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 |
|---|---|---|---|---|---|---|---|---|---|---|
cnn_7layer_alt | import torch
import torch.nn as nn
import torch.nn.functional as F
class cnn_7layer_alt(nn.Module):
def __init__(self, in_ch, in_dim, width=2, linear_size=128):
super(cnn_7layer_alt, self).__init__()
self.conv1 = nn.Conv2d(in_ch, 4 * width, 3, stride=1, padding=1)
self.conv2 = nn.Conv2d(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_... | mnmueller/auto_LiRPA | cnn_7layer_alt | false | 7,278 | [
"BSD-3-Clause"
] | 1 | 55cb270b0b99f07b74541d55706c69fbb9daff66 | https://github.com/mnmueller/auto_LiRPA/tree/55cb270b0b99f07b74541d55706c69fbb9daff66 |
Inception | import torch
import torch.nn as nn
class BasicConv2d(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1,
padding=0, output_relu=True):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=
kernel_size, stride=str... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | moh2236945/pytorch_classification | Inception | false | 7,279 | [
"MIT"
] | 1 | 8816f08af327e06208b348a78d9c63c133b6a628 | https://github.com/moh2236945/pytorch_classification/tree/8816f08af327e06208b348a78d9c63c133b6a628 |
SharedAgent | import torch
import torch.nn.functional as F
import torch.nn as nn
class SharedAgent(torch.nn.Module):
"""
A simple two headed / chimera Actor Critic agent.
The actor and critic share the body of the network.
It is argued that this is because "good" actions
correlate to visiting states with "larg... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | mpgussert/fundamentalRL | SharedAgent | false | 7,280 | [
"MIT"
] | 1 | 4f45436226e0823c21cac316dec8bbf1df697467 | https://github.com/mpgussert/fundamentalRL/tree/4f45436226e0823c21cac316dec8bbf1df697467 |
BoundNot | from _paritybench_helpers import _mock_config
import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import MSELoss
def isnan(x):
if isinstance(x, Patches):
return False
return torch.isnan(x).any()
class Perturbation:
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
import math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_siz... | mnmueller/auto_LiRPA | BoundNot | false | 7,281 | [
"BSD-3-Clause"
] | 1 | 55cb270b0b99f07b74541d55706c69fbb9daff66 | https://github.com/mnmueller/auto_LiRPA/tree/55cb270b0b99f07b74541d55706c69fbb9daff66 |
Net5 | import torch
from torch import nn
from torch.nn.init import kaiming_normal
from torch.nn.init import normal
def weights_init(m):
if isinstance(m, (nn.Conv1d, nn.Linear)):
kaiming_normal(m.weight.data)
try:
kaiming_normal(m.bias.data)
except ValueError:
normal(m.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 import triton_helpers
from torch import nn
from tor... | moritzschaefer/pavooc | Net5 | false | 7,282 | [
"MIT"
] | 1 | 735f5455f9a95a5734436a24e2aa92cf600c91af | https://github.com/moritzschaefer/pavooc/tree/735f5455f9a95a5734436a24e2aa92cf600c91af |
Net4 | import torch
from torch import nn
from torch.nn.init import kaiming_normal
from torch.nn.init import normal
def weights_init(m):
if isinstance(m, (nn.Conv1d, nn.Linear)):
kaiming_normal(m.weight.data)
try:
kaiming_normal(m.bias.data)
except ValueError:
normal(m.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 import triton_helpers
from torch import nn
from tor... | moritzschaefer/pavooc | Net4 | false | 7,283 | [
"MIT"
] | 1 | 735f5455f9a95a5734436a24e2aa92cf600c91af | https://github.com/moritzschaefer/pavooc/tree/735f5455f9a95a5734436a24e2aa92cf600c91af |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | import torch
import torch.nn
import torch.onnx
class NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency(torch
.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency
, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | mrshu/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | false | 7,284 | [
"MIT"
] | 1 | 335edaa2c485ba0dec877bf4cdbd652e2d5d105c | https://github.com/mrshu/onnxruntime/tree/335edaa2c485ba0dec877bf4cdbd652e2d5d105c |
NIN | import string
import torch
import numpy as np
import torch.utils.data
import torch
import torch.nn as nn
def _einsum(a, b, c, x, y):
einsum_str = '{},{}->{}'.format(''.join(a), ''.join(b), ''.join(c))
return torch.einsum(einsum_str, x, y)
def contract_inner(x, y):
"""tensordot(x, y, 1)."""
x_chars =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 string
import numpy as np
import torch.utils.data
import torch
import tor... | mrjavoman/Image-Super-Resolution-via-Iterative-Refinement | NIN | false | 7,285 | [
"Apache-2.0"
] | 1 | 2eb11d972e8e024c3b1d7a84f90895e329b5b408 | https://github.com/mrjavoman/Image-Super-Resolution-via-Iterative-Refinement/tree/2eb11d972e8e024c3b1d7a84f90895e329b5b408 |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | import torch
import torch.nn
import torch.onnx
class NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency(torch.
nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency,
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
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | mrshu/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | false | 7,286 | [
"MIT"
] | 1 | 335edaa2c485ba0dec877bf4cdbd652e2d5d105c | https://github.com/mrshu/onnxruntime/tree/335edaa2c485ba0dec877bf4cdbd652e2d5d105c |
BoundReciprocal | from _paritybench_helpers import _mock_config
import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import MSELoss
def isnan(x):
if isinstance(x, Patches):
return False
return torch.isnan(x).any()
class Perturbation:
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
import math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_siz... | mnmueller/auto_LiRPA | BoundReciprocal | false | 7,287 | [
"BSD-3-Clause"
] | 1 | 55cb270b0b99f07b74541d55706c69fbb9daff66 | https://github.com/mnmueller/auto_LiRPA/tree/55cb270b0b99f07b74541d55706c69fbb9daff66 |
WeightL1Loss | import torch
import torch.nn as nn
class WeightL1Loss(nn.Module):
def __init__(self):
super(WeightL1Loss, self).__init__()
def forward(self, pred_loc, label_loc, loss_weight):
b, _, sh, sw = pred_loc.size()
pred_loc = pred_loc.view(b, 4, -1, sh, sw)
diff = (pred_loc - label_l... | 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
... | mshmoon/siamrpn-lightweight | WeightL1Loss | false | 7,288 | [
"MIT"
] | 1 | f6527e34c9eaaeb45817b12babd78ee73b1c7525 | https://github.com/mshmoon/siamrpn-lightweight/tree/f6527e34c9eaaeb45817b12babd78ee73b1c7525 |
Corr | import torch
import torch.nn as nn
import torch.nn.functional as F
class Corr(nn.Module):
def __init__(self):
super(Corr, self).__init__()
def forward(self, x, kernel):
batch = kernel.size(0)
channel = kernel.size(1)
x = x.view(1, batch * channel, x.size(2), x.size(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | mshmoon/siamrpn-lightweight | Corr | false | 7,289 | [
"MIT"
] | 1 | f6527e34c9eaaeb45817b12babd78ee73b1c7525 | https://github.com/mshmoon/siamrpn-lightweight/tree/f6527e34c9eaaeb45817b12babd78ee73b1c7525 |
BernoulliLogProb | import torch
import torch.nn as nn
import torch.utils
import torch.utils.data
class BernoulliLogProb(nn.Module):
def __init__(self):
super().__init__()
self.bce_with_logits = nn.BCEWithLogitsLoss(reduction='none')
def forward(self, logits, target):
return -self.bce_with_logits(logits... | 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... | msunardi/vae_experiment | BernoulliLogProb | false | 7,290 | [
"MIT"
] | 1 | e3ce39e586f1189d157e753370a90c07713658b3 | https://github.com/msunardi/vae_experiment/tree/e3ce39e586f1189d157e753370a90c07713658b3 |
LogSoftMax | import torch
import torch.nn as nn
import torch.nn.functional as F
class LogSoftMax(nn.Module):
def __init__(self):
super(LogSoftMax, self).__init__()
def forward(self, cls):
b, a2, h, w = cls.size()
cls = cls.view(b, 2, a2 // 2, h, w)
cls = cls.permute(0, 2, 3, 4, 1).contigu... | 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
... | mshmoon/siamrpn-lightweight | LogSoftMax | false | 7,291 | [
