axis=1):
"""
in_channels: real+imag
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | JamesLiao714/FullSubNet | RealConv2d | false | 607 | [
"MIT"
] | 0 | dad740bac35b5d7544c97740ae59101455acdc40 | https://github.com/JamesLiao714/FullSubNet/tree/dad740bac35b5d7544c97740ae59101455acdc40 |
VAE | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Linear(784, 5000)
self.fc2 = nn.Linear(5000, 5000)
self.fc21 = nn.Linear(5000, 20)
self.fc22 = nn.... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | DanIulian/minigrid_rl | VAE | false | 608 | [
"MIT"
] | 0 | d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa | https://github.com/DanIulian/minigrid_rl/tree/d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa |
rmse | import torch
class rmse(torch.nn.Module):
def __init__(self):
super(rmse, self).__init__()
def forward(self, y_true, y_pred):
mse = torch.mean((y_pred - y_true) ** 2, axis=-1)
rmse = torch.sqrt(mse + 1e-07)
return torch.mean(rmse)
def get_inputs():
return [torch.rand([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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | JamesLiao714/FullSubNet | rmse | false | 609 | [
"MIT"
] | 0 | dad740bac35b5d7544c97740ae59101455acdc40 | https://github.com/JamesLiao714/FullSubNet/tree/dad740bac35b5d7544c97740ae59101455acdc40 |
TV_L1Loss | import torch
import torch.nn as nn
import torch.utils.data
class TV_L1Loss(nn.Module):
def __init__(self, tv_loss_weight=1):
super(TV_L1Loss, self).__init__()
def forward(self, x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
count_h = self.tensor_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.triton_helpers import math as tl_math
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch.... | JaguAroo/SRResCGAN | TV_L1Loss | false | 610 | [
"MIT"
] | 0 | 9aac612aff631f7fb9142e0a36de9559cfc1a62d | https://github.com/JaguAroo/SRResCGAN/tree/9aac612aff631f7fb9142e0a36de9559cfc1a62d |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, gamma=2):
super().__init__()
self.gamma = gamma
def forward(self, logit, target, epoch=0):
target = target.float()
max_val = (-logit).clamp(min=0)
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... | Jason-George/irn | FocalLoss | false | 611 | [
"MIT"
] | 0 | b75441b5fb8080c1dbd8dbcb9b05720a4ceb2246 | https://github.com/Jason-George/irn/tree/b75441b5fb8080c1dbd8dbcb9b05720a4ceb2246 |
TV_L1LOSS | import torch
import torch.nn as nn
import torch.utils.data
class TV_L1LOSS(nn.Module):
def __init__(self):
super(TV_L1LOSS, self).__init__()
def forward(self, x, y):
size = x.size()
h_tv_diff = torch.abs(x[:, :, 1:, :] - x[:, :, :-1, :] - (y[:, :, 1
:, :] - y[:, :, :-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
import torch.utils.data
assert_size_stride = torch.... | JaguAroo/SRResCGAN | TV_L1LOSS | false | 612 | [
"MIT"
] | 0 | 9aac612aff631f7fb9142e0a36de9559cfc1a62d | https://github.com/JaguAroo/SRResCGAN/tree/9aac612aff631f7fb9142e0a36de9559cfc1a62d |
RealConvTranspose2d | import torch
import torch.nn as nn
class RealConvTranspose2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=(1, 1),
stride=(1, 1), padding=(0, 0), output_padding=(0, 0), groups=1):
"""
in_channels: real+imag
out_channels: real+imag
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | JamesLiao714/FullSubNet | RealConvTranspose2d | false | 613 | [
"MIT"
] | 0 | dad740bac35b5d7544c97740ae59101455acdc40 | https://github.com/JamesLiao714/FullSubNet/tree/dad740bac35b5d7544c97740ae59101455acdc40 |
DiscShiftLoss | import torch
import torch.nn as nn
class DiscShiftLoss(nn.Module):
"""Disc shift loss.
Args:
loss_weight (float, optional): Loss weight. Defaults to 1.0.
