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 |
|---|---|---|---|---|---|---|---|---|---|---|
HardSigmoid | import torch
import torch.nn as nn
class HardSigmoid(nn.Module):
def __init__(self, bias=1.0, divisor=2.0, min_value=0.0, max_value=1.0):
super(HardSigmoid, self).__init__()
assert divisor != 0, 'divisor is not allowed to be equal to zero'
self.bias = bias
self.divisor = divisor
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | DetectionBLWX/WSDDN.pytorch | HardSigmoid | false | 17,216 | [
"MIT"
] | 7 | 05020d9d0445af90ba0af3f095aa12b18e3da7d2 | https://github.com/DetectionBLWX/WSDDN.pytorch/tree/05020d9d0445af90ba0af3f095aa12b18e3da7d2 |
MaxPoolStride1 | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
import torch._utils
class MaxPoolStride1(nn.Module):
def __init__(self, kernel_size):
super(MaxPoolStride1, self).__init__()
self.kernel_size = kernel_size
self.p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import ... | DatatangAILAB/SuanFaShiXun04 | MaxPoolStride1 | false | 17,217 | [
"Apache-2.0"
] | 5 | f478e40dd84240ac71cbb54e6bacf9ff556fbb3e | https://github.com/DatatangAILAB/SuanFaShiXun04/tree/f478e40dd84240ac71cbb54e6bacf9ff556fbb3e |
SamePadConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class SamePadConv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1, groups=1, bias=True):
super(SamePadConv2d, self).__init__(in_channels, out_channels,
kernel_size, stride,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DandelionLau/NetworkCollections | SamePadConv2d | false | 17,218 | [
"Apache-2.0"
] | 8 | 29e5cd2091f7085b3241209ed9447f2baadbce41 | https://github.com/DandelionLau/NetworkCollections/tree/29e5cd2091f7085b3241209ed9447f2baadbce41 |
NormedLinear | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
class NormedLinear(nn.Module):
def __init__(self, in_features, out_features):
super(NormedLinear, self).__init__()
self.weight = Parameter(torch.Tensor(in_feature... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Data-Designer/Feature-Space-Augmentation-for-Long-Tailed-Data | NormedLinear | false | 17,219 | [
"MIT"
] | 9 | ac6bced6269d6ebaa3fc0935603d905a7f11a6fa | https://github.com/Data-Designer/Feature-Space-Augmentation-for-Long-Tailed-Data/tree/ac6bced6269d6ebaa3fc0935603d905a7f11a6fa |
L | import math
import torch
import torch.nn as nn
def drop_none(**kwargs):
r = {k: v for k, v in kwargs.items() if v is not None}
return r
class L(nn.Module):
def __init__(self, num_linear, input_features, output_features, dtype=
None, device=None):
super().__init__()
options = dro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | DataCanvasIO/YLearn | L | false | 17,220 | [
"Apache-2.0"
] | 3 | d65b5afb83deed154c710de9096317165d95014a | https://github.com/DataCanvasIO/YLearn/tree/d65b5afb83deed154c710de9096317165d95014a |
Downsample | import torch
from torch import nn
class Downsample(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv = nn.Conv2d(dim, dim, 3, 2, 1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | DavidRuhe/simple-variational-diffusion-models | Downsample | false | 17,221 | [
"MIT"
] | 4 | a32355bf052a8f08e9c1919080588d0b22c8de4e | https://github.com/DavidRuhe/simple-variational-diffusion-models/tree/a32355bf052a8f08e9c1919080588d0b22c8de4e |
Upsample | import torch
from torch import nn
class Upsample(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv = nn.ConvTranspose2d(dim, dim, 4, 2, 1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | DavidRuhe/simple-variational-diffusion-models | Upsample | false | 17,222 | [
"MIT"
] | 4 | a32355bf052a8f08e9c1919080588d0b22c8de4e | https://github.com/DavidRuhe/simple-variational-diffusion-models/tree/a32355bf052a8f08e9c1919080588d0b22c8de4e |
SqueezeExcitate | import torch
from torch import nn
import torch.nn.functional as F
class SqueezeExcitate(nn.Module):
def __init__(self, in_channels, se_size, activation=None):
super(SqueezeExcitate, self).__init__()
self.dim_reduce = nn.Conv2d(in_channels=in_channels, out_channels=
se_size, kernel_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | Dogy06/COVID-Efficientnet-Pytorch-1 | SqueezeExcitate | false | 17,223 | [
"MIT"
] | 4 | 3c1f7d9513abe59783152efca28dc886bf4afc2f | https://github.com/Dogy06/COVID-Efficientnet-Pytorch-1/tree/3c1f7d9513abe59783152efca28dc886bf4afc2f |
LabelSmoothingCrossEntropyBCE | import torch
import torch.nn as nn
import torch.nn.functional as F
class LabelSmoothingCrossEntropyBCE(nn.Module):
def __init__(self, smoothing=0.1):
super(LabelSmoothingCrossEntropyBCE, self).__init__()
assert smoothing < 1.0
self.smoothing = smoothing
self.confidence = 1.0 - smo... | 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... | Diyago/Graph-clasification-by-computer-vision | LabelSmoothingCrossEntropyBCE | false | 17,224 | [
"Apache-2.0"
] | 9 | 703c44b98f9875d7a7b6db1c2b96372e11e256d6 | https://github.com/Diyago/Graph-clasification-by-computer-vision/tree/703c44b98f9875d7a7b6db1c2b96372e11e256d6 |
PositiveLinear | import torch
from torch import nn
class PositiveLinear(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int') ->None:
super().__init__()
self.weight = nn.Parameter(torch.randn(in_features, out_features))
self.bias = nn.Parameter(torch.zeros(out_features))
self.sof... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | DavidRuhe/simple-variational-diffusion-models | PositiveLinear | false | 17,225 | [
"MIT"
] | 4 | a32355bf052a8f08e9c1919080588d0b22c8de4e | https://github.com/DavidRuhe/simple-variational-diffusion-models/tree/a32355bf052a8f08e9c1919080588d0b22c8de4e |
Residual_Covolution | import torch
import torch.nn as nn
class Residual_Covolution(nn.Module):
def __init__(self, icol, ocol, num_classes):
super(Residual_Covolution, self).__init__()
self.conv1 = nn.Conv2d(icol, ocol, kernel_size=3, stride=1, padding
=12, dilation=12, bias=True)
self.conv2 = nn.Co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Dayan-Guan/SVMin | Residual_Covolution | false | 17,227 | [
"MIT"
] | 6 | d72b21f65958b1fda0abdbb60bd78d01e9d9cc8f | https://github.com/Dayan-Guan/SVMin/tree/d72b21f65958b1fda0abdbb60bd78d01e9d9cc8f |
GroupNorm | import torch
import torch.nn as nn
class GroupNorm(nn.Module):
def __init__(self, num_features, num_groups=32, eps=1e-05):
super(GroupNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(1, num_features, 1, 1))
self.bias = nn.Parameter(torch.zeros(1, num_features, 1, 1))
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | E-Dreamer-LQ/Astronomical_Target_Detection | GroupNorm | false | 17,228 | [
"MIT"