"MIT"
] | 1 | f6527e34c9eaaeb45817b12babd78ee73b1c7525 | https://github.com/mshmoon/siamrpn-lightweight/tree/f6527e34c9eaaeb45817b12babd78ee73b1c7525 |
DistillLoss | import torch
from torch import nn
import torch.nn.functional as F
class DistillLoss(nn.Module):
def __init__(self, temperature, distillation_weight):
super().__init__()
self.temperature = temperature
self.distillation_weight = distillation_weight
self.kldiv = nn.KLDivLoss(reductio... | 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 ... | mrtunguyen/knowledge_distillation | DistillLoss | false | 7,292 | [
"MIT"
] | 1 | dd114e980dbebda6cc247f658eb801ab948ee6ba | https://github.com/mrtunguyen/knowledge_distillation/tree/dd114e980dbebda6cc247f658eb801ab948ee6ba |
LinRegModel | import torch
import torch.nn as nn
class LinRegModel(nn.Module):
def __init__(self):
super().__init__()
self.a = nn.Parameter(torch.randn(1))
self.b = nn.Parameter(torch.randn(1))
def forward(self, x):
return self.a * x + self.b
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | muellerzr/walk-with-deep-learning | LinRegModel | false | 7,293 | [
"Apache-2.0"
] | 1 | 4adbf26da4885d122ed305eccef3efbb6fb10df5 | https://github.com/muellerzr/walk-with-deep-learning/tree/4adbf26da4885d122ed305eccef3efbb6fb10df5 |
NormalLogProb | import torch
import numpy as np
import torch.nn as nn
import torch.utils
import torch.utils.data
class NormalLogProb(nn.Module):
def __init__(self):
super().__init__()
def forward(self, loc, scale, z):
var = torch.pow(scale, 2)
return -0.5 * torch.log(2 * np.pi * var) - torch.pow(z -... | 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
import torch.utils.data
assert_s... | msunardi/vae_experiment | NormalLogProb | false | 7,294 | [
"MIT"
] | 1 | e3ce39e586f1189d157e753370a90c07713658b3 | https://github.com/msunardi/vae_experiment/tree/e3ce39e586f1189d157e753370a90c07713658b3 |
VGGNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class VGGNet(nn.Module):
def __init__(self):
super(VGGNet, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
self.conv2 = nn.Conv2d(32, 32, kernel_size=(3, 3), st... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | miyosuda/oculomotor | VGGNet | false | 7,295 | [
"Apache-2.0"
] | 1 | 78e7ec61a808d058116c69bff1ea71ecf117c126 | https://github.com/miyosuda/oculomotor/tree/78e7ec61a808d058116c69bff1ea71ecf117c126 |
CNN | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class CNN(nn.Module):
""" CNN for heat shock protein classification """
def __init__(self, model_cfg, in_channels, dropout_rate):
super(CNN, self).__init__()
self.embedder = model_cfg.embedder
if self.emb... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | mswzeus/DeeperHSP | CNN | false | 7,296 | [
"MIT"
] | 1 | 571387f048d3c33fcd78730fdaef57b6c44a27a7 | https://github.com/mswzeus/DeeperHSP/tree/571387f048d3c33fcd78730fdaef57b6c44a27a7 |
BlendLinear | import torch
import torch.nn as nn
import torch.utils.data
class BlendLinear(nn.Module):
def __init__(self, dim_in, dim_out, layer_type=nn.Linear, **unused_kwargs):
super(BlendLinear, self).__init__()
self._layer0 = layer_type(dim_in, dim_out)
self._layer1 = layer_type(dim_in, dim_out)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | musyoku/ffjord | BlendLinear | false | 7,297 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
BlendConv2d | import torch
import torch.nn as nn
import torch.utils.data
class BlendConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False, **unused_kwargs):
super(BlendConv2d, self).__init__()
module = nn.ConvTranspose2d if... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | musyoku/ffjord | BlendConv2d | false | 7,298 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
GINPreTransition | import torch
import typing
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, input_dim, hidden_sizes: 'typing.Iterable[int]',
out_dim, activation_function=nn.Sigmoid(), activation_out=None):
super(MLP, self).__init__()
i_h_sizes = [input_dim] + hidden_sizes
self.mlp =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 typing
impor... | mtiezzi/gnn_site | GINPreTransition | false | 7,299 | [
"BSD-3-Clause"
] | 1 | 79a13603db876ac24e66a152104faa8b76e1d8e7 | https://github.com/mtiezzi/gnn_site/tree/79a13603db876ac24e66a152104faa8b76e1d8e7 |
ConcatSquashLinear | import torch
import torch.nn as nn
import torch.utils.data
class ConcatSquashLinear(nn.Module):
def __init__(self, dim_in, dim_out):
super(ConcatSquashLinear, self).__init__()
self._layer = nn.Linear(dim_in, dim_out)
self._hyper_bias = nn.Linear(1, dim_out, bias=False)
self._hyper... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | musyoku/ffjord | ConcatSquashLinear | false | 7,300 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
ConcatSquashConv2d | import torch
import torch.nn as nn
import torch.utils.data
class ConcatSquashConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatSquashConv2d, self).__init__()
module = nn.ConvTranspose2d if tr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | musyoku/ffjord | ConcatSquashConv2d | false | 7,301 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
GatedConv | import torch
import torch.nn as nn
import torch.utils.data
class GatedConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, groups=1):
super(GatedConv, self).__init__()
self.layer_f = nn.Conv2d(in_channels, out_channels, kernel_size,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | musyoku/ffjord | GatedConv | false | 7,302 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
GatedConv2d | import torch
import torch.nn as nn
import torch.utils.data
class GatedConv2d(nn.Module):
def __init__(self, input_channels, output_channels, kernel_size, stride,
padding, dilation=1, activation=None):
super(GatedConv2d, self).__init__()
self.activation = activation
self.sigmoid = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | musyoku/ffjord | GatedConv2d | false | 7,303 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
GatedConvTranspose | import torch
import torch.nn as nn
import torch.utils.data
class GatedConvTranspose(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, groups=1):
super(GatedConvTranspose, self).__init__()
self.layer_f = nn.ConvTranspose2d(in_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | musyoku/ffjord | GatedConvTranspose | false | 7,304 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
HyperConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1 or classname.find('Conv') != -1:
nn.init.constant_(m.weight, 0)
nn.init.normal_(m.bias, 0, 0.01)
class HyperConv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.functional as F
import torch.utils.data
as... | musyoku/ffjord | HyperConv2d | false | 7,305 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
BasicBlock | import torch
import torch.nn as nn
import torch.utils.data
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, dim):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1, bias=False)
self.bn1 = nn.GroupNorm(2, dim, eps=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 import triton_helpers
from torch._inductor.runtime.... | musyoku/ffjord | BasicBlock | false | 7,306 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
PNTrainingSigmoid | import torch
from torch import nn
class PNTrainingSigmoid(nn.Module):
def __init__(self):
super(PNTrainingSigmoid, self).__init__()
return
def forward(self, output_p, output_n, prior):
cost = prior * torch.mean(torch.sigmoid(-output_p))
cost = cost + (1 - prior) * torch.mean(... | 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... | mxuq/Imbalance-PU | PNTrainingSigmoid | false | 7,307 | [
"MIT"
] | 1 | fd4403b05f98ca6bc8156783e8275888d63f6435 | https://github.com/mxuq/Imbalance-PU/tree/fd4403b05f98ca6bc8156783e8275888d63f6435 |
TwoWordPSDProbe | import torch
import torch.nn as nn
class Probe(nn.Module):
pass
class TwoWordPSDProbe(Probe):
""" Computes squared L2 distance after projection by a matrix.
