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 |
|---|---|---|---|---|---|---|---|---|---|---|
Clamp | import torch
from torch import nn
class Clamp(nn.Module):
"""Clamp energy output"""
def forward(self, x):
x = torch.clamp(x, min=0, max=30)
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
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | abdalazizrashid/idao-21-baseline | Clamp | false | 18,229 | [
"Apache-2.0"
] | 7 | 649c2c70a1754b09fa06bf2264d7e8217b3e10f0 | https://github.com/abdalazizrashid/idao-21-baseline/tree/649c2c70a1754b09fa06bf2264d7e8217b3e10f0 |
Conv2dBlock | import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
import torch.optim
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super().__init__()
self.num_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | agermanidis/HiDT | Conv2dBlock | false | 18,230 | [
"BSD-3-Clause"
] | 4 | 69192bb26785fc4e05038c45d05f2f880dd362d0 | https://github.com/agermanidis/HiDT/tree/69192bb26785fc4e05038c45d05f2f880dd362d0 |
BasicConv | import torch
import torch.nn as nn
import torch.utils.model_zoo
class BasicConv(nn.Module):
def __init__(self, in_feature, out_feature, kernel_size, stride=1,
padding=0, dilation=1, groups=1, relu=True, bn=False, bias=False):
super(BasicConv, self).__init__()
self.conv = nn.Conv2d(in_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | agusgun/EDSR-PyTorch | BasicConv | false | 18,231 | [
"MIT"
] | 6 | 38ff657e2c4e2f148d38b8792bacdf8d81f8bf9f | https://github.com/agusgun/EDSR-PyTorch/tree/38ff657e2c4e2f148d38b8792bacdf8d81f8bf9f |
NormalizeOutput | import torch
import torch.nn.functional as F
from torch import nn
import torch.optim
class NormalizeOutput(nn.Module):
"""
Module that scales the input tensor to the unit norm w.r.t. the specified axis.
Actually, the module analog of `torch.nn.functional.normalize`
"""
def __init__(self, dim=1, p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import ... | agermanidis/HiDT | NormalizeOutput | false | 18,232 | [
"BSD-3-Clause"
] | 4 | 69192bb26785fc4e05038c45d05f2f880dd362d0 | https://github.com/agermanidis/HiDT/tree/69192bb26785fc4e05038c45d05f2f880dd362d0 |
CmapPafHead | import torch
import torch.utils.data
import torch.nn
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn
import torch.optim
assert_size_stride = ... | ajsampathk/trt_pose | CmapPafHead | false | 18,233 | [
"MIT"
] | 7 | 592e038cacaf43b6a502b759a035a4e7cae9db9e | https://github.com/ajsampathk/trt_pose/tree/592e038cacaf43b6a502b759a035a4e7cae9db9e |
LinearModel | import torch
import torch.utils.data
import torch.nn
import torch.optim
class LinearModel(torch.nn.Module):
def __init__(self, _in, out):
super(LinearModel, self).__init__()
self.input = torch.nn.Linear(_in, _in)
self.hidden_1 = torch.nn.Linear(_in, out)
self.hidden_2 = torch.nn.L... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn
import torch.optim
assert_size_stride = ... | ajsampathk/trt_pose | LinearModel | false | 18,234 | [
"MIT"
] | 7 | 592e038cacaf43b6a502b759a035a4e7cae9db9e | https://github.com/ajsampathk/trt_pose/tree/592e038cacaf43b6a502b759a035a4e7cae9db9e |
XnorConv | import torch
import torch.multiprocessing
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.autograd as autograd
import torch.nn.functional as F
class BinarizeWeight(autograd.Function):
@staticmethod
def forward(ctx, sco... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.multiprocessing
import torch.nn as nn
import torch.nn.parallel
impo... | adityakusupati/LLC-2.0 | XnorConv | false | 18,235 | [
"MIT"
] | 10 | 38608bbaa425b15dcf5c971000b7a1b08120fb5c | https://github.com/adityakusupati/LLC-2.0/tree/38608bbaa425b15dcf5c971000b7a1b08120fb5c |
SphereLoss | import torch
import torch.utils.data
import torch.nn as nn
from torchvision.transforms import *
class SphereLoss(nn.Module):
def __init__(self, in_feats, n_classes, scale=14, *args, **kwargs):
super(SphereLoss, self).__init__(*args, **kwargs)
self.scale = scale
self.cross_entropy = nn.Cro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ace19-dev/image-retrieval-pytorch | SphereLoss | false | 18,236 | [
"MIT"
] | 9 | 19bd4ae5efea5b6184c345f693646bcd9a0fc8cf | https://github.com/ace19-dev/image-retrieval-pytorch/tree/19bd4ae5efea5b6184c345f693646bcd9a0fc8cf |
minibatch_discriminator | import torch
import torch.nn as nn
class minibatch_discriminator(nn.Module):
def __init__(self, num_channels, B_dim, C_dim):
super(minibatch_discriminator, self).__init__()
self.B_dim = B_dim
self.C_dim = C_dim
self.num_channels = num_channels
T_init = torch.randn(num_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | aditya30394/Reverse-Image-Captioning | minibatch_discriminator | false | 18,237 | [
"MIT"
] | 5 | a6e427624a64f28d08e5629f48850ff001e48d02 | https://github.com/aditya30394/Reverse-Image-Captioning/tree/a6e427624a64f28d08e5629f48850ff001e48d02 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, smooth=0, eps=1e-07):
super(DiceLoss, self).__init__()
self.smooth = smooth
self.eps = eps
def forward(self, output, target):
return 1 - (2 * torch.sum(output * target) + self.smooth) / (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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | adriangrepo/segmentl | DiceLoss | false | 18,238 | [
"MIT"
] | 5 | 9b520bf6cfd005eef9bba3db36ee6b3bb373b085 | https://github.com/adriangrepo/segmentl/tree/9b520bf6cfd005eef9bba3db36ee6b3bb373b085 |
Accuracy | import torch
import torch.nn as nn
class Accuracy(nn.Module):
def __init__(self, threshold=0.5):
super().__init__()
self.threshold = threshold
def forward(self, y_true, y_pred):
preds = (y_pred > self.threshold).int()
return (preds == y_true).sum().float() / len(preds)
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | alessandroferrari/defeatcovid19-net-pytorch | Accuracy | false | 18,239 | [
"MIT"
] | 9 | fe9ed82563709bae92524093c3bc0bb887fbdf6d | https://github.com/alessandroferrari/defeatcovid19-net-pytorch/tree/fe9ed82563709bae92524093c3bc0bb887fbdf6d |
Hswish | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Hsigmoid(nn.Module):
def __init__(self, inplace=True, add_stub=False):
super().__init__()
self.float_op = nn.quantized.FloatFunctional()