] | 6 | 0c2d6c2e516ff1efa28d44582442123c3a03f079 | https://github.com/E-Dreamer-LQ/Astronomical_Target_Detection/tree/0c2d6c2e516ff1efa28d44582442123c3a03f079 |
FCUDown | import torch
import torch.nn as nn
from functools import partial
class FCUDown(nn.Module):
""" CNN feature maps -> Transformer patch embeddings
"""
def __init__(self, inplanes, outplanes, dw_stride, act_layer=nn.GELU,
norm_layer=partial(nn.LayerNorm, eps=1e-06)):
super(FCUDown, self).__in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Curli-quan/fewshot-select | FCUDown | false | 17,229 | [
"Apache-2.0"
] | 7 | 34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe | https://github.com/Curli-quan/fewshot-select/tree/34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe |
LINEAR_LOGSOFTMAX | import torch
import torch.nn as nn
class LINEAR_LOGSOFTMAX(nn.Module):
def __init__(self, input_dim, nclass):
super(LINEAR_LOGSOFTMAX, self).__init__()
self.fc = nn.Linear(input_dim, nclass)
self.logic = nn.LogSoftmax(dim=1)
self.lossfunction = nn.NLLLoss()
def forward(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.... | Drkun/Lifelong-Zero-Shot-Learning | LINEAR_LOGSOFTMAX | false | 17,231 | [
"Apache-2.0"
] | 9 | 5cea07c25e14aed1c544c83863f4733a8213ddb0 | https://github.com/Drkun/Lifelong-Zero-Shot-Learning/tree/5cea07c25e14aed1c544c83863f4733a8213ddb0 |
GroupBatchnorm2d | import torch
import torch.nn as nn
class GroupBatchnorm2d(nn.Module):
def __init__(self, c_num, group_num=16, eps=1e-10):
super(GroupBatchnorm2d, self).__init__()
self.group_num = group_num
self.gamma = nn.Parameter(torch.ones(c_num, 1, 1))
self.beta = nn.Parameter(torch.zeros(c_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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | E-Dreamer-LQ/Astronomical_Target_Detection | GroupBatchnorm2d | false | 17,232 | [
"MIT"
] | 6 | 0c2d6c2e516ff1efa28d44582442123c3a03f079 | https://github.com/E-Dreamer-LQ/Astronomical_Target_Detection/tree/0c2d6c2e516ff1efa28d44582442123c3a03f079 |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
.. math::
\\begin{array}{ll}
x = context*output \\\\
attn = exp(x_i) / sum_j exp(x_j) \\\\
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | EVASHINJI/Seq2Seq-PyTorch | Attention | false | 17,233 | [
"Apache-2.0"
] | 4 | d53f8d7c240bd7c16ebd1475384774bd064b4b03 | https://github.com/EVASHINJI/Seq2Seq-PyTorch/tree/d53f8d7c240bd7c16ebd1475384774bd064b4b03 |
InstanceNormalization | import torch
import torch.nn as nn
class InstanceNormalization(torch.nn.Module):
"""InstanceNormalization
Improves convergence of neural-style.
ref: https://arxiv.org/pdf/1607.08022.pdf
"""
def __init__(self, dim, eps=1e-09):
super(InstanceNormalization, self).__init__()
self.scal... | 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_... | E-Dreamer-LQ/Astronomical_Target_Detection | InstanceNormalization | false | 17,234 | [
"MIT"
] | 6 | 0c2d6c2e516ff1efa28d44582442123c3a03f079 | https://github.com/E-Dreamer-LQ/Astronomical_Target_Detection/tree/0c2d6c2e516ff1efa28d44582442123c3a03f079 |
ConvLSTMCls | import torch
import torch.nn as nn
class ConvLSTMCls(nn.Module):
def __init__(self, in_channels, out_channels):
"""
Convolutional LSTM block for generation network
Args:
- in_channels: Int. Number of channels of the input of Conv2D
- out_channels: Int. Number of 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.triton_helpers import libdevice
import torch.nn as ... | DveloperY0115/torch-gqn | ConvLSTMCls | false | 17,235 | [
"Apache-2.0"
] | 3 | 3d1be9d73522e3d52f15076e0e9cb485dcab638b | https://github.com/DveloperY0115/torch-gqn/tree/3d1be9d73522e3d52f15076e0e9cb485dcab638b |
FiLMSIREN | import math
import torch
from torch import nn
class FiLMSIREN(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int', omega_0:
'float'=30.0, is_first: 'bool'=False, bias: 'bool'=True):
super().__init__()
self.in_features = in_features
self.out_features = out_featur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
f... | EasternJournalist/pi-GAN | FiLMSIREN | false | 17,236 | [
"MIT"
] | 4 | 3d57611e1c8fca2f3cd00fde1989ec1f9dd94d55 | https://github.com/EasternJournalist/pi-GAN/tree/3d57611e1c8fca2f3cd00fde1989ec1f9dd94d55 |
IOUloss | import torch
import torch.nn as nn
class IOUloss(nn.Module):
def __init__(self, reduction='none', loss_type='iou'):
super(IOUloss, self).__init__()
self.reduction = reduction
self.loss_type = loss_type
def forward(self, pred, target):
assert pred.shape[0] == target.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._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | DerryHub/ByteTrack-attack | IOUloss | false | 17,237 | [
"MIT"
] | 6 | f237894c7985863c0830401933ebd89ca92bde96 | https://github.com/DerryHub/ByteTrack-attack/tree/f237894c7985863c0830401933ebd89ca92bde96 |
NetEnd | import torch
import torch.nn as nn
class NetEnd(nn.Module):
def __init__(self, num_classes: 'int'):
super(NetEnd, self).__init__()
self.num_classes = num_classes
self.fc_net1 = nn.Conv2d(21, self.num_classes, kernel_size=1, stride=1)
assert self.num_classes > 0, 'The number of 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.... | EadCat/Road-Extraction | NetEnd | false | 17,238 | [
"MIT"
] | 4 | 9d4831b6c3a5ef07676cbe1c79b03045fda427ea | https://github.com/EadCat/Road-Extraction/tree/9d4831b6c3a5ef07676cbe1c79b03045fda427ea |
IN_self | import torch
import torch.nn as nn
class IN_self(nn.Module):
def __init__(self, num_features):
super(IN_self, self).__init__()
self.num_features = num_features
self.gamma = nn.Parameter(torch.Tensor(1, num_features, 1, 1),
requires_grad=True)
self.beta = nn.Parameter(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 libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | EkdeepSLubana/BeyondBatchNorm | IN_self | false | 17,239 | [
"MIT"
] | 10 | 2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 |
LN_self | import torch
import torch.nn as nn
class LN_self(nn.Module):
def __init__(self, num_features):
super().__init__()
shape = 1, num_features, 1, 1
self.gamma = nn.Parameter(torch.ones(shape))
self.beta = nn.Parameter(torch.zeros(shape))
def forward(self, X, eps=1e-05):
v... | 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_... | EkdeepSLubana/BeyondBatchNorm | LN_self | false | 17,240 | [
"MIT"
] | 10 | 2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 |
TLU | import torch
import torch.nn as nn
class TLU(nn.Module):
def __init__(self, num_features):
"""max(y, tau) = max(y - tau, 0) + tau = ReLU(y - tau) + tau"""
super(TLU, self).__init__()
self.num_features = num_features
self.tau = nn.Parameter(torch.Tensor(1, num_features, 1, 1),
