entry_point stringlengths 1 65 | original_triton_python_code stringlengths 208 619k | optimised_triton_code stringlengths 1.15k 275k | repo_name stringlengths 7 115 | module_name stringlengths 1 65 | synthetic bool 1
class | uuid int64 0 18.5k | licenses listlengths 1 6 | stars int64 0 19.8k | sha stringlengths 40 40 | repo_link stringlengths 72 180 |
|---|---|---|---|---|---|---|---|---|---|---|
SmoothL1Loss | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
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 math as tl_math
import functools
impor... | Fanzhongjie/ARFE | SmoothL1Loss | false | 456 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
BasicModel_ConvNet_MaxPool1d | import torch
from torch import Tensor
import torch.nn as nn
from typing import no_type_check
class BasicModel_ConvNet_MaxPool1d(nn.Module):
"""Same as above, but with the MaxPool2d replaced
with a MaxPool1d. This is useful because the MaxPool modules
behave differently to other modules from the perspectiv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Europium248/captum | BasicModel_ConvNet_MaxPool1d | false | 457 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
HardSwish | import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
def hard_swish(x, inplace: 'bool'=False):
inner = F.relu6(x + 3.0).div_(6.0)
return x.mul_(inner) if inplace else x.mul(inner)
class HardSwish(nn.Module):
def __init__(self, inplace: 'bool'=False):
... | 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
from torch.nn import functional as F
import torch.nn.parallel
asser... | Fanzhongjie/ARFE | HardSwish | false | 458 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
CecaModule | import math
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
class CecaModule(nn.Module):
"""Constructs a circular ECA module.
ECA module where the conv uses circular padding rather than zero padding.
Unlike the spatial dimension, the channels do not have i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.parallel
assert_size_stride = ... | Fanzhongjie/ARFE | CecaModule | false | 459 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
EcaModule | import math
import torch
import torch.nn as nn
import torch.nn.parallel
class EcaModule(nn.Module):
"""Constructs an ECA module.
Args:
channels: Number of channels of the input feature map for use in adaptive kernel sizes
for actual calculations according to channel.
gamma, be... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.parallel
assert_size_stride = ... | Fanzhongjie/ARFE | EcaModule | false | 460 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
Encoder | import torch
import torch.nn as nn
class Encoder(nn.Module):
"""
This class defines the encoder architecture
"""
def __init__(self, input_size, hidden_size, bottleneck):
super().__init__()
self.linear1 = nn.Linear(input_size, hidden_size)
self.mean = nn.Linear(hidden_size, bottlen... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | FaisalAhmed0/variational-autoencoder | Encoder | false | 461 | [
"MIT"
] | 0 | a6c1c96da8063d822aef2e2bdd69d7cb1b35c2cd | https://github.com/FaisalAhmed0/variational-autoencoder/tree/a6c1c96da8063d822aef2e2bdd69d7cb1b35c2cd |
Sigmoid | import torch
import torch.nn as nn
import torch.nn.parallel
class Sigmoid(nn.Module):
def __init__(self, inplace: 'bool'=False):
super(Sigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return x.sigmoid_() if self.inplace else x.sigmoid()
def get_inputs():
r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | Fanzhongjie/ARFE | Sigmoid | false | 462 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
LocationLayer | import torch
import torch.utils.data
from torch import nn
class LinearNorm(torch.nn.Module):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
super(LinearNorm, self).__init__()
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
torch.nn.init.xavier_unifor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | FadyKhalaf/tacotron2 | LocationLayer | false | 463 | [
"BSD-3-Clause"
] | 0 | d9bf28a6d286aab42bce46df9f26a9a3d7c2f01f | https://github.com/FadyKhalaf/tacotron2/tree/d9bf28a6d286aab42bce46df9f26a9a3d7c2f01f |
MSELoss | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
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 functools
import torch.nn as nn
from torch.nn import functional as F
import torch.... | Fanzhongjie/ARFE | MSELoss | false | 464 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
Aggregator | import torch
import torch.nn as nn
import torch.nn.functional as F
class Aggregator(nn.Module):
def __init__(self, hidden_dim, num_node):
super(Aggregator, self).__init__()
self.W_q = nn.Linear(hidden_dim, hidden_dim)
self.W_k = nn.Linear(hidden_dim, hidden_dim)
self.fc = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | FANTASTPATR/STST | Aggregator | false | 465 | [
"Apache-2.0"
] | 0 | 8f969fcfe31f9555b19e783fb14eecf72def4122 | https://github.com/FANTASTPATR/STST/tree/8f969fcfe31f9555b19e783fb14eecf72def4122 |
Conv2dSame | import math
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
from typing import Tuple
from typing import Optional
from typing import List
def get_same_padding(x: 'int', k: 'int', s: 'int', d: 'int'):
return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
from torch.nn import functional as F
import to... | Fanzhongjie/ARFE | Conv2dSame | false | 466 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
h_swish | import torch
import torch.nn as nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
class h_swish(nn.Module):
def __init__(self, inplace=True)... | 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... | Felicia980317/mytorch | h_swish | false | 467 | [
"Apache-2.0"
] | 0 | e463122c0d402878ec5b4c5a823a0feeba8fdbfe | https://github.com/Felicia980317/mytorch/tree/e463122c0d402878ec5b4c5a823a0feeba8fdbfe |
SigmoidDeepLiftModel | import torch
import torch.nn as nn
class SigmoidDeepLiftModel(nn.Module):
"""
Model architecture from:
https://medium.com/coinmonks/create-a-neural-network-in
-pytorch-and-make-your-life-simpler-ec5367895199
"""
def __init__(self, num_in, num_hidden, num_out):
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
import torch.nn as nn
assert_... | Europium248/captum | SigmoidDeepLiftModel | false | 468 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