self.relu6 = nn.ReLU6(inpla... | 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.quantization import QuantStub
from torch.quantization im... | akosik-anyvision/incubator-tvm | Hswish | false | 18,240 | [
"Apache-2.0"
] | 9 | e1b11712ac09c32614483d24a4c7e0245ee4cb4b | https://github.com/akosik-anyvision/incubator-tvm/tree/e1b11712ac09c32614483d24a4c7e0245ee4cb4b |
Hsigmoid | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Hsigmoid(nn.Module):
def __init__(self, inplace=True, add_stub=False):
super().__init__()
self.float_op = nn.quantized.FloatFunctional()
self.relu6 = nn.ReLU6(inpla... | 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.quantization import QuantStub
from torch.quantization im... | akosik-anyvision/incubator-tvm | Hsigmoid | false | 18,241 | [
"Apache-2.0"
] | 9 | e1b11712ac09c32614483d24a4c7e0245ee4cb4b | https://github.com/akosik-anyvision/incubator-tvm/tree/e1b11712ac09c32614483d24a4c7e0245ee4cb4b |
RNNModel | import torch
class RNNCell(torch.nn.Module):
def __init__(self, input_channels: 'int', hidden_channels: 'int',
non_linearity: 'str'):
"""Manual implementation of a cell of the RNN, necessary when non_linearity is 'sigmoid' since torch.nn.RNNCell
accepts only 'tanh' or 'relu' activations
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | afermanian/rnn-kernel | RNNModel | false | 18,242 | [
"Apache-2.0"
] | 5 | 8c4400c33e61081bfc162fa26d088827cee1028b | https://github.com/afermanian/rnn-kernel/tree/8c4400c33e61081bfc162fa26d088827cee1028b |
BucketingEmbedding | import torch
import torch.nn as nn
class BucketingEmbedding(nn.Module):
def __init__(self, min_val, max_val, count, dim, use_log_scale=False):
super().__init__()
self.min_val = min_val
self.max_val = max_val
self.count = count
self.dim = dim
self.use_log_scale = us... | 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... | aimagelab/LoCoNav | BucketingEmbedding | false | 18,243 | [
"MIT"
] | 9 | 00faf0d22d68a5ac8a4896381f97f2b472613ace | https://github.com/aimagelab/LoCoNav/tree/00faf0d22d68a5ac8a4896381f97f2b472613ace |
SpatialAttentionLayer | import torch
import torch.nn as nn
import torch.utils.model_zoo
class BasicConv(nn.Module):
def __init__(self, in_feature, out_feature, kernel_size, stride=1,
padding=0, dilation=1, groups=1, relu=True, bn=False, bias=False):
super(BasicConv, self).__init__()
self.conv = nn.Conv2d(in_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | agusgun/EDSR-PyTorch | SpatialAttentionLayer | false | 18,244 | [
"MIT"
] | 6 | 38ff657e2c4e2f148d38b8792bacdf8d81f8bf9f | https://github.com/agusgun/EDSR-PyTorch/tree/38ff657e2c4e2f148d38b8792bacdf8d81f8bf9f |
RewardModel | import torch
import torch.nn as nn
from torch.nn import functional as F
class RewardModel(nn.Module):
def __init__(self, hidden_size, state_size, node_size, act_fn='relu'):
super().__init__()
self.act_fn = getattr(F, act_fn)
self.fc_1 = nn.Linear(hidden_size + state_size, node_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | alec-tschantz/planet | RewardModel | false | 18,245 | [
"MIT"
] | 7 | bf68722993c93129263bb9606a582d24cb4f2a58 | https://github.com/alec-tschantz/planet/tree/bf68722993c93129263bb9606a582d24cb4f2a58 |
GLU | import torch
import torch.utils.data
import torch.nn as nn
import torch
from torchvision.transforms import functional as F
import torch.nn.functional as F
import torch.nn.parallel
class GLU(nn.Module):
def __init__(self):
super(GLU, self).__init__()
def forward(self, x):
nc = x.size(1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size... | adymaharana/VLCStoryGan | GLU | false | 18,246 | [
"MIT"
] | 10 | 74112404689e8144c2ed2d375e1e5a1cde09debb | https://github.com/adymaharana/VLCStoryGan/tree/74112404689e8144c2ed2d375e1e5a1cde09debb |
Bridge | import torch
import torch.nn as nn
import torch.nn.functional as F
class Bridge(nn.Sequential):
def __init__(self, in_channels, out_channels):
super(Bridge, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
padding=1)
self.act1 = nn.LeakyReLU(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 as nn
import ... | aiarjun/Monocular-Depth-Estimation | Bridge | false | 18,247 | [
"MIT"
] | 6 | 5989673f1b6d865f822a342448172b374968c234 | https://github.com/aiarjun/Monocular-Depth-Estimation/tree/5989673f1b6d865f822a342448172b374968c234 |
MinibatchStdDev | from torch.nn import Module
import torch
from torch import Tensor
from typing import List
class MinibatchStdDev(Module):
"""
Minibatch standard deviation layer for the discriminator
Args:
group_size: Size of each group into which the batch is split
num_new_features: number of additional fe... | 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.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert... | akanimax/open-styleganv2-pytorch | MinibatchStdDev | false | 18,248 | [
"MIT"
] | 7 | 243f12e335698293a0008d60c8b136d9f80cdacf | https://github.com/akanimax/open-styleganv2-pytorch/tree/243f12e335698293a0008d60c8b136d9f80cdacf |
Vgg16 | import torch
import torch.nn.functional as F
from torch import nn
import torch.optim
class Vgg16(nn.Module):
def __init__(self):
super().__init__()
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | agermanidis/HiDT | Vgg16 | false | 18,249 | [
"BSD-3-Clause"
] | 4 | 69192bb26785fc4e05038c45d05f2f880dd362d0 | https://github.com/agermanidis/HiDT/tree/69192bb26785fc4e05038c45d05f2f880dd362d0 |
ConvDecoder | import torch
import torch.nn as nn
from torch.nn import functional as F
class ConvDecoder(nn.Module):
def __init__(self, hidden_size, state_size, embedding_size, act_fn='relu'):
super().__init__()
self.act_fn = getattr(F, act_fn)
self.embedding_size = embedding_size
self.fc_1 = 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
import torch.nn as nn
from to... | alec-tschantz/planet | ConvDecoder | false | 18,250 | [
"MIT"
] | 7 | bf68722993c93129263bb9606a582d24cb4f2a58 | https://github.com/alec-tschantz/planet/tree/bf68722993c93129263bb9606a582d24cb4f2a58 |
CSAM | import torch
import torch.nn as nn
class CSAM(nn.Module):
"""
Compact Spatial Attention Module
"""
def __init__(self, channels):
super(CSAM, self).__init__()
mid_channels = 4
self.relu1 = nn.ReLU()
self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, padding=0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ZitongYu/pidinet | CSAM | false | 18,251 | [