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | EkdeepSLubana/BeyondBatchNorm | TLU | false | 17,241 | [
"MIT"
] | 10 | 2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.lin1 = nn.Linear(4, 50)
self.lin2 = nn.Linear(50, 50)
self.out = nn.Linear(50, 3)
def forward(self, x):
x = F.relu(self.lin1(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
assert_... | Elli1993/custom_net_on_movidius | Net | false | 17,242 | [
"MIT"
] | 9 | cd7ed784e6d38fe696c1ae1ff0e5a31d1b52c7dc | https://github.com/Elli1993/custom_net_on_movidius/tree/cd7ed784e6d38fe696c1ae1ff0e5a31d1b52c7dc |
CoughNet | import torch
class CoughNet(torch.nn.Module):
def __init__(self, input_size):
super(CoughNet, self).__init__()
self.l1 = torch.nn.Linear(input_size, 512)
self.l2 = torch.nn.Linear(512, 256)
self.l3 = torch.nn.Linear(256, 128)
self.l4 = torch.nn.Linear(128, 64)
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
assert_size_stride = torch._C... | DerWaldi/COVID-19-Cough-Classification | CoughNet | false | 17,243 | [
"MIT"
] | 7 | 40f85133b0b8973c088dc2730c592af1b89b29b7 | https://github.com/DerWaldi/COVID-19-Cough-Classification/tree/40f85133b0b8973c088dc2730c592af1b89b29b7 |
FRN_self | import torch
import torch.nn as nn
class FRN_self(nn.Module):
def __init__(self, num_features, eps=1e-05, is_eps_learnable=True):
super(FRN_self, self).__init__()
self.num_features = num_features
self.init_eps = eps
self.is_eps_learnable = is_eps_learnable
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, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | EkdeepSLubana/BeyondBatchNorm | FRN_self | false | 17,244 | [
"MIT"
] | 10 | 2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 |
custom_embedding | import torch
import torch.nn as nn
import torch.nn.functional as F
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | EpiSci/SoCRATES | custom_embedding | false | 17,245 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d |
ClassifierEnd | import torch
import torch.nn as nn
class ClassifierEnd(nn.Module):
def __init__(self, num_classes: 'int'):
super(ClassifierEnd, self).__init__()
self.num_classes = num_classes
self.fc_net1 = nn.Conv2d(21, self.num_classes, kernel_size=1, stride=1)
self.fc_net2 = nn.Conv2d(self.num... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | EadCat/Road-Extraction | ClassifierEnd | false | 17,246 | [
"MIT"
] | 4 | 9d4831b6c3a5ef07676cbe1c79b03045fda427ea | https://github.com/EadCat/Road-Extraction/tree/9d4831b6c3a5ef07676cbe1c79b03045fda427ea |
CoordConv2D | import torch
from torch import nn
class CoordConv2D(nn.Module):
def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
'int'=3, stride: 'int'=1, padding: 'int'=1, with_r: 'bool'=False):
super().__init__()
self.in_channel = in_channels
self.with_r = with_r
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | EasternJournalist/pi-GAN | CoordConv2D | false | 17,247 | [
"MIT"
] | 4 | 3d57611e1c8fca2f3cd00fde1989ec1f9dd94d55 | https://github.com/EasternJournalist/pi-GAN/tree/3d57611e1c8fca2f3cd00fde1989ec1f9dd94d55 |
DoubleSymLayer | import copy
import math
import torch
import torch.nn as nn
def normalInit(dims):
"""
Essentially, PyTorch's init.xavier_normal_ but clamped
:param K: tensor to be initialized/overwritten
:return: initialized tensor on the device in the nn.Parameter wrapper
"""
K = torch.zeros(dims)
fan_in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import copy
import math
impor... | EmoryMLIP/DynamicBlocks | DoubleSymLayer | false | 17,248 | [
"MIT"
] | 9 | 52acc9fbc1a2640c6ac8922fa18105279ccaea97 | https://github.com/EmoryMLIP/DynamicBlocks/tree/52acc9fbc1a2640c6ac8922fa18105279ccaea97 |
MolDQN | import torch
import torch.nn as nn
class MolDQN(nn.Module):
def __init__(self, input_length, output_length):
super(MolDQN, self).__init__()
self.linear_1 = nn.Linear(input_length, 1024)
self.linear_2 = nn.Linear(1024, 512)
self.linear_3 = nn.Linear(512, 128)
self.linear_4 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | EXJUSTICE/MolDQN-pytorch | MolDQN | false | 17,249 | [
"MIT"
] | 4 | 86828f898461e9f7722ac8a1e0b9fede2c45afe0 | https://github.com/EXJUSTICE/MolDQN-pytorch/tree/86828f898461e9f7722ac8a1e0b9fede2c45afe0 |
Attention | import torch
import torch.nn as nn
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
except:
pass
clas... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | EpiSci/SoCRATES | Attention | false | 17,250 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d |
LinearAttention | import torch
from torch import nn
class LinearAttention(nn.Module):
def __init__(self, dim, heads=4, dim_head=32):
super().__init__()
self.heads = heads
self.dim_head = dim_head
self.hidden_dim = dim_head * heads
self.to_qkv = nn.Conv2d(dim, self.hidden_dim * 3, 1, bias=Fa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DavidRuhe/simple-variational-diffusion-models | LinearAttention | false | 17,251 | [
"MIT"
] | 4 | a32355bf052a8f08e9c1919080588d0b22c8de4e | https://github.com/DavidRuhe/simple-variational-diffusion-models/tree/a32355bf052a8f08e9c1919080588d0b22c8de4e |
EltwiseSubEmbed | import torch
from torch import nn
class EltwiseSubEmbed(nn.Module):
def __init__(self, nonlinearity='square', use_batch_norm=False,
use_classifier=False, num_features=0, num_classes=0):
super(EltwiseSubEmbed, self).__init__()
self.nonlinearity = nonlinearity
if nonlinearity is not... | 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... | Event0511/curling-reid | EltwiseSubEmbed | false | 17,252 | [
"Apache-2.0"
] | 3 | 1494d0faeed951e495573c694362f001df5bf6fd | https://github.com/Event0511/curling-reid/tree/1494d0faeed951e495573c694362f001df5bf6fd |
SNRNetwork | import torch
from torch import nn
class PositiveLinear(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int') ->None:
super().__init__()
self.weight = nn.Parameter(torch.randn(in_features, out_features))
self.bias = nn.Parameter(torch.zeros(out_features))
self.sof... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | DavidRuhe/simple-variational-diffusion-models | SNRNetwork | false | 17,253 | [
"MIT"
] | 4 | a32355bf052a8f08e9c1919080588d0b22c8de4e | https://github.com/DavidRuhe/simple-variational-diffusion-models/tree/a32355bf052a8f08e9c1919080588d0b22c8de4e |
EmbeddingLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | EpiSci/SoCRATES | EmbeddingLayer | false | 17,254 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d |
FakeReLUM | import torch
import torch.nn as nn
import torch.utils.data
class FakeReLU(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input.clamp(min=0)
@staticmethod
def backward(ctx, grad_output):