For a batch of sentences, computes all n^2 pairs of distances
for each sentence in the batch.
"""
def __init__(self, model_dim, probe_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | muziyongshixin/pytorch_SSRP | TwoWordPSDProbe | false | 7,308 | [
"MIT"
] | 1 | e54b3098927ba2ff16bdc8f64f3a2bf46d1f72c5 | https://github.com/muziyongshixin/pytorch_SSRP/tree/e54b3098927ba2ff16bdc8f64f3a2bf46d1f72c5 |
GroupPointWise | import torch
import torch.nn as nn
class GroupPointWise(nn.Module):
def __init__(self, in_dim, n_heads=4, proj_factor=1, target_dim=None):
super().__init__()
if target_dim is not None:
proj_ch = target_dim // proj_factor
else:
proj_ch = in_dim // proj_factor
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | nachiket273/VisTrans | GroupPointWise | false | 7,309 | [
"MIT"
] | 1 | 99129b02f275424ebff900189ec2055f26bb9912 | https://github.com/nachiket273/VisTrans/tree/99129b02f275424ebff900189ec2055f26bb9912 |
Attention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, embed_dim, hidden_dim=None, out_dim=None, n_head=1,
score_function='scaled_dot_product', dropout=0):
""" Attention Mechanism
:param embed_dim:
:param 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
from torch._inductor.runtime.... | n-log-n/ABSA-PyTorch | Attention | false | 7,310 | [
"MIT"
] | 1 | 27b37e05954940fe37369cc679c080d1d8717362 | https://github.com/n-log-n/ABSA-PyTorch/tree/27b37e05954940fe37369cc679c080d1d8717362 |
FitnetRegressor | import torch
import torch.nn.functional as F
class FitnetRegressor(torch.nn.Module):
def __init__(self, in_feature, out_feature):
super(FitnetRegressor, self).__init__()
self.in_feature = in_feature
self.out_feature = out_feature
self.regressor = torch.nn.Conv2d(in_feature, out_fe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | naver-ai/cgl_fairness | FitnetRegressor | false | 7,311 | [
"MIT"
] | 1 | 00d3bec233c9b3e0f88496118abaed8321ca3159 | https://github.com/naver-ai/cgl_fairness/tree/00d3bec233c9b3e0f88496118abaed8321ca3159 |
ZeroOneTest | import torch
from torch import nn
class ZeroOneTest(nn.Module):
def __init__(self):
super(ZeroOneTest, self).__init__()
return
def forward(self, output_p, output_n, prior):
cost = prior * torch.mean((1 - torch.sign(output_p)) / 2)
cost = cost + (1 - prior) * torch.mean((1 + t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | mxuq/Imbalance-PU | ZeroOneTest | false | 7,312 | [
"MIT"
] | 1 | fd4403b05f98ca6bc8156783e8275888d63f6435 | https://github.com/mxuq/Imbalance-PU/tree/fd4403b05f98ca6bc8156783e8275888d63f6435 |
Landsat2ViirsNet | import torch
from torch import nn
from torch.nn import functional as F
class Landsat2ViirsNet(nn.Module):
def __init__(self, latent_dim=64, init_channels=8, kernel_size=4,
image_in_channels=3, image_out_channels=1):
super(Landsat2ViirsNet, self).__init__()
self.enc1 = nn.Conv2d(in_channel... | 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... | mrmauer/detecting_poverty | Landsat2ViirsNet | false | 7,313 | [
"MIT"
] | 1 | 2c8a28295264674f5bfe06ef1fed6dd8b898b8b5 | https://github.com/mrmauer/detecting_poverty/tree/2c8a28295264674f5bfe06ef1fed6dd8b898b8b5 |
VertexDirectEmbedder | import torch
import torch.utils.data
from torch import nn
def normalize_embeddings(embeddings: 'torch.Tensor', epsilon: 'float'=1e-06
) ->torch.Tensor:
"""
Normalize N D-dimensional embedding vectors arranged in a tensor [N, D]
Args:
embeddings (tensor [N, D]): N D-dimensional embedding vecto... | 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.utils.data
from... | nationaldronesau/detectron2 | VertexDirectEmbedder | false | 7,314 | [
"Apache-2.0"
] | 1 | 6afaee60eb6e0032b5b2edfbec1179f7e7b7b75f | https://github.com/nationaldronesau/detectron2/tree/6afaee60eb6e0032b5b2edfbec1179f7e7b7b75f |
xTanH | import torch
import torch.nn
class xTanH(torch.nn.Module):
def forward(self, x: 'torch.Tensor'):
return x - torch.tanh(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride... | nayyarv/bayesnets | xTanH | false | 7,315 | [
"MIT"
] | 1 | 090abd1a0a91c2b9d6d57a182ee5be1f65a22e11 | https://github.com/nayyarv/bayesnets/tree/090abd1a0a91c2b9d6d57a182ee5be1f65a22e11 |
LRN | import torch
import torch.nn as nn
class LRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=
False):
super(LRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if self.ACROSS_CHANNELS:
self.average = nn.AvgPool3d(kernel_size=... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | nbswords/Paper-implemention-by-Pytorch | LRN | false | 7,316 | [
"MIT"
] | 1 | 429514c4f51c41ec7b3013683fb79ad4b4ab4638 | https://github.com/nbswords/Paper-implemention-by-Pytorch/tree/429514c4f51c41ec7b3013683fb79ad4b4ab4638 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def focal_loss(input_values, gamma=10):
"""Computes the focal loss"""
p = torch.exp(-input_values)
loss = (1 - p) ** gamma * input_values
return loss.mean()
class FocalLoss(nn.Module):
def __init__(self, weight=None, gamma=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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | naver-ai/cgl_fairness | FocalLoss | false | 7,317 | [
"MIT"
] | 1 | 00d3bec233c9b3e0f88496118abaed8321ca3159 | https://github.com/naver-ai/cgl_fairness/tree/00d3bec233c9b3e0f88496118abaed8321ca3159 |
MultiHeadSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.distributed
import torch.nn.functional as F
import torch.nn as nn
class MultiHeadSelfAttention(nn.Module):
def __init__(self, config):
super(MultiHeadSelfAttention, self).__init__()
self.query = nn.Linear(config.h... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | myoons/image-gpt-pytorch | MultiHeadSelfAttention | false | 7,318 | [
"Apache-2.0"
] | 1 | d05081250d01ce208796dfb246ea1c9a093237c5 | https://github.com/myoons/image-gpt-pytorch/tree/d05081250d01ce208796dfb246ea1c9a093237c5 |
Matcher | import math
import torch
import torch.nn as nn
class Matcher(nn.Module):
"""
Matching between a pair of nodes to conduct link prediction.