"MIT"
] | 5 | 15cdf9fb056549934877675bf7571b427f86db55 | https://github.com/ZitongYu/pidinet/tree/15cdf9fb056549934877675bf7571b427f86db55 |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.conv1 = nn.Conv2d(2, 4, kernel_size=6, stride=2, bias=False)
self.conv2 = nn.Conv2d(4, 16, kernel_size=6, stride=4)
self.size = 9 * 9... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | aklein1995/Pong_REINFORCE | Policy | false | 18,252 | [
"MIT"
] | 4 | 3b57777fd3ab6e98c8a1191989bd65140e19fc6e | https://github.com/aklein1995/Pong_REINFORCE/tree/3b57777fd3ab6e98c8a1191989bd65140e19fc6e |
PcamPool | import torch
from torch import nn
class PcamPool(nn.Module):
def __init__(self):
super(PcamPool, self).__init__()
def forward(self, feat_map, logit_map):
assert logit_map is not None
prob_map = torch.sigmoid(logit_map)
weight_map = prob_map / prob_map.sum(dim=2, keepdim=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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | alinstein/X_RAY | PcamPool | false | 18,253 | [
"MIT"
] | 4 | 35a39761d3b11ce9e47509025054f25e5f26aab9 | https://github.com/alinstein/X_RAY/tree/35a39761d3b11ce9e47509025054f25e5f26aab9 |
ODEfunc | import torch
import torch.nn as nn
def norm(dim):
return nn.GroupNorm(min(32, dim), dim)
class ConcatConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatConv2d, self).__init__()
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.... | agrimsharma20/Deep-Continuous-Networks | ODEfunc | false | 18,254 | [
"MIT"
] | 4 | 6c2b46dea5d0d7f25682d2fb55c4d5386e30997c | https://github.com/agrimsharma20/Deep-Continuous-Networks/tree/6c2b46dea5d0d7f25682d2fb55c4d5386e30997c |
LxmertAttentionOutput | import torch
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
class LxmertAttentionOutput(nn.Module):
def __init__(self, hidden_size, hidden_dropout_prob):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.LayerNorm = nn.LayerNorm(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | adymaharana/VLCStoryGan | LxmertAttentionOutput | false | 18,255 | [
"MIT"
] | 10 | 74112404689e8144c2ed2d375e1e5a1cde09debb | https://github.com/adymaharana/VLCStoryGan/tree/74112404689e8144c2ed2d375e1e5a1cde09debb |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | adymaharana/VLCStoryGan | BertSelfAttention | false | 18,256 | [
"MIT"
] | 10 | 74112404689e8144c2ed2d375e1e5a1cde09debb | https://github.com/adymaharana/VLCStoryGan/tree/74112404689e8144c2ed2d375e1e5a1cde09debb |
BertPredictionHead | from _paritybench_helpers import _mock_config
import math
import torch
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT"s gelu is slightly different (and gives slightly different ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | adymaharana/VLCStoryGan | BertPredictionHead | false | 18,257 | [
"MIT"
] | 10 | 74112404689e8144c2ed2d375e1e5a1cde09debb | https://github.com/adymaharana/VLCStoryGan/tree/74112404689e8144c2ed2d375e1e5a1cde09debb |
PreActResPath | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class PreActResPath(nn.Module):
def __init__(self, in_features, config, super_block):
super(PreActResPath, self).__init__()
self.number_layers = config['num_layers']
self.activate_dropout = True if config['ac... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | ArlindKadra/DeepLearning | PreActResPath | false | 18,258 | [
"Apache-2.0"
] | 4 | 4e9ffe39bbb8722ca658522e6b6d26c6f2291ef6 | https://github.com/ArlindKadra/DeepLearning/tree/4e9ffe39bbb8722ca658522e6b6d26c6f2291ef6 |
LogSumExpPool | import torch
from torch import nn
class LogSumExpPool(nn.Module):
def __init__(self, gamma):
super(LogSumExpPool, self).__init__()
self.gamma = gamma
def forward(self, feat_map):
"""
Numerically stable implementation of the operation
Arguments:
feat_map(Te... | 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... | alinstein/X_RAY | LogSumExpPool | false | 18,259 | [
"MIT"
] | 4 | 35a39761d3b11ce9e47509025054f25e5f26aab9 | https://github.com/alinstein/X_RAY/tree/35a39761d3b11ce9e47509025054f25e5f26aab9 |
BertPredictionHeadTransform | from _paritybench_helpers import _mock_config
import math
import torch
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT"s gelu is slightly different (and gives slightly different ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | adymaharana/VLCStoryGan | BertPredictionHeadTransform | false | 18,260 | [
"MIT"
] | 10 | 74112404689e8144c2ed2d375e1e5a1cde09debb | https://github.com/adymaharana/VLCStoryGan/tree/74112404689e8144c2ed2d375e1e5a1cde09debb |
NumPredictor | import torch
import torch.nn.functional as F
import torch.nn as nn
class NumPredictor(nn.Module):
def __init__(self, latent_dim):
self.latent_dim = latent_dim
super(NumPredictor, self).__init__()
self.reg_1 = nn.Linear(self.latent_dim, 1)
def forward(self, x):
x = F.relu(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_... | alibaba/FederatedScope | NumPredictor | false | 18,261 | [
"Apache-2.0"
] | 9 | fcf6d237624769ea094cfd68803901622f14fc23 | https://github.com/alibaba/FederatedScope/tree/fcf6d237624769ea094cfd68803901622f14fc23 |
CAModule | import torch
from torch import nn
class CAModule(nn.Module):
"""
Re-implementation of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
code reference:
https://github.com/kobiso/CBAM-keras/blob/master/models/attention_module.py
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | alinstein/X_RAY | CAModule | false | 18,262 | [
"MIT"
] | 4 | 35a39761d3b11ce9e47509025054f25e5f26aab9 | https://github.com/alinstein/X_RAY/tree/35a39761d3b11ce9e47509025054f25e5f26aab9 |
CmapPafHeadAttention | import torch
import torch.utils.data
import torch.nn
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ajsampathk/trt_pose | CmapPafHeadAttention | false | 18,263 | [
"MIT"
] | 7 | 592e038cacaf43b6a502b759a035a4e7cae9db9e | https://github.com/ajsampathk/trt_pose/tree/592e038cacaf43b6a502b759a035a4e7cae9db9e |
AveragedHausdorffLoss | import torch
import torch.nn as nn
def cdist(x, y):
"""
Input: x is a Nxd Tensor
y is a Mxd Tensor
Output: dist is a NxM matrix where dist[i,j] is the norm
between x[i,:] and y[j,:]
i.e. dist[i,j] = ||x[i,:]-y[j,:]||
"""
differences = x.unsqueeze(1) - y.unsqueeze(0)
