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 |
|---|---|---|---|---|---|---|---|---|---|---|
Normalization | import torch
from torch import nn
class Normalization(nn.Module):
def __init__(self, mean=torch.zeros(3), std=torch.ones(3)):
super(Normalization, self).__init__()
self.mean = nn.Parameter(mean.view(-1, 1, 1), requires_grad=False)
self.std = nn.Parameter(std.view(-1, 1, 1), requires_grad=... | 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... | asjir/adain | Normalization | false | 6,265 | [
"MIT"
] | 1 | 1d0f70f161e485ce61ea57ab619d66e8f4ccadde | https://github.com/asjir/adain/tree/1d0f70f161e485ce61ea57ab619d66e8f4ccadde |
BCELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BCELoss(nn.Module):
"""Binary Cross Entropy loss."""
def __init__(self, use_target_weight=False, loss_weight=1.0):
super().__init__()
self.criterion = F.binary_cross_entropy
self.use_target_weight = use_target_we... | 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... | atoaiari/mmpose | BCELoss | false | 6,266 | [
"Apache-2.0"
] | 1 | 256a9117767008e8c33b4038a346aca12233e300 | https://github.com/atoaiari/mmpose/tree/256a9117767008e8c33b4038a346aca12233e300 |
KLDLossWithStandardGaussian | import torch
import torch.nn as nn
import torch.utils.data
class KLDLossWithStandardGaussian(nn.Module):
def forward(self, mu, logvar):
return -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | atmacvit/meronymnet | KLDLossWithStandardGaussian | false | 6,267 | [
"MIT"
] | 1 | 47e1a7caadc0f770439bb26a93b885f790f62804 | https://github.com/atmacvit/meronymnet/tree/47e1a7caadc0f770439bb26a93b885f790f62804 |
KLDLossWithStandardGaussianNoReduction | import torch
import torch.nn as nn
import torch.utils.data
class KLDLossWithStandardGaussianNoReduction(nn.Module):
def forward(self, mu, logvar):
KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=-1)
return KLD
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand(... | 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
assert_size_stride = torch.... | atmacvit/meronymnet | KLDLossWithStandardGaussianNoReduction | false | 6,268 | [
"MIT"
] | 1 | 47e1a7caadc0f770439bb26a93b885f790f62804 | https://github.com/atmacvit/meronymnet/tree/47e1a7caadc0f770439bb26a93b885f790f62804 |
MSELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class MSELoss(nn.Module):
"""MSE loss for coordinate regression."""
def __init__(self, use_target_weight=False, loss_weight=1.0):
super().__init__()
self.criterion = F.mse_loss
self.use_target_weight = use_target_weigh... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dyna... | atoaiari/mmpose | MSELoss | false | 6,269 | [
"Apache-2.0"
] | 1 | 256a9117767008e8c33b4038a346aca12233e300 | https://github.com/atoaiari/mmpose/tree/256a9117767008e8c33b4038a346aca12233e300 |
SpatialEmbedding | import torch
import torch.nn
class SpatialEmbedding(torch.nn.Module):
def __init__(self, in_features, out_features, weight_multiplier=1.0):
super(SpatialEmbedding, self).__init__()
self.b = torch.zeros((in_features, out_features))
self.b.normal_(0, weight_multiplier)
self.b = torc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ashwinpn/Computer-Vision | SpatialEmbedding | false | 6,270 | [
"MIT"
] | 1 | 9dc3abfe416385171b76e2bad6872e10f36a12b4 | https://github.com/ashwinpn/Computer-Vision/tree/9dc3abfe416385171b76e2bad6872e10f36a12b4 |
KLDLoss | import torch
import torch.nn as nn
import torch.utils.data
class KLDLoss(nn.Module):
def forward(self, mu1, logvar1, mu2, logvar2):
batch_size = mu1.shape[0]
sigma1 = logvar1.mul(0.5).exp()
sigma2 = logvar2.mul(0.5).exp()
kld = torch.log(sigma2 / sigma1 + 1e-08) + (torch.exp(logva... | 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
... | atmacvit/meronymnet | KLDLoss | false | 6,271 | [
"MIT"
] | 1 | 47e1a7caadc0f770439bb26a93b885f790f62804 | https://github.com/atmacvit/meronymnet/tree/47e1a7caadc0f770439bb26a93b885f790f62804 |
CuboidPoseHead | import torch
import torch.nn as nn
import torch.nn.functional as F
class CuboidPoseHead(nn.Module):
def __init__(self, beta):
"""Get results from the 3D human pose heatmap. Instead of obtaining
maximums on the heatmap, this module regresses the coordinates of
keypoints via integral pose r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | atoaiari/mmpose | CuboidPoseHead | false | 6,272 | [
"Apache-2.0"
] | 1 | 256a9117767008e8c33b4038a346aca12233e300 | https://github.com/atoaiari/mmpose/tree/256a9117767008e8c33b4038a346aca12233e300 |
KLDLossNoReduction | import torch
import torch.nn as nn
import torch.utils.data
class KLDLossNoReduction(nn.Module):
def forward(self, mu1, logvar1, mu2, logvar2):
sigma1 = logvar1.mul(0.5).exp()
sigma2 = logvar2.mul(0.5).exp()
kld = torch.log(sigma2 / sigma1 + 1e-08) + (torch.exp(logvar1) + (
mu1... | 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
assert_size_stride = torch.... | atmacvit/meronymnet | KLDLossNoReduction | false | 6,273 | [
"MIT"
] | 1 | 47e1a7caadc0f770439bb26a93b885f790f62804 | https://github.com/atmacvit/meronymnet/tree/47e1a7caadc0f770439bb26a93b885f790f62804 |
SimpleSpatialEmbedding | import torch
import torch.nn
class SimpleSpatialEmbedding(torch.nn.Module):
def __init__(self, in_features, out_features, weight_multiplier=1.0):
super(SimpleSpatialEmbedding, self).__init__()
self.b = torch.zeros((in_features, out_features))
self.b.normal_(0, weight_multiplier)
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.triton_helpers import math as tl_math
import torch.... | ashwinpn/Computer-Vision | SimpleSpatialEmbedding | false | 6,274 | [
"MIT"
] | 1 | 9dc3abfe416385171b76e2bad6872e10f36a12b4 | https://github.com/ashwinpn/Computer-Vision/tree/9dc3abfe416385171b76e2bad6872e10f36a12b4 |
MPJPELoss | import torch
import torch.nn as nn
class MPJPELoss(nn.Module):
"""MPJPE (Mean Per Joint Position Error) loss.
Args:
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the loss. Default:... | 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_... | atoaiari/mmpose | MPJPELoss | false | 6,275 | [
"Apache-2.0"
] | 1 | 256a9117767008e8c33b4038a346aca12233e300 | https://github.com/atoaiari/mmpose/tree/256a9117767008e8c33b4038a346aca12233e300 |
SelfGate | import torch
import torch.nn as nn
from torch.nn import functional as F
class SelfGate(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x = self.proj(x)
x, gate = x.chunk(2, dim=-1)
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.triton_helpers import libdevice, math as tl_math
im... | awesome-archive/AI-Writer | SelfGate | false | 6,276 | [
"BSD-3-Clause"
] | 1 | abdcd5582f81fca2f677a020360654865bf82065 | https://github.com/awesome-archive/AI-Writer/tree/abdcd5582f81fca2f677a020360654865bf82065 |
GELU | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
class GELU(nn.Module):
def __init__(self):
super(GELU, self).__init__()
def forward(self, x):
return 0.5 * x * (1 + F.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 *
torch.pow(x, 3))))
def get_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | au55555/classification-pytorch | GELU | false | 6,277 | [
"MIT"
] | 1 | 1937599ae6e688ed7af7470f69964fb6f97241c4 | https://github.com/au55555/classification-pytorch/tree/1937599ae6e688ed7af7470f69964fb6f97241c4 |
Selection | import torch
import torch.nn as nn
class Selection(nn.Module):
"""
Selection neurons to sample from a latent representation for a decoder agent.
