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 |
|---|---|---|---|---|---|---|---|---|---|---|
Conv2dWS | import torch
import torch.nn.functional as F
from torch import nn
class Conv2dWS(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2dWS, self).__init__(in_channels, out_channels,
kernel_size, stride,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | cooked-sashimi/Yet-Another-YOLOv4-Pytorch | Conv2dWS | false | 15,083 | [
"MIT"
] | 133 | c884ef8849987a75b0e17eba1b739c22d3782e90 | https://github.com/cooked-sashimi/Yet-Another-YOLOv4-Pytorch/tree/c884ef8849987a75b0e17eba1b739c22d3782e90 |
InnerProductModel | import torch
class InnerProductModel(torch.nn.Module):
@staticmethod
def is_valid_model_type(model_type):
raise NotImplementedError
@staticmethod
def get_model_from_type(model_type):
raise NotImplementedError
@property
def loss_criterion(self):
return torch.nn.MSELos... | 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
reinterpret... | cuiboyuan/plato | InnerProductModel | false | 15,084 | [
"Apache-2.0"
] | 135 | 260b785cbbf8588c92331d6343211ff72321f90e | https://github.com/cuiboyuan/plato/tree/260b785cbbf8588c92331d6343211ff72321f90e |
Myloss | import torch
import torch.nn as nn
class Myloss(nn.Module):
def __init__(self, epsilon=1e-08):
super(Myloss, self).__init__()
self.epsilon = epsilon
return
def forward(self, input_, label, weight):
entropy = -label * torch.log(input_ + self.epsilon) - (1 - label
)... | 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
... | cuishuhao/HDA | Myloss | false | 15,085 | [
"Apache-2.0"
] | 58 | 1733ca74eee7839b455e9ffd7a169bc54b272745 | https://github.com/cuishuhao/HDA/tree/1733ca74eee7839b455e9ffd7a169bc54b272745 |
AconC | import torch
import torch.nn as nn
class AconC(nn.Module):
""" ACON activation (activate or not).
AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"""
def __i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | cuiboyuan/plato | AconC | false | 15,086 | [
"Apache-2.0"
] | 135 | 260b785cbbf8588c92331d6343211ff72321f90e | https://github.com/cuiboyuan/plato/tree/260b785cbbf8588c92331d6343211ff72321f90e |
ECA | import torch
from torch import nn
class FastGlobalAvgPool2d:
def __init__(self, flatten=False):
self.flatten = flatten
def __call__(self, x):
if self.flatten:
in_size = x.size()
return x.view((in_size[0], in_size[1], -1)).mean(dim=2)
else:
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | cooked-sashimi/Yet-Another-YOLOv4-Pytorch | ECA | false | 15,087 | [
"MIT"
] | 133 | c884ef8849987a75b0e17eba1b739c22d3782e90 | https://github.com/cooked-sashimi/Yet-Another-YOLOv4-Pytorch/tree/c884ef8849987a75b0e17eba1b739c22d3782e90 |
BinaryFocalLoss | import torch
import torch as th
import torch.nn as nn
class BinaryFocalLoss(nn.Module):
def __init__(self, gamma=2.0, alpha=0.25, size_average=True):
super(BinaryFocalLoss, self).__init__()
self.gamma = gamma
self.alpha = alpha
self.size_average = size_average
def forward(sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | cumtchenchang/PPGNet | BinaryFocalLoss | false | 15,088 | [
"MIT"
] | 171 | 9b280aacb887ec584e905b9f9ab006b4f4cb2cc3 | https://github.com/cumtchenchang/PPGNet/tree/9b280aacb887ec584e905b9f9ab006b4f4cb2cc3 |
BCL | import torch
import torch.utils.data
import torch
from torch import nn
class BCL(nn.Module):
"""
batch-balanced contrastive loss
no-change，1
change，-1
"""
def __init__(self, margin=2.0):
super(BCL, self).__init__()
self.margin = margin
def forward(self, distance, label):
... | 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.utils.data
import torch
from torch import nn
assert_size_stride = torch._C._... | cuicaihao/STANet | BCL | false | 15,089 | [
"BSD-2-Clause"
] | 220 | 4c644e2a65bc9516f1d97b29b12ca864638c0c7e | https://github.com/cuicaihao/STANet/tree/4c644e2a65bc9516f1d97b29b12ca864638c0c7e |
MultConst | import torch
import torch.nn as nn
class MultConst(nn.Module):
def forward(self, input):
return 255 * 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | czczup/URST | MultConst | false | 15,090 | [
"Apache-2.0"
] | 119 | 000ec9f7728f12ffad989ec1d07b1dd579514133 | https://github.com/czczup/URST/tree/000ec9f7728f12ffad989ec1d07b1dd579514133 |
FIN2dCyclic | import torch
import torch.utils.data
import torch
import torch.nn as nn
class FIN2dCyclic(nn.Module):
def __init__(self, dim):
super().__init__()
self.instance_norm = nn.InstanceNorm2d(dim, affine=False)
self.a_gamma = nn.Parameter(torch.zeros(dim))
self.b_gamma = nn.Parameter(tor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride =... | cv-rits/CoMoGAN | FIN2dCyclic | false | 15,091 | [
"Apache-2.0"
] | 141 | 09f2f0f694421e289fcad467ca0b23f52e4da7a4 | https://github.com/cv-rits/CoMoGAN/tree/09f2f0f694421e289fcad467ca0b23f52e4da7a4 |
BCELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
def binary_cross_entropy(inputs, target, weight=None, reduction='mean',
smooth_eps=None, from_logits=False):
"""cross entropy loss, with support for label smoothing https://arxiv.org/abs/1512.00567"""
smooth_eps = smooth... | 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... | cwlacewe/SNAS-Series | BCELoss | false | 15,092 | [
"MIT"
] | 133 | 92ac8031f718235aecaefb9967851f8f355dbca0 | https://github.com/cwlacewe/SNAS-Series/tree/92ac8031f718235aecaefb9967851f8f355dbca0 |
GramMatrix | import torch
import torch.nn as nn
class GramMatrix(nn.Module):
def forward(self, y):
b, ch, h, w = y.size()
features = y.view(b, ch, w * h)
features_t = features.transpose(1, 2)
gram = features.bmm(features_t) / (ch * h * w)
return gram
def get_inputs():
return [tor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | czczup/URST | GramMatrix | false | 15,093 | [
"Apache-2.0"
] | 119 | 000ec9f7728f12ffad989ec1d07b1dd579514133 | https://github.com/czczup/URST/tree/000ec9f7728f12ffad989ec1d07b1dd579514133 |
MetaAconC | import torch
import torch.nn as nn
class MetaAconC(nn.Module):
""" ACON activation (activate or not).
MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | cuiboyuan/plato | MetaAconC | false | 15,094 | [
"Apache-2.0"
] | 135 | 260b785cbbf8588c92331d6343211ff72321f90e | https://github.com/cuiboyuan/plato/tree/260b785cbbf8588c92331d6343211ff72321f90e |
PreActBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class PreActBlock(nn.Module):
"""Pre-activation version of the BasicBlock."""
expansion = 1
def __init__(self, in_planes, planes, num_group=4, stride=1, bias=False):
super(PreActBlock, self).__init__()
self.conv1 = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | cwmok/LapIRN | PreActBlock | false | 15,095 | [
"MIT"
] | 53 | d8f96770a704b1f190955cc26297c7b01a270b0a | https://github.com/cwmok/LapIRN/tree/d8f96770a704b1f190955cc26297c7b01a270b0a |
DomainClassifier | import torch
import torch.nn.parallel
import torch.optim
import torch.nn as nn
class DomainClassifier(nn.Module):
def __init__(self, input_dim=1024, ndf=64, with_bias=False):
super(DomainClassifier, self).__init__()
self.conv1 = nn.Conv2d(input_dim, ndf, kernel_size=4, stride=2,
paddi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.parallel
import torch.optim
import torch.nn as nn
assert_size_st... | chaneyddtt/UDA-Animal-Pose | DomainClassifier | false | 15,096 | [
"MIT"
] | 61 | f1ebfda860a2585c60fe86ce1632e910ac97ebc5 | https://github.com/chaneyddtt/UDA-Animal-Pose/tree/f1ebfda860a2585c60fe86ce1632e910ac97ebc5 |
LayerNorm | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class LayerNorm(nn.Module):
def __init__(self, input_dim, cond_dim=0, center=True, scale=True,
epsilon=None, conditional=False, hidden_units=None,
hidden_activation='linear', hidden_initializer='xaiver', **kwargs):
... | 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 torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | cwxcwx0319/Dictionary | LayerNorm | false | 15,097 | [
"Apache-2.0"
] | 82 | 55fb9a602a212f9c3a69a318fec31da1d07279df | https://github.com/cwxcwx0319/Dictionary/tree/55fb9a602a212f9c3a69a318fec31da1d07279df |
ThumbAdaptiveInstanceNorm | import torch
import torch.nn as nn
class ThumbInstanceNorm(nn.Module):
def __init__(self, out_channels=None, affine=True):
super(ThumbInstanceNorm, self).__init__()
self.thumb_mean = None
self.thumb_std = None
self.collection = True
if affine is True:
self.weig... | 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_... | czczup/URST | ThumbAdaptiveInstanceNorm | false | 15,099 | [
"Apache-2.0"
] | 119 | 000ec9f7728f12ffad989ec1d07b1dd579514133 | https://github.com/czczup/URST/tree/000ec9f7728f12ffad989ec1d07b1dd579514133 |
resnet_block | import torch
import torch.nn as nn
import torch.nn.functional as F
class resnet_block(nn.Module):
def __init__(self, dim_in, dim_out):
super(resnet_block, self).__init__()
self.dim_in = dim_in
self.dim_out = dim_out
if self.dim_in == self.dim_out:
self.conv_1 = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | czq142857/DECOR-GAN | resnet_block | false | 15,102 | [
"MIT"
] | 55 | 79c80fc202b8af982989a3e3bb3afe85e606b71f | https://github.com/czq142857/DECOR-GAN/tree/79c80fc202b8af982989a3e3bb3afe85e606b71f |
VQVAEQuantize | import torch
from torch import nn
import torch.nn.functional as F
from scipy.cluster.vq import kmeans2
class VQVAEQuantize(nn.Module):
"""
Neural Discrete Representation Learning, van den Oord et al. 2017
https://arxiv.org/abs/1711.00937
Follows the original DeepMind implementation
https://github... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | crizCraig/deep-vector-quantization | VQVAEQuantize | false | 15,103 | [
"MIT"
] | 326 | c3c026a1ccea369bc892ad6dde5e6d6cd5a508a4 | https://github.com/crizCraig/deep-vector-quantization/tree/c3c026a1ccea369bc892ad6dde5e6d6cd5a508a4 |
ScaledDotProductAttention | import torch
import numpy as np
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super(ScaledDotProductAttention, self).__init__()
self.temperature = temperature
self.dropout = 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.... | dani3l125/TDNet | ScaledDotProductAttention | false | 15,104 | [
"MIT"
] | 195 | 3f8b5378fcc7f97c26b3760ddaf3d4402cf477d1 | https://github.com/dani3l125/TDNet/tree/3f8b5378fcc7f97c26b3760ddaf3d4402cf477d1 |
decoder3 | import torch
import torch.nn as nn
class decoder3(nn.Module):
def __init__(self):
super(decoder3, self).__init__()
self.reflecPad7 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv7 = nn.Conv2d(256, 128, 3, 1, 0)
self.relu7 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingNea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | czczup/URST | decoder3 | false | 15,105 | [
"Apache-2.0"
] | 119 | 000ec9f7728f12ffad989ec1d07b1dd579514133 | https://github.com/czczup/URST/tree/000ec9f7728f12ffad989ec1d07b1dd579514133 |
SelfCorrelationComputation | import torch
import torch.nn as nn
import torch.nn.functional as F
class SelfCorrelationComputation(nn.Module):
def __init__(self, kernel_size=(5, 5), padding=2):
super(SelfCorrelationComputation, self).__init__()
self.kernel_size = kernel_size
self.unfold = nn.Unfold(kernel_size=kernel_s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | dahyun-kang/renet | SelfCorrelationComputation | false | 15,106 | [
"MIT"
] | 50 | 43a4e5af96b56c99a0cd63e35bd272db72f7f3a4 | https://github.com/dahyun-kang/renet/tree/43a4e5af96b56c99a0cd63e35bd272db72f7f3a4 |
discriminator | import torch
import torch.nn as nn
import torch.nn.functional as F
class discriminator(nn.Module):
def __init__(self, d_dim, z_dim):
super(discriminator, self).__init__()
self.d_dim = d_dim
self.z_dim = z_dim
self.conv_1 = nn.Conv3d(1, self.d_dim, 4, stride=1, padding=0, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | czq142857/DECOR-GAN | discriminator | false | 15,107 | [
"MIT"
] | 55 | 79c80fc202b8af982989a3e3bb3afe85e606b71f | https://github.com/czq142857/DECOR-GAN/tree/79c80fc202b8af982989a3e3bb3afe85e606b71f |
EntmaxBisect | from torch.autograd import Function
import torch
import torch.nn as nn
def entmax_bisect(X, alpha=1.5, dim=-1, n_iter=50, ensure_sum_one=True):
"""alpha-entmax: normalizing sparse transform (a la softmax).