return grad_output
class FakeReLUM(nn.Module):
def forward(self, x... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | Ethos-lab/robust-representation-matching | FakeReLUM | false | 17,255 | [
"MIT"
] | 3 | 80d98f11846468c31278146583b9ef4750190211 | https://github.com/Ethos-lab/robust-representation-matching/tree/80d98f11846468c31278146583b9ef4750190211 |
ConditionalBatchNorm | import torch
class ConditionalBatchNorm(torch.nn.Module):
def __init__(self, no, z_dim):
super().__init__()
self.no = no
self.bn = torch.nn.InstanceNorm2d(no, affine=False)
self.condition = torch.nn.Linear(z_dim, 2 * no)
def forward(self, x, z):
cond = self.condition(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ElementAI/beyond-trivial-explanations | ConditionalBatchNorm | false | 17,256 | [
"Apache-2.0"
] | 3 | c517d7bdbab68b6a26f74cee4d15e948b3b47238 | https://github.com/ElementAI/beyond-trivial-explanations/tree/c517d7bdbab68b6a26f74cee4d15e948b3b47238 |
mlp_layer | import torch
import torch.nn as nn
import torch.nn.functional as F
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | EpiSci/SoCRATES | mlp_layer | false | 17,257 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d |
MultVAE_encoder | import torch
import torch.sparse
import torch.nn as nn
class MultVAE_encoder(nn.Module):
def __init__(self, item_dim: 'int', hidden_dim=600, latent_dim=200,
n_hidden_layers=1, dropout=0.5, nonlinearity=nn.Tanh):
super(MultVAE_encoder, self).__init__()
self.item_dim = item_dim
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
import torch.sparse... | EricHe98/sad_final_project | MultVAE_encoder | false | 17,258 | [
"MIT"
] | 3 | 4b2b57e44f939840eede6f134493c5f8d809b1a7 | https://github.com/EricHe98/sad_final_project/tree/4b2b57e44f939840eede6f134493c5f8d809b1a7 |
VAE | import torch
import numpy as np
import torch.utils.data
import torch.nn as nn
class VAE(nn.Module):
def __init__(self, input_size, latent_size):
super(VAE, self).__init__()
self.latent_size = latent_size
self.input_size = input_size
self.mu_layer = nn.Linear(self.input_size, self.... | 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.triton_helpers import math... | ErikHumphrey/sustain-seq2seq | VAE | false | 17,259 | [
"Apache-2.0"
] | 4 | c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 | https://github.com/ErikHumphrey/sustain-seq2seq/tree/c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 |
MLP | import torch
class MLP(torch.nn.Module):
def __init__(self, ni, no, nhidden, nlayers):
super().__init__()
self.nlayers = nlayers
for i in range(nlayers):
if i == 0:
setattr(self, 'linear%d' % i, torch.nn.Linear(ni, nhidden,
bias=False))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | ElementAI/beyond-trivial-explanations | MLP | false | 17,260 | [
"Apache-2.0"
] | 3 | c517d7bdbab68b6a26f74cee4d15e948b3b47238 | https://github.com/ElementAI/beyond-trivial-explanations/tree/c517d7bdbab68b6a26f74cee4d15e948b3b47238 |
merge_layer | import torch
import torch.nn as nn
import torch.nn.functional as F
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | EpiSci/SoCRATES | merge_layer | false | 17,261 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d |
PreactDoubleLayer | import copy
import math
import torch
import torch.nn as nn
def normalInit(dims):
"""
Essentially, PyTorch's init.xavier_normal_ but clamped
:param K: tensor to be initialized/overwritten
:return: initialized tensor on the device in the nn.Parameter wrapper
"""
K = torch.zeros(dims)
fan_in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import copy
import math
impor... | EmoryMLIP/DynamicBlocks | PreactDoubleLayer | false | 17,262 | [
"MIT"
] | 9 | 52acc9fbc1a2640c6ac8922fa18105279ccaea97 | https://github.com/EmoryMLIP/DynamicBlocks/tree/52acc9fbc1a2640c6ac8922fa18105279ccaea97 |
conv_head_pooling | import torch
import torch.nn as nn
class conv_head_pooling(nn.Module):
def __init__(self, in_feature, out_feature, stride, padding_mode='zeros'):
super(conv_head_pooling, self).__init__()
self.conv = nn.Conv2d(in_feature, out_feature, kernel_size=stride +
1, padding=stride // 2, strid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Equationliu/GA-Attack | conv_head_pooling | false | 17,263 | [
"MIT"
] | 8 | b0280674a211f6451774ec6b1d4cee2fc19a4de6 | https://github.com/Equationliu/GA-Attack/tree/b0280674a211f6451774ec6b1d4cee2fc19a4de6 |
Denoise_NormalizeLayer | import torch
import torch.nn as nn
class Denoise_NormalizeLayer(nn.Module):
def __init__(self):
super(Denoise_NormalizeLayer, self).__init__()
def forward(self, inputs: 'torch.tensor'):
permute_RGBtoBGR = [2, 1, 0]
inputs = inputs[:, permute_RGBtoBGR, :, :]
out = inputs / 0.5... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Equationliu/GA-Attack | Denoise_NormalizeLayer | false | 17,264 | [
"MIT"
] | 8 | b0280674a211f6451774ec6b1d4cee2fc19a4de6 | https://github.com/Equationliu/GA-Attack/tree/b0280674a211f6451774ec6b1d4cee2fc19a4de6 |
CEL | import torch
from torch import nn
class CEL(nn.Module):
def __init__(self):
super(CEL, self).__init__()
None
self.eps = 1e-06
def forward(self, pred, target):
pred = pred.sigmoid()
intersection = pred * target
numerator = (pred - intersection).sum() + (target ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Farzanehkaji/MINet | CEL | false | 17,265 | [
"MIT"
] | 9 | cc2852cb2b3b20208f5edf38ec6952363a9b04a7 | https://github.com/Farzanehkaji/MINet/tree/cc2852cb2b3b20208f5edf38ec6952363a9b04a7 |
Conv2d | import torch
from torch import nn
from torch.nn import functional as F
class Conv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2d, self).__init__(in_channels, out_channels, kernel_size,
stride... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Farzanehkaji/MINet | Conv2d | false | 17,266 | [
"MIT"
] | 9 | cc2852cb2b3b20208f5edf38ec6952363a9b04a7 | https://github.com/Farzanehkaji/MINet/tree/cc2852cb2b3b20208f5edf38ec6952363a9b04a7 |
ChannelPool | import torch
import torch.nn as nn
class ChannelPool(nn.Module):
def forward(self, x):
return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1)
.unsqueeze(1)), dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | FVL2020/2DImage_BMI_estimation | ChannelPool | false | 17,267 | [
"MIT"
] | 4 | 3ae8469c3c86aac1afd09b3ba1716ecd94f5ec3f | https://github.com/FVL2020/2DImage_BMI_estimation/tree/3ae8469c3c86aac1afd09b3ba1716ecd94f5ec3f |
ScaleToModel | import torch
import torch.nn as nn
import torch.cuda
from torch import linalg as linalg
class ScaleToModel(nn.Module):
"""
This class acts as an adapter module that scales pixel values from the test run domain to the model domain.