An abstract representation :math:`l_i` is disturbed by a value :math:`r_i` sampled from a normal
standard distribution which is scaled by the selection neur... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | aswanthkrishna/reinforced_scinet | Selection | false | 6,278 | [
"Apache-2.0"
] | 1 | b520f0c73bb1cdf0d0595f0df32372c96946d963 | https://github.com/aswanthkrishna/reinforced_scinet/tree/b520f0c73bb1cdf0d0595f0df32372c96946d963 |
SmoothL1Loss | import torch
import torch.nn as nn
import torch.nn.functional as F
class SmoothL1Loss(nn.Module):
"""SmoothL1Loss loss.
Args:
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the loss... | 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
... | atoaiari/mmpose | SmoothL1Loss | false | 6,279 | [
"Apache-2.0"
] | 1 | 256a9117767008e8c33b4038a346aca12233e300 | https://github.com/atoaiari/mmpose/tree/256a9117767008e8c33b4038a346aca12233e300 |
L1Loss | import torch
import torch.nn as nn
import torch.nn.functional as F
class L1Loss(nn.Module):
"""L1Loss loss ."""
def __init__(self, use_target_weight=False, loss_weight=1.0):
super().__init__()
self.criterion = F.l1_loss
self.use_target_weight = use_target_weight
self.loss_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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | atoaiari/mmpose | L1Loss | false | 6,280 | [
"Apache-2.0"
] | 1 | 256a9117767008e8c33b4038a346aca12233e300 | https://github.com/atoaiari/mmpose/tree/256a9117767008e8c33b4038a346aca12233e300 |
Postnet | import torch
from torch import nn
class Postnet(nn.Module):
"""Postnet is a simple linear layer for predicting the target frames given the
RNN context during training. We don't need the Postnet for feature extraction.
"""
def __init__(self, input_size, output_size=80):
super(Postnet, 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | aviasd/Mockingjay-Speech-Representation | Postnet | false | 6,281 | [
"MIT"
] | 1 | c01aef3f98bbb3fd4b0fc1b61e77fb5d02a0e453 | https://github.com/aviasd/Mockingjay-Speech-Representation/tree/c01aef3f98bbb3fd4b0fc1b61e77fb5d02a0e453 |
DivideMax | import torch
from torch import nn
import torch.utils.data
class DivideMax(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
maxes = x.amax(dim=self.dim, keepdim=True)
return x / maxes
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 import triton_helpers
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards... | avihu111/viewpoint_disentanglement | DivideMax | false | 6,282 | [
"MIT"
] | 1 | 07aa4e119426a500fb1e5b5929909cd791982f27 | https://github.com/avihu111/viewpoint_disentanglement/tree/07aa4e119426a500fb1e5b5929909cd791982f27 |
Mlp | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
class GELU(nn.Module):
def __init__(self):
super(GELU, self).__init__()
def forward(self, x):
return 0.5 * x * (1 + F.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class Mlp(nn.M... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | au55555/classification-pytorch | Mlp | false | 6,283 | [
"MIT"
] | 1 | 1937599ae6e688ed7af7470f69964fb6f97241c4 | https://github.com/au55555/classification-pytorch/tree/1937599ae6e688ed7af7470f69964fb6f97241c4 |
AllocatingLayer | from torch.nn import Module
import torch
from torch.nn.modules.module import Module
class AllocatingLayer(Module):
"""The actor NN base its output for the case of full CSI on a continuous relaxation of the problem. Specifically it gives
a value for every user. This layer will start allocating to the most val... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch.nn.modules.module import Module
assert_si... | avranasa/DRL_Scheduling_Communications | AllocatingLayer | false | 6,284 | [
"MIT"
] | 1 | 2e6cb3a9599e43b73547f4281d82b1e5999271b7 | https://github.com/avranasa/DRL_Scheduling_Communications/tree/2e6cb3a9599e43b73547f4281d82b1e5999271b7 |
TauSTE | from torch.nn import Module
import torch
from typing import Any
import torch.nn.functional as F
class TauSTEFunction(torch.autograd.Function):
@staticmethod
def forward(ctx: 'Any', tau_threshold: 'float', input: 'Any') ->Any:
return (input > tau_threshold).float()
@staticmethod
def backward(... | 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.nn import Module
from typing import Any
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_... | atreyasha/spp-explainability | TauSTE | false | 6,285 | [
"MIT"
] | 1 | c959b837591cc1980d057a67f682e00b1f3e8e37 | https://github.com/atreyasha/spp-explainability/tree/c959b837591cc1980d057a67f682e00b1f3e8e37 |
RoutingCapsules | import torch
import torch.nn as nn
import torch.nn.functional as F
def squash(x, dim=-1, epsilon=1e-08):
norm = (x ** 2).sum(dim=dim, keepdim=True)
x = norm / (norm + 1) * x / (torch.sqrt(norm) + epsilon)
return x
class RoutingCapsules(nn.Module):
"""
input
capsules_num: new feature, num... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ashawkey/CapsNet.pytorch | RoutingCapsules | false | 6,286 | [
"MIT"
] | 1 | 3b796b572bbabe79cc445c35913cd3584733aedf | https://github.com/ashawkey/CapsNet.pytorch/tree/3b796b572bbabe79cc445c35913cd3584733aedf |
MaxMarginRankingLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch as th
import torch.optim
import torch.utils.data
class MaxMarginRankingLoss(nn.Module):
def __init__(self, margin=1):
super(MaxMarginRankingLoss, self).__init__()
self.loss = th.nn.MarginRankingLoss(margin)
se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch as th
import torch.optim
import torch.utils.data
asser... | awesome-archive/Video-to-Online-Platform | MaxMarginRankingLoss | false | 6,287 | [
"Apache-2.0"
] | 1 | 4f91724133a817e79bce91e0abbd46cf38a31167 | https://github.com/awesome-archive/Video-to-Online-Platform/tree/4f91724133a817e79bce91e0abbd46cf38a31167 |
WingLoss | import math
import torch
import torch.nn as nn
class WingLoss(nn.Module):
"""Wing Loss. paper ref: 'Wing Loss for Robust Facial Landmark Localisation
with Convolutional Neural Networks' Feng et al. CVPR'2018.