Use multi-head attention as matching model.
"""
def __init__(self, n_hid):
super(Matcher, self).__init__()
self.left_linear = nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | nchungvh/pyhgt | Matcher | false | 7,319 | [
"MIT"
] | 1 | 3cb08ea856ca02aaf1664aa7486024a8742c7567 | https://github.com/nchungvh/pyhgt/tree/3cb08ea856ca02aaf1664aa7486024a8742c7567 |
Q | import torch
import torch.nn as nn
import torch.nn.functional as F
class Q(nn.Module):
"""
Simple fully connected Q function. Also used for skip-Q when concatenating behaviour action and state together.
Used for simpler environments such as mountain-car or lunar-lander.
"""
def __init__(self, sta... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ndangtt/LeadingOnesDAC | Q | false | 7,320 | [
"Apache-2.0"
] | 1 | 953747d8702f179851d7973c65779a1f830e03a1 | https://github.com/ndangtt/LeadingOnesDAC/tree/953747d8702f179851d7973c65779a1f830e03a1 |
DropoutModel8x8 | import torch
import torch.nn as nn
import torch.nn.functional as func
class DropoutModel8x8(nn.Module):
def __init__(self, channel):
"""
Define useful layers
Argument:
channel: number of channel, or depth or number of different sprite types
"""
super(DropoutModel8x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | mwxely/Cross-domain-PCGML-Level-Generator | DropoutModel8x8 | false | 7,321 | [
"MIT"
] | 1 | baa5d214d6cf22272d144aa6c444a778ac202afe | https://github.com/mwxely/Cross-domain-PCGML-Level-Generator/tree/baa5d214d6cf22272d144aa6c444a778ac202afe |
Attn | import torch
import torch.nn as nn
import torch.nn.functional as F
from numpy import sqrt
class Attn(nn.Module):
def __init__(self, hidden_size, batch_first=True):
super(Attn, self).__init__()
self.hidden_size = hidden_size
self.batch_first = batch_first
self.weights = nn.Paramete... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | nauhc/biLSTM-many-to-one | Attn | false | 7,322 | [
"MIT"
] | 1 | 14dab1c75b395c88bdddfe751461af7dc30e1166 | https://github.com/nauhc/biLSTM-many-to-one/tree/14dab1c75b395c88bdddfe751461af7dc30e1166 |
VAE | import torch
from torch import nn
import torch.nn.functional as F
class VAE(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(784, 400)
self.fc21 = nn.Linear(400, 20)
self.fc22 = nn.Linear(400, 20)
self.fc3 = nn.Linear(20, 400)
self.fc4 = nn.L... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | nd1511/argus | VAE | false | 7,323 | [
"MIT"
] | 1 | 00aaed41ac1321d669ac7060f4d21b24cc3456f0 | https://github.com/nd1511/argus/tree/00aaed41ac1321d669ac7060f4d21b24cc3456f0 |
GCN | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
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.... | negarhdr/PGCN | GCN | false | 7,324 | [
"MIT"
] | 1 | 5143049afcfadc5ab0173e6083ebbb4fd8c8903d | https://github.com/negarhdr/PGCN/tree/5143049afcfadc5ab0173e6083ebbb4fd8c8903d |
Perceptron | import torch
import torch.nn as nn
import torch.nn.functional as F
class Perceptron(nn.Module):
"""Implements a 1-layer perceptron."""
def __init__(self, input_dimension, hidden_dimension, output_dimension):
super(Perceptron, self).__init__()
self._layer1 = nn.Linear(input_dimension, hidden_d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | negotiatorvivian/SAT-Solver | Perceptron | false | 7,325 | [
"MIT"
] | 1 | acbf375ce73103e945aee3e2a225126684a19076 | https://github.com/negotiatorvivian/SAT-Solver/tree/acbf375ce73103e945aee3e2a225126684a19076 |
PerceptronTanh | import torch
import torch.nn as nn
import torch.nn.functional as F
class PerceptronTanh(nn.Module):
"""Implements a 1-layer perceptron with Tanh activaton."""
def __init__(self, input_dimension, hidden_dimension, output_dimension):
super(PerceptronTanh, self).__init__()
self._layer1 = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | negotiatorvivian/SAT-Solver | PerceptronTanh | false | 7,326 | [
"MIT"
] | 1 | acbf375ce73103e945aee3e2a225126684a19076 | https://github.com/negotiatorvivian/SAT-Solver/tree/acbf375ce73103e945aee3e2a225126684a19076 |
ConfidentMSELoss | from torch.nn import Module
import torch
class ConfidentMSELoss(Module):
def __init__(self, threshold=0.96):
self.threshold = threshold
super().__init__()
def forward(self, input, target):
n = input.size(0)
conf_mask = torch.gt(target, self.threshold).float()
input_fl... | 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.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | neuropoly/medicaltorch | ConfidentMSELoss | false | 7,327 | [
"Apache-2.0"
] | 1 | ac129fe894cb1906285dfe380ba4f0aa3bdec787 | https://github.com/neuropoly/medicaltorch/tree/ac129fe894cb1906285dfe380ba4f0aa3bdec787 |
Conv2 | import math
import torch
import torch.nn as nn
class Conv2(nn.Module):
""" A convolution layer with the stride of 2.
Input:
x: (N, 2L+2, in_channels) numeric tensor
global_cond: (N, global_cond_channels) numeric tensor
Output:
y: (N, L, out_channels) numeric te... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | neverix/voice-conv | Conv2 | false | 7,328 | [
"MIT"
] | 1 | 6df0053a59aa26318bdbc096dd312ecc55596ac0 | https://github.com/neverix/voice-conv/tree/6df0053a59aa26318bdbc096dd312ecc55596ac0 |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
"""
Applies an attention mechanism on the query features from the decoder.
.. math::
\\begin{array}{ll}
x = context*query \\\\
attn_scores = 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.... | nguyenxuanhoi2903/SRSF_summarization | Attention | false | 7,329 | [
"MIT"
] | 1 | 3d19e6b7669e0b22bab533fc637a434f379ed392 | https://github.com/nguyenxuanhoi2903/SRSF_summarization/tree/3d19e6b7669e0b22bab533fc637a434f379ed392 |
MinusRbfHSIC | import torch
import torch.nn as nn
class HSIC(nn.Module):
"""Base class for the finite sample estimator of Hilbert-Schmidt Independence Criterion (HSIC)
..math:: HSIC (X, Y) := || C_{x, y} ||^2_{HS}, where HSIC (X, Y) = 0 iif X and Y are independent.