"""
def __init__(self, model_value_range, test_value_range):
"... | 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.cuda
from torch import linalg as linalg
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | Flunzmas/vp-suite | ScaleToModel | false | 17,268 | [
"MIT"
] | 3 | 391570121b5bd9e3fd23aca9a0945a63c4173a24 | https://github.com/Flunzmas/vp-suite/tree/391570121b5bd9e3fd23aca9a0945a63c4173a24 |
MultVae | import torch
import torch.sparse
import torch.nn as nn
class MultVAE_encoder(nn.Module):
def __init__(self, item_dim: 'int', hidden_dim=600, latent_dim=200,
n_hidden_layers=1, dropout=0.5, nonlinearity=nn.Tanh):
super(MultVAE_encoder, self).__init__()
self.item_dim = item_dim
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.... | EricHe98/sad_final_project | MultVae | false | 17,269 | [
"MIT"
] | 3 | 4b2b57e44f939840eede6f134493c5f8d809b1a7 | https://github.com/EricHe98/sad_final_project/tree/4b2b57e44f939840eede6f134493c5f8d809b1a7 |
CrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class CrossEntropyLoss(nn.Module):
"""
cross entropy loss
"""
def __init__(self):
super().__init__()
def forward(self, logits, labels):
return F.cross_entropy(logits, labels, reduction='none')
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Equationliu/GA-Attack | CrossEntropyLoss | false | 17,270 | [
"MIT"
] | 8 | b0280674a211f6451774ec6b1d4cee2fc19a4de6 | https://github.com/Equationliu/GA-Attack/tree/b0280674a211f6451774ec6b1d4cee2fc19a4de6 |
MultiHeadAttn | import math
import torch
from torch import nn
import torch.nn.functional as F
class MultiHeadAttn(nn.Module):
def __init__(self, d_model, n_head, dropout=0.1, scale=False):
super().__init__()
assert d_model % n_head == 0
self.n_head = n_head
self.qkv_linear = nn.Linear(d_model, 3 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | EmanuelaBoros/stacked-ner | MultiHeadAttn | false | 17,271 | [
"MIT"
] | 4 | b57e4fcf777a5ad2519ffa7223364e383975bf7d | https://github.com/EmanuelaBoros/stacked-ner/tree/b57e4fcf777a5ad2519ffa7223364e383975bf7d |
KLLoss | import torch
import torch.nn as nn
import torch.utils.data
class KLLoss(nn.Module):
def __init__(self, size_average=False):
super().__init__()
self.size_average = size_average
def forward(self, mu, logvar):
loss = 0.5 * (mu.pow(2) + logvar.exp() - logvar - 1)
if self.size_ave... | 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
... | ForrestPi/VAEGAN | KLLoss | false | 17,272 | [
"MIT"
] | 8 | c2cfeedcc2dcfad6258468611536d9a8222eb8a3 | https://github.com/ForrestPi/VAEGAN/tree/c2cfeedcc2dcfad6258468611536d9a8222eb8a3 |
AttentionHead | import math
import torch
import torch.utils.data
import torch.nn as nn
class AttentionHead(nn.Module):
def __init__(self, d_model, d_k, d_v, device):
super(AttentionHead, self).__init__()
self.dk = math.sqrt(d_k)
self.query_layer = nn.Linear(d_model, d_k)
self.key_layer = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ErikHumphrey/sustain-seq2seq | AttentionHead | false | 17,273 | [
"Apache-2.0"
] | 4 | c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 | https://github.com/ErikHumphrey/sustain-seq2seq/tree/c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 |
AdditiveAttention | import torch
import torch.utils.data
import torch.nn as nn
class AdditiveAttention(nn.Module):
def __init__(self, enc_hidden_dim, dec_hidden_dim):
super(AdditiveAttention, self).__init__()
self.attention_w1 = nn.Linear(enc_hidden_dim, enc_hidden_dim)
self.attention_w2 = nn.Linear(dec_hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ErikHumphrey/sustain-seq2seq | AdditiveAttention | false | 17,274 | [
"Apache-2.0"
] | 4 | c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 | https://github.com/ErikHumphrey/sustain-seq2seq/tree/c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 |
SimpleLoss | import torch
class SimpleLoss(torch.nn.Module):
def forward(self, output, target):
return output / target
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | FranciscoShi/piepline | SimpleLoss | false | 17,276 | [
"MIT"
] | 5 | 6105788339fc18bab39ea07625b5fd26ad687254 | https://github.com/FranciscoShi/piepline/tree/6105788339fc18bab39ea07625b5fd26ad687254 |
ScaledDotProductAttention | import torch
import torch.nn as nn
import torch.optim
import torch.autograd
import torch.nn
import torch.nn.init
class ScaledDotProductAttention(nn.Module):
def __init__(self, d_model, attention_dropout=0.1):
super(ScaledDotProductAttention, self).__init__()
self.temper = d_model ** 0.5
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.... | FilippoC/-deep-syntactic-dependency-parsing-release | ScaledDotProductAttention | false | 17,277 | [
"MIT"
] | 4 | 30e2571ea930c2fd81559f5a2a971e3738cc6d39 | https://github.com/FilippoC/-deep-syntactic-dependency-parsing-release/tree/30e2571ea930c2fd81559f5a2a971e3738cc6d39 |
ConcatELU | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConcatELU(nn.Module):
"""
Activation function that applies ELU in both direction (inverted and plain).
Allows non-linearity while providing strong gradients for any input (important for final convolution)
"""
def forward(sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | GBATZOLIS/CAFLOW | ConcatELU | false | 17,278 | [
"MIT"
] | 6 | ea33f84c424bd8e46999be59cd5d52bd8f0a3a77 | https://github.com/GBATZOLIS/CAFLOW/tree/ea33f84c424bd8e46999be59cd5d52bd8f0a3a77 |
HDRLoss | import torch
import torch.nn as nn
class HDRLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, out_img, ref_img):
return torch.mean((out_img - ref_img) ** 2)
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Galaxies99/NHDRRNet-pytorch | HDRLoss | false | 17,279 | [
"MIT"
] | 10 | b20aae987e586a6cf9c9c52fc07b0884ce6fdf37 | https://github.com/Galaxies99/NHDRRNet-pytorch/tree/b20aae987e586a6cf9c9c52fc07b0884ce6fdf37 |
LayerNorm | import torch
import torch.nn as nn
import torch.optim
import torch.autograd
import torch.nn
import torch.nn.init
class LayerNorm(nn.Module):
def __init__(self, dim, mean=0.0, std=1.0, fixed=False, eps=1e-06, ball
=False):
super(LayerNorm, self).__init__()
self.eps = eps
self.ball ... | 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.optim
import torch.autograd
import torch.nn
... | FilippoC/-deep-syntactic-dependency-parsing-release | LayerNorm | false | 17,280 | [
"MIT"
] | 4 | 30e2571ea930c2fd81559f5a2a971e3738cc6d39 | https://github.com/FilippoC/-deep-syntactic-dependency-parsing-release/tree/30e2571ea930c2fd81559f5a2a971e3738cc6d39 |
FencepostModule | import torch
import torch.nn as nn
import torch.optim
import torch.autograd
import torch.nn
import torch.nn.init
class FencepostModule(nn.Module):
def __init__(self, input_dim, repr_dim, n_labels, disentangle=False,
label_bias=True, span_bias=False, activation='tanh'):
super(FencepostModule, 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
import torch.nn as ... | FilippoC/-deep-syntactic-dependency-parsing-release | FencepostModule | false | 17,281 | [
"MIT"
] | 4 | 30e2571ea930c2fd81559f5a2a971e3738cc6d39 | https://github.com/FilippoC/-deep-syntactic-dependency-parsing-release/tree/30e2571ea930c2fd81559f5a2a971e3738cc6d39 |
CenterCosineSimilarity | import torch
import torch.nn as nn
class CenterCosineSimilarity(nn.Module):
def __init__(self, feat_dim, num_centers, eps=1e-08):
super(CenterCosineSimilarity, self).__init__()
self.centers = nn.Parameter(torch.randn(num_centers, feat_dim))
self.eps = eps
def forward(self, feat):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | GT-RIPL/DistillMatch-SSCL | CenterCosineSimilarity | false | 17,282 | [
"MIT"
] | 9 | e572671fd6994b3c43ad6e46e9efb3588804524c | https://github.com/GT-RIPL/DistillMatch-SSCL/tree/e572671fd6994b3c43ad6e46e9efb3588804524c |
BBoxTransform | import torch
import torch.nn as nn
class BBoxTransform(nn.Module):
def forward(self, anchors, regression):
"""
decode_box_outputs adapted from https://github.com/google/automl/blob/master/efficientdet/anchors.py
Args:
anchors: [batchsize, boxes, (y1, x1, y2, x2)]
... | 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... | DerryHub/the-TaobaoLive-Commodity-Identify-Competition | BBoxTransform | false | 17,283 | [
"MIT"
] | 4 | 7e5e5c4fbddd9949fe01810d58bd7994889c007c | https://github.com/DerryHub/the-TaobaoLive-Commodity-Identify-Competition/tree/7e5e5c4fbddd9949fe01810d58bd7994889c007c |
SmoothCrossEntropyLoss | import torch
from torch.nn.modules.loss import _WeightedLoss
import torch.nn.functional as F
class SmoothCrossEntropyLoss(_WeightedLoss):
"""
Smooth labelling for pytorch.