Args:
omega (float): Also referred to as width.
epsilon (float): Also referred t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | atoaiari/mmpose | WingLoss | false | 6,288 | [
"Apache-2.0"
] | 1 | 256a9117767008e8c33b4038a346aca12233e300 | https://github.com/atoaiari/mmpose/tree/256a9117767008e8c33b4038a346aca12233e300 |
SoftWingLoss | import math
import torch
import torch.nn as nn
class SoftWingLoss(nn.Module):
"""Soft Wing Loss 'Structure-Coherent Deep Feature Learning for Robust Face
Alignment' Lin et al. TIP'2021.
loss =
1. |x| , if |x| < omega1
2. omega2*ln(1+|x|/epsilon) + B, if |x| >= om... | 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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | atoaiari/mmpose | SoftWingLoss | false | 6,289 | [
"Apache-2.0"
] | 1 | 256a9117767008e8c33b4038a346aca12233e300 | https://github.com/atoaiari/mmpose/tree/256a9117767008e8c33b4038a346aca12233e300 |
SSLoss | import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
import torch
def sum_tensor(inp, axes, keepdim=False):
axes = np.unique(axes).astype(int)
if keepdim:
for ax in axes:
inp = inp.sum(int(ax), keepdim=True)
else:
for ax in sorted(axes, reverse=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
import numpy as np
import torch.nn as nn
import torch.utils.data
import torch
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | ayanglab/HDL | SSLoss | false | 6,290 | [
"Apache-2.0"
] | 1 | 5ff778d713331671ffa85e9fb63378d8c0a57769 | https://github.com/ayanglab/HDL/tree/5ff778d713331671ffa85e9fb63378d8c0a57769 |
ScaledDotProductAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
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.... | awesome-archive/FEAT | ScaledDotProductAttention | false | 6,291 | [
"MIT"
] | 1 | 940d525fcbf2a40528d284392a03e4b0193344a7 | https://github.com/awesome-archive/FEAT/tree/940d525fcbf2a40528d284392a03e4b0193344a7 |
GraphConvolution | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, dropout=0.3):
super(GraphConvolution, self).__init__()
self.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
assert_... | ayyyq/T-LSTM | GraphConvolution | false | 6,292 | [
"MIT"
] | 1 | 36dbc88ac710d3925851cd87c2368ecfc7061b70 | https://github.com/ayyyq/T-LSTM/tree/36dbc88ac710d3925851cd87c2368ecfc7061b70 |
LayerNorm | import torch
import torch.nn as nn
import torch.optim
class LayerNorm(nn.Module):
"""A Layer Normalization layer.
Lei Ba, Jimmy, Jamie Ryan Kiros, and Geoffrey E. Hinton.
arXiv preprint arXiv:1607.06450 (2016).
"""
def __init__(self, dim):
super(LayerNorm, self).__init__()
s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.... | awesome-archive/nmtpytorch | LayerNorm | false | 6,293 | [
"MIT"
] | 1 | 7c0ea21b29fc85a1f30ef4400d62b9d8e3d88be4 | https://github.com/awesome-archive/nmtpytorch/tree/7c0ea21b29fc85a1f30ef4400d62b9d8e3d88be4 |
AttentionModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttentionModule(nn.Module):
""" A neural module that takes a feature map, attends to the features, and
produces an attention.
"""
def __init__(self, dim):
super().__init__()
self.conv1 = nn.Conv2d(dim, dim, kerne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | aymenx17/ShapeCount | AttentionModule | false | 6,294 | [
"Apache-2.0"
] | 1 | 6d2fb780684335ccd0127b3084bf40674203bcf1 | https://github.com/aymenx17/ShapeCount/tree/6d2fb780684335ccd0127b3084bf40674203bcf1 |
GDiceLossV2 | import torch
import torch.nn as nn
import torch.utils.data
import torch
from torch.autograd import Variable
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
import torch
assert_size_stride = torch._C.... | ayanglab/HDL | GDiceLossV2 | false | 6,295 | [
"Apache-2.0"
] | 1 | 5ff778d713331671ffa85e9fb63378d8c0a57769 | https://github.com/ayanglab/HDL/tree/5ff778d713331671ffa85e9fb63378d8c0a57769 |
BCELoss | import torch
import torch.nn as nn
class BCELoss(nn.BCELoss):
def __init__(self, **kwargs):
super(BCELoss, self).__init__(**kwargs)
def forward(self, input, target):
input = input.squeeze(1)
target = target.float()
return super(BCELoss, self).forward(input, target)
def get_... | 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... | azxj/BRRNet | BCELoss | false | 6,296 | [
"MIT"
] | 1 | 274068efd5453f2c1fb07bfaad448d048b9c793b | https://github.com/azxj/BRRNet/tree/274068efd5453f2c1fb07bfaad448d048b9c793b |
LogLoss | import torch
from torch.nn import MSELoss
class LogLoss(MSELoss):
def __init__(self):
super(LogLoss, self).__init__()
self.loss = torch.nn.MSELoss()
self.loss2 = torch.nn.MSELoss()
def forward(self, input, target):
tgt = torch.atan(target)
inp = torch.atan(input)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import MSELoss... | aykuttasil/mindsdb | LogLoss | false | 6,297 | [
"MIT"
] | 1 | 2c36b6f75f13d7104fe4d3dbb7ca307fa84f45ad | https://github.com/aykuttasil/mindsdb/tree/2c36b6f75f13d7104fe4d3dbb7ca307fa84f45ad |
QueryModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class QueryModule(nn.Module):
""" A neural module that takes as input a feature map and an attention and produces a feature
map as output.
"""
def __init__(self, dim):
super().__init__()
self.conv1 = nn.Conv2d(dim, 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
import torch.nn as nn
assert_... | aymenx17/ShapeCount | QueryModule | false | 6,298 | [
"Apache-2.0"
] | 1 | 6d2fb780684335ccd0127b3084bf40674203bcf1 | https://github.com/aymenx17/ShapeCount/tree/6d2fb780684335ccd0127b3084bf40674203bcf1 |
GCN | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, dropout=0.3):
super(GraphConvolution, self).__init__()
self.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 ... | ayyyq/T-LSTM | GCN | false | 6,299 | [
"MIT"
] | 1 | 36dbc88ac710d3925851cd87c2368ecfc7061b70 | https://github.com/ayyyq/T-LSTM/tree/36dbc88ac710d3925851cd87c2368ecfc7061b70 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 94 * 94, 120)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | arefmalek/Demographics_Disenfranchisement | Net | false | 6,300 | [
"MIT"
] | 1 | f4ae8c0965cf1b1cab9b245c3f5f54d3b5fe9aba | https://github.com/arefmalek/Demographics_Disenfranchisement/tree/f4ae8c0965cf1b1cab9b245c3f5f54d3b5fe9aba |
ExgLayer | import torch
import torch.nn as nn
class ExgLayer(nn.Module):
def __init__(self, x_size, h_size, g_size, out_size):
super(ExgLayer, self).__init__()
self.h_size = h_size
self.g_size = g_size
self.out_size = out_size
self.x_size = x_size
self.linear_x2 = nn.Linear(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ayyyq/T-LSTM | ExgLayer | false | 6,301 | [
"MIT"
] | 1 | 36dbc88ac710d3925851cd87c2368ecfc7061b70 | https://github.com/ayyyq/T-LSTM/tree/36dbc88ac710d3925851cd87c2368ecfc7061b70 |
myCustomModel | import logging
import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class BaseModel(nn.Module):
"""
Base class for all models
All models require an initialization 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.... | awoloshuk/NephNet | myCustomModel | false | 6,302 | [
"MIT"
] | 1 | 562431364874fef1680069c7a5235c67b96504b8 | https://github.com/awoloshuk/NephNet/tree/562431364874fef1680069c7a5235c67b96504b8 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, smooth=1.0):
super(DiceLoss, self).__init__()
self.smooth = smooth
def forward(self, input, target):
n = input.shape[0]
input = input.view(n, -1)
target = target.view(n, -1)
inter... | 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... | azxj/BRRNet | DiceLoss | false | 6,303 | [
"MIT"
] | 1 | 274068efd5453f2c1fb07bfaad448d048b9c793b | https://github.com/azxj/BRRNet/tree/274068efd5453f2c1fb07bfaad448d048b9c793b |
CombinedTargetMSELoss | import torch
import torch.nn as nn
class CombinedTargetMSELoss(nn.Module):
"""MSE loss for combined target.
