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 |
|---|---|---|---|---|---|---|---|---|---|---|
AddSubNet | import torch
from torch import nn
import torch.utils.data
class AddSubNet(nn.Module):
"""
Simple AddSub network in PyTorch. This network outputs the sum and
subtraction of the inputs.
"""
def __init__(self):
super(AddSubNet, self).__init__()
def forward(self, input0, input1):
... | 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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | funny000/python_project | AddSubNet | false | 3,507 | [
"MIT"
] | 0 | 190289765d0bdd908ce289c78969b3702a2c4292 | https://github.com/funny000/python_project/tree/190289765d0bdd908ce289c78969b3702a2c4292 |
RelativeL1 | import torch
import torch.nn as nn
class RelativeL1(nn.Module):
""" Relative L1 loss.
Comparing to the regular L1, introducing the division by |c|+epsilon
better models the human vision system’s sensitivity to variations
in the dark areas. (where epsilon = 0.01, to prevent values of 0 in the
denom... | 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
... | grofit/traiNNer | RelativeL1 | false | 15,474 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
Decoder5 | import torch
import torch.nn as nn
class Decoder5(nn.Module):
def __init__(self, model=None, fixed=False):
super(Decoder5, self).__init__()
self.fixed = fixed
self.conv51 = nn.Conv2d(512, 512, 3, 1, 0)
self.conv44 = nn.Conv2d(512, 512, 3, 1, 0)
self.conv43 = nn.Conv2d(512,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MingSun-Tse/pytorch-AdaIN | Decoder5 | false | 2,748 | [
"MIT"
] | 0 | 02ae320345232983c754ea233613aedc21e4d348 | https://github.com/MingSun-Tse/pytorch-AdaIN/tree/02ae320345232983c754ea233613aedc21e4d348 |
ImageDiscriminator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | HotaekHan/Synthetically_Supervised_Text_Recognition | ImageDiscriminator | false | 17,391 | [
"MIT"
] | 8 | a6bb7d3039b1280c6efe177b69d8b985d2e13285 | https://github.com/HotaekHan/Synthetically_Supervised_Text_Recognition/tree/a6bb7d3039b1280c6efe177b69d8b985d2e13285 |
Mean | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from typing import Optional
from torch import nn
assert_size_stride = torch._C._dynamo.gu... | HiroakiMikami/mlprogram | Mean | false | 17,362 | [
"MIT"
] | 9 | 573e94c567064705fa65267dd83946bf183197de | https://github.com/HiroakiMikami/mlprogram/tree/573e94c567064705fa65267dd83946bf183197de |
Critic | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | joyce-fang/deep-reinforcement-learning | Critic | false | 3,777 | [
"MIT"
] | 0 | 62cedab584465bd1c3ef112eb149e8fc611546e3 | https://github.com/joyce-fang/deep-reinforcement-learning/tree/62cedab584465bd1c3ef112eb149e8fc611546e3 |
CosineActivation | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | sungreong/PyTimeSeries | CosineActivation | false | 4,399 | [
"MIT"
] | 0 | d5321c1226fc7fb6a45fec7009843894be417594 | https://github.com/sungreong/PyTimeSeries/tree/d5321c1226fc7fb6a45fec7009843894be417594 |
LinearZeros | import torch
from torch import nn
class LinearZeros(nn.Linear):
def __init__(self, in_channels, out_channels, logscale_factor=3):
super().__init__(in_channels, out_channels)
self.logscale_factor = logscale_factor
self.register_parameter('logs', nn.Parameter(torch.zeros(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._inductor.runtime.triton_helpers import math as tl_math
from torch im... | americast/glow-pytorch | LinearZeros | false | 6,185 | [
"MIT"
] | 1 | bbc576b96a5218417d25ae76b60f04ae24621de3 | https://github.com/americast/glow-pytorch/tree/bbc576b96a5218417d25ae76b60f04ae24621de3 |
SameBlock2d | import torch
import torch.nn.functional as F
from torch import nn
class SameBlock2d(nn.Module):
"""
Simple block, preserve spatial resolution.