"""
def __init__(self, loss_weight=0.1):
super().__init__()
self.loss_weight = loss_weight
def forward(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Jason-Khan/mmediting | DiscShiftLoss | false | 614 | [
"Apache-2.0"
] | 0 | d187f95a675dff3eb975a575bd9278d643b5b645 | https://github.com/Jason-Khan/mmediting/tree/d187f95a675dff3eb975a575bd9278d643b5b645 |
ComplexConvTranspose2d | import torch
import torch.nn as nn
class ComplexConvTranspose2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=(1, 1),
stride=(1, 1), padding=(0, 0), output_padding=(0, 0), causal=False,
complex_axis=1, groups=1):
"""
in_channels: real+imag
o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | JamesLiao714/FullSubNet | ComplexConvTranspose2d | false | 615 | [
"MIT"
] | 0 | dad740bac35b5d7544c97740ae59101455acdc40 | https://github.com/JamesLiao714/FullSubNet/tree/dad740bac35b5d7544c97740ae59101455acdc40 |
ComplexConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class ComplexConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=(1, 1),
stride=(1, 1), padding=(0, 0), dilation=1, groups=1, causal=True,
complex_axis=1):
"""
in_channels: real+imag
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | JamesLiao714/FullSubNet | ComplexConv2d | false | 616 | [
"MIT"
] | 0 | dad740bac35b5d7544c97740ae59101455acdc40 | https://github.com/JamesLiao714/FullSubNet/tree/dad740bac35b5d7544c97740ae59101455acdc40 |
CharbonnierLoss | import functools
import torch
from torch.nn import 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".
Returns:
Tensor: Reduced lo... | 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 functools
from torch.... | Jason-Khan/mmediting | CharbonnierLoss | false | 617 | [
"Apache-2.0"
] | 0 | d187f95a675dff3eb975a575bd9278d643b5b645 | https://github.com/Jason-Khan/mmediting/tree/d187f95a675dff3eb975a575bd9278d643b5b645 |
SplitAndConcat | import torch
import torch.nn as nn
import torch.quantization.quantize_fx
import torch.utils.data
class SplitAndConcat(nn.Module):
"""Split the data from split_dim and concatenate in concat_dim.
@param split_dim from which axis the data will be chunk
@param concat_dim to which axis the data will be concat... | 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.quantization.quantize_fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | JacobSzwejbka/d2go | SplitAndConcat | false | 618 | [
"Apache-2.0"
] | 0 | d86ecc92eb97f14fcd97d626185f61c6817351e4 | https://github.com/JacobSzwejbka/d2go/tree/d86ecc92eb97f14fcd97d626185f61c6817351e4 |
ResidualBlock | import torch
import torch.nn as nn
import torch.utils.data
class ResidualBlock(nn.Module):
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.prelu = nn.PReLU()
self.conv2 = nn.Conv2d(channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | JaguAroo/SRResCGAN | ResidualBlock | false | 619 | [
"MIT"
] | 0 | 9aac612aff631f7fb9142e0a36de9559cfc1a62d | https://github.com/JaguAroo/SRResCGAN/tree/9aac612aff631f7fb9142e0a36de9559cfc1a62d |
SigmoidFocalClassificationLoss | import torch
import torch.nn as nn
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init__(self, gamma: 'float'=2.0, alpha: 'float'=0.25):
"""
Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
... | 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... | Javier-DlaP/OpenPCDet | SigmoidFocalClassificationLoss | false | 620 | [
"Apache-2.0"
] | 0 | c4d308ea8052dd92948e2377b161b2519254275b | https://github.com/Javier-DlaP/OpenPCDet/tree/c4d308ea8052dd92948e2377b161b2519254275b |
PinballLoss | import torch
from torch import nn
class PinballLoss(nn.Module):
"""
Calculates the quantile loss function.
Attributes
----------
self.pred : torch.tensor
Predictions.
self.target : torch.tensor
Target to predict.