CombinedTarget: The combination of classification target
(response map) and regression target (offset map).
Paper ref: Huang et al. The Devil is in the Details: Delving into
... | 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... | atoaiari/mmpose | CombinedTargetMSELoss | false | 6,304 | [
"Apache-2.0"
] | 1 | 256a9117767008e8c33b4038a346aca12233e300 | https://github.com/atoaiari/mmpose/tree/256a9117767008e8c33b4038a346aca12233e300 |
DiceLoss | import torch
import torch.nn as nn
import torch.utils.data
import torch
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, pred, target):
pred = pred.squeeze(dim=1)
dice = 2 * (pred * target).sum(dim=1).sum(dim=1).sum(dim=1) / (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
import torch.nn as nn
import torch.utils.data
import torch
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | ayanglab/HDL | DiceLoss | false | 6,305 | [
"Apache-2.0"
] | 1 | 5ff778d713331671ffa85e9fb63378d8c0a57769 | https://github.com/ayanglab/HDL/tree/5ff778d713331671ffa85e9fb63378d8c0a57769 |
SigmoidBCELoss | import torch
import torch.nn as nn
class SigmoidBCELoss(nn.BCEWithLogitsLoss):
def __init__(self, **kwargs):
super(SigmoidBCELoss, self).__init__(**kwargs)
def forward(self, input, target):
input = input.squeeze(1)
target = target.float()
return super(SigmoidBCELoss, self).fo... | 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... | azxj/BRRNet | SigmoidBCELoss | false | 6,306 | [
"MIT"
] | 1 | 274068efd5453f2c1fb07bfaad448d048b9c793b | https://github.com/azxj/BRRNet/tree/274068efd5453f2c1fb07bfaad448d048b9c793b |
SqueezeExcitation | import torch
from torch import Tensor
import torch.nn.functional as F
from torch import nn
from torchvision.models.mobilenetv2 import _make_divisible
class SqueezeExcitation(nn.Module):
def __init__(self, input_channels: 'int', squeeze_factor: 'int'=4):
super().__init__()
squeeze_channels = _make... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 Tensor
impo... | ayrna/ordinal-cnn-ecoc | SqueezeExcitation | false | 6,307 | [
"BSD-3-Clause"
] | 1 | 2b7909d036612727a45a174c891c4e749c3b60c4 | https://github.com/ayrna/ordinal-cnn-ecoc/tree/2b7909d036612727a45a174c891c4e749c3b60c4 |
templateModel | import logging
import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class BaseModel(nn.Module):
"""
Base class for all models
All models require an initialization 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
import logging
import numpy a... | awoloshuk/NephNet | templateModel | false | 6,308 | [
"MIT"
] | 1 | 562431364874fef1680069c7a5235c67b96504b8 | https://github.com/awoloshuk/NephNet/tree/562431364874fef1680069c7a5235c67b96504b8 |
ScaledDotProductAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention --baseline version"""
def __init__(self, dropout=0.3):
super().__init__()
self.dropout = nn.Dropout(dropout)
def forward(self, q, k, v, mask=Non... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ayyyq/T-LSTM | ScaledDotProductAttention | false | 6,309 | [
"MIT"
] | 1 | 36dbc88ac710d3925851cd87c2368ecfc7061b70 | https://github.com/ayyyq/T-LSTM/tree/36dbc88ac710d3925851cd87c2368ecfc7061b70 |
SentenceMatrixLayer | import torch
import torch.nn as nn
class SentenceMatrixLayer(nn.Module):
def __init__(self, in_size, out_size=1, p_Asem=0.6):
super(SentenceMatrixLayer, self).__init__()
self.in_size = in_size
self.out_size = out_size
self.p_Asem = p_Asem
self.linear = nn.Linear(in_size * ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ayyyq/T-LSTM | SentenceMatrixLayer | false | 6,310 | [
"MIT"
] | 1 | 36dbc88ac710d3925851cd87c2368ecfc7061b70 | https://github.com/ayyyq/T-LSTM/tree/36dbc88ac710d3925851cd87c2368ecfc7061b70 |
SplitDim | import torch
import torch.nn as nn
class SplitDim(nn.Module):
def __init__(self, nonlin_col=1, nonlin_type=torch.nn.functional.
softplus, correction=True):
super(SplitDim, self).__init__()
self.nonlinearity = nonlin_type
self.col = nonlin_col
if correction:
sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | b4thesunrise/drbayes | SplitDim | false | 6,311 | [
"BSD-2-Clause"
] | 1 | 9bc827aea2c7f084fb1ee77a4bd9f3c9726ecf8c | https://github.com/b4thesunrise/drbayes/tree/9bc827aea2c7f084fb1ee77a4bd9f3c9726ecf8c |
ReSentenceMatrixLayer | import torch
import torch.nn as nn
class ReSentenceMatrixLayer(nn.Module):
def __init__(self, in_size, out_size=1):
super(ReSentenceMatrixLayer, self).__init__()
self.in_size = in_size
self.out_size = out_size
self.a_Asem = nn.Parameter(torch.tensor(0.0))
self.linear = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ayyyq/T-LSTM | ReSentenceMatrixLayer | false | 6,312 | [
"MIT"
] | 1 | 36dbc88ac710d3925851cd87c2368ecfc7061b70 | https://github.com/ayyyq/T-LSTM/tree/36dbc88ac710d3925851cd87c2368ecfc7061b70 |
Qnet | import random
import torch
import torch.nn as nn
import torch.nn.functional as F
class Qnet(nn.Module):
def __init__(self):
super(Qnet, self).__init__()
self.fc1 = nn.Linear(4, 128)
self.fc2 = nn.Linear(128, 128)
self.fc3 = nn.Linear(128, 2)
def forward(self, x):
x = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import random
import torch.nn... | azeye/QuickstartRL | Qnet | false | 6,313 | [
"MIT"
] | 1 | ae1a9eb8bc0c5f52700fa0ac19ce5abcf3ccdefa | https://github.com/azeye/QuickstartRL/tree/ae1a9eb8bc0c5f52700fa0ac19ce5abcf3ccdefa |
HardSwish | import torch
import torch.nn as nn
import torchvision.transforms.functional as F
import torch.nn.functional as F
def hard_swish(x: 'torch.Tensor', inplace: 'bool'=False):
"""Hard swish."""