Empirically, we use the finite sample estimator of HSIC... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | naver-ai/cgl_fairness | MinusRbfHSIC | false | 7,330 | [
"MIT"
] | 1 | 00d3bec233c9b3e0f88496118abaed8321ca3159 | https://github.com/naver-ai/cgl_fairness/tree/00d3bec233c9b3e0f88496118abaed8321ca3159 |
LayerNorm | import torch
class LayerNorm(torch.nn.Module):
"""
A vanilla implementation of layer normalization https://arxiv.org/pdf/1607.06450.pdf
norm_x = (x - mean) / sqrt((x - mean) ^ 2)
This does not include the trainable parameters gamma and beta for performance speed.
Typically, this is norm_x * 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | netdrones/ml-agents | LayerNorm | false | 7,331 | [
"Apache-2.0"
] | 1 | 7d7d6f149c92ea2067d7cea364d92c8c3b8db3f4 | https://github.com/netdrones/ml-agents/tree/7d7d6f149c92ea2067d7cea364d92c8c3b8db3f4 |
TokenClassifier | import torch
import torch.nn as nn
def transformer_weights_init(module, std_init_range=0.02, xavier=True):
"""
Initialize different weights in Transformer model.
Args:
module: torch.nn.Module to be initialized
std_init_range: standard deviation of normal initializer
xavier: if 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 import triton_helpers
from torch._inductor.runtime.... | ngxingyu/Domain-Transfer-for-Punctuation-Retrieval | TokenClassifier | false | 7,332 | [
"Apache-2.0"
] | 1 | f5aa0ea0946c68aaf7fcf49a5085e6c823766a2f | https://github.com/ngxingyu/Domain-Transfer-for-Punctuation-Retrieval/tree/f5aa0ea0946c68aaf7fcf49a5085e6c823766a2f |
MultichannelIamge | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class ModulatedConv2d(nn.Module):
def __init__(self, channels_in, channels_out, style_dim, kernel_size,
demodulate=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(channels_out, channels_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
import math
import torch.nn as nn
import torch.nn.functional as F
assert_size_st... | nhorton04/mobile_styletransfer | MultichannelIamge | false | 7,333 | [
"Apache-2.0"
] | 1 | db8b9a61b67fd58b9e4d61457ee58e36800cfbbe | https://github.com/nhorton04/mobile_styletransfer/tree/db8b9a61b67fd58b9e4d61457ee58e36800cfbbe |
Net | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Net(nn.Module):
def __init__(self, input_size, num_classes):
super(Net, self).__init__()
self.linear1 = nn.Linear(input_size, 128)
self.linear2 = nn.Linear(128, 256)
self.linear3 = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | nce3xin/spam | Net | false | 7,334 | [
"MIT"
] | 1 | 908421d5cf2dd103e2a7044bf1c8586aaf5f2ada | https://github.com/nce3xin/spam/tree/908421d5cf2dd103e2a7044bf1c8586aaf5f2ada |
ResidualBlock | import torch
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
padding = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(padding)
self.conv2d = nn.Conv2d(in_channels, ou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | naver-ai/cgl_fairness | ResidualBlock | false | 7,335 | [
"MIT"
] | 1 | 00d3bec233c9b3e0f88496118abaed8321ca3159 | https://github.com/naver-ai/cgl_fairness/tree/00d3bec233c9b3e0f88496118abaed8321ca3159 |
PositionGenerator | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""Construct a layernorm module (See citation for details)."""
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(fe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | nigelnnk/MATCh-sensitivity | PositionGenerator | false | 7,336 | [
"MIT"
] | 1 | aaf2b924ac98c8c5925bbf431481724d11a102f8 | https://github.com/nigelnnk/MATCh-sensitivity/tree/aaf2b924ac98c8c5925bbf431481724d11a102f8 |
EdgeFeaturesLayer | import torch
import torch.nn as nn
class EdgeFeaturesLayer(nn.Module):
def __init__(self, d_model, d_edge, h, dropout):
super(EdgeFeaturesLayer, self).__init__()
assert d_model % h == 0
d_model // h
self.linear = nn.Linear(d_edge, 1, bias=False)
with torch.no_grad():
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | nigelnnk/MATCh-sensitivity | EdgeFeaturesLayer | false | 7,337 | [
"MIT"
] | 1 | aaf2b924ac98c8c5925bbf431481724d11a102f8 | https://github.com/nigelnnk/MATCh-sensitivity/tree/aaf2b924ac98c8c5925bbf431481724d11a102f8 |
Generator | import math
import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""Construct a layernorm module (See citation for details)."""
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | nigelnnk/MATCh-sensitivity | Generator | false | 7,338 | [
"MIT"
] | 1 | aaf2b924ac98c8c5925bbf431481724d11a102f8 | https://github.com/nigelnnk/MATCh-sensitivity/tree/aaf2b924ac98c8c5925bbf431481724d11a102f8 |
SqueezeNet | import copy
import torch
import torch.nn as nn
import torch.utils.data
from torchvision.models.squeezenet import squeezenet1_0
from torchvision.models.squeezenet import squeezenet1_1
import torch.nn.modules.activation
class GramMatrix(nn.Module):
def forward(self, x):
b, c, h, w = x.size()
F = 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 copy
import torch.nn a... | matherm/ummon3 | SqueezeNet | false | 7,339 | [
"BSD-3-Clause"
] | 1 | 08476d21ce17cc95180525d48202a1690dfc8a08 | https://github.com/matherm/ummon3/tree/08476d21ce17cc95180525d48202a1690dfc8a08 |
SequenceClassifier | import torch
import torch.nn as nn
import torch.nn.functional as F
def transformer_weights_init(module, std_init_range=0.02, xavier=True):
"""
Initialize different weights in Transformer model.
Args:
module: torch.nn.Module to be initialized
std_init_range: standard deviation of normal ini... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ngxingyu/Domain-Transfer-for-Punctuation-Retrieval | SequenceClassifier | false | 7,340 | [
"Apache-2.0"
] | 1 | f5aa0ea0946c68aaf7fcf49a5085e6c823766a2f | https://github.com/ngxingyu/Domain-Transfer-for-Punctuation-Retrieval/tree/f5aa0ea0946c68aaf7fcf49a5085e6c823766a2f |
ScaleNorm | import math
import torch
import torch.nn as nn
class ScaleNorm(nn.Module):
"""ScaleNorm"""
"""All g’s in SCALE NORM are initialized to sqrt(d)"""
def __init__(self, scale, eps=1e-05):
super(ScaleNorm, self).__init__()
self.scale = nn.Parameter(torch.tensor(math.sqrt(scale)))
self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch.nn ... | nigelnnk/MATCh-sensitivity | ScaleNorm | false | 7,341 | [
"MIT"
] | 1 | aaf2b924ac98c8c5925bbf431481724d11a102f8 | https://github.com/nigelnnk/MATCh-sensitivity/tree/aaf2b924ac98c8c5925bbf431481724d11a102f8 |
StyleResidual | import torch
from torch import nn
import torch.utils.data
import torch.optim
class StyleResidual(nn.Module):
"""Styling."""
def __init__(self, d_channel: 'int', d_style: 'int', kernel_size: 'int'=1):
super().__init__()
self.rs = nn.Conv1d(in_channels=d_style, out_channels=d_channel,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.optim
assert_size_stri... | niklub/NeMo | StyleResidual | false | 7,342 | [
"Apache-2.0"
] | 1 | 4bcb2321cd16835f63afe3dfe993e6d56bcf2c0c | https://github.com/niklub/NeMo/tree/4bcb2321cd16835f63afe3dfe993e6d56bcf2c0c |
CQLAgent | import torch
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from scipy import optimize
class CQLAgent(nn.Module):
def __init__(self, input_shape, n_actions, n_opponent_actions,
hidden_dim=64):
super(CQLAgent, self).__init__()
self.fc1 = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | netlab-lcy/CMIX | CQLAgent | false | 7,343 | [
"MIT"
] | 1 | 53e2d8794af2b380295efe06dcb05235089953c1 | https://github.com/netlab-lcy/CMIX/tree/53e2d8794af2b380295efe06dcb05235089953c1 |
MultiHeadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
"""
Applies an multi-head attention mechanism on the output features from the decoder.