L1Loss | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
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 math as tl_math
import functools
impor... | Fanzhongjie/ARFE | L1Loss | false | 469 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
Scale1Minus1 | import torch
class Scale1Minus1(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x = x / 254.0 * 2 - 1
return 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Filco306/WASP-GANs | Scale1Minus1 | false | 470 | [
"Apache-2.0"
] | 0 | e50cf096a5e3eb26d33a3cbf164d728b9789e41b | https://github.com/Filco306/WASP-GANs/tree/e50cf096a5e3eb26d33a3cbf164d728b9789e41b |
RMSELoss | import torch
import torch.nn as nn
class RMSELoss(nn.Module):
def __init__(self):
super(RMSELoss, self).__init__()
def forward(self, x, y):
criterion = nn.MSELoss()
loss = torch.sqrt(criterion(x, y))
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | FizzerYu/CollaborativeVAE | RMSELoss | false | 471 | [
"MIT"
] | 0 | 4714cce49acba258600b1b5bbcd3a1a4762385e6 | https://github.com/FizzerYu/CollaborativeVAE/tree/4714cce49acba258600b1b5bbcd3a1a4762385e6 |
Rescale | import torch
class Rescale(torch.nn.Module):
def __init__(self, old_min, old_max, new_min, new_max):
super(Rescale, self).__init__()
self.old_min = old_min
self.old_max = old_max
self.new_min = new_min
self.new_max = new_max
def forward(self, x):
x = (x - 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Filco306/WASP-GANs | Rescale | false | 472 | [
"Apache-2.0"
] | 0 | e50cf096a5e3eb26d33a3cbf164d728b9789e41b | https://github.com/Filco306/WASP-GANs/tree/e50cf096a5e3eb26d33a3cbf164d728b9789e41b |
LRN | import torch
import torch.optim
import torch.nn as nn
class LRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=True
):
super(LRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:
self.average = nn.AvgPool3d... | 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.optim
import torch.nn as nn
assert_size_stride = torch._C._dynamo.... | Fyy10/UESTC-Thesis-DA | LRN | false | 473 | [
"MIT"
] | 0 | 6cb16efd1f80aa569c90874a806a62dec8afaec4 | https://github.com/Fyy10/UESTC-Thesis-DA/tree/6cb16efd1f80aa569c90874a806a62dec8afaec4 |
MMDLoss | import torch
import torch.optim
import torch.nn as nn
class MMDLoss(nn.Module):
def __init__(self):
"""
Maximum Mean Discrepancy Loss
"""
super(MMDLoss, self).__init__()
self.eps = 1e-08
def forward(self, f1: 'torch.Tensor', f2: 'torch.Tensor') ->torch.Tensor:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.optim
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | Fyy10/UESTC-Thesis-DA | MMDLoss | false | 474 | [
"MIT"
] | 0 | 6cb16efd1f80aa569c90874a806a62dec8afaec4 | https://github.com/Fyy10/UESTC-Thesis-DA/tree/6cb16efd1f80aa569c90874a806a62dec8afaec4 |
ConvWS2d | import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
def conv_ws_2d(input, weight, bias=None, stride=1, padding=0, dilation=1,
groups=1, eps=1e-05):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1)
mean = weight_flat.mean(dim=1, keepdim=True).view(c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Fanzhongjie/ARFE | ConvWS2d | false | 475 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
selfVLoss | import torch
import torch.nn as nn
class selfVLoss(nn.Module):
def __init__(self, lambda_v, lambda_r):
super(selfVLoss, self).__init__()
self.lambda_v = lambda_v
self.lambda_r = lambda_r
def forward(self, v, z):
return 1.0 * self.lambda_v / self.lambda_r * torch.mean(torch.su... | 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... | FizzerYu/CollaborativeVAE | selfVLoss | false | 476 | [
"MIT"
] | 0 | 4714cce49acba258600b1b5bbcd3a1a4762385e6 | https://github.com/FizzerYu/CollaborativeVAE/tree/4714cce49acba258600b1b5bbcd3a1a4762385e6 |
ConvLayer | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super(SpectralNorm, self).__init__()
self.module = module
self.n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch.... | GEN418/EventGAN | ConvLayer | false | 477 | [
"MIT"
] | 0 | 372318bc8f285f513db4babf7786b5c04e97c86d | https://github.com/GEN418/EventGAN/tree/372318bc8f285f513db4babf7786b5c04e97c86d |
GeM | import torch
import torch.nn.functional as F
from torch import nn
def gem(x, p=3, eps=1e-06):
return F.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x.size(-1))).pow(
1.0 / p)
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-06, requires_grad: 'bool'=True):
super().__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.functional a... | Fkaneko/kaggle_g2net_gravitational_wave_detection- | GeM | false | 478 | [
"Apache-2.0"
] | 0 | 8bb32cc675e6b56171da8a3754fffeda41e934bb | https://github.com/Fkaneko/kaggle_g2net_gravitational_wave_detection-/tree/8bb32cc675e6b56171da8a3754fffeda41e934bb |
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... | GeoffNN/robustness | FakeReLUM | false | 479 | [
"MIT"
] | 0 | 2cefabb5b0ceab62a77e0572f209144d7124cc9f | https://github.com/GeoffNN/robustness/tree/2cefabb5b0ceab62a77e0572f209144d7124cc9f |
NetVLAD | import torch
import numpy as np
import torch.utils.data
import torch
import torch.nn.functional as F
from torch import nn
class NetVLAD(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, num_clusters=64, dim=128, normalize_input=True):
"""
Args:
num_clusters : 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._inductor.runtime.... | DanielPollithy/UncertainToDayGAN | NetVLAD | false | 480 | [
"BSD-2-Clause"
] | 0 | bd16fa1a34878dbdc765d548169b7058a56864ff | https://github.com/DanielPollithy/UncertainToDayGAN/tree/bd16fa1a34878dbdc765d548169b7058a56864ff |
Encoder | import torch
from torch import nn
class Encoder(nn.Module):
def __init__(self, input_dim, hidden_dim, latent_dim):
super(Encoder, self).__init__()
self.FC_input = nn.Linear(input_dim, hidden_dim)
self.FC_mean = nn.Linear(hidden_dim, latent_dim)
self.FC_var = nn.Linear(hidden_dim, ... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | FrederikKromann/dtu_mlops | Encoder | false | 481 | [
"Apache-2.0"
] | 0 | b82e43e1a1b58f0ba208414092e4c0ea63c5d4ff | https://github.com/FrederikKromann/dtu_mlops/tree/b82e43e1a1b58f0ba208414092e4c0ea63c5d4ff |
LinearTextualHead | import torch
from torch import nn
from typing import Optional
class TextualHead(nn.Module):
"""
Base class for all textual heads. All child classes can simply inherit
from :class:`~torch.nn.Module`, however this is kept here for uniform
type annotations.