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._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | adriangrepo/segmentl | AveragedHausdorffLoss | false | 18,264 | [
"MIT"
] | 5 | 9b520bf6cfd005eef9bba3db36ee6b3bb373b085 | https://github.com/adriangrepo/segmentl/tree/9b520bf6cfd005eef9bba3db36ee6b3bb373b085 |
GCN | import math
import torch
import torch.nn as nn
from torch.nn import functional as F
class GraphConvLayer(nn.Module):
""" A Graph Convolution Layer as per https://arxiv.org/pdf/1609.02907.pdf with Glorot initialisation """
def __init__(self, in_features: 'int', out_filters: 'int',
dropout_ratio: 'floa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | alecokas/swahili-text-gcn | GCN | false | 18,265 | [
"MIT"
] | 4 | 14b8196b30baac2a05c869a1f6c17a912d1adcea | https://github.com/alecokas/swahili-text-gcn/tree/14b8196b30baac2a05c869a1f6c17a912d1adcea |
ConvLayer | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
import torch.nn as nn
class ConvLayer(nn.Module):
"""Conv layer for qa output"""
def __init__(self, config):
"""
Args:
config (ModelArguments): ModelArguments
"""
super().__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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Amber-Chaeeunk/Open-Domain-Question-Answering | ConvLayer | false | 18,266 | [
"MIT"
] | 5 | 725e369a4409c54bf11bcfb9db53865d8fc1f935 | https://github.com/Amber-Chaeeunk/Open-Domain-Question-Answering/tree/725e369a4409c54bf11bcfb9db53865d8fc1f935 |
PatchEmbedding | import torch
from torch import Tensor
from torch import nn
class PatchEmbedding(nn.Module):
"""Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, embed_dim=768):
super().__init__()
assert img_size % patch_size == 0, 'Image size must be divisible by 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | alhamami/Object-Detection-And-Tracking | PatchEmbedding | false | 18,267 | [
"MIT"
] | 5 | a211a1dc103e812c539cd0ee16a2da4251943bed | https://github.com/alhamami/Object-Detection-And-Tracking/tree/a211a1dc103e812c539cd0ee16a2da4251943bed |
BertPooler | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class BertPooler(nn.Module):
def __init__(self, config):
super(BertPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Ahren09/FinerFact | BertPooler | false | 18,268 | [
"MIT"
] | 9 | 68df3799fbfadd56fa69b019ca6fba0c482f21d3 | https://github.com/Ahren09/FinerFact/tree/68df3799fbfadd56fa69b019ca6fba0c482f21d3 |
BertSelfOutput | from _paritybench_helpers import _mock_config
import torch
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
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).
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | adymaharana/VLCStoryGan | BertSelfOutput | false | 18,269 | [
"MIT"
] | 10 | 74112404689e8144c2ed2d375e1e5a1cde09debb | https://github.com/adymaharana/VLCStoryGan/tree/74112404689e8144c2ed2d375e1e5a1cde09debb |
ClassAttention | import torch
from torch import Tensor
from torch import nn
class ClassAttention(nn.Module):
"""ClassAttention as in CaiT
"""
def __init__(self, dim: 'int', heads: 'int'):
super().__init__()
self.num_heads = heads
self.scale = (dim // heads) ** -0.5
self.qkv = nn.Linear(dim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | alhamami/Object-Detection-And-Tracking | ClassAttention | false | 18,270 | [
"MIT"
] | 5 | a211a1dc103e812c539cd0ee16a2da4251943bed | https://github.com/alhamami/Object-Detection-And-Tracking/tree/a211a1dc103e812c539cd0ee16a2da4251943bed |
IdentityMappingZero | import torch
import torch.nn as nn
class IdentityMappingZero(nn.Module):
def __init__(self, out_channels: 'int', stride: 'int') ->None:
super(IdentityMappingZero, self).__init__()
self.out_channels = out_channels
self.stride = stride
pad_value = self.out_channels // 4
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 torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | alvarobartt/understanding-resnet | IdentityMappingZero | false | 18,271 | [
"MIT"
] | 6 | 1e95aba607bf3fead740affb9ceafb7fde3ee0c6 | https://github.com/alvarobartt/understanding-resnet/tree/1e95aba607bf3fead740affb9ceafb7fde3ee0c6 |
Neumann | import math
import torch
import torch.nn as nn
class Neumann(nn.Module):
def __init__(self, n_features, depth, residual_connection, mlp_depth,
init_type):
super().__init__()
self.depth = depth
self.n_features = n_features
self.residual_connection = residual_connection
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | alexprz/NeuMiss | Neumann | false | 18,272 | [
"BSD-3-Clause"
] | 9 | bf4f68ba4dd29b51ec3de9d6eef85deecebfa68d | https://github.com/alexprz/NeuMiss/tree/bf4f68ba4dd29b51ec3de9d6eef85deecebfa68d |
PositionwiseFeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedForward(nn.Module):
""" Two-layer position-wise feed-forward neural network. """
def __init__(self, d_in, d_hid, dropout=0.1, normalize_before=True):
super().__init__()
self.normalize_before = normalize_b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | alipay/Pyraformer | PositionwiseFeedForward | false | 18,273 | [
"Apache-2.0"
] | 7 | 84af4dbd93b7b96975b5034f0dde412005260123 | https://github.com/alipay/Pyraformer/tree/84af4dbd93b7b96975b5034f0dde412005260123 |
XCA | import torch
from torch import Tensor
import torch.nn.functional as F
from torch import nn
class XCA(nn.Module):
""" Cross-Covariance Attention (XCA) operation where the channels are updated using a weighted
sum. The weights are obtained from the (softmax normalized) Cross-covariance
matrix (Q^T K \\in d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | alhamami/Object-Detection-And-Tracking | XCA | false | 18,274 | [
"MIT"
] | 5 | a211a1dc103e812c539cd0ee16a2da4251943bed | https://github.com/alhamami/Object-Detection-And-Tracking/tree/a211a1dc103e812c539cd0ee16a2da4251943bed |
BackwardsNet | import torch
from torch import nn
class BackwardsNet(nn.Module):
def __init__(self, h, ydim):
super().__init__()
self.loss = torch.nn.CrossEntropyLoss()
self.fc1 = torch.nn.Linear(2 * h, h)
self.fc2 = torch.nn.Linear(h, ydim)
def forward(self, phiPrev, phi, atn):
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.... | alexandonian/neural-mmo | BackwardsNet | false | 18,275 | [
"MIT"
] | 4 | a4879c3399971ede81b64f507ee81706ba0d3366 | https://github.com/alexandonian/neural-mmo/tree/a4879c3399971ede81b64f507ee81706ba0d3366 |