self.quantiles : torch.tensor
"""
def __init__(self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Javicadserres/wind-production-forecast | PinballLoss | false | 621 | [
"MIT"
] | 0 | 903fbf53d2ea34dc1a63e89cee252e76d6c25876 | https://github.com/Javicadserres/wind-production-forecast/tree/903fbf53d2ea34dc1a63e89cee252e76d6c25876 |
SpatialGatherModule | import torch
from torch.nn import functional as F
import torch.nn as nn
class SpatialGatherModule(nn.Module):
"""Aggregate the context features according to the initial predicted
probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
def __init__(self, scale):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Jason-Khan/mmediting | SpatialGatherModule | false | 622 | [
"Apache-2.0"
] | 0 | d187f95a675dff3eb975a575bd9278d643b5b645 | https://github.com/Jason-Khan/mmediting/tree/d187f95a675dff3eb975a575bd9278d643b5b645 |
MultiHeadAttention | import torch
from torch import nn
class MultiHeadAttention(nn.Module):
def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.0):
super().__init__()
self.num_heads = num_heads
head_dim = dim_self // num_heads
self.scale = head_dim ** -0.5
self.to_queries = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JAVI897/CLIP_prefix_caption | MultiHeadAttention | false | 623 | [
"MIT"
] | 0 | f4569891d01a5a790e9cdf850fb7feda3a0affc7 | https://github.com/JAVI897/CLIP_prefix_caption/tree/f4569891d01a5a790e9cdf850fb7feda3a0affc7 |
PolicyNet | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.optim
import torch.autograd
from torch.distributions import Normal
class PolicyNet(nn.Module):
def __init__(self, learning_rate, lr_alpha, init_alpha, target_entropy,
in_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.... | ChangQingAAS/Deep-Reinforcement-Learning | PolicyNet | false | 624 | [
"MIT"
] | 0 | 3bc1381c632b1730a48e63e972aea62086c4287c | https://github.com/ChangQingAAS/Deep-Reinforcement-Learning/tree/3bc1381c632b1730a48e63e972aea62086c4287c |
BasicBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
import torch.utils.data
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=0, bias=True)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | JaguAroo/SRResCGAN | BasicBlock | false | 625 | [
"MIT"
] | 0 | 9aac612aff631f7fb9142e0a36de9559cfc1a62d | https://github.com/JaguAroo/SRResCGAN/tree/9aac612aff631f7fb9142e0a36de9559cfc1a62d |
MSEGradLoss | import torch
import torch.nn as nn
import torch.utils.data
class MSEGradLoss(nn.Module):
def __init__(self, grad=False):
super(MSEGradLoss, self).__init__()
self.grad = grad
def forward(self, input, target):
err = input - target
loss = err.norm(p=2).pow(2).div(err.numel())
... | 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
import... | JaguAroo/SRResCGAN | MSEGradLoss | false | 626 | [
"MIT"
] | 0 | 9aac612aff631f7fb9142e0a36de9559cfc1a62d | https://github.com/JaguAroo/SRResCGAN/tree/9aac612aff631f7fb9142e0a36de9559cfc1a62d |
L1CompositionLoss | import functools
import torch
from torch.nn import 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".
Returns:
Tensor: Reduced lo... | 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
from ... | Jason-Khan/mmediting | L1CompositionLoss | false | 627 | [
"Apache-2.0"
] | 0 | d187f95a675dff3eb975a575bd9278d643b5b645 | https://github.com/Jason-Khan/mmediting/tree/d187f95a675dff3eb975a575bd9278d643b5b645 |
KeypointRCNNPredictorNoUpscale | import torch
import torch.nn as nn
import torch.quantization.quantize_fx
import torch.utils.data
class KeypointRCNNPredictorNoUpscale(nn.Module):
def __init__(self, in_channels, num_keypoints):
super(KeypointRCNNPredictorNoUpscale, self).__init__()
input_features = in_channels
deconv_kern... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.quantization.quantize_fx
import torch.utils.d... | JacobSzwejbka/d2go | KeypointRCNNPredictorNoUpscale | false | 628 | [
"Apache-2.0"
] | 0 | d86ecc92eb97f14fcd97d626185f61c6817351e4 | https://github.com/JacobSzwejbka/d2go/tree/d86ecc92eb97f14fcd97d626185f61c6817351e4 |
TV_L2Loss | import torch
import torch.nn as nn
import torch.utils.data
class TV_L2Loss(nn.Module):
def __init__(self):
super(TV_L2Loss, self).__init__()
def forward(self, x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
count_h = self.tensor_size(x[:, :, 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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | JaguAroo/SRResCGAN | TV_L2Loss | false | 629 | [
"MIT"
] | 0 | 9aac612aff631f7fb9142e0a36de9559cfc1a62d | https://github.com/JaguAroo/SRResCGAN/tree/9aac612aff631f7fb9142e0a36de9559cfc1a62d |
SmoothPinballLoss | import torch
from torch import nn
from torch.nn import functional
class SmoothPinballLoss(nn.Module):
"""
Smoth version of the pinball loss function.
Parameters
----------
quantiles : torch.tensor
alpha : int
Smoothing rate.
Attributes
----------
self.pred : torch.tensor
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.gua... | Javicadserres/wind-production-forecast | SmoothPinballLoss | false | 630 | [
"MIT"
] | 0 | 903fbf53d2ea34dc1a63e89cee252e76d6c25876 | https://github.com/Javicadserres/wind-production-forecast/tree/903fbf53d2ea34dc1a63e89cee252e76d6c25876 |
MSECompositionLoss | import functools
import torch
from torch.nn import 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".