Args:
visual_feature_size: Size (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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | GeorgeBatch/arch-pre-training | LinearTextualHead | false | 482 | [
"MIT"
] | 0 | 7ed75868689e9283d61d11360fdbf4e77d4ebd2e | https://github.com/GeorgeBatch/arch-pre-training/tree/7ed75868689e9283d61d11360fdbf4e77d4ebd2e |
LinearRegression | import torch
from torch import nn
class LinearRegression(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int', bias:
'bool'=True):
super().__init__()
self.weights = nn.Parameter(torch.randn(out_features, in_features))
self.bias = bias
if bias:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | GaspardLeMarque/Competition-Models | LinearRegression | false | 483 | [
"MIT"
] | 0 | 3bd0b75de369420ac3a3011659f1de5942a867e1 | https://github.com/GaspardLeMarque/Competition-Models/tree/3bd0b75de369420ac3a3011659f1de5942a867e1 |
ResidualBlock | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super(SpectralNorm, self).__init__()
self.module = module
self.n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | GEN418/EventGAN | ResidualBlock | false | 484 | [
"MIT"
] | 0 | 372318bc8f285f513db4babf7786b5c04e97c86d | https://github.com/GEN418/EventGAN/tree/372318bc8f285f513db4babf7786b5c04e97c86d |
MyNet | import torch
import torch.nn as nn
class MyNet(nn.Module):
def __init__(self):
super(MyNet, self).__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=8, kernel_size
=5, stride=2, padding=0)
self.conv2_1 = nn.Conv2d(8, 16, 3, 1, 0)
self.conv2_2 = nn.Conv2d(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DeepDuke/Face_Keypoints_Dectection | MyNet | false | 485 | [
"MIT"
] | 0 | 9f09e1ad113734a9ba5d006d3f817a497db572aa | https://github.com/DeepDuke/Face_Keypoints_Dectection/tree/9f09e1ad113734a9ba5d006d3f817a497db572aa |
CAM_Module | import torch
import torch.nn as nn
import torch._utils
class CAM_Module(nn.Module):
""" Channel attention module"""
def __init__(self, in_dim):
super(CAM_Module, self).__init__()
self.chanel_in = in_dim
self.gamma = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(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.... | GhadeerElmkaiel/Trans2Seg | CAM_Module | false | 486 | [
"Apache-2.0"
] | 0 | 6717db602205cbed494ae1913ac7cbbca8e83463 | https://github.com/GhadeerElmkaiel/Trans2Seg/tree/6717db602205cbed494ae1913ac7cbbca8e83463 |
HistogramLayerUNET | import torch
import numpy as np
import torch.nn as nn
class HistogramLayerUNET(nn.Module):
def __init__(self, in_channels, kernel_size, dim=2, num_bins=4, stride=
None, padding=0, normalize_count=True, normalize_bins=True,
count_include_pad=False, ceil_mode=False, skip_connection=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 math as tl_math
import torch.... | GatorSense/Histological_Segmentation | HistogramLayerUNET | false | 487 | [
"MIT"
] | 0 | 12849fff3d9d58c1fe419b18dba49294db375488 | https://github.com/GatorSense/Histological_Segmentation/tree/12849fff3d9d58c1fe419b18dba49294db375488 |
selfCrossEntropy | import torch
import torch.nn as nn
class selfCrossEntropy(nn.Module):
def __init__(self):
super(selfCrossEntropy, self).__init__()
def forward(self, output, target):
output = nn.functional.softmax(output, dim=0)
return -torch.mean(torch.sum(target * torch.log(torch.clamp(output,
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | FizzerYu/CollaborativeVAE | selfCrossEntropy | false | 488 | [
"MIT"
] | 0 | 4714cce49acba258600b1b5bbcd3a1a4762385e6 | https://github.com/FizzerYu/CollaborativeVAE/tree/4714cce49acba258600b1b5bbcd3a1a4762385e6 |
ChannelPool | import torch
from torch import nn
class ChannelPool(nn.Module):
def forward(self, x):
return torch.mean(x, 1).unsqueeze(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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | GhadeerElmkaiel/MirrorNet | ChannelPool | false | 489 | [
"BSD-3-Clause"
] | 0 | 1a0389abc5b1ccbe7fde7bd1df772cb9df30c072 | https://github.com/GhadeerElmkaiel/MirrorNet/tree/1a0389abc5b1ccbe7fde7bd1df772cb9df30c072 |
run_latent | import torch
import torch.nn as nn
class run_latent(nn.Module):
def __init__(self, in_dim, hidden_dim):
super(run_latent, self).__init__()
self.fc_z_mean = nn.Linear(in_dim, hidden_dim)
self.fc_z_log_sigma = nn.Linear(in_dim, hidden_dim)
self.fc_gen = nn.Linear(hidden_dim, in_dim)... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | FizzerYu/CollaborativeVAE | run_latent | false | 490 | [
"MIT"
] | 0 | 4714cce49acba258600b1b5bbcd3a1a4762385e6 | https://github.com/FizzerYu/CollaborativeVAE/tree/4714cce49acba258600b1b5bbcd3a1a4762385e6 |
Attention_Decoder | import torch
import torch.nn as nn
import torch._utils
class Attention_Decoder(nn.Module):
def __init__(self, dim, num_heads=1, qkv_bias=False, qk_scale=None,
attn_drop=0.0, proj_drop=0.0):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scal... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | GhadeerElmkaiel/Trans2Seg | Attention_Decoder | false | 491 | [
"Apache-2.0"
] | 0 | 6717db602205cbed494ae1913ac7cbbca8e83463 | https://github.com/GhadeerElmkaiel/Trans2Seg/tree/6717db602205cbed494ae1913ac7cbbca8e83463 |
ParsingRelationLoss | import torch
import torch.nn.modules
import torch.nn as nn
class ParsingRelationLoss(nn.Module):
def __init__(self):
super(ParsingRelationLoss, self).__init__()
def forward(self, logits):
_n, _c, h, _w = logits.shape
loss_all = []
for i in range(0, h - 1):
loss_al... | 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.modules
import torch.nn as nn
assert_size_stride = torch.... | Glutamat42/Ultra-Fast-Lane-Detection | ParsingRelationLoss | false | 492 | [
"MIT"
] | 0 | 175448f39574d64a7cc6fd35ec92e3c5539c9837 | https://github.com/Glutamat42/Ultra-Fast-Lane-Detection/tree/175448f39574d64a7cc6fd35ec92e3c5539c9837 |
selfLatentLoss | import torch
import torch.nn as nn
class selfLatentLoss(nn.Module):
def __init__(self):
super(selfLatentLoss, self).__init__()
def forward(self, z_mean, z_log_sigma_sq):
return torch.mean(torch.sum(torch.pow(z_mean, 2) + torch.exp(
z_log_sigma_sq) - z_log_sigma_sq - 1, 1))
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | FizzerYu/CollaborativeVAE | selfLatentLoss | false | 493 | [