inner = F.relu6(x + 3.0).div_(6.0)
return x.mul_(inner) if inplace else x.mul(inner)
class HardSwish(nn.Module):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torchvision.transforms.functional as F
import torch.nn.funct... | bcaitech1/p4-mod-model_diet | HardSwish | false | 6,314 | [
"MIT"
] | 1 | 36d8a747e12c375b07d132ed4d08f9fc77126a8b | https://github.com/bcaitech1/p4-mod-model_diet/tree/36d8a747e12c375b07d132ed4d08f9fc77126a8b |
PoswiseFeedForwardNet | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class PoswiseFeedForwardNet(nn.Module):
""" feed forward """
def __init__(self, config):
super().__init__()
self.config = config
self.conv1 = nn.Conv1d(in_channels=self.con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | bage79/transformer-evolution-bage | PoswiseFeedForwardNet | false | 6,315 | [
"Apache-2.0"
] | 1 | 715bdf61421dc19e21fb0f66bfa4b564305987f8 | https://github.com/bage79/transformer-evolution-bage/tree/715bdf61421dc19e21fb0f66bfa4b564305987f8 |
CNNHead | import torch
import torch.nn as nn
class CNNHead(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.conv = nn.Conv1d(in_channels=input_dim, out_channels=2,
kernel_size=3, padding=1)
self.relu = nn.ReLU()
def forward(self, x):
return self.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_... | baseballChatbot7/KBO_MRC | CNNHead | false | 6,316 | [
"MIT"
] | 1 | ad11318d785bacdf29a12adfd25afe90d7ff2779 | https://github.com/baseballChatbot7/KBO_MRC/tree/ad11318d785bacdf29a12adfd25afe90d7ff2779 |
IdentityMessage | import torch
import torch.utils.data
class IdentityMessage(torch.nn.Module):
def __init__(self, raw_msg_dim: 'int', memory_dim: 'int', time_dim: 'int'):
super(IdentityMessage, self).__init__()
self.out_channels = raw_msg_dim + 2 * memory_dim + time_dim
def forward(self, z_src, z_dst, raw_msg... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | beneisner/pytorch_geometric | IdentityMessage | false | 6,317 | [
"MIT"
] | 1 | 53d44a96bd2de2753b1ab1d7153c026c92606a81 | https://github.com/beneisner/pytorch_geometric/tree/53d44a96bd2de2753b1ab1d7153c026c92606a81 |
SigmoidDiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, smooth=1.0):
super(DiceLoss, self).__init__()
self.smooth = smooth
def forward(self, input, target):
n = input.shape[0]
input = input.view(n, -1)
target = target.view(n, -1)
inter... | 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... | azxj/BRRNet | SigmoidDiceLoss | false | 6,318 | [
"MIT"
] | 1 | 274068efd5453f2c1fb07bfaad448d048b9c793b | https://github.com/azxj/BRRNet/tree/274068efd5453f2c1fb07bfaad448d048b9c793b |
PolicyModuleAlt | import torch
import torch.nn as nn
import torch.nn.functional as F
class PolicyModuleAlt(nn.Module):
def __init__(self, input_dim, hid_dim, n_actions):
super().__init__()
self.fc_1 = nn.Linear(input_dim, hid_dim)
self.fc_2 = nn.Linear(hid_dim, hid_dim)
self.fc_a = nn.Linear(hid_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | bentrevett/task-oriented-language-grounding | PolicyModuleAlt | false | 6,319 | [
"MIT"
] | 1 | 812a7bc21ee622030eb0594c576c7d60dc630148 | https://github.com/bentrevett/task-oriented-language-grounding/tree/812a7bc21ee622030eb0594c576c7d60dc630148 |
feedforwardLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class feedforwardLayer(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.3):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hid)
self.w_2 = nn.Linear(d_hid, d_in)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ayyyq/T-LSTM | feedforwardLayer | false | 6,320 | [
"MIT"
] | 1 | 36dbc88ac710d3925851cd87c2368ecfc7061b70 | https://github.com/ayyyq/T-LSTM/tree/36dbc88ac710d3925851cd87c2368ecfc7061b70 |
Envelope | import torch
import torch.utils.data
class Envelope(torch.nn.Module):
def __init__(self, exponent):
super(Envelope, self).__init__()
self.p = exponent + 1
self.a = -(self.p + 1) * (self.p + 2) / 2
self.b = self.p * (self.p + 2)
self.c = -self.p * (self.p + 1) / 2
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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | beneisner/pytorch_geometric | Envelope | false | 6,321 | [
"MIT"
] | 1 | 53d44a96bd2de2753b1ab1d7153c026c92606a81 | https://github.com/beneisner/pytorch_geometric/tree/53d44a96bd2de2753b1ab1d7153c026c92606a81 |
CR | import torch
from typing import List
from typing import Union
import torch.nn as nn
def autopad(kernel_size: 'Union[int, List[int]]', padding:
'Union[int, None]'=None) ->Union[int, List[int]]:
"""Auto padding calculation for pad='same' in TensorFlow."""
if isinstance(kernel_size, int):
kernel_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from typing import List
from ... | bcaitech1/p4-mod-model_diet | CR | false | 6,322 | [
"MIT"
] | 1 | 36d8a747e12c375b07d132ed4d08f9fc77126a8b | https://github.com/bcaitech1/p4-mod-model_diet/tree/36d8a747e12c375b07d132ed4d08f9fc77126a8b |
bodypose_model | import torch
from collections import OrderedDict
import torch.nn as nn
def make_layers(block, no_relu_layers):
layers = []
for layer_name, v in block.items():
if 'pool' in layer_name:
layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], padding=v[2])
layers.append((layer_name, l... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from collections import Order... | alanlee-chn/handpose-est | bodypose_model | false | 6,323 | [
"MIT"
] | 1 | 241a6beb45e045e65a328aade22ce536f4dcd893 | https://github.com/alanlee-chn/handpose-est/tree/241a6beb45e045e65a328aade22ce536f4dcd893 |
ShiftedSoftplus | import torch
import torch.nn.functional as F
import torch.utils.data
class ShiftedSoftplus(torch.nn.Module):
def __init__(self):
super(ShiftedSoftplus, self).__init__()
self.shift = torch.log(torch.tensor(2.0)).item()
def forward(self, x):
return F.softplus(x) - self.shift
def get_... | 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.utils.data
assert_size_stride = torch._C._dynamo.... | beneisner/pytorch_geometric | ShiftedSoftplus | false | 6,324 | [
"MIT"
] | 1 | 53d44a96bd2de2753b1ab1d7153c026c92606a81 | https://github.com/beneisner/pytorch_geometric/tree/53d44a96bd2de2753b1ab1d7153c026c92606a81 |
ImageProcessingModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class ImageProcessingModule(nn.Module):
def __init__(self, n_filters):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=n_filters,
kernel_size=7, stride=7)
def forward(self, observation):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | bentrevett/task-oriented-language-grounding | ImageProcessingModule | false | 6,325 | [
"MIT"
] | 1 | 812a7bc21ee622030eb0594c576c7d60dc630148 | https://github.com/bentrevett/task-oriented-language-grounding/tree/812a7bc21ee622030eb0594c576c7d60dc630148 |
BinaryChunk | import math
import torch
class BinaryChunk(torch.nn.Module):
def __init__(self, nCls, isLogit=False, pooling='max', chunk_dim=-1):
super(BinaryChunk, self).__init__()
self.nClass = nCls
self.nChunk = int(math.ceil(math.log2(self.nClass)))
self.pooling = pooling
self.isLogi... | 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 math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | azopticsinc/optical-neural-network | BinaryChunk | false | 6,326 | [
"MIT"
] | 1 | 28280014a6c1fc717a5077ed5e3c3496a4b103ac | https://github.com/azopticsinc/optical-neural-network/tree/28280014a6c1fc717a5077ed5e3c3496a4b103ac |
Accuracy | from torch.nn import Module
import torch
from torch import Tensor
class Accuracy(Module):
"""
Class for calculating the accuracy for a given prediction and the labels
for comparison.