QuadraticModel | import torch
class QuadraticModel(torch.nn.Module):
def __init__(self, in_channels, class_num):
super(QuadraticModel, self).__init__()
x = torch.ones((in_channels, 1))
self.x = torch.nn.parameter.Parameter(x.uniform_(-10.0, 10.0).float())
def forward(self, A):
return torch.su... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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_cu... | alibaba/FederatedScope | QuadraticModel | false | 18,276 | [
"Apache-2.0"
] | 9 | fcf6d237624769ea094cfd68803901622f14fc23 | https://github.com/alibaba/FederatedScope/tree/fcf6d237624769ea094cfd68803901622f14fc23 |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 3, kernel_size=7, stride=1, bias=False,
padding=3)
self.conv2 = nn.Conv2d(3, 3, kernel_size=7, stride=1, bias=False,
padding=3)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | alirezadavoudi/tensorflow-vs-pytorch | Net | false | 18,277 | [
"MIT"
] | 4 | 1c0ccda8004591f3f29d4787d7b3bbfbc397523f | https://github.com/alirezadavoudi/tensorflow-vs-pytorch/tree/1c0ccda8004591f3f29d4787d7b3bbfbc397523f |
BlurPool2d | import torch
from torch.nn import *
import torch.nn as nn
class BlurPool2d(nn.Sequential):
"""Blur Pooling Layer (MaxPool2d replacement)
See: https://richzhang.github.io/antialiased-cnns/
Paper: https://arxiv.org/abs/1904.11486
"""
__constants__ = ['in_features']
_blur_kernel = torch.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
from torch.nn import *
import... | aktgpt/brevis | BlurPool2d | false | 18,278 | [
"MIT"
] | 8 | 0c3dcabd241ea50cafbc2012250804e1ecb7555e | https://github.com/aktgpt/brevis/tree/0c3dcabd241ea50cafbc2012250804e1ecb7555e |
ResNetModel | import torch
from typing import Dict
from abc import abstractmethod
from torch import nn
import torch.nn.functional as F
class DetectionModel(nn.Module):
"""
Base class describing any single object detection model
"""
def __init__(self, params: '{}'):
self._params = params
assert para... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from typing import Dict
from ... | aethersis/VisualEyeTracker | ResNetModel | false | 18,279 | [
"MIT"
] | 7 | 53723bd68972954249b53d6ba0ac1cbe93b8844f | https://github.com/aethersis/VisualEyeTracker/tree/53723bd68972954249b53d6ba0ac1cbe93b8844f |
TopkMSELoss | import torch
class TopkMSELoss(torch.nn.Module):
def __init__(self, topk) ->None:
super().__init__()
self.topk = topk
self.criterion = torch.nn.MSELoss(reduction='none')
def forward(self, output, label):
losses = self.criterion(output, label).mean(2).mean(1)
losses = ... | 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... | alipay/Pyraformer | TopkMSELoss | false | 18,280 | [
"Apache-2.0"
] | 7 | 84af4dbd93b7b96975b5034f0dde412005260123 | https://github.com/alipay/Pyraformer/tree/84af4dbd93b7b96975b5034f0dde412005260123 |
MMTensorNorm | import torch
import torch.nn as nn
class MMTensorNorm(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
mean = torch.mean(x, dim=self.dim).unsqueeze(self.dim)
std = torch.std(x, dim=self.dim).unsqueeze(self.dim)
return (x - m... | 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_... | amaankhan02/ChaLearn-2021-LAP | MMTensorNorm | false | 18,281 | [
"Apache-2.0",
"MIT"
] | 5 | 73227d642ebd69c3bde4065f22c6ad99b0cbe9f4 | https://github.com/amaankhan02/ChaLearn-2021-LAP/tree/73227d642ebd69c3bde4065f22c6ad99b0cbe9f4 |
CPNLoss | import torch
import torch.nn.functional as F
from torch import nn
class CPNLoss(nn.Module):
"""This is the loss function used for Cascaded Pyramid Net. Note that the
original paper (arXiv:1711.07319) uses L2 loss. However the author (Shiyu)
who participated in the FashionAI Keypoints competition found tha... | 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.functi... | alwc/fashionAI-keypoints-detection-pytorch | CPNLoss | false | 18,282 | [
"Apache-2.0"
] | 7 | 92061f66d89283e62093990dcb2dbdb03b8fa676 | https://github.com/alwc/fashionAI-keypoints-detection-pytorch/tree/92061f66d89283e62093990dcb2dbdb03b8fa676 |
GeneratorLoss | import torch
from torch import nn
class GeneratorLoss(nn.Module):
"""
Generator (BCE) loss function
Args:
alpha (default: int=1): Coefficient by which map loss will be multiplied
beta (default: int=1): Coefficient by which point loss will be multiplied
"""
def __init__(self, alph... | 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 ... | akanametov/pathgan | GeneratorLoss | false | 18,283 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d |
ConvEncoder | import torch
import torch.nn as nn
from torch.nn import functional as F
class ConvEncoder(nn.Module):
def __init__(self, embedding_size, act_fn='relu'):
super().__init__()
self.act_fn = getattr(F, act_fn)
self.embedding_size = embedding_size
self.conv_1 = nn.Conv2d(3, 32, 4, strid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | alec-tschantz/planet | ConvEncoder | false | 18,284 | [
"MIT"
] | 7 | bf68722993c93129263bb9606a582d24cb4f2a58 | https://github.com/alec-tschantz/planet/tree/bf68722993c93129263bb9606a582d24cb4f2a58 |
PositionwiseFeedForward | import torch
import torch.nn as nn
class LayerNormalization(nn.Module):
""" Layer normalization module """
def __init__(self, d_hid, eps=0.001):
super(LayerNormalization, self).__init__()
self.eps = eps
self.a_2 = nn.Parameter(torch.ones(d_hid), requires_grad=True)
self.b_2 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | amaankhan02/ChaLearn-2021-LAP | PositionwiseFeedForward | false | 18,285 | [
"Apache-2.0",
"MIT"
] | 5 | 73227d642ebd69c3bde4065f22c6ad99b0cbe9f4 | https://github.com/amaankhan02/ChaLearn-2021-LAP/tree/73227d642ebd69c3bde4065f22c6ad99b0cbe9f4 |
MemoryUpdater | from _paritybench_helpers import _mock_config
import math
import torch
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | adymaharana/VLCStoryGan | MemoryUpdater | false | 18,286 | [
"MIT"
] | 10 | 74112404689e8144c2ed2d375e1e5a1cde09debb | https://github.com/adymaharana/VLCStoryGan/tree/74112404689e8144c2ed2d375e1e5a1cde09debb |
GaussianKullbackLeiblerLoss | import torch
import torch.nn as nn
class Loss(nn.Module):
"""Base loss class."""
def __init__(self):
super(Loss, self).__init__()
class GaussianKullbackLeiblerLoss(Loss):
"""Gaussian empirical KL divergence class."""