Solves the optimization problem:
max_p <x, p> - H_a(p) s.t. p >= 0, sum(p) == 1.
wh... | 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.autograd import F... | antoniogois/entmax | EntmaxBisect | false | 15,108 | [
"MIT"
] | 298 | 7ff3fa6b09ee53e04514173aacae9de90c95ca75 | https://github.com/antoniogois/entmax/tree/7ff3fa6b09ee53e04514173aacae9de90c95ca75 |
decoder4 | import torch
import torch.nn as nn
class decoder4(nn.Module):
def __init__(self):
super(decoder4, self).__init__()
self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(512, 256, 3, 1, 0)
self.relu11 = nn.ReLU(inplace=True)
self.unpool = nn.Upsampling... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | czczup/URST | decoder4 | false | 15,109 | [
"Apache-2.0"
] | 119 | 000ec9f7728f12ffad989ec1d07b1dd579514133 | https://github.com/czczup/URST/tree/000ec9f7728f12ffad989ec1d07b1dd579514133 |
CrossEntropyLossWithAuxiliary | import torch
import torch.nn as nn
import torch.nn.parallel
from torch.optim.lr_scheduler import *
from torchvision.models import *
from torchvision.transforms import *
class CrossEntropyLossWithAuxiliary(nn.CrossEntropyLoss):
"""Cross-entropy loss that can add auxiliary loss if present."""
def forward(self,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | dani3l125/torchprune | CrossEntropyLossWithAuxiliary | false | 15,110 | [
"MIT"
] | 74 | f2589ec7514bd531ddaa7da3aed6388bb13712d3 | https://github.com/dani3l125/torchprune/tree/f2589ec7514bd531ddaa7da3aed6388bb13712d3 |
SelfAttention | import torch
import torch.nn as nn
class SelfAttention(nn.Module):
"""A simple self-attention solution."""
def __init__(self, data_dim, dim_q):
super(SelfAttention, self).__init__()
self._layers = []
self._fc_q = nn.Linear(data_dim, dim_q)
self._layers.append(self._fc_q)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | daia99/brain-tokyo-workshop | SelfAttention | false | 15,111 | [
"Apache-2.0"
] | 1,097 | cd470255230afddba2b80d99a9641b682f4d0762 | https://github.com/daia99/brain-tokyo-workshop/tree/cd470255230afddba2b80d99a9641b682f4d0762 |
FPNOutput | import torch
import torch.nn as nn
class ConvBNReLU(nn.Module):
def __init__(self, in_chan, out_chan, ks=1, stride=1, padding=0,
norm_layer=None, bias=True, *args, **kwargs):
super(ConvBNReLU, self).__init__()
self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | dani3l125/TDNet | FPNOutput | false | 15,112 | [
"MIT"
] | 195 | 3f8b5378fcc7f97c26b3760ddaf3d4402cf477d1 | https://github.com/dani3l125/TDNet/tree/3f8b5378fcc7f97c26b3760ddaf3d4402cf477d1 |
NSELoss | import torch
class NSELoss(torch.nn.Module):
"""Calculate (batch-wise) NSE Loss.
Each sample i is weighted by 1 / (std_i + eps)^2, where std_i is the standard deviation of the
discharge from the basin, to which the sample belongs.
Parameters:
-----------
eps : float
Constant, added ... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | danielsuo/toy_flood | NSELoss | false | 15,113 | [
"MIT"
] | 49 | 471d3c4091d86d4a00fbf910937d4e60fdaf79a1 | https://github.com/danielsuo/toy_flood/tree/471d3c4091d86d4a00fbf910937d4e60fdaf79a1 |
MLP_CRITIC | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('BatchNorm') != -1:
m.weight.data.norm... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Huihui-z/CE-GZSL | MLP_CRITIC | false | 15,114 | [
"MIT"
] | 58 | 7bf5358ac4727ea1dc2dc9dec2f453b014500bd8 | https://github.com/Huihui-z/CE-GZSL/tree/7bf5358ac4727ea1dc2dc9dec2f453b014500bd8 |
OhemCELoss2D | import math
import torch
import torch.nn as nn
class OhemCELoss2D(nn.CrossEntropyLoss):
"""2D Cross Entropy Loss with Auxilary Loss"""
def __init__(self, n_min, thresh=0.7, ignore_index=-1):
super(OhemCELoss2D, self).__init__(None, None, ignore_index,
reduction='none')
self.thresh... | 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 math
import tor... | dani3l125/TDNet | OhemCELoss2D | false | 15,115 | [
"MIT"
] | 195 | 3f8b5378fcc7f97c26b3760ddaf3d4402cf477d1 | https://github.com/dani3l125/TDNet/tree/3f8b5378fcc7f97c26b3760ddaf3d4402cf477d1 |
RewardCriterion | import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.autograd import *
def to_contiguous(tensor):
if tensor.is_contiguous():
return tensor
else:
return tensor.contiguous()
class RewardCriterion(nn.Module):
def __init__(self):
super(RewardCriterion, 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
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | daqingliu/CAVP | RewardCriterion | false | 15,116 | [
"MIT"
] | 49 | d383affde78dbc75e369095c27954dcdd79478d0 | https://github.com/daqingliu/CAVP/tree/d383affde78dbc75e369095c27954dcdd79478d0 |
CircularPad | import torch
from torch import nn
class CircularPad(nn.Module):
def __init__(self, pad):
super(CircularPad, self).__init__()
self.pad = pad
self.zeropad = torch.nn.modules.padding.ConstantPad2d((pad, pad, 0,
0), 0)
def forward(self, x):
x = torch.cat([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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | daniilidis-group/emvn | CircularPad | false | 15,117 | [
"MIT"
] | 46 | 1888e2a47b02e911e08afa40ba7341662cf3d6ea | https://github.com/daniilidis-group/emvn/tree/1888e2a47b02e911e08afa40ba7341662cf3d6ea |
classifier | import torch
import torch.nn as nn
import torch.nn.functional as F
class classifier(nn.Module):
def __init__(self, ef_dim, z_dim, class_num, voxel_size):
super(classifier, self).__init__()
self.ef_dim = ef_dim
self.z_dim = z_dim
self.class_num = class_num
self.voxel_size =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | czq142857/DECOR-GAN | classifier | false | 15,118 | [
"MIT"
] | 55 | 79c80fc202b8af982989a3e3bb3afe85e606b71f | https://github.com/czq142857/DECOR-GAN/tree/79c80fc202b8af982989a3e3bb3afe85e606b71f |
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.... | carson-sky/Patch-NetVLAD | NetVLAD | false | 15,119 | [
"MIT"
] | 278 | 7b913626b34dbbe250d6921a6a093512ee513eac | https://github.com/carson-sky/Patch-NetVLAD/tree/7b913626b34dbbe250d6921a6a093512ee513eac |
SingleSP | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torch.autograd import *
import torch.nn.functional as F
class SingleSP(nn.Module):
def __init__(self, opt):
super(SingleSP, self).__init__()
self.rnn_size = opt.rnn_size
self.att_hid_size = opt.att_hid_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | daqingliu/CAVP | SingleSP | false | 15,120 | [
"MIT"
] | 49 | d383affde78dbc75e369095c27954dcdd79478d0 | https://github.com/daqingliu/CAVP/tree/d383affde78dbc75e369095c27954dcdd79478d0 |