Returns:
Tensor: Reduced lo... | 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 functools
from torch.nn import functional as F
import torch.nn as nn
assert_size_s... | Jason-Khan/mmediting | MSECompositionLoss | false | 631 | [
"Apache-2.0"
] | 0 | d187f95a675dff3eb975a575bd9278d643b5b645 | https://github.com/Jason-Khan/mmediting/tree/d187f95a675dff3eb975a575bd9278d643b5b645 |
MaskedDense | from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from torch.nn.modules import Module
class MaskedDense(Module):
def __init__(self, in_dim, out_dim, bias=False):
super(MaskedDense, self).__init__()
self.in_dim = in_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.nn import Module
import torch.nn as nn
from torch.nn import init
from... | DwaraknathT/pyfl | MaskedDense | false | 632 | [
"MIT"
] | 0 | e9a4d1ca98c6167a567d0d46771ac9e1c7bb7322 | https://github.com/DwaraknathT/pyfl/tree/e9a4d1ca98c6167a567d0d46771ac9e1c7bb7322 |
CharbonnierCompLoss | import functools
import torch
from torch.nn import 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".
Returns:
Tensor: Reduced lo... | 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 functools
from torch.... | Jason-Khan/mmediting | CharbonnierCompLoss | false | 633 | [
"Apache-2.0"
] | 0 | d187f95a675dff3eb975a575bd9278d643b5b645 | https://github.com/Jason-Khan/mmediting/tree/d187f95a675dff3eb975a575bd9278d643b5b645 |
Fire | import torch
import torch.nn as nn
from torchvision.transforms.functional import *
class Fire(nn.Module):
def __init__(self, inplanes, squeeze_planes, expand1x1_planes,
expand3x3_planes):
super(Fire, self).__init__()
self.inplanes = inplanes
self.squeeze = nn.Conv2d(inplanes, sque... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 to... | BiggerBinBin/e3d_handpose_x-master | Fire | false | 634 | [
"Apache-2.0"
] | 0 | 20d091a8a019d85de26c81d02985868f79d5de84 | https://github.com/BiggerBinBin/e3d_handpose_x-master/tree/20d091a8a019d85de26c81d02985868f79d5de84 |
MaskedConv | from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from torch.nn.modules import Module
from torch.nn.modules.utils import _pair
class MaskedConv(Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, 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.nn import Module
import torch.nn as nn
from torch.nn import init
from... | DwaraknathT/pyfl | MaskedConv | false | 635 | [
"MIT"
] | 0 | e9a4d1ca98c6167a567d0d46771ac9e1c7bb7322 | https://github.com/DwaraknathT/pyfl/tree/e9a4d1ca98c6167a567d0d46771ac9e1c7bb7322 |
WeightedCrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class WeightedCrossEntropyLoss(nn.Module):
"""
Transform input to fit the fomation of PyTorch offical cross entropy loss
with anchor-wise weighting.
"""
def __init__(self):
super(WeightedCrossEntropyLoss, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Javier-DlaP/OpenPCDet | WeightedCrossEntropyLoss | false | 636 | [
"Apache-2.0"
] | 0 | c4d308ea8052dd92948e2377b161b2519254275b | https://github.com/Javier-DlaP/OpenPCDet/tree/c4d308ea8052dd92948e2377b161b2519254275b |
FusedLeakyReLU | import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
if input.ndim == 3:
return F.leaky_relu(input + bias.view(1, *rest_dim, bias.shape[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 import nn
from torch.nn import functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda... | Jerry2001/StyleCLIP | FusedLeakyReLU | false | 637 | [
"MIT"
] | 0 | 806216b4ce7b4c001ff05d7bd707b28d20ea6191 | https://github.com/Jerry2001/StyleCLIP/tree/806216b4ce7b4c001ff05d7bd707b28d20ea6191 |
Img_decoder_v3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, input_channel, output_channel, upsample=True):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(input_channel, output_channel, kernel_size=3,
padding=0)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Holmes-Alan/Photo2Sketch | Img_decoder_v3 | false | 638 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
ScaledDotProduction | import torch
import torch.nn as nn
class ScaledDotProduction(nn.Module):
"""Scaled Dot Production"""
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
self.softmax = nn.Softmax(dim=2)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Jincheng-Sun/Kylearn-pytorch | ScaledDotProduction | false | 639 | [
"MIT"
] | 0 | e72f2ab45a3f4724e843a27bec37664d3612fdca | https://github.com/Jincheng-Sun/Kylearn-pytorch/tree/e72f2ab45a3f4724e843a27bec37664d3612fdca |
AdditiveAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class AdditiveAttention(torch.nn.Module):
"""
A general additive attention module.