Source: https://stackoverflow.com/questions/55681502/label-smoothing-in-pytorch
"""
def __init__(self, weight=None, reduction... | 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.nn.modules.... | Fuminides/athena | SmoothCrossEntropyLoss | false | 17,284 | [
"MIT"
] | 10 | 78ad7ad5236dc8f12adc0401c52add3931292e69 | https://github.com/Fuminides/athena/tree/78ad7ad5236dc8f12adc0401c52add3931292e69 |
LinearExcitability | import math
import torch
from torch import nn
from torch.nn.parameter import Parameter
def linearExcitability(input, weight, excitability=None, bias=None):
"""
Applies a linear transformation to the incoming data: :math:`y = c(xA^T) + b`.
Shape:
- input: :math:`(N, *, 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
import math
from torch import nn
from torch.nn.parameter import Parameter
assert... | GMvandeVen/progressive-learning-pytorch | LinearExcitability | false | 17,285 | [
"MIT"
] | 4 | 165645b2d7595d94d036f765c9a311d505e667a3 | https://github.com/GMvandeVen/progressive-learning-pytorch/tree/165645b2d7595d94d036f765c9a311d505e667a3 |
LSGanLoss | import torch
from torch import nn
import torch.optim
class LSGanLoss(nn.Module):
def __init__(self, layer=3):
super(LSGanLoss, self).__init__()
self.layer = layer
def forward(self, real, fake):
loss_G = 0
loss_D = 0
for i in range(self.layer):
loss_G = los... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | ForrestPi/faceSwapProjects | LSGanLoss | false | 17,286 | [
"MIT"
] | 5 | daf2649a2791a25aa541c4d6d3b7e1d6552be5d7 | https://github.com/ForrestPi/faceSwapProjects/tree/daf2649a2791a25aa541c4d6d3b7e1d6552be5d7 |
conv_layer | import torch
from torch import nn
class conv_layer(nn.Module):
"""Standard convolutional layer. Possible to return pre-activations."""
def __init__(self, in_planes, out_planes, kernel_size=3, stride=1,
padding=1, drop=0, batch_norm=False, nl=nn.ReLU(), bias=True, gated
=False):
super(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | GMvandeVen/progressive-learning-pytorch | conv_layer | false | 17,288 | [
"MIT"
] | 4 | 165645b2d7595d94d036f765c9a311d505e667a3 | https://github.com/GMvandeVen/progressive-learning-pytorch/tree/165645b2d7595d94d036f765c9a311d505e667a3 |
Downsample | import torch
import torch.nn as nn
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | GastonMazzei/escher-project-website | Downsample | false | 17,289 | [
"MIT"
] | 5 | b3861eeeca11a7c31502f539ded9ae718f3d9e2e | https://github.com/GastonMazzei/escher-project-website/tree/b3861eeeca11a7c31502f539ded9ae718f3d9e2e |
CPUForgetMult | import torch
import torch.nn.init
class CPUForgetMult(torch.nn.Module):
def __init__(self):
super(CPUForgetMult, self).__init__()
def forward(self, f, x, hidden_init=None):
result = []
forgets = f.split(1, dim=0)
prev_h = hidden_init
for i, h in enumerate((f * x).spli... | 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.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | FurKan7/resim_renklendirme-colorizing-of-picture- | CPUForgetMult | false | 17,290 | [
"MIT"
] | 8 | a431a42cd00a60f85948795bc872a272897fbc76 | https://github.com/FurKan7/resim_renklendirme-colorizing-of-picture-/tree/a431a42cd00a60f85948795bc872a272897fbc76 |
MLP | import torch
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, width):
super().__init__()
self.width = width
self.c_fc = nn.Linear(width, width * 4)
self.c_proj = nn.Linear(width * 4, width)
self.gelu = nn.GELU()
def forward(self, x):
return 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
import torch.nn as ... | GastonMazzei/escher-project-website | MLP | false | 17,291 | [
"MIT"
] | 5 | b3861eeeca11a7c31502f539ded9ae718f3d9e2e | https://github.com/GastonMazzei/escher-project-website/tree/b3861eeeca11a7c31502f539ded9ae718f3d9e2e |
rSoftMax | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import F
class rSoftMax(nn.Module):
def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | DerryHub/the-TaobaoLive-Commodity-Identify-Competition | rSoftMax | false | 17,292 | [
"MIT"
] | 4 | 7e5e5c4fbddd9949fe01810d58bd7994889c007c | https://github.com/DerryHub/the-TaobaoLive-Commodity-Identify-Competition/tree/7e5e5c4fbddd9949fe01810d58bd7994889c007c |
GroupNorm32 | import torch
import torch.nn.functional as F
import torch.nn as nn
class GroupNorm32(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-05):
super().__init__(num_groups=num_groups, num_channels=num_channels,
eps=eps)
self.swish = swish
def forward(self, x):... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | GastonMazzei/escher-project-website | GroupNorm32 | false | 17,293 | [
"MIT"
] | 5 | b3861eeeca11a7c31502f539ded9ae718f3d9e2e | https://github.com/GastonMazzei/escher-project-website/tree/b3861eeeca11a7c31502f539ded9ae718f3d9e2e |
downsampleLayer | import torch
import torch.nn as nn
class downsampleLayer(nn.Module):
"""
A downsample layer of UNet. LeakyReLU is used as the activation func.