"""
def __init__(self, in_features, out_features, groups=1, kernel_size=3,
padding=1):
super(SameBlock2d, self).__init__()
self.conv =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | KangweiiLiu/Thin-Plate-Spline-Motion-Model | SameBlock2d | false | 5,430 | [
"MIT"
] | 1 | 0ec14f6c06f5beeef159340142ec5182a1be9bc7 | https://github.com/KangweiiLiu/Thin-Plate-Spline-Motion-Model/tree/0ec14f6c06f5beeef159340142ec5182a1be9bc7 |
Biaffine | import torch
import torch.nn as nn
class Biaffine(nn.Module):
def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
super(Biaffine, self).__init__()
self.n_in = n_in
self.n_out = n_out
self.bias_x = bias_x
self.bias_y = bias_y
self.weight = nn.Parameter(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | CNLPT/lightNLP | Biaffine | false | 13,451 | [
"Apache-2.0"
] | 889 | c7f128422ba5b16f514bb294145cb3b562e95829 | https://github.com/CNLPT/lightNLP/tree/c7f128422ba5b16f514bb294145cb3b562e95829 |
DynamicWeights | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | lzrobots/dgmn | DynamicWeights | false | 15,983 | [
"MIT"
] | 54 | 515476b5c6a07dcc3b7a4d2243c541377624bb33 | https://github.com/lzrobots/dgmn/tree/515476b5c6a07dcc3b7a4d2243c541377624bb33 |
UpsampleConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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
import math
from torchvision.datasets import *
from ... | Womcos/SCARF | UpsampleConv2d | false | 5,984 | [
"MIT"
] | 1 | b90251bc23410cb810a7082ca75147a7aae21dec | https://github.com/Womcos/SCARF/tree/b90251bc23410cb810a7082ca75147a7aae21dec |
CosineClassifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Danden1/DER-ClassIL.pytorch | CosineClassifier | false | 13,553 | [
"MIT"
] | 79 | 66ccdb45890d3da335f4dcb841160cbea8719c15 | https://github.com/Danden1/DER-ClassIL.pytorch/tree/66ccdb45890d3da335f4dcb841160cbea8719c15 |
ResBlock | import torch
from torch import nn
import torch.distributed
class ResBlock(nn.Module):
def __init__(self, feature_size, action_size):
super(ResBlock, self).__init__()
self.lin_1 = nn.Linear(feature_size + action_size, feature_size)
self.lin_2 = nn.Linear(feature_size + action_size, feature... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.distributed
assert_size_stride = torch._C._dyn... | Improbable-AI/curiosity_baselines | ResBlock | false | 17,442 | [
"MIT"
] | 5 | 42dca92b2fb66c0790a72206bf48595d3b5b487f | https://github.com/Improbable-AI/curiosity_baselines/tree/42dca92b2fb66c0790a72206bf48595d3b5b487f |
StridedNet | import torch
import torch.nn.functional as F
import torch.nn as nn
class StridedNet(nn.Module):
def __init__(self):
super(StridedNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=10, kernel_size=
6, stride=1, dilation=1)
self.pool1 = nn.MaxPool2d(kernel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JHorcasitas/cnn_document_binarization | StridedNet | false | 17,466 | [
"MIT"
] | 9 | 075f76aed375ca14a53011f4dfeb12379debb5b3 | https://github.com/JHorcasitas/cnn_document_binarization/tree/075f76aed375ca14a53011f4dfeb12379debb5b3 |
AUXModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class AUXModule(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.linear = nn.Linear(in_features, out_features)
def forward(self, x):
x = F.adaptive_max_pool2d(x, output_size=(1, 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
import torch.nn as nn
assert_... | AhmadQasim/unet-segmentator-brats | AUXModule | false | 18,397 | [
"MIT"
] | 2 | 3e94cc234d55867957024bb5d05df6ec16882bbf | https://github.com/AhmadQasim/unet-segmentator-brats/tree/3e94cc234d55867957024bb5d05df6ec16882bbf |
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Stanwang1210/HW3_1_Source_Seperation | Encoder | false | 2,857 | [
"MIT"
] | 0 | 8c05850fa4f0f0845c460f9afd06fd8fe3e29dc9 | https://github.com/Stanwang1210/HW3_1_Source_Seperation/tree/8c05850fa4f0f0845c460f9afd06fd8fe3e29dc9 |
InterpolationBlock | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | earhian/imgclsmob | InterpolationBlock | false | 6,621 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
AmdimNCELoss | import torch
import torch.nn as nn
def tanh_clip(x, clip_val=10.0):
"""
soft clip values to the range [-clip_val, +clip_val]
"""
if clip_val is not None:
x_clip = clip_val * torch.tanh(1.0 / clip_val * x)
else:
x_clip = x
return x_clip
class AmdimNCELoss(nn.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.... | jfrancis71/pytorch-lightning-bolts | AmdimNCELoss | false | 3,827 | [
"Apache-2.0"
] | 0 | 8a4cf8f61644c28d6df54ccffe3a52d6f5fce5a6 | https://github.com/jfrancis71/pytorch-lightning-bolts/tree/8a4cf8f61644c28d6df54ccffe3a52d6f5fce5a6 |
MinPoolTrinary | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | MinPoolTrinary | false | 17,130 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