Refer to 「State-of-the-art Speech Recognition With Sequence-to-Sequence Models」 Paper
https://arxiv.org/abs/17... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ngbsLab/Korean-Speech-Recognition | MultiHeadAttention | false | 7,344 | [
"Apache-2.0"
] | 1 | 3867bf7d23222da6812c9b98a93d3c6f7b3c80fc | https://github.com/ngbsLab/Korean-Speech-Recognition/tree/3867bf7d23222da6812c9b98a93d3c6f7b3c80fc |
FocalLoss | import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
import torch.autograd
class FocalLoss(nn.Module):
def __init__(self, gamma=0, eps=1e-07):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.eps = eps
self.ce = torch.nn.CrossEntropyLoss()
de... | 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
... | nikitajz/google-landmarks | FocalLoss | false | 7,345 | [
"MIT"
] | 1 | 2051462be4450c193c98b237fc7ebdae783e2b28 | https://github.com/nikitajz/google-landmarks/tree/2051462be4450c193c98b237fc7ebdae783e2b28 |
ClassWisePool | import sys
from torch.autograd import Function
import torch
from torch import nn
class ClassWisePoolFunction(Function):
@staticmethod
def forward(ctx, input, args):
ctx.num_maps = args
batch_size, num_channels, h, w = input.size()
if num_channels % ctx.num_maps != 0:
None
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import sys
from torch.autograd import Function
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_st... | nishanthta/wsl | ClassWisePool | false | 7,346 | [
"MIT"
] | 1 | 5fda3b909a314b7f88ffa9ab27a6a142de6b0159 | https://github.com/nishanthta/wsl/tree/5fda3b909a314b7f88ffa9ab27a6a142de6b0159 |
WassersteinLoss | import torch
def torch_cdf_loss(tensor_a, tensor_b, p=1):
tensor_a = tensor_a / (torch.sum(tensor_a, dim=-1, keepdim=True) + 1e-14)
tensor_b = tensor_b / (torch.sum(tensor_b, dim=-1, keepdim=True) + 1e-14)
cdf_tensor_a = torch.cumsum(tensor_a, dim=-1)
cdf_tensor_b = torch.cumsum(tensor_b, dim=-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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | nikitadhawan/SimCLR | WassersteinLoss | false | 7,347 | [
"MIT"
] | 1 | 7d87b384b1edb68e7ba86601b26f76e6da214718 | https://github.com/nikitadhawan/SimCLR/tree/7d87b384b1edb68e7ba86601b26f76e6da214718 |
CoxPHLossSorted | import torch
def cox_ph_loss_sorted(log_h, event, eps=1e-07):
"""Requires the input to be sorted by descending duration time.
See DatasetDurationSorted.
We calculate the negative log of $( rac{h_i}{\\sum_{j \\in R_i} h_j})^d$,
where h = exp(log_h) are the hazards and R is the risk set, and d is event... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | nikolase90/pycox | CoxPHLossSorted | false | 7,348 | [
"BSD-2-Clause"
] | 1 | 1c780253da7bab7eba0dc02e1436a68a9b812a66 | https://github.com/nikolase90/pycox/tree/1c780253da7bab7eba0dc02e1436a68a9b812a66 |
leaky_hardtanh | import torch
import torch.nn as nn
class leaky_hardtanh(nn.Module):
def __init__(self, min=-1, max=1, slope=0.01):
super(leaky_hardtanh, self).__init__()
self.min = min
self.max = max
self.slope = slope
def forward(self, x):
x = torch.where(x < self.min, self.min + 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | nikolasmorshuis/gadolinium_prediction | leaky_hardtanh | false | 7,349 | [
"Apache-2.0"
] | 1 | 7d6640df5b62ce578a947d3a9b9c701c3d1ccd79 | https://github.com/nikolasmorshuis/gadolinium_prediction/tree/7d6640df5b62ce578a947d3a9b9c701c3d1ccd79 |
GlobalAttention | import torch
import torch.nn as nn
import torch.cuda
def aeq(*args):
base = args[0]
for a in args[1:]:
assert a == base, str(args)
class Bottle(nn.Module):
def forward(self, input):
if len(input.size()) <= 2:
return super(Bottle, self).forward(input)
size = input.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._inductor.runtime.... | nikhilweee/syntactic-seq2seq | GlobalAttention | false | 7,350 | [
"MIT"
] | 1 | 807e524167b064fc85c91e5e2fa994de6b739455 | https://github.com/nikhilweee/syntactic-seq2seq/tree/807e524167b064fc85c91e5e2fa994de6b739455 |
NetVLAD | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from sklearn.neighbors import NearestNeighbors
class NetVLAD(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, num_clusters=64, dim=128, normalize_input=True,
vladv2=False, use_faiss=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 import triton_helpers
from torch._inductor.runtime.... | leochien1110/Patch-NetVLAD | NetVLAD | false | 7,351 | [
"MIT"
] | 1 | 9282217dd2c9bcf0446a05400fd277e651cecf4e | https://github.com/leochien1110/Patch-NetVLAD/tree/9282217dd2c9bcf0446a05400fd277e651cecf4e |
Net | import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.data
class Net(nn.Module):
def __init__(self, in_dim):
super().__init__()
self.fc1 = nn.Linear(in_dim, 120, bias=False)
nn.init.normal_(self.fc1.weight, mean=0, std=1)
self.fc2 = nn.Linear(120, 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 import nn
import t... | nmichlo/msc-research | Net | false | 7,352 | [
"MIT"
] | 1 | 625e57eca77bbfbc4728ccebdb0733e1613bd258 | https://github.com/nmichlo/msc-research/tree/625e57eca77bbfbc4728ccebdb0733e1613bd258 |
StdConv3d | import torch
from torch import nn
import torch.jit
import torch.nn.functional as F
import torch.nn.functional
class StdConv3d(nn.Conv3d):
def forward(self, x):
w = self.weight
v, m = torch.var_mean(w, dim=[1, 2, 3, 4], keepdim=True, unbiased=False
)
w = (w - m) / torch.sqrt(v ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | nntrongnghia/TDSI21-Shoulder-Muscle-Segmentation | StdConv3d | false | 7,353 | [
"Apache-2.0"
] | 1 | 29f0f83d93e4fdd8127261283dcf9242d9914ba6 | https://github.com/nntrongnghia/TDSI21-Shoulder-Muscle-Segmentation/tree/29f0f83d93e4fdd8127261283dcf9242d9914ba6 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 4, (3, 3), 1)
self.dropout2d_1 = nn.Dropout2d(p=0.5)
self.conv2 = nn.Conv2d(4, 32, (3, 3), 1)
self.dropout2d_2 = nn.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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | nathantau/BigBrain | Net | false | 7,354 | [
"MIT"
] | 1 | b9e81ee3ca91fadeccd59043dcc0062af1e6d365 | https://github.com/nathantau/BigBrain/tree/b9e81ee3ca91fadeccd59043dcc0062af1e6d365 |
FF | import torch
import torch.nn as nn
def conv(in_channels, out_channels, kernel_size, bias=False, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size, padding=