"MIT"
] | 0 | 4714cce49acba258600b1b5bbcd3a1a4762385e6 | https://github.com/FizzerYu/CollaborativeVAE/tree/4714cce49acba258600b1b5bbcd3a1a4762385e6 |
OneLayerFCBodyWithAction | import torch
import torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class OneLayerFCBodyWithAction(nn.Module):
def __init__(self, state_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Fieps1/p3-tennis | OneLayerFCBodyWithAction | false | 494 | [
"MIT"
] | 0 | 29f3dab5810d7cd7f84120416a615956d266c256 | https://github.com/Fieps1/p3-tennis/tree/29f3dab5810d7cd7f84120416a615956d266c256 |
GRUCell | import torch
import numpy as np
import torch.nn.functional as F
import torch.utils.data
import torch.nn as nn
class GRUCell(nn.Module):
def __init__(self, input_size, hidden_size, bias=True):
super(GRUCell, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | GregoryLand/PyGrid | GRUCell | false | 495 | [
"Apache-2.0"
] | 0 | 00271f73db825eaf378095ea5c4363d4a04d38a6 | https://github.com/GregoryLand/PyGrid/tree/00271f73db825eaf378095ea5c4363d4a04d38a6 |
VarifocalLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Guoning-Chen/mmdetection | VarifocalLoss | false | 496 | [
"Apache-2.0"
] | 0 | f1d1c5a19dbe6aa2e74fc9ca2e9578db4532fc64 | https://github.com/Guoning-Chen/mmdetection/tree/f1d1c5a19dbe6aa2e74fc9ca2e9578db4532fc64 |
CrossEntropy | import torch
from torch import nn
from torch.nn import functional as F
class CrossEntropy(nn.Module):
def __init__(self, ignore_label=-1, weight=None):
super(CrossEntropy, self).__init__()
self.ignore_label = ignore_label
self.criterion = nn.CrossEntropyLoss(weight=weight, ignore_index=
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | Gummary/Pytorch-Project-Template | CrossEntropy | false | 497 | [
"MIT"
] | 0 | 56bc5e253627d40fb8771eccdb2bb663c833beb3 | https://github.com/Gummary/Pytorch-Project-Template/tree/56bc5e253627d40fb8771eccdb2bb663c833beb3 |
FeatureSelect | import torch
import torch.optim
import torch.nn as nn
class FeatureSelect(nn.Module):
def __init__(self, in_dim=84, ratio=0.5):
"""
Feature Select via Sorting
Args:
in_dim: the number of dimensions of raw features
ratio: the portion of selected features
""... | 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.optim
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.ass... | Fyy10/UESTC-Thesis-DA | FeatureSelect | false | 498 | [
"MIT"
] | 0 | 6cb16efd1f80aa569c90874a806a62dec8afaec4 | https://github.com/Fyy10/UESTC-Thesis-DA/tree/6cb16efd1f80aa569c90874a806a62dec8afaec4 |
GeM | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-06):
"""
Args:
p : int
Number of the pooling parameter
eps : float
lower-bound o... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | GiaVit97/project_vg | GeM | false | 499 | [
"MIT"
] | 0 | 410de0861f479a86e9c4611bd4f0e270566bcd49 | https://github.com/GiaVit97/project_vg/tree/410de0861f479a86e9c4611bd4f0e270566bcd49 |
L2Norm | from torch.nn import Module
import torch
import torch.nn.functional as F
class L2Norm(Module):
def forward(self, input):
return F.normalize(input)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
... | HAOCHENYE/Silent-Face-Anti-Spoofing-master-yehc | L2Norm | false | 500 | [
"Apache-2.0"
] | 0 | 014c781d4109733f87a50b10d10508ba5e431581 | https://github.com/HAOCHENYE/Silent-Face-Anti-Spoofing-master-yehc/tree/014c781d4109733f87a50b10d10508ba5e431581 |
SIG_LOSS | import torch
from torch import nn
class SIG_LOSS(nn.Module):
def __init__(self, device):
super(SIG_LOSS, self).__init__()
self.m_device = device
self.m_criterion = nn.BCEWithLogitsLoss(reduction='mean')
def forward(self, preds, targets):
loss = self.m_criterion(preds, targets... | 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 ... | HCDM/XRec | SIG_LOSS | false | 501 | [
"MIT"
] | 0 | dae7d3e1237b8e41913656eb33d81e78c61424ea | https://github.com/HCDM/XRec/tree/dae7d3e1237b8e41913656eb33d81e78c61424ea |
SigmoidFocalLoss | import torch
import torch.utils.data.distributed
import torch
import torch.nn as nn
from numpy import int64 as int64
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.ignor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | HRTNet/HRTNet | SigmoidFocalLoss | false | 502 | [
"MIT"
] | 0 | 6a51c9c34568988ea6125a1638794c63d8fadbea | https://github.com/HRTNet/HRTNet/tree/6a51c9c34568988ea6125a1638794c63d8fadbea |
GaussianFocalLoss | import functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | Guoning-Chen/mmdetection | GaussianFocalLoss | false | 503 | [
"Apache-2.0"
] | 0 | f1d1c5a19dbe6aa2e74fc9ca2e9578db4532fc64 | https://github.com/Guoning-Chen/mmdetection/tree/f1d1c5a19dbe6aa2e74fc9ca2e9578db4532fc64 |
InnerProductDecoder | import torch
import torch.fx
import torch.utils.data
class InnerProductDecoder(torch.nn.Module):
"""The inner product decoder from the `"Variational Graph Auto-Encoders"
<https://arxiv.org/abs/1611.07308>`_ paper
.. math::
\\sigma(\\mathbf{Z}\\mathbf{Z}^{\\top})
where :math:`\\mathbf{Z} \\in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | HWSelf/pytorch_geometric | InnerProductDecoder | false | 504 | [
"MIT"
] | 0 | c1214de674079b5e39e57c045d0f844b60caf590 | https://github.com/HWSelf/pytorch_geometric/tree/c1214de674079b5e39e57c045d0f844b60caf590 |
SeparableConv2d_same | import torch
from torch import nn
from torch.nn import functional as F
def fixed_padding(inputs, kernel_size, rate):
kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
pad_total = kernel_size_effective - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
padded_inputs = F.p... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import functional as F
assert_size_stride = t... | Gummary/Pytorch-Project-Template | SeparableConv2d_same | false | 505 | [
"MIT"
] | 0 | 56bc5e253627d40fb8771eccdb2bb663c833beb3 | https://github.com/Gummary/Pytorch-Project-Template/tree/56bc5e253627d40fb8771eccdb2bb663c833beb3 |
perceptron | import torch
from torch import nn
import torch.nn.functional as F
class perceptron(nn.Module):
def __init__(self, n_channels):
super(perceptron, self).__init__()
self.L = nn.Linear(n_channels, 10)
def forward(self, x):
x = self.L(x)
x = F.softmax(x, dim=1)
return 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