VonmisesLossBiternion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_s... | TUI-NICR/multi-task-person-perception | VonmisesLossBiternion | false | 17,966 | [
"BSD-3-Clause"
] | 4 | 81666eb42be9522fd726448e82e8bbf04138ffa3 | https://github.com/TUI-NICR/multi-task-person-perception/tree/81666eb42be9522fd726448e82e8bbf04138ffa3 |
NonLocal2d | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.transforms import functional as F
from torch.nn import functional as F
import torch.utils.data
class NonLocal2d(nn.Module):
def __init__(self, dim_in, dim_inner, dim_out, max_pool_stride=2,
use_maxpool=True, use_gn=False,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | shunya-toyokawa/qanet_human_parts_segmentatiom | NonLocal2d | false | 16,519 | [
"MIT"
] | 72 | 5527b247acd65534b455c26e3692a14b31669602 | https://github.com/shunya-toyokawa/qanet_human_parts_segmentatiom/tree/5527b247acd65534b455c26e3692a14b31669602 |
Conv1dResBlock | import torch
import torch.nn as nn
class Conv1d(nn.Conv1d):
"""
Convolution 1d
Args:
x: (N, T, C_in)
Returns:
y: (N, T, C_out)
"""
def __init__(self, in_channels, out_channels, kernel_size,
activation_fn=None, drop_rate=0.0, stride=1, padding='same',
dilation=1, groups=1, bias=Tr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Jackson-Kang/VQVC-Pytorch | Conv1dResBlock | false | 8,313 | [
"MIT"
] | 13 | d2267b5c52253b6ae11a5767963a65320ae335c2 | https://github.com/Jackson-Kang/VQVC-Pytorch/tree/d2267b5c52253b6ae11a5767963a65320ae335c2 |
InvertibleChannelMixing1D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
from torch import nn
from warnings import wa... | cetmann/iunets | InvertibleChannelMixing1D | false | 15,015 | [
"MIT"
] | 86 | 80ed7cce0e505a0396c42359eaf27819222d71f6 | https://github.com/cetmann/iunets/tree/80ed7cce0e505a0396c42359eaf27819222d71f6 |
SmallMnistNoDropout | import torch
import torch.nn as nn
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class SmallMnistNoDropout(nn.Module):
def __init__(self):
super(SmallMnistNoDropout, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Rohan-Chaudhury/aimet | SmallMnistNoDropout | false | 17,952 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a |
DiceLoss | import functools
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch._C
import torch.serialization
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | SeHwanJoo/mmsegmentation_body | DiceLoss | false | 1,041 | [
"Apache-2.0"
] | 0 | 31c4bf27c3dc0a84bfbb06a0c017c5908c17f0ac | https://github.com/SeHwanJoo/mmsegmentation_body/tree/31c4bf27c3dc0a84bfbb06a0c017c5908c17f0ac |
ResizeConv1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | BalintHompot/uncertainty | ResizeConv1d | false | 132 | [
"Apache-2.0"
] | 0 | 544c6c5cf22464d69316a31f97fc87355cd10b7e | https://github.com/BalintHompot/uncertainty/tree/544c6c5cf22464d69316a31f97fc87355cd10b7e |
ResBlock3d | import torch
from torch import nn
class ResBlock3d(nn.Module):
def __init__(self, in_ch, out_ch):
super(ResBlock3d, self).__init__()
self.conv1 = nn.Conv3d(in_ch, out_ch, 3, 1, padding=1)
self.conv2 = nn.Conv3d(out_ch, out_ch, 3, 1, padding=1)
self.bn = nn.InstanceNorm3d(in_ch)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | ldlasso2/hologan-pytorch | ResBlock3d | false | 15,880 | [
"BSD-3-Clause"
] | 61 | baec67d3673cc68e51434516d19465f3d6dd0a1b | https://github.com/ldlasso2/hologan-pytorch/tree/baec67d3673cc68e51434516d19465f3d6dd0a1b |
IOUloss | import torch
import torch.nn as nn
import torch.utils.data
class IOUloss(nn.Module):
def __init__(self, reduction='none', loss_type='iou'):
super(IOUloss, self).__init__()
self.reduction = reduction
self.loss_type = loss_type
def forward(self, pred, target):
assert pred.shape... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | kuazhangxiaoai/YOLOX | IOUloss | false | 3,858 | [
"Apache-2.0"
] | 0 | 7aff49b25a8a80c4c33e941da416500eda72b1a2 | https://github.com/kuazhangxiaoai/YOLOX/tree/7aff49b25a8a80c4c33e941da416500eda72b1a2 |
layer_2_to_1 | import torch
import numpy as np
import torch.nn as nn
def contractions_2_to_1(inputs, dim, normalization='inf', normalization_val=1.0
):
diag_part = torch.diagonal(inputs, dim1=2, dim2=3)
sum_diag_part = torch.sum(diag_part, dim=2).unsqueeze(dim=2)
sum_of_rows = torch.sum(inputs, dim=3)
sum_of_col... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | HyTruongSon/InvariantGraphNetworks-PyTorch | layer_2_to_1 | false | 17,413 | [
"Apache-2.0"
] | 7 | da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 | https://github.com/HyTruongSon/InvariantGraphNetworks-PyTorch/tree/da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 |
_Residual_Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | twtygqyy/pytorch-EDSR | _Residual_Block | false | 16,661 | [
"MIT"
] | 59 | 001031b6563fcc45d4e7edb7e14c41fb9982ce64 | https://github.com/twtygqyy/pytorch-EDSR/tree/001031b6563fcc45d4e7edb7e14c41fb9982ce64 |
SimpleMulModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | opti-mix/glow | SimpleMulModule | false | 7,408 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | graphbuilder/BNN | BasicBlock | false | 6,759 | [
"MIT"
] | 1 | d99eb5c7ef19f8b0c14a135d40a489f154a3c894 | https://github.com/graphbuilder/BNN/tree/d99eb5c7ef19f8b0c14a135d40a489f154a3c894 |
AdaptiveFeatureNorm | import torch
import torch.nn as nn
import torch.utils.data
class AdaptiveFeatureNorm(nn.Module):
"""
The `Stepwise Adaptive Feature Norm loss (ICCV 2019) <https://arxiv.org/pdf/1811.07456v2.pdf>`_
Instead of using restrictive scalar R to match the corresponding feature norm, Stepwise Adaptive Feature Nor... | 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.utils.data
assert_size_stride = torch._C._dy... | XianyuanLiu/Transfer-Learning-Library | AdaptiveFeatureNorm | false | 10,130 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
MnistMLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | IST-DASLab/ACDC | MnistMLP | false | 17,455 | [
"Apache-2.0"
] | 6 | ac53210b6adc1f2506ff909de08172ed9cad25d5 | https://github.com/IST-DASLab/ACDC/tree/ac53210b6adc1f2506ff909de08172ed9cad25d5 |
Spatial_Attention | import torch
import torch.nn as nn
class Spatial_Attention(nn.Module):
def __init__(self, channels, length):
super(Spatial_Attention, self).__init__()
self.conv_3x3 = nn.Conv2d(in_channels=2, out_channels=2,
kernel_size=3, stride=2, padding=3 // 2)
self.resize_bilinear = nn.Up... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | yhf2022/APAN | Spatial_Attention | false | 4,621 | [
"MIT"
] | 0 | b4dd9a5585f42cccefe01e9525cdc8c59727bdf2 | https://github.com/yhf2022/APAN/tree/b4dd9a5585f42cccefe01e9525cdc8c59727bdf2 |
PositionwiseFeedForward | import torch
import torch.nn as nn
class PositionwiseFeedForward(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_hid, d_inner_hid=None, dropout=0):
super(PositionwiseFeedForward, self).__init__()
if d_inner_hid is None:
d_inner_hid = d_hid
self.w_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
import torch.nn as nn
assert_... | HeGuanyuan/ABSA-PyTorch | PositionwiseFeedForward | false | 2,345 | [
"MIT"
] | 0 | 8244aeb39007a2714ccbfd54629ddbbb013ea87e | https://github.com/HeGuanyuan/ABSA-PyTorch/tree/8244aeb39007a2714ccbfd54629ddbbb013ea87e |
LayerReLU6Test | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | goldbattle/onnx2keras | LayerReLU6Test | false | 12,455 | [
"MIT"
] | 0 | dcf52041299ce4216552d1132ec86eb4debd5303 | https://github.com/goldbattle/onnx2keras/tree/dcf52041299ce4216552d1132ec86eb4debd5303 |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | YacobBY/vedastr | ScaledDotProductAttention | false | 1,253 | [
"Apache-2.0"
] | 0 | 2353780489b58d2398b9af49d238ef0df3f45f2a | https://github.com/YacobBY/vedastr/tree/2353780489b58d2398b9af49d238ef0df3f45f2a |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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
from torch.optim.lr_scheduler import *
from torch.nn impo... | ashishbaghudana/san_mrc | LayerNorm | false | 6,248 | [
"BSD-3-Clause"
] | 1 | 03ed7d94c735f1fe2854bb9c208385b5fde44905 | https://github.com/ashishbaghudana/san_mrc/tree/03ed7d94c735f1fe2854bb9c208385b5fde44905 |
TrueDynamics | import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
class TrueDynamics(nn.Module):
def __init__(self, env, hidden_size=200, drop_prob=0.0):
super().__init__()
self.env = env
self.hidden_size = hidden_size
self.drop_prob = drop_prob
sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Alfo5123/ConcreteDropout | TrueDynamics | false | 16,868 | [
"MIT"
] | 7 | c442871553e20a2de078c0fbac7fa52302d50abf | https://github.com/Alfo5123/ConcreteDropout/tree/c442871553e20a2de078c0fbac7fa52302d50abf |
AvgReducePool1d | import torch
from torch import nn
class AvgReducePool1d(nn.Module):
"""A subclass of :torch_nn:`Module`.
Avg Pool layer for 1D inputs. The same as :torch_nn:`AvgPool1d` except that
the pooling dimension is entirely reduced (i.e., `pool_size=input_length`).
"""
def forward(self, input: 'torch.Tens... | 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... | atif93/texar-pytorch | AvgReducePool1d | false | 6,256 | [
"Apache-2.0"
] | 1 | 88163619ec69382e1bbe57fa8bce06260bfc76a2 | https://github.com/atif93/texar-pytorch/tree/88163619ec69382e1bbe57fa8bce06260bfc76a2 |
LavaLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
class LavaLoss(nn.Module):
"""
Depth gradient Loss for instance segmentation
"""
def __init__(self):
super(LavaLoss, self).__init__()
pass
def forward(self, seg_masks, gradient_map):
gt_size = gradient_map... | 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... | EryiXie/PlaneRecNet | LavaLoss | false | 8,079 | [
"MIT"
] | 34 | 534e23e6c5db2235ab1e5a9419fb4bfec3ffa943 | https://github.com/EryiXie/PlaneRecNet/tree/534e23e6c5db2235ab1e5a9419fb4bfec3ffa943 |