FLogSigmoid | import torch
import torch.nn as nn
class FLogSigmoid(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FLogSigmoid, self).__init__()
def forward(self, x):
from torch.nn import functional as F
return F.logsigmoid(x)
def get_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, math as tl_math
import torc... | dawnclaude/onnx2keras | FLogSigmoid | false | 15,121 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
SpatialAttention | import torch
import torch.nn as nn
import torch.nn
import torch.utils.data
class SpatialAttention(nn.Module):
def __init__(self, input_dim, context_dim):
super().__init__()
self.conv_context = nn.Conv2d(context_dim, input_dim, 1, stride=1,
padding=0, bias=False)
self.sm = nn.S... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dariopavllo/textured-3d-gan | SpatialAttention | false | 15,122 | [
"MIT"
] | 77 | d419cee94c5913a900e08b15c0438eb2c89ce4d4 | https://github.com/dariopavllo/textured-3d-gan/tree/d419cee94c5913a900e08b15c0438eb2c89ce4d4 |
AsymmetricLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class AsymmetricLoss(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08,
disable_torch_grad_focal_loss=False):
super(AsymmetricLoss... | 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... | davidaderup/query2labels | AsymmetricLoss | false | 15,123 | [
"MIT"
] | 164 | 5a10c861dda85d94ba01ec6ad4119eef67a9f441 | https://github.com/davidaderup/query2labels/tree/5a10c861dda85d94ba01ec6ad4119eef67a9f441 |
AsymmetricLossOptimized | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class AsymmetricLossOptimized(nn.Module):
""" Notice - optimized version, minimizes memory allocation and gpu uploading,
favors inplace operations"""
def __init__(sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | davidaderup/query2labels | AsymmetricLossOptimized | false | 15,124 | [
"MIT"
] | 164 | 5a10c861dda85d94ba01ec6ad4119eef67a9f441 | https://github.com/davidaderup/query2labels/tree/5a10c861dda85d94ba01ec6ad4119eef67a9f441 |
FHardtanh | import random
import torch
import torch.nn as nn
class FHardtanh(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FHardtanh, self).__init__()
self.min_val = random.random()
self.max_val = self.min_val + random.random()
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 random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | dawnclaude/onnx2keras | FHardtanh | false | 15,125 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
OutputSP | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torch.autograd import *
import torch.nn.functional as F
class OutputSP(nn.Module):
def __init__(self, opt):
super(OutputSP, self).__init__()
self.rnn_size = opt.rnn_size
self.att_hid_size = opt.att_hid_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | daqingliu/CAVP | OutputSP | false | 15,126 | [
"MIT"
] | 49 | d383affde78dbc75e369095c27954dcdd79478d0 | https://github.com/daqingliu/CAVP/tree/d383affde78dbc75e369095c27954dcdd79478d0 |
FClipTest | import torch
import numpy as np
import torch.nn as nn
class FClipTest(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
self.low = np.random.uniform(-1, 1)
self.high = np.random.uniform(1, 2)
super(FClipTest, self).__init__()
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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.ass... | dawnclaude/onnx2keras | FClipTest | false | 15,127 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
EALSTM | import torch
from typing import Tuple
import torch.nn as nn
class EALSTM(nn.Module):
"""Implementation of the Entity-Aware-LSTM (EA-LSTM)
TODO: Include paper ref and latex equations
Parameters
----------
input_size_dyn : int
Number of dynamic features, which are those, passed to the LSTM... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | danielsuo/toy_flood | EALSTM | false | 15,128 | [
"MIT"
] | 49 | 471d3c4091d86d4a00fbf910937d4e60fdaf79a1 | https://github.com/danielsuo/toy_flood/tree/471d3c4091d86d4a00fbf910937d4e60fdaf79a1 |
FMul | import torch
import torch.nn as nn
class FMul(nn.Module):
def __init__(self):
super(FMul, self).__init__()
def forward(self, x, y):
x = x * y
x = x * 10.0
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | dawnclaude/onnx2keras | FMul | false | 15,129 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
GroupWiseLinear | 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
class GroupWiseLinear(nn.Module):
def __init__(self, num_class, hidden_dim, bias=True):
super().__init__()
self.num_class = num_class
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 torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
as... | davidaderup/query2labels | GroupWiseLinear | false | 15,130 | [
"MIT"
] | 164 | 5a10c861dda85d94ba01ec6ad4119eef67a9f441 | https://github.com/davidaderup/query2labels/tree/5a10c861dda85d94ba01ec6ad4119eef67a9f441 |
EncoderImagePrecomp | import torch
import numpy as np
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
def l2norm(x, dim=-1):
return x / x.norm(2, dim=dim, keepdim=True).clamp(min=1e-06)
class EncoderImagePrecomp(nn.Module):
""" image encoder """
def __init__(self, img_dim, embed_size, no_imgno... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | davidatbu/MLVGSNL | EncoderImagePrecomp | false | 15,131 | [
"MIT"
] | 97 | 88d42424a0a7badb43e22cd3950948c9522faaa1 | https://github.com/davidatbu/MLVGSNL/tree/88d42424a0a7badb43e22cd3950948c9522faaa1 |
FDiv | import torch
import torch.nn as nn
class FDiv(nn.Module):
def __init__(self):
super(FDiv, self).__init__()
def forward(self, x, y):
x = x / 2
y = y / 2
x = x / y
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_ini... | 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... | dawnclaude/onnx2keras | FDiv | false | 15,132 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
img_encoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class resnet_block(nn.Module):
def __init__(self, dim_in, dim_out):
super(resnet_block, self).__init__()
self.dim_in = dim_in
self.dim_out = dim_out
if self.dim_in == self.dim_out:
self.conv_1 = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | czq142857/DECOR-GAN | img_encoder | false | 15,133 | [
"MIT"
] | 55 | 79c80fc202b8af982989a3e3bb3afe85e606b71f | https://github.com/czq142857/DECOR-GAN/tree/79c80fc202b8af982989a3e3bb3afe85e606b71f |
LSTM | import torch
from typing import Tuple
import torch.nn as nn
class LSTM(nn.Module):
"""Implementation of the standard LSTM.