from torch._inductor.runtime.... | GuilhermeSenna/Testes-TCC | perceptron | false | 506 | [
"MIT"
] | 0 | ed38baf864d8993685427affa1f009e6cf7c5dcb | https://github.com/GuilhermeSenna/Testes-TCC/tree/ed38baf864d8993685427affa1f009e6cf7c5dcb |
Invertible1x1Conv | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import torch.utils.data
import torch.nn
class Invertible1x1Conv(torch.nn.Module):
"""
The layer outputs both the convolution, and the log determinant
of its weight matrix. If reverse=True it does convolution with
inverse... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.functional as F
from torch.autograd import Variable
import torch... | GreyZzzzzzXh/TensorRT | Invertible1x1Conv | false | 507 | [
"Apache-2.0"
] | 0 | ba5b1b4f1ade5896c7fae206e43570a2712498d4 | https://github.com/GreyZzzzzzXh/TensorRT/tree/ba5b1b4f1ade5896c7fae206e43570a2712498d4 |
IdentityMessage | import torch
import torch.fx
import torch.utils.data
class IdentityMessage(torch.nn.Module):
def __init__(self, raw_msg_dim: 'int', memory_dim: 'int', time_dim: 'int'):
super(IdentityMessage, self).__init__()
self.out_channels = raw_msg_dim + 2 * memory_dim + time_dim
def forward(self, z_src... | 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.fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | HWSelf/pytorch_geometric | IdentityMessage | false | 508 | [
"MIT"
] | 0 | c1214de674079b5e39e57c045d0f844b60caf590 | https://github.com/HWSelf/pytorch_geometric/tree/c1214de674079b5e39e57c045d0f844b60caf590 |
wide_basic | import torch
import torch.nn as nn
def get_norm(n_filters, norm):
if norm is None:
return Identity()
elif norm == 'batch':
return nn.BatchNorm2d(n_filters, momentum=0.9)
elif norm == 'instance':
return nn.InstanceNorm2d(n_filters, affine=True)
elif norm == 'layer':
retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | FloralZhao/unlimited_labeled_data_project_strong_augmen | wide_basic | false | 509 | [
"MIT"
] | 0 | caf90d70145e5d841a38b2e2f18a710703264a28 | https://github.com/FloralZhao/unlimited_labeled_data_project_strong_augmen/tree/caf90d70145e5d841a38b2e2f18a710703264a28 |
DeConvNet2 | import torch
import torch.nn as nn
import torch.nn.functional as F
def spectral_norm(module, init=True, std=1, bound=False):
if init:
nn.init.normal_(module.weight, 0, std)
if hasattr(module, 'bias') and module.bias is not None:
module.bias.data.zero_()
SpectralNorm.apply(module, 'weight',... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | GloryyrolG/normalized-autoencoders | DeConvNet2 | false | 510 | [
"MIT"
] | 0 | 27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 | https://github.com/GloryyrolG/normalized-autoencoders/tree/27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 |
KMomentLoss | import torch
import torch.optim
import torch.nn as nn
class KMomentLoss(nn.Module):
def __init__(self, k: 'int'=4):
"""
k moment distance, where `k` represents the highest order of moment.
"""
super(KMomentLoss, self).__init__()
self.eps = 1e-08
self.k = k
def... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.optim
import torch.nn as nn
assert_size_stride = torch._C._dynamo.... | Fyy10/UESTC-Thesis-DA | KMomentLoss | false | 511 | [
"MIT"
] | 0 | 6cb16efd1f80aa569c90874a806a62dec8afaec4 | https://github.com/Fyy10/UESTC-Thesis-DA/tree/6cb16efd1f80aa569c90874a806a62dec8afaec4 |
DDPGConvBody | import torch
import torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class DDPGConvBody(nn.Module):
def __init__(self, in_channels=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.triton_helpers import libdevice
import torch.nn as ... | Fieps1/p3-tennis | DDPGConvBody | false | 512 | [
"MIT"
] | 0 | 29f3dab5810d7cd7f84120416a615956d266c256 | https://github.com/Fieps1/p3-tennis/tree/29f3dab5810d7cd7f84120416a615956d266c256 |
CenteredLayer | import torch
from torch import nn
class CenteredLayer(nn.Module):
def __init__(self, **kwargs):
super(CenteredLayer, self).__init__(**kwargs)
def forward(self, x):
return x - x.mean()
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Halo1236/Dive-into-DL-PyTorch | CenteredLayer | false | 513 | [
"Apache-2.0"
] | 0 | 586b4e9ca77b2121ce5f5bec8b0a893b33f1b574 | https://github.com/Halo1236/Dive-into-DL-PyTorch/tree/586b4e9ca77b2121ce5f5bec8b0a893b33f1b574 |
DenseBlock | import torch
import torch.nn as nn
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
super().__init__()
self.padding = dilation
self.causal_conv = nn.Conv1d(in_channels, out_channels, kernel_size,
padding=self.padding, dil... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Hao-Kailong/DisFeb | DenseBlock | false | 514 | [
"MIT"
] | 0 | 2877edd587556e127d6648ee211ed22838c8d015 | https://github.com/Hao-Kailong/DisFeb/tree/2877edd587556e127d6648ee211ed22838c8d015 |
WordAVGModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class WordAVGModel(nn.Module):
def __init__(self, vocab_size, embedding_dim, output_dim, dropout=0.5):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.fc1 = nn.Linear(embedding_dim, int((em... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | HanielF/siRNA-Predict-and-RSC-Test-System | WordAVGModel | false | 515 | [
"Apache-2.0"
] | 0 | 59c41558e0c579ee03168ee860490770ecb0a7a3 | https://github.com/HanielF/siRNA-Predict-and-RSC-Test-System/tree/59c41558e0c579ee03168ee860490770ecb0a7a3 |
Attention | import math
import torch
import torch.nn.functional as F
import torch.fx
import torch.utils.data
def restricted_softmax(src, dim: 'int'=-1, margin: 'float'=0.0):
src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0.0)
out = (src - src_max).exp()
out = out / (out.sum(dim=dim, keepdim=True) + (mar... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HWSelf/pytorch_geometric | Attention | false | 516 | [
"MIT"
] | 0 | c1214de674079b5e39e57c045d0f844b60caf590 | https://github.com/HWSelf/pytorch_geometric/tree/c1214de674079b5e39e57c045d0f844b60caf590 |
PreNormTransformerDecoderLayer | import torch
from torch import nn
class PreNormTransformerDecoderLayer(nn.TransformerDecoderLayer):
"""
A variant of :class:`torch.nn.TransformerDecoderLayer` where layer
normalization is included inside the residual branch, and performed before
self-attention and feedforward layers.