Dense | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | cdslabamotong/GCNPP | Dense | false | 3,392 | [
"MIT"
] | 0 | 8445ed3f960e986e12e5a4d65e99e4125e6153c1 | https://github.com/cdslabamotong/GCNPP/tree/8445ed3f960e986e12e5a4d65e99e4125e6153c1 |
PositionwiseFeedForward | import math
import torch
from torch import nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class PositionwiseFeedForward(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super()... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | desmarg/ehr_ml | PositionwiseFeedForward | false | 6,560 | [
"MIT"
] | 1 | 48a385fe2ebdbef655bd4c6b6dd9a73a4e3f76b4 | https://github.com/desmarg/ehr_ml/tree/48a385fe2ebdbef655bd4c6b6dd9a73a4e3f76b4 |
RMSELoss | import torch
import torch.nn as nn
class RMSELoss(nn.Module):
def __init__(self):
super(RMSELoss, self).__init__()
def forward(self, x, y):
criterion = nn.MSELoss()
loss = torch.sqrt(criterion(x, y))
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | FizzerYu/CollaborativeVAE | RMSELoss | false | 471 | [
"MIT"
] | 0 | 4714cce49acba258600b1b5bbcd3a1a4762385e6 | https://github.com/FizzerYu/CollaborativeVAE/tree/4714cce49acba258600b1b5bbcd3a1a4762385e6 |
net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | brabeem/deep-reinforcement-learning | net | false | 12,183 | [
"MIT"
] | 0 | aff919545a1b6d9d44f5aaaa13b9981c888e7169 | https://github.com/brabeem/deep-reinforcement-learning/tree/aff919545a1b6d9d44f5aaaa13b9981c888e7169 |
Dueling_QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Brandon-HY-Lin/deep-reinforcement-learning | Dueling_QNetwork | false | 187 | [
"MIT"
] | 0 | d809851b6f98d1089379392d4687e2acaf1c0c79 | https://github.com/Brandon-HY-Lin/deep-reinforcement-learning/tree/d809851b6f98d1089379392d4687e2acaf1c0c79 |
Naked | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | vishalbelsare/pfhedge | Naked | false | 16,679 | [
"MIT"
] | 81 | 4d7ff173995e0795942bc6ec55f3fdc5bfb7a5f1 | https://github.com/vishalbelsare/pfhedge/tree/4d7ff173995e0795942bc6ec55f3fdc5bfb7a5f1 |
SelfDisLoss | import torch
from torch import nn
from torch import einsum
class SelfDisLoss(nn.Module):
def __init__(self):
super(SelfDisLoss, self).__init__()
def forward(self, feat, mean_feat):
sim = einsum('nc,nc->n', [feat, mean_feat])
dis = torch.sqrt(2.0 * (1 - sim))
loss = torch.mean... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | catcodee/cluster-contrast-reid | SelfDisLoss | false | 3,266 | [
"MIT"
] | 0 | f6359990a4326375f23c3fd654df3fc6dcc9c579 | https://github.com/catcodee/cluster-contrast-reid/tree/f6359990a4326375f23c3fd654df3fc6dcc9c579 |
GatedLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | D-hash-code/ffjord-rnode-finalweek-mnist | GatedLinear | false | 2,151 | [
"MIT"
] | 0 | 4cabcbadda79c68df53ec25f1f8fe03cfeee78f9 | https://github.com/D-hash-code/ffjord-rnode-finalweek-mnist/tree/4cabcbadda79c68df53ec25f1f8fe03cfeee78f9 |
Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | CsekM8/dtu_mlops | Decoder | false | 11,318 | [
"Apache-2.0"
] | 0 | 5c96a9afac0298fab57b7d47e4c08497f4a5d8d9 | https://github.com/CsekM8/dtu_mlops/tree/5c96a9afac0298fab57b7d47e4c08497f4a5d8d9 |
CRF | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | yezhengli-Mr9/torchnlp | CRF | false | 13,148 | [
"Apache-2.0"
] | 0 | 0f2ad6d149a413da9f03c6f6694c429746de6551 | https://github.com/yezhengli-Mr9/torchnlp/tree/0f2ad6d149a413da9f03c6f6694c429746de6551 |
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 |
ConvertPointsFromHomogeneous | import torch
import torch.nn as nn
def convert_points_from_homogeneous(points):
"""Function that converts points from homogeneous to Euclidean space.
See :class:`~torchgeometry.ConvertPointsFromHomogeneous` for details.
Examples::
>>> input = torch.rand(2, 4, 3) # BxNx3
>>> output = tg... | 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... | JudyYe/frankmocap | ConvertPointsFromHomogeneous | false | 9,178 | [
"BSD-3-Clause"
] | 0 | b6e63f344e852ebdbca0095643b5bc0466370891 | https://github.com/JudyYe/frankmocap/tree/b6e63f344e852ebdbca0095643b5bc0466370891 |
RobertaSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class RobertaSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if (config.hidden_size % config.num_attention_heads != 0 and not
hasattr(config, 'embedding_size')):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BlackNoodle/TUCORE-GCN | RobertaSelfAttention | false | 8,790 | [
"MIT"
] | 27 | 16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 | https://github.com/BlackNoodle/TUCORE-GCN/tree/16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 |
ResnetBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torchvision.transforms import *
import torch.nn as nn
import torch.utils.da... | Minsoo2022/graf | ResnetBlock | false | 9,480 | [
"MIT"