def __init__(self) ->None:
super(GaussianKullbackLeiblerLoss, 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... | aloctavodia/kulprit | GaussianKullbackLeiblerLoss | false | 18,287 | [
"MIT"
] | 4 | ab017074f7428154b8834515512db259c5f635e8 | https://github.com/aloctavodia/kulprit/tree/ab017074f7428154b8834515512db259c5f635e8 |
STLayer | import torch
class StraightThrough(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return torch.sign(x)
@staticmethod
def backward(ctx, grad):
return grad.clamp(-1.0, 1.0)
class STLayer(torch.nn.Module):
def __init__(self):
super(STLayer, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | alper111/affordance-learning | STLayer | false | 18,288 | [
"MIT"
] | 3 | 21b70f689a8299c6af7cd4ed763fc3133cf1681f | https://github.com/alper111/affordance-learning/tree/21b70f689a8299c6af7cd4ed763fc3133cf1681f |
AdaptiveGeneratorLoss | import torch
from torch import nn
class AdaptiveGeneratorLoss(nn.Module):
"""
Adaptive Generator (BCE) loss function (depends on losses of Discriminators)
Args:
alpha (default: int=3): Coefficient for map and point losses
"""
def __init__(self, alpha=3):
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, math as tl_math
from torch ... | akanametov/pathgan | AdaptiveGeneratorLoss | false | 18,289 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d |
MultiHeadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.2):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | alipay/Pyraformer | MultiHeadAttention | false | 18,290 | [
"Apache-2.0"
] | 7 | 84af4dbd93b7b96975b5034f0dde412005260123 | https://github.com/alipay/Pyraformer/tree/84af4dbd93b7b96975b5034f0dde412005260123 |
MultiheadConvAttention | import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.data
from torch.nn import Parameter
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class MultiheadConvAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | amaurySabran/fairseq | MultiheadConvAttention | false | 18,291 | [
"BSD-3-Clause"
] | 4 | e6d5dd36678224e8b06aa0e97749f7a1c20a9949 | https://github.com/amaurySabran/fairseq/tree/e6d5dd36678224e8b06aa0e97749f7a1c20a9949 |
DiceLoss | import torch
from torch import nn
class DiceLoss(nn.Module):
"""
DICE loss function
Args:
alpha (default: int=10): Coefficient in exp of sigmoid function
smooth (default: int=1): To prevent zero in nominator
"""
def __init__(self, alpha=10, smooth=1):
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 math as tl_math
from torch import nn
a... | akanametov/pathgan | DiceLoss | false | 18,292 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d |
KLDivergence | import torch
from torch import nn
def kl_divergence(px, py):
eps = 1e-08
kl_div = px * (torch.log(px + eps) - torch.log(py + eps))
return kl_div
class KLDivergence(nn.Module):
"""
Kullback–Leibler divergence
Args:
- None -
"""
def __init__(self):
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.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | akanametov/pathgan | KLDivergence | false | 18,293 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d |
BiDAFAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
def masked_softmax(logits, mask, dim=-1, log_softmax=False):
"""Take the softmax of `logits` over given dimension, and set
entries to 0 wherever `mask` is 0.
Args:
logits (torch.Tensor): Inputs to the softmax function.
mas... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | amankhullar/MMBiDAF | BiDAFAttention | false | 18,294 | [
"MIT"
] | 4 | 510a0c4f3bdeb7a84fb1554d8daee6b3fada3d61 | https://github.com/amankhullar/MMBiDAF/tree/510a0c4f3bdeb7a84fb1554d8daee6b3fada3d61 |
PixelwiseLossMSE | import torch
from torch import nn
class PixelwiseLossMSE(nn.Module):
"""
MSE loss function
Args:
alpha (default: int=20): Coefficient by which loss will be multiplied
"""
def __init__(self, alpha=20):
super().__init__()
self.alpha = alpha
def forward(self, fake, real... | 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... | akanametov/pathgan | PixelwiseLossMSE | false | 18,295 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d |
DiscriminatorLoss | import torch
from torch import nn
class DiscriminatorLoss(nn.Module):
"""
Discriminator (BCE) loss function
Args:
- None -
"""
def __init__(self):
super().__init__()
self.adv_criterion = nn.BCEWithLogitsLoss()
def forward(self, fake_pred, real_pred):
fake_tar... | 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 ... | akanametov/pathgan | DiscriminatorLoss | false | 18,296 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d |
Critic | import torch
import torch.nn.functional as f
from torch import nn
class Critic(nn.Module):
def __init__(self, input_dim):
super(Critic, self).__init__()
self._input_dim = input_dim
self.dense1 = nn.Linear(self._input_dim, self._input_dim)
self.dense2 = nn.Linear(self._input_dim, 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | amirarsalan90/TabFairGAN | Critic | false | 18,297 | [
"MIT"
] | 5 | 402c434e0aa7a335fda652a67e72b132edb5f663 | https://github.com/amirarsalan90/TabFairGAN/tree/402c434e0aa7a335fda652a67e72b132edb5f663 |
TimeEncode | import torch
import numpy as np
class TimeEncode(torch.nn.Module):
def __init__(self, dim):
super(TimeEncode, self).__init__()
self.dim = dim
self.w = torch.nn.Linear(1, dim)
self.w.weight = torch.nn.Parameter(torch.from_numpy(1 / 10 ** np.
linspace(0, 9, dim, dtype=np... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 numpy ... | amazon-research/tgl | TimeEncode | false | 18,298 | [
"Apache-2.0"
] | 9 | 5d852b8ae643b64b591a10dfbe8a1d10f696b200 | https://github.com/amazon-research/tgl/tree/5d852b8ae643b64b591a10dfbe8a1d10f696b200 |
GaussianKernel | import torch
from torch import nn
class GaussianKernel(nn.Module):
"""
Gaussian kernel module.
:param mu: Float, mean of the kernel.
:param sigma: Float, sigma of the kernel.