kernel_size // 2, bias=bias, stride=stride)
class FF(nn.Module):
def __init__(self, n_feat, kernel_size=3, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | noxsine/WTSDNet | FF | false | 7,355 | [
"MIT"
] | 1 | 7f25fb62c705c730c4d2fab6c86f9cf3535e6d80 | https://github.com/noxsine/WTSDNet/tree/7f25fb62c705c730c4d2fab6c86f9cf3535e6d80 |
PSNRLoss | import torch
from torch import nn
class PSNRLoss(nn.Module):
def __init__(self):
super(PSNRLoss, self).__init__()
self.criterion = nn.MSELoss(size_average=True)
def __repr__(self):
return 'PSNR'
def forward(self, output, target):
mse = self.criterion(output, 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._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | nthuy190991/geoseg | PSNRLoss | false | 7,356 | [
"MIT"
] | 1 | b679af5dc558720df36dddc7abfd4e6ecb46d7de | https://github.com/nthuy190991/geoseg/tree/b679af5dc558720df36dddc7abfd4e6ecb46d7de |
CELoss | import torch
from torch import nn
class CELoss(nn.Module):
def __init__(self):
super(CELoss, self).__init__()
self.criterionBinary = nn.BCELoss(size_average=True)
self.criterionMulti = nn.NLLLoss(size_average=True)
def __repr__(self):
return 'CE'
def forward(self, 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.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | nthuy190991/geoseg | CELoss | false | 7,357 | [
"MIT"
] | 1 | b679af5dc558720df36dddc7abfd4e6ecb46d7de | https://github.com/nthuy190991/geoseg/tree/b679af5dc558720df36dddc7abfd4e6ecb46d7de |
RatioModel | import torch
import torch.nn.functional as F
class RatioModel(torch.nn.Module):
def __init__(self, D_in, hidden_unit_num):
super().__init__()
None
self.l1 = torch.nn.Linear(D_in, hidden_unit_num)
self.l2 = torch.nn.Linear(hidden_unit_num, hidden_unit_num)
self.l3 = torch.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.triton_helpers import libdevice, math as tl_math
as... | numahha/wmopo | RatioModel | false | 7,358 | [
"MIT"
] | 1 | 1557dab2e8168c1f2e53ffbc435b4000680f1d28 | https://github.com/numahha/wmopo/tree/1557dab2e8168c1f2e53ffbc435b4000680f1d28 |
SchedulerTestNet | import torch
from torch import nn as nn
from torch.nn import functional as F
from torch import optim as optim
class SchedulerTestNet(torch.nn.Module):
"""
adapted from: https://github.com/pytorch/pytorch/blob/master/test/test_optim.py
"""
def __init__(self):
super(SchedulerTestNet, 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 import triton_helpers
from torch import nn as nn
fr... | oke-aditya/pytorch-lightning-bolts | SchedulerTestNet | false | 7,359 | [
"Apache-2.0"
] | 1 | 268df20bb442e7385b709b1488d37fd2767aba3c | https://github.com/oke-aditya/pytorch-lightning-bolts/tree/268df20bb442e7385b709b1488d37fd2767aba3c |
DynamicsModel | import torch
class DynamicsModel(torch.nn.Module):
def __init__(self, D_in, D_out, hidden_unit_num):
None
super(DynamicsModel, self).__init__()
self.l1 = torch.nn.Linear(D_in, hidden_unit_num)
self.l2 = torch.nn.Linear(hidden_unit_num, D_out)
self.logvar = torch.nn.Paramet... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | numahha/wmopo | DynamicsModel | false | 7,360 | [
"MIT"
] | 1 | 1557dab2e8168c1f2e53ffbc435b4000680f1d28 | https://github.com/numahha/wmopo/tree/1557dab2e8168c1f2e53ffbc435b4000680f1d28 |
MultipleInputModel | import torch
from torch import nn as nn
from torch import optim as optim
class TemplateModel(nn.Module):
def __init__(self, mix_data=False):
""" Base model for testing. The setting ``mix_data=True`` simulates a wrong implementation. """
super().__init__()
self.mix_data = mix_data
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 as nn
from torch import optim as optim
assert_size_stride =... | oke-aditya/pytorch-lightning-bolts | MultipleInputModel | false | 7,361 | [
"Apache-2.0"
] | 1 | 268df20bb442e7385b709b1488d37fd2767aba3c | https://github.com/oke-aditya/pytorch-lightning-bolts/tree/268df20bb442e7385b709b1488d37fd2767aba3c |
FakeRKHSConvNet | import math
import torch
import numpy as np
from torch import nn as nn
from torch import optim as optim
class MaybeBatchNorm2d(nn.Module):
def __init__(self, n_ftr, affine, use_bn):
super(MaybeBatchNorm2d, self).__init__()
self.bn = nn.BatchNorm2d(n_ftr, affine=affine)
self.use_bn = use_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._inductor.runtime.... | oke-aditya/pytorch-lightning-bolts | FakeRKHSConvNet | false | 7,362 | [
"Apache-2.0"
] | 1 | 268df20bb442e7385b709b1488d37fd2767aba3c | https://github.com/oke-aditya/pytorch-lightning-bolts/tree/268df20bb442e7385b709b1488d37fd2767aba3c |
AgentA2C | import torch
import torch.nn as nn
class AgentA2C(nn.Module):
def __init__(self, state_shape, n_actions):
super().__init__()
self.name = 'a2c'
self.n_actions = n_actions
self.state_shape = state_shape
self.hidden1 = nn.Linear(self.state_shape, 100)
self.act1 = nn.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._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | onimaru/Reinforcement_Learning | AgentA2C | false | 7,363 | [
"MIT"
] | 1 | 4c45b51a095cb0cb3c18f6a1542befdcab8a58a4 | https://github.com/onimaru/Reinforcement_Learning/tree/4c45b51a095cb0cb3c18f6a1542befdcab8a58a4 |
VishalNet | import torch
import torch.nn as nn
class VishalNet(nn.Module):
def __init__(self):
super(VishalNet, self).__init__()
self.cnn1 = nn.Conv1d(1, 60, 81, 1, 40)
self.cnn2 = nn.Conv1d(60, 1, 301, 1, 150)
def forward(self, input):
out1 = nn.functional.relu(self.cnn1(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
import torch.nn as nn
assert_... | olivesgatech/Geophysics-2021-Joint-learning-for-spatial-context-based-inversion | VishalNet | false | 7,364 | [
"MIT"
] | 1 | 56f506dfe62ac3557febb4c8e3c62542b1624a1b | https://github.com/olivesgatech/Geophysics-2021-Joint-learning-for-spatial-context-based-inversion/tree/56f506dfe62ac3557febb4c8e3c62542b1624a1b |
L2Norm | import torch
from torch import nn
class L2Norm(nn.Module):
def forward(self, x, eps=1e-06):
norm = x.norm(dim=1, keepdim=True).clamp(min=eps)
return x / norm
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
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | onlyrico/vit-pytorch | L2Norm | false | 7,365 | [
"MIT"
] | 1 | e52ac4195550faa9c3372533d325bf649f7354ad | https://github.com/onlyrico/vit-pytorch/tree/e52ac4195550faa9c3372533d325bf649f7354ad |