] | 0 | e763dd4ef59db1695dfc4bfc7e3f716c92d480a8 | https://github.com/Minsoo2022/graf/tree/e763dd4ef59db1695dfc4bfc7e3f716c92d480a8 |
ResidualBlock | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_add_0(in_ptr0, out_p... | boxiXia/pytorch_sac | ResidualBlock | false | 1,565 | [
"MIT"
] | 0 | ad570845c482498769217b398c22fafaff2ff2f1 | https://github.com/boxiXia/pytorch_sac/tree/ad570845c482498769217b398c22fafaff2ff2f1 |
Conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional
import torch.backends.cudnn
assert_size_stride = torc... | xolbynz/EfficientNetV2-PyTorch- | Conv2d | false | 13,117 | [
"Apache-2.0"
] | 0 | 4b5039755adbd0e5f8ee0611e3d6b5be8c13ecd2 | https://github.com/xolbynz/EfficientNetV2-PyTorch-/tree/4b5039755adbd0e5f8ee0611e3d6b5be8c13ecd2 |
ConcatAvgMaxPooling | import torch
import torch.nn as nn
class ConcatAvgMaxPooling(nn.Module):
def __init__(self, kernel_size=12, stride=1):
super(ConcatAvgMaxPooling, self).__init__()
self.avgpool = nn.AvgPool2d(kernel_size, stride=1)
self.maxpool = nn.MaxPool2d(kernel_size, stride=1)
def forward(self, x... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | SamitHuang/CELNet | ConcatAvgMaxPooling | false | 5,794 | [
"MIT"
] | 1 | 51e067fdb16e723a45a0a60399d568b58cdc011d | https://github.com/SamitHuang/CELNet/tree/51e067fdb16e723a45a0a60399d568b58cdc011d |
BertOutput | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | IsaacChanghau/ReLoCLNet | BertOutput | false | 8,783 | [
"MIT"
] | 31 | 56cb666ce516cce9acbcfce78fb4e95d81e11e54 | https://github.com/IsaacChanghau/ReLoCLNet/tree/56cb666ce516cce9acbcfce78fb4e95d81e11e54 |
KL_Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Little0o0/FedML | KL_Loss | false | 5,551 | [
"Apache-2.0"
] | 1 | 720015c90fcfec88d465a81b1e8fb45676dce9fb | https://github.com/Little0o0/FedML/tree/720015c90fcfec88d465a81b1e8fb45676dce9fb |
NeuralNetMultiplePositionalArguments | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class NeuralNetMultiplePositionalArguments(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArguments, self).__init__()
self.fc1 = torch.nn.Linear(input_size, h... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | almiliMSFT/onnxruntime | NeuralNetMultiplePositionalArguments | false | 14,811 | [
"MIT"
] | 6,036 | c002dc86a364852859ca9642698fcfc5edf22c9d | https://github.com/almiliMSFT/onnxruntime/tree/c002dc86a364852859ca9642698fcfc5edf22c9d |
LandmarkHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | chennnnnnnnn/face_detection | LandmarkHead | false | 3,355 | [
"MIT"
] | 0 | 77d5a9098d9e1a65ac5093a23620ed5d99dc0723 | https://github.com/chennnnnnnnn/face_detection/tree/77d5a9098d9e1a65ac5093a23620ed5d99dc0723 |
ConvTranspose2d | import torch
import torch.nn as nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
class ConvTranspose2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, groups=1, bias=True, dilation=1):
super(ConvTranspose2d, 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
import torch.nn as nn
import torch.cuda
import torch.backends.cudnn
import torch... | JudeDavis1/intel-extension-for-pytorch | ConvTranspose2d | false | 2,581 | [
"Apache-2.0"
] | 0 | 364e34cb4917a709f5108c07d4005bf82f3d5067 | https://github.com/JudeDavis1/intel-extension-for-pytorch/tree/364e34cb4917a709f5108c07d4005bf82f3d5067 |
L1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Dogacel/mmfashion | L1Loss | false | 11,409 | [
"Apache-2.0"
] | 0 | e49613245c8501042edd7aeeaa8fb93e5ea13238 | https://github.com/Dogacel/mmfashion/tree/e49613245c8501042edd7aeeaa8fb93e5ea13238 |
Downsample | import torch
import torch.nn as nn
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | AranKomat/Diff-DALLE | Downsample | false | 13,281 | [
"MIT"
] | 53 | 9418e98e97b599c5c65f16ee168fedf76a29095f | https://github.com/AranKomat/Diff-DALLE/tree/9418e98e97b599c5c65f16ee168fedf76a29095f |
Encoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
"""Estimation of the nonnegative mixture weight by a 1-D conv layer.
"""
def __init__(self, L, N):
super(Encoder, self).__init__()
self.L, self.N = L, N
self.conv1d_U = nn.Conv1d(1, N, ker... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Stanwang1210/HW3_1_Source_Seperation | Encoder | false | 2,857 | [
"MIT"
] | 0 | 8c05850fa4f0f0845c460f9afd06fd8fe3e29dc9 | https://github.com/Stanwang1210/HW3_1_Source_Seperation/tree/8c05850fa4f0f0845c460f9afd06fd8fe3e29dc9 |
Gate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from scipy.stats import entropy as entropy
from scipy.spat... | yanda-wang/AMHSC | Gate | false | 4,737 | [
"MIT"
] | 0 | 9b0a48d1f0992ca3272e7089835a946c49d5f50d | https://github.com/yanda-wang/AMHSC/tree/9b0a48d1f0992ca3272e7089835a946c49d5f50d |
Encoder | import torch
import torch.nn as nn
class Encoder(nn.Module):
"""
Takes in data, returns mu and sigma for variational approximation of latent variable.