Examples:
>>> import torch
>>> kernel = GaussianKernel()
>>> x = torch.randn(4, 5, 10)
>>... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | amberhuang01/LearningFromFactCheckers | GaussianKernel | false | 18,299 | [
"MIT"
] | 9 | 3c21684709bf5e331c4585c7d62596960dd44732 | https://github.com/amberhuang01/LearningFromFactCheckers/tree/3c21684709bf5e331c4585c7d62596960dd44732 |
IoUnionLoss | import torch
from torch import nn
class IoUnionLoss(nn.Module):
"""
Intersection over Union loss function
Args:
alpha (default: int=10): Coefficient in exp of sigmoid function
smooth (default: int=1): To prevent zero in nominator
"""
def __init__(self, alpha=10, smooth=1):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | akanametov/pathgan | IoUnionLoss | false | 18,300 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d |
Discriminator | import math
import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, n_hidden):
super(Discriminator, self).__init__()
self.weight = nn.Parameter(torch.Tensor(n_hidden, n_hidden))
self.reset_parameters()
def uniform(self, size, tensor):
bound = 1.0... | 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
assert_size_stride = torch._C._dynamo.guards.a... | amazon-research/panrep | Discriminator | false | 18,301 | [
"Apache-2.0"
] | 10 | 57e6f71bb70c0908f3db28be97af0d818a863e19 | https://github.com/amazon-research/panrep/tree/57e6f71bb70c0908f3db28be97af0d818a863e19 |
EdgePredictor | import torch
class EdgePredictor(torch.nn.Module):
def __init__(self, dim_in):
super(EdgePredictor, self).__init__()
self.dim_in = dim_in
self.src_fc = torch.nn.Linear(dim_in, dim_in)
self.dst_fc = torch.nn.Linear(dim_in, dim_in)
self.out_fc = torch.nn.Linear(dim_in, 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
assert_size_stride = torch._C... | amazon-research/tgl | EdgePredictor | false | 18,302 | [
"Apache-2.0"
] | 9 | 5d852b8ae643b64b591a10dfbe8a1d10f696b200 | https://github.com/amazon-research/tgl/tree/5d852b8ae643b64b591a10dfbe8a1d10f696b200 |
MSE_loss | import torch
import torch.nn as nn
import torch.utils.data
import torch.optim
class MSE_loss(nn.Module):
def __init__(self):
super(MSE_loss, self).__init__()
def forward(self, prediction, gt, epoch=0):
err = prediction[:, 0:1] - gt
mask = (gt > 0).detach()
mse_loss = torch.me... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | alopezgit/project-adapt | MSE_loss | false | 18,303 | [
"MIT"
] | 8 | e93ab350344a5504f76f4e460002e0163996f88a | https://github.com/alopezgit/project-adapt/tree/e93ab350344a5504f76f4e460002e0163996f88a |
PixelwiseLossL1 | import torch
from torch import nn
class PixelwiseLossL1(nn.Module):
"""
L1 loss function
Args:
alpha (default: int=1): Coefficient by which loss will be multiplied
"""
def __init__(self, alpha=1):
super().__init__()
self.alpha = alpha
self.criterion = nn.L1Loss()
... | 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... | akanametov/pathgan | PixelwiseLossL1 | false | 18,304 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d |
RankingLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class RankingLoss(nn.Module):
def __init__(self, margin_lambda: 'float'=0.01) ->None:
super(RankingLoss, self).__init__()
self.margin_lambda = margin_lambda
def forward(self, candidates_scores: 'torch.Tensor', summary_scores:... | 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... | andrejmiscic/simcls-pytorch | RankingLoss | false | 18,305 | [
"MIT"
] | 5 | 516315c4b35955e4201677fc838f5f38a6e8fd54 | https://github.com/andrejmiscic/simcls-pytorch/tree/516315c4b35955e4201677fc838f5f38a6e8fd54 |
RankCrossEntropyLoss | import torch
import torch.nn.functional as F
from torch import nn
class RankCrossEntropyLoss(nn.Module):
"""Creates a criterion that measures rank cross entropy loss."""
__constants__ = ['num_neg']
def __init__(self, num_neg: 'int'=1):
"""
:class:`RankCrossEntropyLoss` constructor.
... | 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... | amberhuang01/LearningFromFactCheckers | RankCrossEntropyLoss | false | 18,306 | [
"MIT"
] | 9 | 3c21684709bf5e331c4585c7d62596960dd44732 | https://github.com/amberhuang01/LearningFromFactCheckers/tree/3c21684709bf5e331c4585c7d62596960dd44732 |
DecoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.2):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | alipay/Pyraformer | DecoderLayer | false | 18,307 | [
"Apache-2.0"
] | 7 | 84af4dbd93b7b96975b5034f0dde412005260123 | https://github.com/alipay/Pyraformer/tree/84af4dbd93b7b96975b5034f0dde412005260123 |
F | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
class F(nn.Module):
def __init__(self, input_size, hidden_size, output_size, learning_rate=
0.001):
super().__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 torch.nn as ... | amolk/AGI-experiments | F | false | 18,309 | [
"MIT"
] | 5 | ddb352c884d513ff4d9a843d0901699acb9e39b9 | https://github.com/amolk/AGI-experiments/tree/ddb352c884d513ff4d9a843d0901699acb9e39b9 |
LayerNormalization | import torch
import torch.nn as nn
class LayerNormalization(nn.Module):
def __init__(self, hidden_size, eps=1e-05):
super(LayerNormalization, self).__init__()
self.eps = eps
self.hidden_size = hidden_size
self.a2 = nn.Parameter(torch.ones(hidden_size), requires_grad=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 torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | analvikingur/RGAN | LayerNormalization | false | 18,310 | [
"MIT"
] | 8 | b1893c2f53d11c9173c7a30f63f6d93d72232493 | https://github.com/analvikingur/RGAN/tree/b1893c2f53d11c9173c7a30f63f6d93d72232493 |
AdaIN | import torch
import torch.optim
class AdaIN(torch.nn.Module):
def __init__(self, epsilon: 'float'=1e-05):
super(AdaIN, self).__init__()
self.epsilon = epsilon
def calc_vector_mean_std(self, x):
std = torch.sqrt(torch.var(x, dim=1) + self.epsilon)
mean = torch.mean(x, dim=1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_str... | ai-in-motion/moai | AdaIN | false | 18,311 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
Downsample2d | import functools
import torch
import torch.optim
class Downsample2d(torch.nn.Module):
def __init__(self, scale: 'float'=0.5, mode: 'str'='bilinear'):
super(Downsample2d, self).__init__()
self.downsample = functools.partial(torch.nn.functional.interpolate,
scale_factor=scale, mode=mode... | 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.optim
assert_size_stride = torch._C._dynamo.guards.assert_s... | ai-in-motion/moai | Downsample2d | false | 18,312 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
SoftArgmax | import torch
import torch as t
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class SoftArgmax(nn.Module):
def __init__(self, temperature=0.001):
super(SoftArgmax, self).__init__()
self.temperature = temperature
def forward(self, input, sampling=Fal... | 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 as t
impo... | analvikingur/RGAN | SoftArgmax | false | 18,313 | [
"MIT"
] | 8 | b1893c2f53d11c9173c7a30f63f6d93d72232493 | https://github.com/analvikingur/RGAN/tree/b1893c2f53d11c9173c7a30f63f6d93d72232493 |
EncoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.2):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | alipay/Pyraformer | EncoderLayer | false | 18,314 | [
"Apache-2.0"
] | 7 | 84af4dbd93b7b96975b5034f0dde412005260123 | https://github.com/alipay/Pyraformer/tree/84af4dbd93b7b96975b5034f0dde412005260123 |
MinusOne | import torch
import torch.optim
class MinusOne(torch.nn.Module):
def __init__(self):
super(MinusOne, self).__init__()
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
return x - 1.0
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.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | MinusOne | false | 18,315 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
KL | import torch
import torch.optim
class KL(torch.nn.KLDivLoss):
def __init__(self, is_input_log: 'bool'=False, is_target_log: 'bool'=False
):
super(KL, self).__init__(reduction='none', log_target=is_target_log)
self.is_input_log = is_input_log
def forward(self, gt: 'torch.Tensor', pred... | 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.optim
assert_size_stride = torch._C._dynamo.guard... | ai-in-motion/moai | KL | false | 18,316 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
GemanMcClure | import torch
import torch.optim
class L2(torch.nn.Module):
def __init__(self):
super(L2, self).__init__()
def forward(self, pred: 'torch.Tensor', gt: 'torch.Tensor'=None,
weights: 'torch.Tensor'=None, mask: 'torch.Tensor'=None
) ->torch.Tensor:
l2 = (gt - pred) ** 2 if gt is ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | GemanMcClure | false | 18,317 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
SplitAndConcat | import torch
import torch.nn as nn
import torch.quantization.quantize_fx
import torch.utils.data
class SplitAndConcat(nn.Module):
"""Split the data from split_dim and concatenate in concat_dim.