Originally for NAML.
"""
def __init__(self, query_vector_dim, candidate_vector_dim, writer=None,
tag=None, names=None):
super(Addit... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Janelovelzy/NewsRecommendation | AdditiveAttention | false | 640 | [
"MIT"
] | 0 | bd2d70e828ffa66ea4cbbd3c6ac09f14e7f0179b | https://github.com/Janelovelzy/NewsRecommendation/tree/bd2d70e828ffa66ea4cbbd3c6ac09f14e7f0179b |
ScaledDotProductAttention | import torch
import numpy as np
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
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
from torch._inductor.runtime.... | Jincheng-Sun/Kylearn-pytorch | ScaledDotProductAttention | false | 641 | [
"MIT"
] | 0 | e72f2ab45a3f4724e843a27bec37664d3612fdca | https://github.com/Jincheng-Sun/Kylearn-pytorch/tree/e72f2ab45a3f4724e843a27bec37664d3612fdca |
BCE_loss | import torch
import torch.nn as nn
class BCE_loss(nn.Module):
def __init__(self):
super(BCE_loss, self).__init__()
def forward(self, pred, gt):
bce_loss = nn.BCELoss(size_average=True)
bce_out = bce_loss(pred, gt)
return bce_out
def get_inputs():
return [torch.rand([4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Jianrong-Lu/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival | BCE_loss | false | 642 | [
"MIT"
] | 0 | 257cf17ce6d405166dd8449f3b34e305cb5103b2 | https://github.com/Jianrong-Lu/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival/tree/257cf17ce6d405166dd8449f3b34e305cb5103b2 |
ReactionDotProduction | import torch
import torch.nn as nn
class ReactionDotProduction(nn.Module):
""" Scaled Dot Productionss """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
self.softmax = nn.Softmax(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.... | Jincheng-Sun/Kylearn-pytorch | ReactionDotProduction | false | 643 | [
"MIT"
] | 0 | e72f2ab45a3f4724e843a27bec37664d3612fdca | https://github.com/Jincheng-Sun/Kylearn-pytorch/tree/e72f2ab45a3f4724e843a27bec37664d3612fdca |
GAT | import torch
import torch.nn as nn
import torch.nn.functional as F
class GATLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, alpha=0.2):
super(GATLayer, self).__init__()
self.in_features = in_features... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | JLUVicent/DAEGC | GAT | false | 644 | [
"MIT"
] | 0 | 9a4cc50e40e8521fafb00960d1adf8216674c8f6 | https://github.com/JLUVicent/DAEGC/tree/9a4cc50e40e8521fafb00960d1adf8216674c8f6 |
LinearConvExpansion | import torch
import numpy as np
import torch.nn as nn
class LinearConvExpansion(nn.Module):
"""expansion 1D -> 2D"""
def __init__(self, d_features, n_channel, n_depth):
super().__init__()
self.d_features = d_features
self.n_channel = n_channel
self.n_depth = n_depth
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | Jincheng-Sun/Kylearn-pytorch | LinearConvExpansion | false | 645 | [
"MIT"
] | 0 | e72f2ab45a3f4724e843a27bec37664d3612fdca | https://github.com/Jincheng-Sun/Kylearn-pytorch/tree/e72f2ab45a3f4724e843a27bec37664d3612fdca |
PlainRefiner | import torch
import torch.nn as nn
class PlainRefiner(nn.Module):
"""Simple refiner from Deep Image Matting.
Args:
conv_channels (int): Number of channels produced by the three main
convolutional layer.