Expects the inputs to be from a range of 0 to 1 and sets a crossing threshold at 0.5
the labels are similarly round... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
from torch import Tensor
assert_size_stride = torch._C._dynam... | bharadwaj1098/sparseml | Accuracy | false | 6,327 | [
"Apache-2.0"
] | 1 | b43dc3edc9f7e6cd32368937b7ed3352180abe52 | https://github.com/bharadwaj1098/sparseml/tree/b43dc3edc9f7e6cd32368937b7ed3352180abe52 |
Attention | import math
import torch
import torch.nn.functional as F
import torch.utils.data
def restricted_softmax(src, dim: 'int'=-1, margin: 'float'=0.0):
src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0.0)
out = (src - src_max).exp()
out = out / (out.sum(dim=dim, keepdim=True) + (margin - src_max).e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | beneisner/pytorch_geometric | Attention | false | 6,328 | [
"MIT"
] | 1 | 53d44a96bd2de2753b1ab1d7153c026c92606a81 | https://github.com/beneisner/pytorch_geometric/tree/53d44a96bd2de2753b1ab1d7153c026c92606a81 |
DentReLU | import torch
import torch.nn as nn
class DentReLUFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, input, p):
ctx.save_for_backward(input)
ctx.p = p
output = input.clone()
mask1 = p <= input
mask2 = input <= 0
output[mask1 & mask2] = 0
r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | bfeng/pytorch-cifar | DentReLU | false | 6,329 | [
"MIT"
] | 1 | 6de257bb4b489429785502d487044c55bec62aae | https://github.com/bfeng/pytorch-cifar/tree/6de257bb4b489429785502d487044c55bec62aae |
LuongAttentionConcat | import torch
import torch.nn as nn
import torch.nn.functional as F
class LuongAttentionConcat(nn.Module):
def __init__(self, units, hidden_size):
super().__init__()
self.W = nn.Linear(2 * hidden_size, units)
self.V = nn.Linear(units, 1)
def forward(self, query, values):
query... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | beroguedou/nmt-pytorch | LuongAttentionConcat | false | 6,330 | [
"MIT"
] | 1 | 8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 | https://github.com/beroguedou/nmt-pytorch/tree/8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 |
BahdanauAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class BahdanauAttention(nn.Module):
def __init__(self, units, hidden_size):
super().__init__()
self.W1 = nn.Linear(hidden_size, units)
self.W2 = nn.Linear(hidden_size, units)
self.V = nn.Linear(units, 1)
def f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | beroguedou/nmt-pytorch | BahdanauAttention | false | 6,331 | [
"MIT"
] | 1 | 8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 | https://github.com/beroguedou/nmt-pytorch/tree/8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 |
RC | import torch
import torch.nn as nn
import torch.nn.functional as F
class RC(nn.Module):
"""
A wrapper class for ReflectionPad2d, Conv2d and an optional relu
"""
def __init__(self, in_dim, out_dim, kernel_size=3, padding=1,
activation_function=True):
super().__init__()
self.pad... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | benningtonlee7/AdaIn_Style_Transfer_From_Scratch_In_Pytorch | RC | false | 6,332 | [
"MIT"
] | 1 | 50dfe4bdcbcdd0f4e647f9ee45de2a3f81eb6722 | https://github.com/benningtonlee7/AdaIn_Style_Transfer_From_Scratch_In_Pytorch/tree/50dfe4bdcbcdd0f4e647f9ee45de2a3f81eb6722 |
Decoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class Decoder(nn.Module):
""" Encoder
"""
def __init__(self, n_levels, n_color, n_eccentricity, n_azimuth,
n_theta, n_phase):
super(Decoder, self).__init__()
self.n_levels = n_levels
self.n_color = n_color
... | 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... | bicv/POLO | Decoder | false | 6,333 | [
"MIT"
] | 1 | b8d4f9014796a4eb24c178d8be611a0b3b4c44df | https://github.com/bicv/POLO/tree/b8d4f9014796a4eb24c178d8be611a0b3b4c44df |
ImageProcessingModuleAlt | import torch
import torch.nn as nn
import torch.nn.functional as F
class ImageProcessingModuleAlt(nn.Module):
def __init__(self, n_filters):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=n_filters * 2,
kernel_size=7)
self.conv2 = nn.Conv2d(in_channels=n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | bentrevett/task-oriented-language-grounding | ImageProcessingModuleAlt | false | 6,334 | [
"MIT"
] | 1 | 812a7bc21ee622030eb0594c576c7d60dc630148 | https://github.com/bentrevett/task-oriented-language-grounding/tree/812a7bc21ee622030eb0594c576c7d60dc630148 |
MultimodalFusionModule | import torch
import torch.nn as nn
class MultimodalFusionModule(nn.Module):
def __init__(self, emb_dim, n_filters):
super().__init__()
self.fc_h = nn.Linear(emb_dim, n_filters)
def forward(self, image, instruction):
_batch_size, _n_filters, _height, _width = image.shape
a = t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | bentrevett/task-oriented-language-grounding | MultimodalFusionModule | false | 6,335 | [
"MIT"
] | 1 | 812a7bc21ee622030eb0594c576c7d60dc630148 | https://github.com/bentrevett/task-oriented-language-grounding/tree/812a7bc21ee622030eb0594c576c7d60dc630148 |
LuongAttentionDot | import torch
import torch.nn as nn
import torch.nn.functional as F
class LuongAttentionDot(nn.Module):
def __init__(self):
super().__init__()
def forward(self, query, values):
query = torch.squeeze(query, 0)
query = torch.unsqueeze(query, 1)
query_transposed = query.transpose... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | beroguedou/nmt-pytorch | LuongAttentionDot | false | 6,336 | [
"MIT"
] | 1 | 8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 | https://github.com/beroguedou/nmt-pytorch/tree/8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, feature_num):
super(Net, self).__init__()
self.layer_1 = nn.Linear(feature_num, 500)
self.layer_2 = nn.Linear(500, 20)
def forward(self, x):
x = F.relu(self.layer_1(x))... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | bm2-lab/scPrivacy | Net | false | 6,337 | [
"MIT"
] | 1 | 444c8f3a5e7b890c299cd823359e5414f73d6205 | https://github.com/bm2-lab/scPrivacy/tree/444c8f3a5e7b890c299cd823359e5414f73d6205 |
MLP | import torch
from torch import nn
from torch.nn import functional as F
class MLP(nn.Module):
"""
Multi-Layer Perceptron
:param in_dim: int, size of input feature
:param n_classes: int, number of output classes
:param hidden_dim: int, size of hidden vector
:param dropout: fl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import functional as F
assert_size_stride = t... | bigdata-ustc/DisenQNet | MLP | false | 6,338 | [
"MIT"
] | 1 | 908fadeb9b8d278450213deff70205703bd91da6 | https://github.com/bigdata-ustc/DisenQNet/tree/908fadeb9b8d278450213deff70205703bd91da6 |
PairwiseBCELoss | import torch
from abc import abstractmethod
import torch.utils.data.dataloader
import torch.nn.functional as F
import torch.nn as nn
import torch.nn
import torch.optim.optimizer
class SimilarityLoss(nn.Module):
def __init__(self):
super(SimilarityLoss, self).__init__()
@abstractmethod