TODO: Include ref and LaTeX equations
Parameters
----------
input_size : int
Number of input features
hidden_size : int
Number of hidden/memory cells.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | danielsuo/toy_flood | LSTM | false | 15,134 | [
"MIT"
] | 49 | 471d3c4091d86d4a00fbf910937d4e60fdaf79a1 | https://github.com/danielsuo/toy_flood/tree/471d3c4091d86d4a00fbf910937d4e60fdaf79a1 |
GatedConv2d | import torch
import torch.nn as nn
from torch.nn import functional as F
class GatedConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation=1):
super(GatedConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, 2 * out_channels, kernel_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | davidreiman/nsf | GatedConv2d | false | 15,136 | [
"MIT"
] | 231 | ed70316c3bf1acd4ffdf309f1773172c34e48320 | https://github.com/davidreiman/nsf/tree/ed70316c3bf1acd4ffdf309f1773172c34e48320 |
decoder5 | import torch
import torch.nn as nn
class decoder5(nn.Module):
def __init__(self):
super(decoder5, self).__init__()
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu15 = nn.ReLU(inplace=True)
self.unpool = nn.Upsampling... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | czczup/URST | decoder5 | false | 15,137 | [
"Apache-2.0"
] | 119 | 000ec9f7728f12ffad989ec1d07b1dd579514133 | https://github.com/czczup/URST/tree/000ec9f7728f12ffad989ec1d07b1dd579514133 |
FPELU | import random
import torch
import torch.nn as nn
class FPELU(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FPELU, self).__init__()
self.alpha = random.random()
def forward(self, x):
from torch.nn import functional as F
return F.elu(x, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guard... | dawnclaude/onnx2keras | FPELU | false | 15,138 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
FLeakyReLU | import random
import torch
import torch.nn as nn
class FLeakyReLU(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FLeakyReLU, self).__init__()
self.negative_slope = random.random()
def forward(self, x):
from torch.nn import functional as F
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.gu... | dawnclaude/onnx2keras | FLeakyReLU | false | 15,139 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
FSELU | import torch
import torch.nn as nn
class FSELU(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FSELU, self).__init__()
def forward(self, x):
from torch.nn import functional as F
return F.selu(x)
def get_inputs():
return [torch.rand([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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | dawnclaude/onnx2keras | FSELU | false | 15,140 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
FThreshold | import random
import torch
import torch.nn as nn
class FThreshold(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FThreshold, self).__init__()
self.threshold = random.random()
self.value = self.threshold + random.random()
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
import random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.gu... | dawnclaude/onnx2keras | FThreshold | false | 15,141 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
LayerLeakyReLU | import random
import torch
import torch.nn as nn
class LayerLeakyReLU(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super(LayerLeakyReLU, self).__init__()
self.negative_slope = random.random()
self.leaky_relu = nn.LeakyReLU(negative_slope=self.negative... | 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 random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.gu... | dawnclaude/onnx2keras | LayerLeakyReLU | false | 15,142 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
MultiHeadAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
"""
:param q: queries, B x N_HEADS x seq_len x d_k
:param k: keys, same dim as q
:param v: values, same dim as q
:param d_k: d_model/n_heads = 128/8 = 16
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | davide-belli/generative-graph-transformer | MultiHeadAttention | false | 15,143 | [
"MIT"
] | 51 | 949aacf57246e8c28df7dfa38e5c59bf8b2b0ee8 | https://github.com/davide-belli/generative-graph-transformer/tree/949aacf57246e8c28df7dfa38e5c59bf8b2b0ee8 |
FSub | import torch
import torch.nn as nn
class FSub(nn.Module):
def __init__(self):
super(FSub, self).__init__()
def forward(self, x, y):
x = x - y - 8.3
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | dawnclaude/onnx2keras | FSub | false | 15,144 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
FFloorTest | import torch
import torch.nn as nn
class FFloorTest(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FFloorTest, self).__init__()
def forward(self, x):
return x.floor()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | dawnclaude/onnx2keras | FFloorTest | false | 15,145 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
FTanh | import torch
import torch.nn as nn
class FTanh(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FTanh, self).__init__()
def forward(self, x):
from torch.nn import functional as F
return F.tanh(x)
def get_inputs():
return [torch.rand([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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | dawnclaude/onnx2keras | FTanh | false | 15,146 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
LayerTanh | import torch
import torch.nn as nn
class LayerTanh(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super(LayerTanh, self).__init__()
self.tanh = nn.Tanh()
def forward(self, x):
x = self.tanh(x)
return x
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | dawnclaude/onnx2keras | LayerTanh | false | 15,147 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
PointNetfeat | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
class PointNetfeat(nn.Module):
"""
Simple PointNet that extracts point-wise feature by concatenating local and global features.
Uses group norm instead of batch norm.