Refer docume... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | GeorgeBatch/arch-pre-training | PreNormTransformerDecoderLayer | false | 517 | [
"MIT"
] | 0 | 7ed75868689e9283d61d11360fdbf4e77d4ebd2e | https://github.com/GeorgeBatch/arch-pre-training/tree/7ed75868689e9283d61d11360fdbf4e77d4ebd2e |
CausalConv1d | import torch
import torch.nn as nn
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
super().__init__()
self.padding = dilation
self.causal_conv = nn.Conv1d(in_channels, out_channels, kernel_size,
padding=self.padding, dil... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Hao-Kailong/DisFeb | CausalConv1d | false | 518 | [
"MIT"
] | 0 | 2877edd587556e127d6648ee211ed22838c8d015 | https://github.com/Hao-Kailong/DisFeb/tree/2877edd587556e127d6648ee211ed22838c8d015 |
ConvNet2FC | import torch
import torch.nn as nn
def spectral_norm(module, init=True, std=1, bound=False):
if init:
nn.init.normal_(module.weight, 0, std)
if hasattr(module, 'bias') and module.bias is not None:
module.bias.data.zero_()
SpectralNorm.apply(module, 'weight', bound=bound)
return module
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | GloryyrolG/normalized-autoencoders | ConvNet2FC | false | 519 | [
"MIT"
] | 0 | 27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 | https://github.com/GloryyrolG/normalized-autoencoders/tree/27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 |
Envelope | import torch
import torch.fx
import torch.utils.data
class Envelope(torch.nn.Module):
def __init__(self, exponent):
super(Envelope, self).__init__()
self.p = exponent + 1
self.a = -(self.p + 1) * (self.p + 2) / 2
self.b = self.p * (self.p + 2)
self.c = -self.p * (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
import torch.fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | HWSelf/pytorch_geometric | Envelope | false | 520 | [
"MIT"
] | 0 | c1214de674079b5e39e57c045d0f844b60caf590 | https://github.com/HWSelf/pytorch_geometric/tree/c1214de674079b5e39e57c045d0f844b60caf590 |
Attn | import torch
from torch import nn
class Attn(torch.nn.Module):
"""
Attention:
feature_dim: dimension of feature embedding
method: method to calculate attention, (general, dot, concat)
input_dim: dimension of input embedding, default is the same as feature_dim; method dot is only available wh... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HCDM/XRec | Attn | false | 521 | [
"MIT"
] | 0 | dae7d3e1237b8e41913656eb33d81e78c61424ea | https://github.com/HCDM/XRec/tree/dae7d3e1237b8e41913656eb33d81e78c61424ea |
Encoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
"""利用卷积 + 最大池化得到句子嵌入"""
def __init__(self, max_length, word_embedding_dim=50, pos_embedding_dim
=5, hidden_size=230):
nn.Module.__init__(self)
self.max_length = max_length
self.hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Hao-Kailong/DisFeb | Encoder | false | 522 | [
"MIT"
] | 0 | 2877edd587556e127d6648ee211ed22838c8d015 | https://github.com/Hao-Kailong/DisFeb/tree/2877edd587556e127d6648ee211ed22838c8d015 |
CrossNet | import torch
from torch import nn
class CrossNet(nn.Module):
"""The Cross Network part of Deep&Cross Network model,
which leans both low and high degree cross feature.
Input shape
- 2D tensor with shape: ``(batch_size, units)``.
Output shape
- 2D tensor with shape: ``(batch_size, u... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | HCDM/XRec | CrossNet | false | 523 | [
"MIT"
] | 0 | dae7d3e1237b8e41913656eb33d81e78c61424ea | https://github.com/HCDM/XRec/tree/dae7d3e1237b8e41913656eb33d81e78c61424ea |
VGGASPP | import torch
from torch import nn
class FCReLUDrop(nn.Sequential):
def __init__(self, in_ch, out_ch, kernel_size, dilation, padding,
layer_idx, branch_idx):
super(FCReLUDrop, self).__init__()
self.add_module(f'fc{layer_idx}_{branch_idx}', nn.Conv2d(in_ch,
out_ch, 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
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | HAL-42/DeepLabV2YQ | VGGASPP | false | 524 | [
"Apache-2.0"
] | 0 | 96bfcf1055da7adeb4a7c1ed841f6ec29957be59 | https://github.com/HAL-42/DeepLabV2YQ/tree/96bfcf1055da7adeb4a7c1ed841f6ec29957be59 |
InstanceNorm2D | import torch
import torch.nn as nn
class InstanceNorm2D(nn.Module):
def __init__(self, num_channels, epsilon=1e-05, momentum=0.9, rescale=True
):
super(InstanceNorm2D, self).__init__()
self.num_channels = num_channels
self.epsilon = epsilon
self.momentum = momentum
... | 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_... | HarmanDotpy/Normalizations-in-Deep-Learning | InstanceNorm2D | false | 525 | [
"MIT"
] | 0 | 3e1899837fb3ba625f515ef1a995f3573b65456d | https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning/tree/3e1899837fb3ba625f515ef1a995f3573b65456d |
NN | import torch
from torch import nn
import torch.nn.functional as F
class NN(nn.Module):
def __init__(self, input_size, num_classes):
super(NN, self).__init__()
self.fc1 = nn.Linear(input_size, 50)
self.fc2 = nn.Linear(50, num_classes)
def forward(self, x):
x = F.relu(self.fc1(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | HaowenWeiJohn/CV_Project | NN | false | 526 | [
"MIT"
] | 0 | 8e2414796f60a8c3fe452f3721e4a6ef7edfdb11 | https://github.com/HaowenWeiJohn/CV_Project/tree/8e2414796f60a8c3fe452f3721e4a6ef7edfdb11 |
LayerNorm2D | import torch
import torch.nn as nn
class LayerNorm2D(nn.Module):
def __init__(self, num_channels, epsilon=1e-05):
super(LayerNorm2D, self).__init__()
self.num_channels = num_channels
self.epsilon = epsilon
self.gamma = nn.Parameter(torch.ones(num_channels))
self.beta = 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | HarmanDotpy/Normalizations-in-Deep-Learning | LayerNorm2D | false | 527 | [
"MIT"
] | 0 | 3e1899837fb3ba625f515ef1a995f3573b65456d | https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning/tree/3e1899837fb3ba625f515ef1a995f3573b65456d |
GroupNorm2D | import torch
import torch.nn as nn
class GroupNorm2D(nn.Module):
def __init__(self, num_channels, num_groups=4, epsilon=1e-05):
super(GroupNorm2D, self).__init__()
self.num_channels = num_channels
self.num_groups = num_channels // 4
self.epsilon = epsilon
self.gamma = nn.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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | HarmanDotpy/Normalizations-in-Deep-Learning | GroupNorm2D | false | 528 | [
"MIT"
] | 0 | 3e1899837fb3ba625f515ef1a995f3573b65456d | https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning/tree/3e1899837fb3ba625f515ef1a995f3573b65456d |
InstanceNorm | import torch
import torch.nn as nn
class InstanceNorm(nn.Module):
def __init__(self, epsilon=1e-08):
""" avoid in-place ops.