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from abc im... | bogdankostic/flair | PairwiseBCELoss | false | 6,339 | [
"MIT"
] | 1 | 8cf03eab19512e94c1bcb4a30409bb065d37fe25 | https://github.com/bogdankostic/flair/tree/8cf03eab19512e94c1bcb4a30409bb065d37fe25 |
FociDetector | import torch
import torch.nn as nn
import torch.utils.data
class FociDetector(nn.Module):
def __init__(self, input_channels=3, input_size=17, ksize=5,
hidden_channels=10):
super(FociDetector, self).__init__()
self.conv1 = nn.Conv2d(input_channels, hidden_channels, ksize,
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
import ... | bharath272/centrosome-analysis | FociDetector | false | 6,340 | [
"MIT"
] | 1 | 6ae3744be464812b3767909420d7b78cea9da670 | https://github.com/bharath272/centrosome-analysis/tree/6ae3744be464812b3767909420d7b78cea9da670 |
LuongAttentionGeneral | import torch
import torch.nn as nn
import torch.nn.functional as F
class LuongAttentionGeneral(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.W = nn.Linear(hidden_size, hidden_size)
def forward(self, query, values):
query = torch.squeeze(query, 0)
query ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | beroguedou/nmt-pytorch | LuongAttentionGeneral | false | 6,341 | [
"MIT"
] | 1 | 8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 | https://github.com/beroguedou/nmt-pytorch/tree/8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 |
Actor | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_unit(layer):
inp = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(inp)
return -lim, lim
class Actor(nn.Module):
def __init__(self, state_size, action_size, seed=2, fc_units=256):
super(Actor, 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.... | bnriiitb/Deep-Reinforcement-Learning | Actor | false | 6,342 | [
"MIT"
] | 1 | 5649a9d86fbec32fe3ac9cbb923d0d3a4c692d1e | https://github.com/bnriiitb/Deep-Reinforcement-Learning/tree/5649a9d86fbec32fe3ac9cbb923d0d3a4c692d1e |
PositionwiseFeedforwardLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedforwardLayer(nn.Module):
def __init__(self, hid_dim: 'int', pf_dim: 'int', dropout: 'float') ->None:
super().__init__()
self.fc_1 = nn.Linear(hid_dim, pf_dim)
self.fc_2 = nn.Linear(pf_dim, hid_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
import torch.nn as nn
assert_... | bob80333/investigating_extrapolation | PositionwiseFeedforwardLayer | false | 6,343 | [
"MIT"
] | 1 | fc4f72baa46b8490968f7ad546897937feb8b25d | https://github.com/bob80333/investigating_extrapolation/tree/fc4f72baa46b8490968f7ad546897937feb8b25d |
KopoinANNNetwork | import torch
import torch.nn as nn
class KopoinANNNetwork(nn.Module):
def __init__(self, featShape):
super(KopoinANNNetwork, self).__init__()
self.featShape = featShape
self.act = nn.Sigmoid()
self.layer0 = nn.Linear(featShape, featShape // 2)
self.layer1 = nn.Linear(featS... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | bmd2007/benchmark_eval | KopoinANNNetwork | false | 6,344 | [
"MIT"
] | 1 | aa42bb3369e79db4cb63e1963afcc8af6d8f5696 | https://github.com/bmd2007/benchmark_eval/tree/aa42bb3369e79db4cb63e1963afcc8af6d8f5696 |
BertPooler | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional
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.GELU()... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.fun... | bj1103/FaST-VGS-Family | BertPooler | false | 6,345 | [
"BSD-3-Clause"
] | 1 | 824f987a5bd647fc17aa34b98eb1d9109441d64b | https://github.com/bj1103/FaST-VGS-Family/tree/824f987a5bd647fc17aa34b98eb1d9109441d64b |
PatchMerge | import torch
from torch import nn
class PatchMerge(nn.Module):
"""
Implements the Patch Merge operator from Swin Transformer
"""
def __init__(self, channels: 'int', window_size: 'int'=2):
super(PatchMerge, self).__init__()
self.merger = nn.Conv2d(in_channels=channels, out_channels=
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | bradezard131/swin-transformer | PatchMerge | false | 6,346 | [
"MIT"
] | 1 | 72e38cbae8bda332d03dced814d10b45185c04de | https://github.com/bradezard131/swin-transformer/tree/72e38cbae8bda332d03dced814d10b45185c04de |
PatchEmbed | import torch
import torch.nn as nn
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
num_patches = img_size // patch_size * (img_size // patch_size)
self.img_size = img_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | bmi-imaginelab/CD-Net-Histopathology-Representation-Learning-using-Pyramidal-Context-Detail-Network | PatchEmbed | false | 6,347 | [
"Apache-2.0"
] | 1 | cc4dad85cdeea7295cb48f6f947fd1ac25d8862e | https://github.com/bmi-imaginelab/CD-Net-Histopathology-Representation-Learning-using-Pyramidal-Context-Detail-Network/tree/cc4dad85cdeea7295cb48f6f947fd1ac25d8862e |
LunarLanderDQN | import torch
import torch.nn as nn
import torch.nn.functional as F
class LunarLanderDQN(nn.Module):
def __init__(self, state_space_dim, action_space_dim, hidden=12):
super(LunarLanderDQN, self).__init__()
self.hidden = hidden
self.fc1 = nn.Linear(state_space_dim, hidden)
self.fc2 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | breno-aberle/rl-pong-project | LunarLanderDQN | false | 6,348 | [
"MIT"
] | 1 | 9dc0d12e4bbcdb2905d46f66e84fac6d70c7831d | https://github.com/breno-aberle/rl-pong-project/tree/9dc0d12e4bbcdb2905d46f66e84fac6d70c7831d |
DuelingQNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class DuelingQNetwork(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=48):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimen... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | bobiblazeski/navigation | DuelingQNetwork | false | 6,349 | [
"MIT"
] | 1 | bb863b4475a90ff26bede20af647ae4882a0f6fb | https://github.com/bobiblazeski/navigation/tree/bb863b4475a90ff26bede20af647ae4882a0f6fb |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 5)
self.pool = nn.MaxPool2d(2, 2)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
return x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | bongsang/face-landmark | Net | false | 6,350 | [
"MIT"
] | 1 | bc7644480be1ddf8d35c2875d251bc84c00ccaa7 | https://github.com/bongsang/face-landmark/tree/bc7644480be1ddf8d35c2875d251bc84c00ccaa7 |
RankingLoss | import torch
from abc import abstractmethod
import torch.utils.data.dataloader
import torch.nn.functional as F
import torch.nn as nn
import torch.nn
import torch.optim.optimizer
class SimilarityLoss(nn.Module):
def __init__(self):
super(SimilarityLoss, self).__init__()
@abstractmethod
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
from torch._inductor.runtime import triton_helpers
from abc import abstractmethod
import torch.utils.data.dataloader
import torch.nn as nn
i... | bogdankostic/flair | RankingLoss | false | 6,351 | [
"MIT"
] | 1 | 8cf03eab19512e94c1bcb4a30409bb065d37fe25 | https://github.com/bogdankostic/flair/tree/8cf03eab19512e94c1bcb4a30409bb065d37fe25 |
AttnModel | import torch
from torch import nn
from torch.nn import functional as F
class MLP(nn.Module):
"""
Multi-Layer Perceptron
:param in_dim: int, size of input feature