AgentReinforce | import torch
import torch.nn as nn
class AgentReinforce(nn.Module):
def __init__(self, state_shape, n_actions):
super().__init__()
self.name = 'reinforce'
self.n_actions = n_actions
self.state_shape = state_shape
self.hidden1 = nn.Linear(self.state_shape, 100)
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | onimaru/Reinforcement_Learning | AgentReinforce | false | 7,366 | [
"MIT"
] | 1 | 4c45b51a095cb0cb3c18f6a1542befdcab8a58a4 | https://github.com/onimaru/Reinforcement_Learning/tree/4c45b51a095cb0cb3c18f6a1542befdcab8a58a4 |
AmdimNCELoss | import torch
from torch import nn as nn
from torch import optim as optim
def tanh_clip(x, clip_val=10.0):
"""
soft clip values to the range [-clip_val, +clip_val]
"""
if clip_val is not None:
x_clip = clip_val * torch.tanh(1.0 / clip_val * x)
else:
x_clip = x
return x_clip
cl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | oke-aditya/pytorch-lightning-bolts | AmdimNCELoss | false | 7,367 | [
"Apache-2.0"
] | 1 | 268df20bb442e7385b709b1488d37fd2767aba3c | https://github.com/oke-aditya/pytorch-lightning-bolts/tree/268df20bb442e7385b709b1488d37fd2767aba3c |
ConditionTime | import torch
from torch import nn as nn
def condition_time(x, i=0, size=(12, 16), seq_len=15):
"""create one hot encoded time image-layers, i in [1, seq_len]"""
assert i < seq_len
times = torch.eye(seq_len, dtype=x.dtype, device=x.device)[i].unsqueeze(-1
).unsqueeze(-1)
ones = torch.ones(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 import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | openclimatefix/MetNet | ConditionTime | false | 7,368 | [
"MIT"
] | 1 | 06eed550e93da6325641958b0d36c15adde1d928 | https://github.com/openclimatefix/MetNet/tree/06eed550e93da6325641958b0d36c15adde1d928 |
_ImpalaBlock | import torch
from torch import nn
class _ImpalaResBlock(nn.Module):
def __init__(self, n_channels: 'int'):
super().__init__()
self.n_channels = n_channels
kernel_size = 3
padding = 1
self.relu = nn.ReLU()
self.relu_inplace = nn.ReLU()
self.conv1 = nn.Conv2d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | nrfulton/vsrl-framework | _ImpalaBlock | false | 7,369 | [
"MIT"
] | 1 | c778824b3285e3e994a4c5846c7b1c2ac03c669b | https://github.com/nrfulton/vsrl-framework/tree/c778824b3285e3e994a4c5846c7b1c2ac03c669b |
MILLR | import torch
import numpy as np
from torch import nn
import torch as tc
from sklearn.metrics import *
from torch.utils.data import DataLoader
from torch.utils.data import WeightedRandomSampler
class myDataset(torch.utils.data.Dataset):
def __init__(self, x, y):
self.x = x
self.y = y
def __le... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from torch... | mhbl3/PrecursorAnalysis | MILLR | false | 7,370 | [
"MIT"
] | 1 | aaa2fe0219ad579b9126fef9cc8594a59ae66815 | https://github.com/mhbl3/PrecursorAnalysis/tree/aaa2fe0219ad579b9126fef9cc8594a59ae66815 |
PEG | import torch
from torch import nn
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(x, **kwargs) + x
class PEG(nn.Module):
def __init__(self, dim, kernel_size=3):
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | onlyrico/vit-pytorch | PEG | false | 7,371 | [
"MIT"
] | 1 | e52ac4195550faa9c3372533d325bf649f7354ad | https://github.com/onlyrico/vit-pytorch/tree/e52ac4195550faa9c3372533d325bf649f7354ad |
SpatialGather_Module | import torch
from torchvision.transforms import functional as F
import torch.nn as nn
import torch.nn.functional as F
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial predicted probability distribution.
Employ the soft-weighted method to aggregate ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | openseg-group/panoptic-deeplab | SpatialGather_Module | false | 7,372 | [
"Apache-2.0"
] | 1 | 818887597e75af77ba32185eb67d8aeac47b54fe | https://github.com/openseg-group/panoptic-deeplab/tree/818887597e75af77ba32185eb67d8aeac47b54fe |
Qux | import torch
import torch.jit
import torch.onnx
import torch.nn
class Qux(torch.nn.Module):
def __init__(self, x):
super(Qux, self).__init__()
self.x = x
def forward(self, a, b):
return a - b - self.x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | opti-mix/glow | Qux | false | 7,373 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
Discriminator | import torch
import numpy as np
from torch import nn as nn
from torch.nn import functional as F
from torch import optim as optim
class Discriminator(nn.Module):
def __init__(self, img_shape, hidden_dim=1024):
super().__init__()
in_dim = int(np.prod(img_shape))
self.fc1 = nn.Linear(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
import numpy as np
from torch import nn as nn
from torch import optim as optim
a... | oke-aditya/pytorch-lightning-bolts | Discriminator | false | 7,374 | [
"Apache-2.0"
] | 1 | 268df20bb442e7385b709b1488d37fd2767aba3c | https://github.com/oke-aditya/pytorch-lightning-bolts/tree/268df20bb442e7385b709b1488d37fd2767aba3c |
ResBlock | import torch
from torch import nn
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, dilation=1,
bias=False):
super(CausalConv1d, self).__init__()
self.padding = padding = (kernel_size - 1) * dilation
self.conv = nn.Conv1d(in_channels, out_ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | oleges1/TTS | ResBlock | false | 7,375 | [
"MIT"
] | 1 | 19b389714078729fae29faf9c23112bdbe4c8dec | https://github.com/oleges1/TTS/tree/19b389714078729fae29faf9c23112bdbe4c8dec |
RepeatModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class RepeatModule(torch.nn.Module):
def __init__(self, repeats):
super(RepeatModule, self).__init__()
self.repeats = repeats
def forward(self, tensor):
tensor = tensor + tensor
return tensor.repeat(self.repeats)... | 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | opti-mix/glow | RepeatModule | false | 7,376 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
Grounding | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch as th
from torchvision.ops.boxes import *
from torchvision.transforms.functional import *
class Grounding(nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.u... | necla-ml/ML-Vision | Grounding | false | 7,377 | [
"BSD-3-Clause"
] | 1 | 66229b29fc0f67c75dbe6304cdb8c5e93fe0bacf | https://github.com/necla-ml/ML-Vision/tree/66229b29fc0f67c75dbe6304cdb8c5e93fe0bacf |
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