"""
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._inductor.runtime.... | davrempe/caspr | PointNetfeat | false | 15,148 | [
"MIT"
] | 65 | a02edb4be11f5ccfe563b2a7869ee8e731e0f8ff | https://github.com/davrempe/caspr/tree/a02edb4be11f5ccfe563b2a7869ee8e731e0f8ff |
LayerThreshold | import random
import torch
import torch.nn as nn
class LayerThreshold(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super(LayerThreshold, self).__init__()
self.threshold = random.random()
self.value = self.threshold + random.random()
self.thres... | 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 random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.gu... | dawnclaude/onnx2keras | LayerThreshold | false | 15,149 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
LayerReLU6 | import torch
import torch.nn as nn
class LayerReLU6(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super(LayerReLU6, self).__init__()
self.relu = nn.ReLU6()
def forward(self, x):
x = self.relu(x)
return x
def get_inputs():
return [tor... | 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... | dawnclaude/onnx2keras | LayerReLU6 | false | 15,150 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
LayerHardtanh | import random
import torch
import torch.nn as nn
class LayerHardtanh(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super(LayerHardtanh, self).__init__()
self.min_val = random.random()
self.max_val = self.min_val + random.random()
self.htanh = n... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | dawnclaude/onnx2keras | LayerHardtanh | false | 15,151 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
ffnn | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
def get_shape(t):
return list(t.shape)
class ffnn(nn.Module):
def __init__(self, emb_size, num_layers, hidden_size, output_size,
dropout, output_weights_initializer=None):
super(ffnn, 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
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
assert_... | db-bionlp/CLNER | ffnn | false | 15,152 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
LayerELU | import random
import torch
import torch.nn as nn
class LayerELU(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super(LayerELU, self).__init__()
self.alpha = random.random()
self.elu = nn.ELU(alpha=self.alpha)
def forward(self, 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
from torch._inductor.runtime.triton_helpers import libdevice
import random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guard... | dawnclaude/onnx2keras | LayerELU | false | 15,153 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
VoxelFeatureExtractor | import torch
from torch import nn
class VoxelFeatureExtractor(nn.Module):
"""Computes mean of non-zero points within voxel."""
def forward(self, feature, occupancy):
"""
:feature FloatTensor of shape (N, K, C)
:return FloatTensor of shape (N, C)
"""
denominator = occup... | 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... | dd-iuonac/vision3d | VoxelFeatureExtractor | false | 15,154 | [
"MIT"
] | 131 | 9ea514c80eb99d265c3247321e59bfc1c2ccd94a | https://github.com/dd-iuonac/vision3d/tree/9ea514c80eb99d265c3247321e59bfc1c2ccd94a |
ScalarMix | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class ScalarMix(nn.Module):
def __init__(self, n_layers, dropout=0):
super(ScalarMix, self).__init__()
self.n_layers = n_layers
self.dropout = dropout
self.weights = nn.Parameter(torch.zeros(n_la... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
... | db-bionlp/CLNER | ScalarMix | false | 15,155 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
EmissionModel | import torch
import torch.utils.data
class EmissionModel(torch.nn.Module):
"""
- forward(): computes the log probability of an observation.
- sample(): given a state, sample an observation for that state.
"""
def __init__(self, N, M):
super(EmissionModel, self).__init__()
self.N = N
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | dendisuhubdy/pytorch_HMM | EmissionModel | false | 15,156 | [
"Apache-2.0"
] | 88 | 3235326027328e1b0377b17f9dad8fcc56a3668c | https://github.com/dendisuhubdy/pytorch_HMM/tree/3235326027328e1b0377b17f9dad8fcc56a3668c |
BiaffineAttention | import torch
import torch.nn as nn
import torch.utils.data.dataloader
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.nn
class BiaffineAttention(nn.Module):
"""
Adopted from NeuroNLP2:
https://github.com/XuezheMax/NeuroNLP2/blob/master/neuronlp2/nn/modules/attentio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | db-bionlp/CLNER | BiaffineAttention | false | 15,157 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
HDRLoss | import torch
import torch.nn as nn
class HDRLoss(nn.Module):
"""High dynamic range loss."""
def __init__(self, eps=0.01):
"""Initializes loss with numerical stability epsilon."""
super(HDRLoss, self).__init__()
self._eps = eps
def forward(self, denoised, target):
"""Compu... | 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... | delldu/Noise2Noise | HDRLoss | false | 15,158 | [
"MIT"
] | 224 | f519f208776a60efadac208c109c9b7f432504b5 | https://github.com/delldu/Noise2Noise/tree/f519f208776a60efadac208c109c9b7f432504b5 |
Conv2d | import torch
import torch.nn as nn
from torch.distributions import transforms as transform
class Flow(transform.Transform, nn.Module):
"""
Main class for a single flow.
"""
def __init__(self, amortized='none'):
""" Initialize as both transform and module """
transform.Transform.__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
import torch.nn as nn
from torch.distributions import transforms as transform
as... | dendisuhubdy/flow_synthesizer | Conv2d | false | 15,159 | [
"MIT"
] | 93 | 1561e8ce2520258acb3d228beebbb626a8abc04f | https://github.com/dendisuhubdy/flow_synthesizer/tree/1561e8ce2520258acb3d228beebbb626a8abc04f |
cnn_layer | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class cnn_layer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, bias=True):
super(cnn_layer, self).__init__()
self.conv = torch.nn.Conv1d(in_channels=in_channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | db-bionlp/CLNER | cnn_layer | false | 15,160 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
bilinear_classifier | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class Sparse_dropout(nn.Module):
def __init__(self, p):
super(Sparse_dropout, self).__init__()
self.dropout_rate = p
def forward(self, input, noise_shape):
if not self.training:
return 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 torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
assert_... | db-bionlp/CLNER | bilinear_classifier | false | 15,161 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
LSID | import math
import torch
import torch.nn as nn
def pixel_shuffle(input, upscale_factor, depth_first=False):
"""Rearranges elements in a tensor of shape :math:`[*, C*r^2, H, W]` to a
tensor of shape :math:`[C, H*r, W*r]`.
See :class:`~torch.nn.PixelShuffle` for details.
Args:
input (Tensor): ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 math
import torch.nn a... | cydonia999/Learning_to_See_in_the_Dark_PyTorch | LSID | false | 15,162 | [
"MIT"
] | 77 | 470a6a8e9c6367d8fa88ee6d1dea211dd9fb1f81 | https://github.com/cydonia999/Learning_to_See_in_the_Dark_PyTorch/tree/470a6a8e9c6367d8fa88ee6d1dea211dd9fb1f81 |
HexaLinearScore | import math
import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class HexaLinearScore(nn.Module):
"""
Outer product version of hexalinear function for sequence labeling.
"""
def __init__(self, wemb_size, tagset_size, temb_size=20, rank=396, std=
0.1545, norma... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.utils.data.dataloader
import torc... | db-bionlp/CLNER | HexaLinearScore | false | 15,163 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
GraphAttentionLayer | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.autograd import Variable
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
def __init__(self, requires_grad=True):
super(GraphAttentionLayer, self).__init__()
if requires_grad:
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dawnranger/pytorch-AGNN | GraphAttentionLayer | false | 15,164 | [
"MIT"
] | 137 | 461f71b45e5eaddb50cff31a537b06cb1a50ba8f | https://github.com/dawnranger/pytorch-AGNN/tree/461f71b45e5eaddb50cff31a537b06cb1a50ba8f |
QuadriLinearScore | import math
import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class QuadriLinearScore(nn.Module):
"""
Outer product version of quadrilinear function for sequence labeling.