:param n_classes: int, number of output classes
:param hidden_dim: int, size of hidden vector
:param dropout: fl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | bigdata-ustc/DisenQNet | AttnModel | false | 6,352 | [
"MIT"
] | 1 | 908fadeb9b8d278450213deff70205703bd91da6 | https://github.com/bigdata-ustc/DisenQNet/tree/908fadeb9b8d278450213deff70205703bd91da6 |
QNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class QNetwork(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=48):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | bobiblazeski/navigation | QNetwork | false | 6,353 | [
"MIT"
] | 1 | bb863b4475a90ff26bede20af647ae4882a0f6fb | https://github.com/bobiblazeski/navigation/tree/bb863b4475a90ff26bede20af647ae4882a0f6fb |
Block | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
random_tensor = keep_prob +... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | au55555/classification-pytorch | Block | false | 6,354 | [
"MIT"
] | 1 | 1937599ae6e688ed7af7470f69964fb6f97241c4 | https://github.com/au55555/classification-pytorch/tree/1937599ae6e688ed7af7470f69964fb6f97241c4 |
MaskedLinear | import torch
import torch.cuda
from torch.nn.functional import *
class MaskedLinear(torch.nn.Linear):
def forward(self, x, mask):
out = super().forward(x)
if mask.is_floating_point():
out = out * mask
else:
out = out * mask.type_as(out)
return out
def get... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.cuda
from torch.nn.functional import *
assert_size_stride = torch._... | bratao/DeepSpeed | MaskedLinear | false | 6,355 | [
"MIT"
] | 1 | c50d8955e942e5e26cf81835d59ec3f20ef8540d | https://github.com/bratao/DeepSpeed/tree/c50d8955e942e5e26cf81835d59ec3f20ef8540d |
CartpoleDQN | import torch
import torch.nn as nn
import torch.nn.functional as F
class CartpoleDQN(nn.Module):
def __init__(self, state_space_dim, action_space_dim, hidden=12):
super(CartpoleDQN, self).__init__()
self.hidden = hidden
self.fc1 = nn.Linear(state_space_dim, hidden)
self.fc2 = nn.L... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | breno-aberle/rl-pong-project | CartpoleDQN | false | 6,356 | [
"MIT"
] | 1 | 9dc0d12e4bbcdb2905d46f66e84fac6d70c7831d | https://github.com/breno-aberle/rl-pong-project/tree/9dc0d12e4bbcdb2905d46f66e84fac6d70c7831d |
AvgPool2d | from torch.nn import Module
import torch
import torch as th
class AvgPool2d(Module):
"""
This class is the beginning of an exact python port of the torch.nn.AvgPool2d
module. Because PySyft cannot hook into layers which are implemented in C++,
our special functionalities (such as encrypted computation... | 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.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | brandonhee/PySyft | AvgPool2d | false | 6,357 | [
"Apache-2.0"
] | 1 | 31217f28aa3d996b2bb84477fb15a990f0cb9a80 | https://github.com/brandonhee/PySyft/tree/31217f28aa3d996b2bb84477fb15a990f0cb9a80 |
Critic | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_unit(layer):
inp = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(inp)
return -lim, lim
class Critic(nn.Module):
def __init__(self, state_size, action_size, seed=2, fc1_units=256,
fc2_units=256... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.nn as nn
assert_size_stride = torch._C._dynamo.g... | bnriiitb/Deep-Reinforcement-Learning | Critic | false | 6,358 | [
"MIT"
] | 1 | 5649a9d86fbec32fe3ac9cbb923d0d3a4c692d1e | https://github.com/bnriiitb/Deep-Reinforcement-Learning/tree/5649a9d86fbec32fe3ac9cbb923d0d3a4c692d1e |
SimpleModel | import torch
import torch.cuda
from torch.nn.functional import *
class SimpleModel(torch.nn.Module):
def __init__(self, hidden_dim, empty_grad=False, rank=0):
super(SimpleModel, self).__init__()
self.linear = torch.nn.Linear(hidden_dim, hidden_dim)
if empty_grad:
self.linear2 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | bratao/DeepSpeed | SimpleModel | false | 6,359 | [
"MIT"
] | 1 | c50d8955e942e5e26cf81835d59ec3f20ef8540d | https://github.com/bratao/DeepSpeed/tree/c50d8955e942e5e26cf81835d59ec3f20ef8540d |
Mid_block | import torch
import torch.nn as nn
import torch.utils.data
class Mid_block(nn.Module):
def __init__(self, chanIn, chanOut, ks=3, stride=1):
super().__init__()
self.conv1 = nn.Conv3d(chanIn, chanOut, ks, padding=1)
self.conv2 = nn.Conv3d(chanOut, chanOut, ks, padding=1)
def forward(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | basharbme/3d_segmentation | Mid_block | false | 6,360 | [
"MIT"
] | 1 | efcd966f74ebb74614515c38930e820ea1c4744e | https://github.com/basharbme/3d_segmentation/tree/efcd966f74ebb74614515c38930e820ea1c4744e |
MaskedLinearSeqDup | import torch
import torch.cuda
from torch.nn.functional import *
class MaskedLinear(torch.nn.Linear):
def forward(self, x, mask):
out = super().forward(x)
if mask.is_floating_point():
out = out * mask
else:
out = out * mask.type_as(out)
return out
class M... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.cuda
from torch.nn.functional import *
assert_size_stride = torch._... | bratao/DeepSpeed | MaskedLinearSeqDup | false | 6,361 | [
"MIT"
] | 1 | c50d8955e942e5e26cf81835d59ec3f20ef8540d | https://github.com/bratao/DeepSpeed/tree/c50d8955e942e5e26cf81835d59ec3f20ef8540d |
MultiChannelCombinedScorer | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
class FociDetector(nn.Module):
def __init__(self, input_channels=3, input_size=17, ksize=5,
hidden_channels=10):
super(FociDetector, self).__init__()
self.conv1 = nn.Conv2d(input_channels, hidden_ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | bharath272/centrosome-analysis | MultiChannelCombinedScorer | false | 6,362 | [
"MIT"
] | 1 | 6ae3744be464812b3767909420d7b78cea9da670 | https://github.com/bharath272/centrosome-analysis/tree/6ae3744be464812b3767909420d7b78cea9da670 |
SmoothBCEwLogits | import torch
import torch.utils.data
import torch.nn.functional as F
from torch.nn.modules.loss import _WeightedLoss
class SmoothBCEwLogits(_WeightedLoss):
def __init__(self, weight=None, reduction='mean', smoothing=0.0,
pos_weight=None):
super().__init__(weight=weight, reduction=reduction)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | broadinstitute/lincs-profiling-comparison | SmoothBCEwLogits | false | 6,363 | [
"BSD-3-Clause"
] | 1 | 075c3bc60eeb3934fc42c30bae6aeed8cda1cd6d | https://github.com/broadinstitute/lincs-profiling-comparison/tree/075c3bc60eeb3934fc42c30bae6aeed8cda1cd6d |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, input_seq_length, output_num_classes):
"""Initialize model layers"""
super(Net, self).__init__()
self.input_seq_length = input_seq_length
self.output_num_classes = output_nu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | bradford415/multiclassification | Net | false | 6,364 | [
"MIT"
] | 1 | ee0234ec0a85b04f78cd86c3e5c52e5d658f19ac | https://github.com/bradford415/multiclassification/tree/ee0234ec0a85b04f78cd86c3e5c52e5d658f19ac |
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