"""
def __init__(self, alph_size, seq_len, z_dim=30, hidden_architecture=[
1500, 1500]):
super(Encoder, self).__init__()
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
from torch._inductor.runtime.... | rorymaizels/AC299r | Encoder | false | 7,581 | [
"MIT"
] | 1 | eb4b76ad52a10b9af0579ec3f725ec8fc90b00f1 | https://github.com/rorymaizels/AC299r/tree/eb4b76ad52a10b9af0579ec3f725ec8fc90b00f1 |
TreeStandardize | import torch
from torch import nn
import torch.utils.data
class TreeStandardize(nn.Module):
def forward(self, trees):
mu = torch.mean(trees[0], dim=(1, 2)).unsqueeze(1).unsqueeze(1)
s = torch.std(trees[0], dim=(1, 2)).unsqueeze(1).unsqueeze(1)
standardized = (trees[0] - mu) / (s + 1e-05)
... | 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
import torch.utils.data
assert_size_stride = torch._C._dyn... | balsa-project/balsa | TreeStandardize | false | 3,158 | [
"Apache-2.0"
] | 0 | 36f3fb35d33589928d761b89de52367d18d08fd8 | https://github.com/balsa-project/balsa/tree/36f3fb35d33589928d761b89de52367d18d08fd8 |
IDiv | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_div_0(in_ptr0, in_ptr1, out_ptr1, xnumel,... | NVIDIA-AI-IOT-private/torch2trt | IDiv | false | 10,512 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
resnet_block | import torch
import torch.nn as nn
import torch.nn.functional as F
class resnet_block(nn.Module):
def __init__(self, ef_dim):
super(resnet_block, self).__init__()
self.ef_dim = ef_dim
self.conv_1 = nn.Conv3d(self.ef_dim, self.ef_dim, 1, stride=1,
padding=0, bias=True)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | lwkobe/NMC | resnet_block | false | 15,974 | [
"MIT"
] | 74 | a59c187d35b2f929ea3a94fc2b434061d7f7993a | https://github.com/lwkobe/NMC/tree/a59c187d35b2f929ea3a94fc2b434061d7f7993a |
SimpleConv | import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleConv(nn.Module):
def __init__(self):
super(SimpleConv, self).__init__()
self.conv1 = nn.Conv2d(3, 50, 5, 1)
self.conv2 = nn.Conv2d(50, 100, 5, 1)
self.fc1 = nn.Linear(21 * 21 * 100, 1600)
self.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
import torch.nn as nn
assert_... | junoon53/pcam_challenge | SimpleConv | false | 7,151 | [
"MIT"
] | 1 | 283c98b2d2e211424cdcb56d8230a7a29dc5af46 | https://github.com/junoon53/pcam_challenge/tree/283c98b2d2e211424cdcb56d8230a7a29dc5af46 |
RepeatChannel | import torch
import torch.nn as nn
import torch.nn.parallel
class RepeatChannel(nn.Module):
def __init__(self, repeat):
super(RepeatChannel, self).__init__()
self.repeat = repeat
def forward(self, img):
return img.repeat(1, self.repeat, 1, 1)
def get_inputs():
return [torch.ran... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | AyushExel/GANSketching | RepeatChannel | false | 13,352 | [
"MIT"
] | 598 | c72524ac4425de898087af7a4c554b777a4e2218 | https://github.com/AyushExel/GANSketching/tree/c72524ac4425de898087af7a4c554b777a4e2218 |
SpatialDepthWiseSharedConvolution | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 import nn
import torch.utils.data
import ... | techthiyanes/annotated_deep_learning_paper_implementations | SpatialDepthWiseSharedConvolution | false | 16,565 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
LinearPool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | C3-ASV-Team/torchxrayvision | LinearPool | false | 4,919 | [
"Apache-2.0"
] | 1 | 7e53f0606986562f17a1ffd9f31d006756eff78d | https://github.com/C3-ASV-Team/torchxrayvision/tree/7e53f0606986562f17a1ffd9f31d006756eff78d |
Pointwise | import torch
import torch.nn as nn
import torch.nn.functional as F
class Pointwise(nn.Module):
def __init__(self, Cin=4, K=1, Cout=10):
super(Pointwise, self).__init__()
self.conv1 = nn.Conv2d(Cin, Cout, kernel_size=K, bias=False,
padding=0, stride=1)
def forward(self, 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_... | sfu-arch/TensorBricks | Pointwise | false | 4,692 | [
"MIT"
] | 0 | c46c60d0939b7deb65f103bf34961d47419ce571 | https://github.com/sfu-arch/TensorBricks/tree/c46c60d0939b7deb65f103bf34961d47419ce571 |
LastLevelMaxPool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | Amir4g/maskrcnn-benchmark | LastLevelMaxPool | false | 11,547 | [
"MIT"
] | 0 | c734fef962c3a2782e0055cfb6f825505a4b0c26 | https://github.com/Amir4g/maskrcnn-benchmark/tree/c734fef962c3a2782e0055cfb6f825505a4b0c26 |
Hsigmoid | import torch
from torch import nn
import torch.nn.functional as F
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
def get_inputs():
ret... | 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... | EricFH/SOR | Hsigmoid | false | 8,088 | [
"Apache-2.0"
] | 14 | d644469da16169dd269c6ecaac51b1762649e17a | https://github.com/EricFH/SOR/tree/d644469da16169dd269c6ecaac51b1762649e17a |