"""
def __init__(self, wemb_size, tagset_size, temb_size=20, rank=396, std=
0.1545, w... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.utils.data.dataloader
import torc... | db-bionlp/CLNER | QuadriLinearScore | false | 15,165 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
DecoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
"""
:param q: queries, B x N_HEADS x seq_len x d_k
:param k: keys, same dim as q
:param v: values, same dim as q
:param d_k: d_model/n_heads = 128/8 = 16
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | davide-belli/generative-graph-transformer | DecoderLayer | false | 15,166 | [
"MIT"
] | 51 | 949aacf57246e8c28df7dfa38e5c59bf8b2b0ee8 | https://github.com/davide-belli/generative-graph-transformer/tree/949aacf57246e8c28df7dfa38e5c59bf8b2b0ee8 |
LayerNorm | import math
import torch
import torch as th
import torch.nn as nn
from torch.nn import Parameter
class LayerNorm(nn.Module):
"""
Layer Normalization based on Ba & al.:
'Layer Normalization'
https://arxiv.org/pdf/1607.06450.pdf
"""
def __init__(self, input_size: 'int', learnable: 'bool'=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.triton_helpers import libdevice
import math
import torch as th
import torch.nn as nn
from torch.nn import Param... | denizetkar/lstms.pth | LayerNorm | false | 15,167 | [
"Apache-2.0"
] | 130 | c1d6af1e106e17c51604ae8acdb5114828adff19 | https://github.com/denizetkar/lstms.pth/tree/c1d6af1e106e17c51604ae8acdb5114828adff19 |
BaLayerNorm | import torch
import torch as th
import torch.nn as nn
from torch.nn import Parameter
class BaLayerNorm(nn.Module):
"""
Layer Normalization based on Ba & al.:
'Layer Normalization'
https://arxiv.org/pdf/1607.06450.pdf
This implementation mimicks the original torch implementation at:
https://gi... | 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 th
import torch.nn as nn
from torch.nn import Parameter
assert_... | denizetkar/lstms.pth | BaLayerNorm | false | 15,168 | [
"Apache-2.0"
] | 130 | c1d6af1e106e17c51604ae8acdb5114828adff19 | https://github.com/denizetkar/lstms.pth/tree/c1d6af1e106e17c51604ae8acdb5114828adff19 |
GatedConv2d | import torch
import torch.nn as nn
class GatedConv2d(nn.Module):
def __init__(self, in_c, out_c, kernel, stride, pad, dilation=1, act=
torch.relu):
super(GatedConv2d, self).__init__()
self.activation = act
self.sigmoid = nn.Sigmoid()
self.h = nn.Conv2d(in_c, out_c, kernel,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | dendisuhubdy/flow_synthesizer | GatedConv2d | false | 15,169 | [
"MIT"
] | 93 | 1561e8ce2520258acb3d228beebbb626a8abc04f | https://github.com/dendisuhubdy/flow_synthesizer/tree/1561e8ce2520258acb3d228beebbb626a8abc04f |
MinibatchStddev | import torch
from torch import nn
def Tstdeps(val):
return torch.sqrt(((val - val.mean()) ** 2).mean() + 1e-08)
class MinibatchStddev(nn.Module):
def __init__(self):
super(MinibatchStddev, self).__init__()
self.eps = 1.0
def forward(self, x):
stddev_mean = Tstdeps(x)
ne... | 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_... | deepsound-project/pggan-pytorch | MinibatchStddev | false | 15,170 | [
"MIT"
] | 115 | dab2ec79229c3800253a209304dbb1e7ac1d1219 | https://github.com/deepsound-project/pggan-pytorch/tree/dab2ec79229c3800253a209304dbb1e7ac1d1219 |
ChanNorm | import torch
from torch import nn
class ChanNorm(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
std = torch.var(x,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | destefani/lightweight-gan | ChanNorm | false | 15,171 | [
"MIT"
] | 1,187 | 5ba61c21c8c9c8d4574a4a3ddd4759f86debf9bf | https://github.com/destefani/lightweight-gan/tree/5ba61c21c8c9c8d4574a4a3ddd4759f86debf9bf |
GatedDense | import torch
import torch.nn as nn
class GatedDense(nn.Module):
def __init__(self, input_size, output_size, activation=torch.relu):
super(GatedDense, self).__init__()
self.activation = activation
self.sigmoid = nn.Sigmoid()
self.h = nn.Linear(input_size, output_size)
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_... | dendisuhubdy/flow_synthesizer | GatedDense | false | 15,172 | [
"MIT"
] | 93 | 1561e8ce2520258acb3d228beebbb626a8abc04f | https://github.com/dendisuhubdy/flow_synthesizer/tree/1561e8ce2520258acb3d228beebbb626a8abc04f |
LinearBlock | import torch
from functools import partial
import torch.nn as nn
def dispatcher(dispatch_fn):
def decorated(key, *args):
if callable(key):
return key
if key is None:
key = 'none'
return dispatch_fn(key, *args)
return decorated
@dispatcher
def activ_dispatch(a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from functools import partial... | derwind/dmfont | LinearBlock | false | 15,173 | [
"MIT"
] | 95 | 17a91a9cc1917d2485eaa8e92b68245578920c76 | https://github.com/derwind/dmfont/tree/17a91a9cc1917d2485eaa8e92b68245578920c76 |
PopulationColourRGBTransforms | from _paritybench_helpers import _mock_config
import torch
import numpy as np
class PopulationColourRGBTransforms(torch.nn.Module):
"""RGB color transforms and ordering of patches."""