https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 """
... | 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_... | Holmes-Alan/Photo2Sketch | InstanceNorm | false | 529 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
TVLoss | import torch
class TVLoss(torch.nn.Module):
def __init__(self):
super(TVLoss, self).__init__()
def forward(self, x):
x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
self._tensor_size(x[:, :, 1:, :])
self._tensor_size(x[:, :, :, 1:])
h_tv = torch.pow(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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Holmes-Alan/Photo2Sketch | TVLoss | false | 530 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
Net | import torch
from torch import nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 100, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(100, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Halo1236/Dive-into-DL-PyTorch | Net | false | 531 | [
"Apache-2.0"
] | 0 | 586b4e9ca77b2121ce5f5bec8b0a893b33f1b574 | https://github.com/Halo1236/Dive-into-DL-PyTorch/tree/586b4e9ca77b2121ce5f5bec8b0a893b33f1b574 |
GlobalAvgPool2d | import torch
from torch import nn
import torch.nn.functional as F
class GlobalAvgPool2d(nn.Module):
def __init__(self):
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
return F.avg_pool2d(x, kernel_size=x.size()[2:])
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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... | Halo1236/Dive-into-DL-PyTorch | GlobalAvgPool2d | false | 532 | [
"Apache-2.0"
] | 0 | 586b4e9ca77b2121ce5f5bec8b0a893b33f1b574 | https://github.com/Halo1236/Dive-into-DL-PyTorch/tree/586b4e9ca77b2121ce5f5bec8b0a893b33f1b574 |
DeConvNet3 | import torch
import torch.nn as nn
def get_activation(s_act):
if s_act == 'relu':
return nn.ReLU(inplace=True)
elif s_act == 'sigmoid':
return nn.Sigmoid()
elif s_act == 'softplus':
return nn.Softplus()
elif s_act == 'linear':
return None
elif s_act == 'tanh':
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | GloryyrolG/normalized-autoencoders | DeConvNet3 | false | 533 | [
"MIT"
] | 0 | 27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 | https://github.com/GloryyrolG/normalized-autoencoders/tree/27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 |
AvgPoolPad | import torch
import torch.nn as nn
from math import *
class AvgPoolPad(nn.Module):
def __init__(self, stride=2, padding=1):
super(AvgPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.AvgPool2d(3, stride=stride, padding=padding,
count_include_pad=Fa... | 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
from math import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | Helicopt/torchreid-preprocess | AvgPoolPad | false | 534 | [
"MIT"
] | 0 | 2597e502eef079705a5f8a9115a9a1980a9d080d | https://github.com/Helicopt/torchreid-preprocess/tree/2597e502eef079705a5f8a9115a9a1980a9d080d |
SharpenSoftmax | import torch
import torch.nn as nn
class SharpenSoftmax(nn.Module):
def __init__(self, tau, dim=0):
super().__init__()
self.tau = tau
self.dim = dim
def forward(self, pred):
pred = pred / self.tau
return pred.log_softmax(self.dim)
def get_inputs():
return [torch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Hayoung93/UDA | SharpenSoftmax | false | 535 | [
"Apache-2.0"
] | 0 | a587b01c76141d64e7cead55b62e0f3ed75890bf | https://github.com/Hayoung93/UDA/tree/a587b01c76141d64e7cead55b62e0f3ed75890bf |
GramMatrix | import torch
import torch.nn as nn
class GramMatrix(nn.Module):
def forward(self, input):
b, c, h, w = input.size()
f = input.view(b, c, h * w)
G = torch.bmm(f, f.transpose(1, 2))
return G.div_(c * h * w)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Holmes-Alan/Photo2Sketch | GramMatrix | false | 536 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
HardAttn | import torch
from torch.nn import functional as F
import torch.nn as nn
from math import *
class HardAttn(nn.Module):
"""Hard Attention (Sec. 3.1.II)"""
def __init__(self, in_channels):
super(HardAttn, self).__init__()
self.fc = nn.Linear(in_channels, 4 * 2)
self.init_params()
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Helicopt/torchreid-preprocess | HardAttn | false | 537 | [
"MIT"
] | 0 | 2597e502eef079705a5f8a9115a9a1980a9d080d | https://github.com/Helicopt/torchreid-preprocess/tree/2597e502eef079705a5f8a9115a9a1980a9d080d |
SiamusicLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class SiamusicLoss(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def neg_cos_sim(self, p, z):
z = z.detach()
p = F.normalize(p, dim=self.dim)
z = F.normalize(z, dim=self.dim)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | HongSungRae/SiamRec | SiamusicLoss | false | 538 | [
"MIT"
] | 0 | 2ab3b973bc6503eeea66c15c563fdd75b8e5bea1 | https://github.com/HongSungRae/SiamRec/tree/2ab3b973bc6503eeea66c15c563fdd75b8e5bea1 |
styleLoss_v2 | import torch
import torch.nn as nn
def calc_mean_std(feat, eps=1e-05):
size = feat.size()
assert len(size) == 4
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
return 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 ... | Holmes-Alan/Photo2Sketch | styleLoss_v2 | false | 539 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
InnerProductDecoder | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.modules.loss
class InnerProductDecoder(nn.Module):
"""Decoder for using inner product for prediction."""