"""
def __init__(self, infeature, outfeature, kernelSize, strides=2,
paddings=1, bn=False):
super(downsampleLayer, self).__init__()
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | GentleDell/DEBOR | downsampleLayer | false | 17,294 | [
"BSD-3-Clause"
] | 4 | cd566f173599fe7419e7baf312f63830c28d5de2 | https://github.com/GentleDell/DEBOR/tree/cd566f173599fe7419e7baf312f63830c28d5de2 |
GraphLinear | import torch
import torch.nn as nn
class GraphLinear(nn.Module):
"""
Generalization of 1x1 convolutions on Graphs
"""
def __init__(self, in_channels, out_channels):
super(GraphLinear, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | GentleDell/DEBOR | GraphLinear | false | 17,295 | [
"BSD-3-Clause"
] | 4 | cd566f173599fe7419e7baf312f63830c28d5de2 | https://github.com/GentleDell/DEBOR/tree/cd566f173599fe7419e7baf312f63830c28d5de2 |
Upsample | import torch
import torch.nn.functional as F
import torch.nn as nn
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | GastonMazzei/escher-project-website | Upsample | false | 17,296 | [
"MIT"
] | 5 | b3861eeeca11a7c31502f539ded9ae718f3d9e2e | https://github.com/GastonMazzei/escher-project-website/tree/b3861eeeca11a7c31502f539ded9ae718f3d9e2e |
upsampleLayer | import torch
import torch.nn as nn
class upsampleLayer(nn.Module):
"""
A upsample layer of UNet. ReLU is the activation func. The skip connection
can be cutted if not given. Because RGB-UV is not a completion task but a
image transition task.
"""
def __init__(self, infeature, outfeature, ker... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | GentleDell/DEBOR | upsampleLayer | false | 17,297 | [
"BSD-3-Clause"
] | 4 | cd566f173599fe7419e7baf312f63830c28d5de2 | https://github.com/GentleDell/DEBOR/tree/cd566f173599fe7419e7baf312f63830c28d5de2 |
Attention | import torch
import torch.optim
from torch import nn
class Attention(nn.Module):
"""
Attention network for calculate attention value
"""
def __init__(self, encoder_dim, decoder_dim, attention_dim):
"""
:param encoder_dim: input size of encoder network
:param decoder_dim: input... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Fpiotro/MOLECULAR-TRANSLATION | Attention | false | 17,298 | [
"MIT"
] | 5 | 050dd0c093ee4e68326c2404c5b4dbf53ca6c8a0 | https://github.com/Fpiotro/MOLECULAR-TRANSLATION/tree/050dd0c093ee4e68326c2404c5b4dbf53ca6c8a0 |
USConv2d | import torch
import torch.nn as nn
import torch.utils
def make_divisible(v, divisor=8, min_value=1):
"""
forked from slim:
https://github.com/tensorflow/models/blob/ 0344c5503ee55e24f0de7f37336a6e08f10976fd/ research/slim/nets/mobilenet/mobilenet.py#L62-L69
"""
if min_value is 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
import torch.nn as nn
import torch.utils
assert_size_stride = torch._C._dynamo.g... | Gaussianer/FasterSeg | USConv2d | false | 17,299 | [
"MIT"
] | 6 | f2e102b433275ac9f3387a8c2ae8439b2687bfda | https://github.com/Gaussianer/FasterSeg/tree/f2e102b433275ac9f3387a8c2ae8439b2687bfda |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch.utils
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
in_size = inputs.size()
inputs = inp... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | Gaussianer/FasterSeg | GlobalAvgPool2d | false | 17,300 | [
"MIT"
] | 6 | f2e102b433275ac9f3387a8c2ae8439b2687bfda | https://github.com/Gaussianer/FasterSeg/tree/f2e102b433275ac9f3387a8c2ae8439b2687bfda |
TwoMLPHead | import torch
from torchvision.transforms import functional as F
from torch import nn
import torch.nn.functional as F
import torch.utils.data
class TwoMLPHead(nn.Module):
"""
Standard heads for FPN-based models
Arguments:
in_channels (int): number of input channels
representation_size (int... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | BoChenYS/ROPE | TwoMLPHead | false | 17,301 | [
"BSD-3-Clause"
] | 6 | 3e50f134259b555cf547e4a3ef8b14cf5cda4e00 | https://github.com/BoChenYS/ROPE/tree/3e50f134259b555cf547e4a3ef8b14cf5cda4e00 |
F_fully_connected_leaky | import torch
from torch import nn
class F_fully_connected_leaky(nn.Module):
"""Fully connected tranformation, not reversible, but used below."""
def __init__(self, size_in, size, internal_size=None, dropout=0.0,
batch_norm=False, leaky_slope=0.01):
super(F_fully_connected_leaky, self).__init_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Goobley/Radynversion | F_fully_connected_leaky | false | 17,302 | [
"MIT"
] | 7 | f44edc77b6eb7ef2bdbd8e8aabda3bf9822d3695 | https://github.com/Goobley/Radynversion/tree/f44edc77b6eb7ef2bdbd8e8aabda3bf9822d3695 |
SqueezeAndExcitation | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class SqueezeAndExcitation(nn.Module):
def __init__(self, planes, squeeze):
super(SqueezeAndExcitation, self).__init__()
self.squeeze = nn.Linear(planes, 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
import ... | FujitsuLaboratories/CAC | SqueezeAndExcitation | false | 17,303 | [
"Apache-2.0"
] | 8 | d12df8e47f61eaf7d7b0ed355e2d1aa296453f86 | https://github.com/FujitsuLaboratories/CAC/tree/d12df8e47f61eaf7d7b0ed355e2d1aa296453f86 |
SigmoidFocalLoss | import torch
import torch.nn as nn
import torch.utils
class SigmoidFocalLoss(nn.Module):
def __init__(self, ignore_label, gamma=2.0, alpha=0.25, reduction='mean'):
super(SigmoidFocalLoss, self).__init__()
self.ignore_label = ignore_label
self.gamma = gamma
self.alpha = alpha
... | 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
... | Gaussianer/FasterSeg | SigmoidFocalLoss | false | 17,304 | [
"MIT"
] | 6 | f2e102b433275ac9f3387a8c2ae8439b2687bfda | https://github.com/Gaussianer/FasterSeg/tree/f2e102b433275ac9f3387a8c2ae8439b2687bfda |
PairwiseLoss | import torch
import torch.nn as nn
class PairwiseLoss(nn.Module):
def __init__(self):
super().__init__()
self.m = 0
def forward(self, pos_out, neg_out):
distance = 1 - pos_out + neg_out
loss = torch.mean(torch.max(torch.tensor(0), distance))
return loss
def get_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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | GrantXie/wikidata-wikifier | PairwiseLoss | false | 17,305 | [
"MIT"
] | 3 | a65c9b71596e390999af9de7638eb8c8c13c1581 | https://github.com/GrantXie/wikidata-wikifier/tree/a65c9b71596e390999af9de7638eb8c8c13c1581 |
MultiheadAttention | import math
import torch
import torch as th
import torch.nn as nn
class QKVMultiheadAttention(nn.Module):
def __init__(self, n_heads: 'int', n_ctx: 'int'):
super().__init__()
self.n_heads = n_heads
self.n_ctx = n_ctx
def forward(self, qkv):
bs, n_ctx, width = qkv.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.... | GastonMazzei/escher-project-website | MultiheadAttention | false | 17,306 | [
"MIT"
] | 5 | b3861eeeca11a7c31502f539ded9ae718f3d9e2e | https://github.com/GastonMazzei/escher-project-website/tree/b3861eeeca11a7c31502f539ded9ae718f3d9e2e |
PairwiseNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class PairwiseNetwork(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.fc1 = nn.Linear(hidden_size, 2 * hidden_size)