loss_refine (dict): Config of the loss of the refiner. Default: None.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Jason-Khan/mmediting | PlainRefiner | false | 646 | [
"Apache-2.0"
] | 0 | d187f95a675dff3eb975a575bd9278d643b5b645 | https://github.com/Jason-Khan/mmediting/tree/d187f95a675dff3eb975a575bd9278d643b5b645 |
AffineGridGen | from torch.nn import Module
import torch
import torch.nn.functional as F
import torch.nn
from torch.nn.modules.module import Module
class AffineGridGen(Module):
def __init__(self, out_h=240, out_w=240, out_ch=3, use_cuda=True):
super(AffineGridGen, self).__init__()
self.out_h = out_h
self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.nn
from torch.nn.modules.module import Module
assert_size_stride = torch._C._dynamo.guards.assert_s... | JiwonCocoder/matching1 | AffineGridGen | false | 647 | [
"MIT"
] | 0 | 75274312a4ab6ba318c3d241abc0eb292f6ce69c | https://github.com/JiwonCocoder/matching1/tree/75274312a4ab6ba318c3d241abc0eb292f6ce69c |
FeatureCorrelation | import torch
import torch.nn as nn
import torch.nn
def featureL2Norm(feature):
epsilon = 1e-06
norm = torch.pow(torch.sum(torch.pow(feature, 2), 1) + epsilon, 0.5
).unsqueeze(1).expand_as(feature)
return torch.div(feature, norm)
class FeatureCorrelation(torch.nn.Module):
def __init__(self, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JiwonCocoder/matching1 | FeatureCorrelation | false | 648 | [
"MIT"
] | 0 | 75274312a4ab6ba318c3d241abc0eb292f6ce69c | https://github.com/JiwonCocoder/matching1/tree/75274312a4ab6ba318c3d241abc0eb292f6ce69c |
StyleMod | import torch
from torch import nn
from torch.nn import functional as F
import torchvision.transforms.functional as F
import torch.nn.functional as F
class MyLinear(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 functional as F
import torchvision.tra... | AnimeshKoratana/blurryface | StyleMod | false | 649 | [
"Apache-2.0"
] | 0 | c6cb5feec02f6d5af3acb1678336800390715d65 | https://github.com/AnimeshKoratana/blurryface/tree/c6cb5feec02f6d5af3acb1678336800390715d65 |
Actor | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from math import *
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Jeyhooon/deep-reinforcement-learning | Actor | false | 650 | [
"MIT"
] | 0 | 7a6f1974493a2058635539a4868512cdf3fb5bdb | https://github.com/Jeyhooon/deep-reinforcement-learning/tree/7a6f1974493a2058635539a4868512cdf3fb5bdb |
Critic | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from math import *
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | Jeyhooon/deep-reinforcement-learning | Critic | false | 651 | [
"MIT"
] | 0 | 7a6f1974493a2058635539a4868512cdf3fb5bdb | https://github.com/Jeyhooon/deep-reinforcement-learning/tree/7a6f1974493a2058635539a4868512cdf3fb5bdb |
ModulatedConv2d | import math
import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
if input.ndim == 3:
return F.leaky_relu(input + bias.view(1, *rest_dim, bias.shape[0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | Jerry2001/StyleCLIP | ModulatedConv2d | false | 652 | [
"MIT"
] | 0 | 806216b4ce7b4c001ff05d7bd707b28d20ea6191 | https://github.com/Jerry2001/StyleCLIP/tree/806216b4ce7b4c001ff05d7bd707b28d20ea6191 |
Bottleneck | import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hid)
self.w_2 = nn.Linear(d_hid, d_in)
self.layer_norm = nn.LayerNorm(d_in, eps=1e-06)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Jincheng-Sun/Kylearn-pytorch | Bottleneck | false | 653 | [
"MIT"
] | 0 | e72f2ab45a3f4724e843a27bec37664d3612fdca | https://github.com/Jincheng-Sun/Kylearn-pytorch/tree/e72f2ab45a3f4724e843a27bec37664d3612fdca |
ConvExpansion | import torch
import torch.nn as nn
class ConvExpansion(nn.Module):
"""expansion 1D -> 2D"""
def __init__(self, d_features, n_channel, n_depth):
super().__init__()
self.d_features = d_features
self.n_channel = n_channel
self.n_depth = n_depth
self.conv = nn.Conv1d(1, n_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Jincheng-Sun/Kylearn-pytorch | ConvExpansion | false | 654 | [
"MIT"
] | 0 | e72f2ab45a3f4724e843a27bec37664d3612fdca | https://github.com/Jincheng-Sun/Kylearn-pytorch/tree/e72f2ab45a3f4724e843a27bec37664d3612fdca |
ChannelSELayer3D | import torch
import torch.nn as nn
class ChannelSELayer3D(nn.Module):
"""
3D extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
"""
def __init__(self, num_channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Jianrong-Lu/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival | ChannelSELayer3D | false | 655 | [
"MIT"
] | 0 | 257cf17ce6d405166dd8449f3b34e305cb5103b2 | https://github.com/Jianrong-Lu/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival/tree/257cf17ce6d405166dd8449f3b34e305cb5103b2 |
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