@param split_dim from which axis the data will be chunk
@param concat_dim to which axis the data will be concat... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.quantization.quantize_fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | ananthsub/d2go | SplitAndConcat | false | 18,318 | [
"Apache-2.0"
] | 3 | 8c3618d9e73518d32350ab4e6d0fb6509c9e08b6 | https://github.com/ananthsub/d2go/tree/8c3618d9e73518d32350ab4e6d0fb6509c9e08b6 |
MatchModule | import torch
import torch.nn.functional as F
from torch import nn
class MatchModule(nn.Module):
"""
Computing the match representation for Match LSTM.
:param hidden_size: Size of hidden vectors.
:param dropout_rate: Dropout rate of the projection layer. Defaults to 0.
Examples:
>>> impor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | amberhuang01/LearningFromFactCheckers | MatchModule | false | 18,319 | [
"MIT"
] | 9 | 3c21684709bf5e331c4585c7d62596960dd44732 | https://github.com/amberhuang01/LearningFromFactCheckers/tree/3c21684709bf5e331c4585c7d62596960dd44732 |
ClassAttentionBlock | import torch
from torch import Tensor
from torch import nn
class MLP(nn.Module):
def __init__(self, dim, hidden_dim, out_dim=None) ->None:
super().__init__()
out_dim = out_dim or dim
self.fc1 = nn.Linear(dim, hidden_dim)
self.act = nn.GELU()
self.fc2 = nn.Linear(hidden_dim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | alhamami/Object-Detection-And-Tracking | ClassAttentionBlock | false | 18,320 | [
"MIT"
] | 5 | a211a1dc103e812c539cd0ee16a2da4251943bed | https://github.com/alhamami/Object-Detection-And-Tracking/tree/a211a1dc103e812c539cd0ee16a2da4251943bed |
Clamp | import torch
import torch.optim
class Clamp(torch.nn.Module):
min_value: 'float'
max_value: 'float'
def __init__(self, min_value: 'float'=0.0, max_value: 'float'=1.0):
super(Clamp, self).__init__()
self.min_value = min_value
self.max_value = max_value
def forward(self, x: 'to... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_... | ai-in-motion/moai | Clamp | false | 18,321 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
MAE_loss | import torch
import torch.nn as nn
import torch.utils.data
import torch.optim
class MAE_loss(nn.Module):
def __init__(self):
super(MAE_loss, self).__init__()
def forward(self, prediction, gt, epoch=0):
prediction = prediction[:, 0:1]
abs_err = torch.abs(prediction - gt)
mask ... | 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
import torch.optim
assert_s... | alopezgit/project-adapt | MAE_loss | false | 18,322 | [
"MIT"
] | 8 | e93ab350344a5504f76f4e460002e0163996f88a | https://github.com/alopezgit/project-adapt/tree/e93ab350344a5504f76f4e460002e0163996f88a |
CosineDistance | import torch
import numpy as np
import torch.optim
def _acos_safe(x: 'torch.Tensor', eps: 'float'=0.0001):
slope = np.arccos(1.0 - eps) / eps
buf = torch.empty_like(x)
good = torch.abs(x) <= 1.0 - eps
bad = ~good
sign = torch.sign(x[bad])
buf[good] = torch.acos(x[good])
buf[bad] = torch.ac... | 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 numpy as np
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | ai-in-motion/moai | CosineDistance | false | 18,323 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
Discriminator2d | import torch
import torch.nn as nn
import torch.utils.data
import torch
class Discriminator2d(nn.Module):
def __init__(self, ngpu, wd, nc_d):
super(Discriminator2d, self).__init__()
self.ngpu = ngpu
self.conv0 = nn.Conv2d(nc_d, 2 ** (wd - 4), 4, 2, 1)
self.conv1 = nn.Conv2d(2 ** (... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | amirDahari1/SuperRes | Discriminator2d | false | 18,324 | [
"MIT"
] | 6 | 6e7500b803136d6a60d1571630b16e81bec5f888 | https://github.com/amirDahari1/SuperRes/tree/6e7500b803136d6a60d1571630b16e81bec5f888 |
Lambda | import torch
import torch.optim
class KL(torch.nn.KLDivLoss):
def __init__(self, is_input_log: 'bool'=False, is_target_log: 'bool'=False
):
super(KL, self).__init__(reduction='none', log_target=is_target_log)
self.is_input_log = is_input_log
def forward(self, gt: 'torch.Tensor', pred... | 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.optim
assert_size_stride = torch._C._dynamo.guards.assert_si... | ai-in-motion/moai | Lambda | false | 18,325 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
Dense | import math
import torch
import torch.nn as nn
from string import ascii_lowercase
import torch.optim
class Dense(nn.Module):
def __init__(self, input_features, output_features=None):
super(Dense, self).__init__()
self.input_features = input_features
self.output_features = (input_features ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
from string import ascii_lowercase
import torc... | andrew-xu-monash/UMM-Modified | Dense | false | 18,326 | [
"Apache-2.0"
] | 4 | 18729dc34733c203e8cd3873fec2b9f7d0b56dba | https://github.com/andrew-xu-monash/UMM-Modified/tree/18729dc34733c203e8cd3873fec2b9f7d0b56dba |
MSE_log_loss | import torch
import torch.nn as nn
import torch.utils.data
import torch.optim
class MSE_log_loss(nn.Module):
def __init__(self):
super(MSE_log_loss, self).__init__()
def forward(self, prediction, gt):
prediction = torch.clamp(prediction, min=0)
err = torch.log(prediction + 1e-06) - t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | alopezgit/project-adapt | MSE_log_loss | false | 18,327 | [
"MIT"
] | 8 | e93ab350344a5504f76f4e460002e0163996f88a | https://github.com/alopezgit/project-adapt/tree/e93ab350344a5504f76f4e460002e0163996f88a |
AngleError | import torch
import torch.optim
def _angular_error(gt: 'torch.Tensor', pred: 'torch.Tensor', radians: 'bool'):
relative = gt @ torch.transpose(pred, -2, -1)
trace = relative[:, 0, 0] + relative[:, 1, 1] + relative[:, 2, 2]
trace = torch.clamp(trace, -1.0, 3.0)
phi = 0.5 * (trace - 1.0)
return phi.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ai-in-motion/moai | AngleError | false | 18,328 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
VisibilityFOV | import torch
import torch.optim
class VisibilityFOV(torch.nn.Module):
def __init__(self, width: 'int'=1, height: 'int'=1, coord_type: 'str'=
'coord'):
super(VisibilityFOV, self).__init__()
self.coord_type = coord_type
self.width = width
self.height = height
def forwar... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | VisibilityFOV | false | 18,329 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
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