self.fc2 = nn.Linear(2 * hidden_size, hidden_size)
self.fc3 = nn.Linear(hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | GrantXie/wikidata-wikifier | PairwiseNetwork | false | 17,307 | [
"MIT"
] | 3 | a65c9b71596e390999af9de7638eb8c8c13c1581 | https://github.com/GrantXie/wikidata-wikifier/tree/a65c9b71596e390999af9de7638eb8c8c13c1581 |
SoftCrossEntropyLoss2d | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
class SoftCrossEntropyLoss2d(nn.Module):
def __init__(self):
super(SoftCrossEntropyLoss2d, self).__init__()
def forward(self, inputs, targets):
loss = 0
inputs = -F.log_softmax(inputs, dim=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
from torch._inductor.runtime.... | Gaussianer/FasterSeg | SoftCrossEntropyLoss2d | false | 17,308 | [
"MIT"
] | 6 | f2e102b433275ac9f3387a8c2ae8439b2687bfda | https://github.com/Gaussianer/FasterSeg/tree/f2e102b433275ac9f3387a8c2ae8439b2687bfda |
Affine | import math
import torch
from torch import nn
import torch.autograd
from torch.nn import init
class Affine(nn.Module):
"""
This module implements the affine parameters gamma and beta seen in
Eq. 10 in Pezeshki et al. (2016). It differs from the way affine
is used in batchnorm out of the box of PyTorch... | 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
import torch.autograd
from torch.nn import init
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Goschjann/ssltsc | Affine | false | 17,309 | [
"MIT"
] | 5 | 08d6b1bf711bb1c8f19f9bfb66a98d4e423e932e | https://github.com/Goschjann/ssltsc/tree/08d6b1bf711bb1c8f19f9bfb66a98d4e423e932e |
PositionEmbs | import torch
from torch import nn
class PositionEmbs(nn.Module):
def __init__(self, num_patches, emb_dim, dropout_rate=0.1):
super(PositionEmbs, self).__init__()
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 2,
emb_dim))
if dropout_rate > 0:
self.d... | 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... | Graeme22/VisionTransformer-Pytorch | PositionEmbs | false | 17,310 | [
"Apache-2.0"
] | 5 | 4e8abecf27e92dffd8d00f3d9b5ad4a21079cd0e | https://github.com/Graeme22/VisionTransformer-Pytorch/tree/4e8abecf27e92dffd8d00f3d9b5ad4a21079cd0e |
DistilledLoss | import torch
from torch import nn
import torch.nn.functional as F
class DistilledLoss(nn.Module):
"""
Intended for use with a DistillationTrainer.
Combines vanilla cross entropy loss with a modified form of KL divergence loss.
Attempts to minimize the KL divergence between the student and distilled 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, math as tl_math
from torch ... | Graeme22/VisionTransformer-Pytorch | DistilledLoss | false | 17,311 | [
"Apache-2.0"
] | 5 | 4e8abecf27e92dffd8d00f3d9b5ad4a21079cd0e | https://github.com/Graeme22/VisionTransformer-Pytorch/tree/4e8abecf27e92dffd8d00f3d9b5ad4a21079cd0e |
APLayer | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class ZIF(torch.autograd.Function):
@staticmethod
def forward(ctx, input, gama):
out = (input > 0).float()
L = torch.tensor([gama])
ctx.save_for_b... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
assert_size_st... | Gus-Lab/temporal_efficient_training | APLayer | false | 17,312 | [
"MIT"
] | 5 | f9bde4107ed653cc8dd3ee58689bf3b55f6b89ba | https://github.com/Gus-Lab/temporal_efficient_training/tree/f9bde4107ed653cc8dd3ee58689bf3b55f6b89ba |
Conv2dSame | import torch
from torchvision.transforms import *
import torch.nn
import torch
import torch.nn as 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_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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torchvis... | COMHTVM/lensless | Conv2dSame | false | 17,313 | [
"MIT"
] | 6 | 0d67a310bab08551d7422fa792f3422a7ee7d9cb | https://github.com/COMHTVM/lensless/tree/0d67a310bab08551d7422fa792f3422a7ee7d9cb |
Combinator | import torch
from torch import nn
import torch.autograd
class Combinator(nn.Module):
"""
The vanilla combinator function g() that combines vertical and
lateral connections as explained in Pezeshki et al. (2016).
The weights are initialized as described in Eq. 17
and the g() is defined in Eq. 16.
... | 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.autograd
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | Goschjann/ssltsc | Combinator | false | 17,314 | [
"MIT"
] | 5 | 08d6b1bf711bb1c8f19f9bfb66a98d4e423e932e | https://github.com/Goschjann/ssltsc/tree/08d6b1bf711bb1c8f19f9bfb66a98d4e423e932e |
ResidualAttentionBlock | import math
import torch
import torch as th
import torch.nn as nn
class LayerNorm(nn.LayerNorm):
"""
Implementation that supports fp16 inputs but fp32 gains/biases.
"""
def forward(self, x: 'th.Tensor'):
return super().forward(x.float())
class QKVMultiheadAttention(nn.Module):
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | GastonMazzei/escher-project-website | ResidualAttentionBlock | false | 17,315 | [
"MIT"
] | 5 | b3861eeeca11a7c31502f539ded9ae718f3d9e2e | https://github.com/GastonMazzei/escher-project-website/tree/b3861eeeca11a7c31502f539ded9ae718f3d9e2e |
BasicBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, inplanes, planes, droprate=0.2, attention=None):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, padding=1,
bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Galaxies99/alpha-protein | BasicBlock | false | 17,316 | [
"MIT"
] | 4 | db4b77ab48d5905ade5d4a66004f8387773718fa | https://github.com/Galaxies99/alpha-protein/tree/db4b77ab48d5905ade5d4a66004f8387773718fa |
SpaceToDepth | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
class SpaceToDepth(nn.Module):
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
def forward(self, x):
N, C, H, W ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.... | GuillaumeAI/gia-labeling-ImageNet21K | SpaceToDepth | false | 17,317 | [
"MIT"
] | 4 | 825ff49f1558f848fc8a798e2e393b708e75bb0e | https://github.com/GuillaumeAI/gia-labeling-ImageNet21K/tree/825ff49f1558f848fc8a798e2e393b708e75bb0e |
PA | import torch
from torch import nn
class PA(nn.Module):
def __init__(self, dim):
super().__init__()
self.pa_conv = nn.Conv2d(dim, dim, 3, 1, 1, groups=dim)
def forward(self, x):
return x * self.pa_conv(x).sigmoid()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Genevievekim/segformer | PA | false | 17,318 | [
"MIT"
] | 10 | 4a0800746ade51101ec2573c683b06eccadb9683 | https://github.com/Genevievekim/segformer/tree/4a0800746ade51101ec2573c683b06eccadb9683 |
DilatedBasicBlock | import torch
import torch.nn as nn
class DilatedBasicBlock(nn.Module):
def __init__(self, inplanes, planes, kernel_size=3, dilation=1):
super(DilatedBasicBlock, self).__init__()
padding_size = kernel_size + (kernel_size - 1) * (dilation - 1) - 1
assert padding_size % 2 == 0
paddin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Galaxies99/alpha-protein | DilatedBasicBlock | false | 17,319 | [
"MIT"
] | 4 | db4b77ab48d5905ade5d4a66004f8387773718fa | https://github.com/Galaxies99/alpha-protein/tree/db4b77ab48d5905ade5d4a66004f8387773718fa |
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