def __init__(self, dropout, act=torch.sigmoid):
super(InnerProductDecoder, self).__init__()
self.dropout ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.modules.loss
assert_size_stride = torch._C... | HongyiZhu/EHI | InnerProductDecoder | false | 540 | [
"MIT"
] | 0 | 9fbbc6046546dd7fc6de5d831b4c941bc4404e02 | https://github.com/HongyiZhu/EHI/tree/9fbbc6046546dd7fc6de5d831b4c941bc4404e02 |
AddReadout | import torch
import torch.nn as nn
class AddReadout(nn.Module):
"""Handles readout operation when `readout` parameter is `add`. Removes `cls_token` or `readout_token` from tensor and adds it to the rest of tensor"""
def __init__(self, start_index=1):
super(AddReadout, self).__init__()
self.s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | HrithikNambiar/vformer | AddReadout | false | 541 | [
"MIT"
] | 0 | 5bd902a45e5cae70ab001ca6c217f12f923561f1 | https://github.com/HrithikNambiar/vformer/tree/5bd902a45e5cae70ab001ca6c217f12f923561f1 |
MaxPoolPad | import torch
import torch.nn as nn
from math import *
class MaxPoolPad(nn.Module):
def __init__(self):
super(MaxPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.MaxPool2d(3, stride=2, padding=1)
def forward(self, x):
x = self.pad(x)
x = s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from math import *
assert_size_stride = torch._C._dynamo.guards.ass... | Helicopt/torchreid-preprocess | MaxPoolPad | false | 542 | [
"MIT"
] | 0 | 2597e502eef079705a5f8a9115a9a1980a9d080d | https://github.com/Helicopt/torchreid-preprocess/tree/2597e502eef079705a5f8a9115a9a1980a9d080d |
DeConvNet64 | import torch
import torch.nn as nn
def get_activation(s_act):
if s_act == 'relu':
return nn.ReLU(inplace=True)
elif s_act == 'sigmoid':
return nn.Sigmoid()
elif s_act == 'softplus':
return nn.Softplus()
elif s_act == 'linear':
return None
elif s_act == 'tanh':
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | GloryyrolG/normalized-autoencoders | DeConvNet64 | false | 543 | [
"MIT"
] | 0 | 27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 | https://github.com/GloryyrolG/normalized-autoencoders/tree/27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 |
RoundPass | import torch
import torch as t
import torch.utils.data
class RoundPass(t.nn.Module):
def forward(self, x):
y = x.round()
y_grad = x
return (y - y_grad).detach() + y_grad
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 as t
import torch.utils.data
assert_size_stride = torch._C._dynamo... | HumberMe/lsq-net | RoundPass | false | 544 | [
"MIT"
] | 0 | 7dcd75bff4aa7ff2d9c8a7902198fe411a38eb4c | https://github.com/HumberMe/lsq-net/tree/7dcd75bff4aa7ff2d9c8a7902198fe411a38eb4c |
GraphConvolution | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.modules.loss
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __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.nn import Module
i... | HongyiZhu/EHI | GraphConvolution | false | 545 | [
"MIT"
] | 0 | 9fbbc6046546dd7fc6de5d831b4c941bc4404e02 | https://github.com/HongyiZhu/EHI/tree/9fbbc6046546dd7fc6de5d831b4c941bc4404e02 |
HardMish | import torch
import torch.nn as nn
import torch.nn.parallel
def hard_mish(x, inplace: 'bool'=False):
""" Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
if inplace:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guar... | HotaekHan/detr_pytorch | HardMish | false | 546 | [
"MIT"
] | 0 | 730e02db0ac8910ef782234a3990587771ad67f9 | https://github.com/HotaekHan/detr_pytorch/tree/730e02db0ac8910ef782234a3990587771ad67f9 |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch.nn.parallel
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
return nn.functional.adaptive_avg_pool... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | HotaekHan/detr_pytorch | GlobalAvgPool2d | false | 547 | [
"MIT"
] | 0 | 730e02db0ac8910ef782234a3990587771ad67f9 | https://github.com/HotaekHan/detr_pytorch/tree/730e02db0ac8910ef782234a3990587771ad67f9 |
Selection | import torch
import torch.nn as nn
class Selection(nn.Module):
"""
Selection neurons to sample from a latent representation for a decoder agent.
An abstract representation :math:`l_i` is disturbed by a value :math:`r_i` sampled from a normal
standard distribution which is scaled by the selection neur... | 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... | HendrikPN/intervention-based-autoencoder | Selection | false | 548 | [
"Apache-2.0"
] | 0 | 90018d8ea264681cc9b9b55ba9e531e36275136f | https://github.com/HendrikPN/intervention-based-autoencoder/tree/90018d8ea264681cc9b9b55ba9e531e36275136f |
Pad_Pool2d | import torch
from torch import nn
class Pad_Pool2d(nn.Module):
"""
Implements a padding layer in front of pool1d layers used in our architectures to achieve padding=same output shape
Pads 0 to the left and 1 to the right side of x
"""
def __init__(self, left=0, right=1, top=0, bottom=1, value=0... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Hullimulli/EEGEyeNet | Pad_Pool2d | false | 549 | [
"MIT"
] | 0 | 677a791b39800f44dc254553b16ee2f92e62c423 | https://github.com/Hullimulli/EEGEyeNet/tree/677a791b39800f44dc254553b16ee2f92e62c423 |
GCNModelVAE | from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.modules.loss
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | HongyiZhu/EHI | GCNModelVAE | false | 550 | [
"MIT"
] | 0 | 9fbbc6046546dd7fc6de5d831b4c941bc4404e02 | https://github.com/HongyiZhu/EHI/tree/9fbbc6046546dd7fc6de5d831b4c941bc4404e02 |
Pad_Conv2d | import math
import torch
from torch import nn
class Pad_Conv2d(nn.Module):
"""
Implements a padding layer in front of conv2d layers used in our architectures to achieve padding=same output shape
Pads 0 to the left and 1 to the right side of x
Input:
kernel as a tuple (kx, ky)
Output:
Pad... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guard... | Hullimulli/EEGEyeNet | Pad_Conv2d | false | 551 | [
"MIT"
] | 0 | 677a791b39800f44dc254553b16ee2f92e62c423 | https://github.com/Hullimulli/EEGEyeNet/tree/677a791b39800f44dc254553b16ee2f92e62c423 |
PatchEmbedding | import torch
import torch.nn as nn
def pair(t):
"""
Parameters
----------
t: tuple[int] or int
"""
return t if isinstance(t, tuple) else (t, t)
class PatchEmbedding(nn.Module):
"""
Parameters
----------
img_size: int
Image Size
patch_size: int
Patch Size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | HrithikNambiar/vformer | PatchEmbedding | false | 552 | [
"MIT"
] | 0 | 5bd902a45e5cae70ab001ca6c217f12f923561f1 | https://github.com/HrithikNambiar/vformer/tree/5bd902a45e5cae70ab001ca6c217f12f923561f1 |
SEModule | import torch
import torch.nn as nn
import torch.nn.parallel
class FastGlobalAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super(FastGlobalAvgPool2d, self).__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
in_size = x.size()
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | HotaekHan/detr_pytorch | SEModule | false | 553 | [
"MIT"
] | 0 | 730e02db0ac8910ef782234a3990587771ad67f9 | https://github.com/HotaekHan/detr_pytorch/tree/730e02db0ac8910ef782234a3990587771ad67f9 |
ConvNet64 | import torch
import torch.nn as nn
def get_activation(s_act):
if s_act == 'relu':
return nn.ReLU(inplace=True)
elif s_act == 'sigmoid':
return nn.Sigmoid()
elif s_act == 'softplus':
return nn.Softplus()
elif s_act == 'linear':
return None
elif s_act == 'tanh':
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | GloryyrolG/normalized-autoencoders | ConvNet64 | false | 554 | [
"MIT"
] | 0 | 27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 | https://github.com/GloryyrolG/normalized-autoencoders/tree/27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | HumberMe/mmclassification | FocalLoss | false | 555 | [
"Apache-2.0"
] | 0 | 68f1542068d3af4db932c97e6a728181432fff0c | https://github.com/HumberMe/mmclassification/tree/68f1542068d3af4db932c97e6a728181432fff0c |
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