def __init__(self, config, device, num_patches=1, pop_size=1,
requires_grad=True):
super(PopulationColourRGBT... | 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 numpy as np
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_... | deepmind/arnheim | PopulationColourRGBTransforms | false | 15,174 | [
"Apache-2.0"
] | 186 | cc9d2dd12391faa460b58bff1cc5be82145a5965 | https://github.com/deepmind/arnheim/tree/cc9d2dd12391faa460b58bff1cc5be82145a5965 |
ConvBlock | import torch
import torch.nn.functional as F
from functools import partial
import torch.nn as nn
def dispatcher(dispatch_fn):
def decorated(key, *args):
if callable(key):
return key
if key is None:
key = 'none'
return dispatch_fn(key, *args)
return decorated
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 functools import partial... | derwind/dmfont | ConvBlock | false | 15,175 | [
"MIT"
] | 95 | 17a91a9cc1917d2485eaa8e92b68245578920c76 | https://github.com/derwind/dmfont/tree/17a91a9cc1917d2485eaa8e92b68245578920c76 |
Attention | import torch
import torch.nn.functional as F
import torch.nn as nn
def dispatcher(dispatch_fn):
def decorated(key, *args):
if callable(key):
return key
if key is None:
key = 'none'
return dispatch_fn(key, *args)
return decorated
def spectral_norm(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
from torch._inductor.runtime.... | derwind/dmfont | Attention | false | 15,176 | [
"MIT"
] | 95 | 17a91a9cc1917d2485eaa8e92b68245578920c76 | https://github.com/derwind/dmfont/tree/17a91a9cc1917d2485eaa8e92b68245578920c76 |
EncoderImageWeightNormPrecomp | import torch
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
from torch.nn.utils.weight_norm import weight_norm
def l2norm(X, dim, eps=1e-08):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
retur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 collections im... | devilslot/SCAN | EncoderImageWeightNormPrecomp | false | 15,177 | [
"Apache-2.0"
] | 428 | 01812aa98e2ebe39695c8906589b6fe66b2a0d6e | https://github.com/devilslot/SCAN/tree/01812aa98e2ebe39695c8906589b6fe66b2a0d6e |
CopyChannels | import torch
class CopyChannels(torch.nn.Module):
def __init__(self, multiple=3, dim=1):
super(CopyChannels, self).__init__()
self.multiple = multiple
self.dim = dim
def forward(self, x):
return torch.cat([x for _ in range(self.multiple)], dim=self.dim)
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret... | dianjixz/AutoDL | CopyChannels | false | 15,178 | [
"Apache-2.0"
] | 1,044 | 48db4eb04d55ce69e93d4a3bdc24592bdb34a868 | https://github.com/dianjixz/AutoDL/tree/48db4eb04d55ce69e93d4a3bdc24592bdb34a868 |
CReLU | import torch
import torch.nn.functional as F
import torch.nn as nn
class CReLU(nn.Module):
def __init__(self):
super(CReLU, self).__init__()
def forward(self, x):
return torch.cat((F.leaky_relu(x, 0.01, inplace=True), F.leaky_relu
(-x, 0.01, inplace=True)), 1)
def get_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | dipikakhullar/ocr | CReLU | false | 15,179 | [
"MIT"
] | 284 | a55e70d82f42803be5ed63f8f59e4fa597fcf8d6 | https://github.com/dipikakhullar/ocr/tree/a55e70d82f42803be5ed63f8f59e4fa597fcf8d6 |
ResBlock | import torch
import torch.nn.functional as F
from functools import partial
import torch.nn as nn
def dispatcher(dispatch_fn):
def decorated(key, *args):
if callable(key):
return key
if key is None:
key = 'none'
return dispatch_fn(key, *args)
return decorated
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.functional as... | derwind/dmfont | ResBlock | false | 15,180 | [
"MIT"
] | 95 | 17a91a9cc1917d2485eaa8e92b68245578920c76 | https://github.com/derwind/dmfont/tree/17a91a9cc1917d2485eaa8e92b68245578920c76 |
IdentityPadding | import torch
import torch.nn as nn
import torch.nn.functional as F
class IdentityPadding(nn.Module):
def __init__(self, in_channels, out_channels, stride):
super(IdentityPadding, self).__init__()
self.pooling = nn.MaxPool2d(1, stride=stride)
self.add_channels = out_channels - in_channels
... | 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... | dnddnjs/pytorch-vision | IdentityPadding | false | 15,181 | [
"MIT"
] | 48 | d432b467774f838bef37372d6cff3576c6559803 | https://github.com/dnddnjs/pytorch-vision/tree/d432b467774f838bef37372d6cff3576c6559803 |
BertSelfOutput | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, 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.triton_helpers import libdevice
import torch.nn as ... | dfhby0/CBLUE | BertSelfOutput | false | 15,182 | [
"Apache-2.0"
] | 293 | 36bdb52f17c4379d4a5f8b407890ba294017b5e2 | https://github.com/dfhby0/CBLUE/tree/36bdb52f17c4379d4a5f8b407890ba294017b5e2 |
TwoLayerNet | import torch
import torch.nn
class TwoLayerNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
super(TwoLayerNet, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H)
self.linear2 = torch.nn.Linear(H, D_out)
def forward(self, x):
h_relu = self.linear1(x).clamp(min=0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
assert_size_s... | dionhaefner/delve | TwoLayerNet | false | 15,183 | [
"MIT"
] | 69 | 811756520cbfd8dce4427c53203ac193f61a94d1 | https://github.com/dionhaefner/delve/tree/811756520cbfd8dce4427c53203ac193f61a94d1 |
MultiHeadAttention | import torch
import numpy as np
def scaled_dot_product_attention(q, k, v, mask):
matmul_qk = torch.matmul(q, k.permute(0, 1, 3, 2))
dk = k.shape[-1]
scaled_attention_logits = matmul_qk / np.sqrt(dk)
if mask is not None:
scaled_attention_logits += mask * -1000000000.0
attention_weights = to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | depengchen123/ctrl | MultiHeadAttention | false | 15,184 | [
"BSD-3-Clause"
] | 1,559 | 8673e9ec1bf6441ad8d793a626cdfd8c1fd9c4e4 | https://github.com/depengchen123/ctrl/tree/8673e9ec1bf6441ad8d793a626cdfd8c1fd9c4e4 |
BatchNorm | import torch
import numpy as np
from abc import abstractmethod
from torch import tensor
import torch.nn as nn
import numpy.random as rng
class BaseFlow(nn.Module):
""" """
def __init__(self, n_inputs, **kwargs):
super().__init__()
self.n_inputs = n_inputs
@abstractmethod
def forward(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
from abc import abstractmethod
from torch i... | diana-hep/madminer | BatchNorm | false | 15,185 | [
"MIT"
] | 46 | 3a585d2887a31886cdeadddb0a284f0472146fce | https://github.com/diana-hep/madminer/tree/3a585d2887a31886cdeadddb0a284f0472146fce |
LayerCake | import torch
import torch.nn
class LayerCake(torch.nn.Module):
def __init__(self, D_in, H1, H2, H3, H4, H5, D_out):
"""
In the constructor we instantiate two nn.Linear modules and assign them as
member variables.
"""
super(LayerCake, self).__init__()
self.linear1 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
assert_size_s... | dionhaefner/delve | LayerCake | false | 15,186 | [
"MIT"
] | 69 | 811756520cbfd8dce4427c53203ac193f61a94d1 | https://github.com/dionhaefner/delve/tree/811756520cbfd8dce4427c53203ac193f61a94d1 |
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