Spatial_Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | yhf2022/APAN | Spatial_Attention | false | 4,621 | [
"MIT"
] | 0 | b4dd9a5585f42cccefe01e9525cdc8c59727bdf2 | https://github.com/yhf2022/APAN/tree/b4dd9a5585f42cccefe01e9525cdc8c59727bdf2 |
AddNorm | import torch
import torch.nn.functional as F
import torch.nn as nn
class TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch_... | 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.functional as F
import torch.nn as nn
assert_size_stride = torc... | JakeForsey/pytorch-forecasting | AddNorm | false | 9,124 | [
"MIT"
] | 0 | e5291df3dd8f8d72ecd2b21869f69cebf9456028 | https://github.com/JakeForsey/pytorch-forecasting/tree/e5291df3dd8f8d72ecd2b21869f69cebf9456028 |
SamplingSearch | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | PaccMann/paccmann_chemistry | SamplingSearch | false | 18,367 | [
"MIT"
] | 9 | f7e9735aafb936f837c38b5055c654be178f385f | https://github.com/PaccMann/paccmann_chemistry/tree/f7e9735aafb936f837c38b5055c654be178f385f |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, ignore_target=-1):
super().__init__()
self.ignore_target = ignore_target
def forward(self, input, target):
"""
:param input: (N), logit
:param target: (N), {0, 1}
:return:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | hlesmqh/WS3D | DiceLoss | false | 15,522 | [
"MIT"
] | 100 | 6816eeb135923a59de34ee5d94be2d0fd3ec83f9 | https://github.com/hlesmqh/WS3D/tree/6816eeb135923a59de34ee5d94be2d0fd3ec83f9 |
FC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | TOMeoww/STGAN | FC | false | 1,124 | [
"MIT"
] | 0 | 090a4024999e68f017140312ecfdd0d4dc3dc425 | https://github.com/TOMeoww/STGAN/tree/090a4024999e68f017140312ecfdd0d4dc3dc425 |
ISAB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ernoult/set_transformer | ISAB | false | 12,362 | [
"MIT"
] | 0 | 4b380106e1f43b7eb6315624c57d4d1d38737b78 | https://github.com/ernoult/set_transformer/tree/4b380106e1f43b7eb6315624c57d4d1d38737b78 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Qsingle/MedicalImage | DiceLoss | false | 963 | [
"MIT"
] | 0 | a5020d7d2266669a4d6ffec224430e8b25cc1dfc | https://github.com/Qsingle/MedicalImage/tree/a5020d7d2266669a4d6ffec224430e8b25cc1dfc |
EntmaxBisect | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.autograd import F... | cifkao/entmax | EntmaxBisect | false | 15,222 | [
"MIT"
] | 298 | f18bab9318f9d2471a36545ee0b4c97be6d48a87 | https://github.com/cifkao/entmax/tree/f18bab9318f9d2471a36545ee0b4c97be6d48a87 |
CrossLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.optim
assert_size_stride = torch._C._dynamo.g... | piers-hinds/rtdl | CrossLayer | false | 10,627 | [
"Apache-2.0"
] | 0 | 66cf9b90d2269395152dabf32653bdd599ddb12e | https://github.com/piers-hinds/rtdl/tree/66cf9b90d2269395152dabf32653bdd599ddb12e |
DAInsHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | Flsahkong/Domain-Adaptive-Faster-RCNN-PyTorch | DAInsHead | false | 5,258 | [
"MIT"
] | 1 | 2d3ed73714ea5d5ff52d0b2ea51396a498ae6abe | https://github.com/Flsahkong/Domain-Adaptive-Faster-RCNN-PyTorch/tree/2d3ed73714ea5d5ff52d0b2ea51396a498ae6abe |
DAInsHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | shreyasrajesh/DA-Object-Detection | DAInsHead | false | 4,405 | [
"MIT"
] | 0 | b1919fdf49a9f1589c48c63e0a3122852e5557ce | https://github.com/shreyasrajesh/DA-Object-Detection/tree/b1919fdf49a9f1589c48c63e0a3122852e5557ce |
SAN | import torch
import torch.nn as nn
class SAN(nn.Module):
def __init__(self, d_model, nhead, dropout=0.1):
super(SAN, self).__init__()
self.d_model = d_model
self.nhead = nhead
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.dropout = nn.Dropout... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | yuriy-os/russian-reviews-bert-e2e-absa | SAN | false | 16,784 | [
"Apache-2.0"
] | 293 | 369a6179353e3bf28643e8e9347b624078e38bf4 | https://github.com/yuriy-os/russian-reviews-bert-e2e-absa/tree/369a6179353e3bf28643e8e9347b624078e38bf4 |
ClippedLinearQuantization | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | Emily0219/distiller | ClippedLinearQuantization | false | 5,141 | [
"Apache-2.0"
] | 1 | 445ed35b671fb54586acc280b53d951f18bf97ae | https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae |
WL1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | tccoin/UM-545-Machine-Learning | WL1Loss | false | 4,410 | [
"MIT"
] | 0 | 0854d7ad7e546c009edeb4a4d3e507ce95b99cf8 | https://github.com/tccoin/UM-545-Machine-Learning/tree/0854d7ad7e546c009edeb4a4d3e507ce95b99cf8 |
Sinkhorn_Net | import torch
from torch import nn
import torch.cuda
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Features(nn.Module):
def __init__(self, latent_dim, output_dim, dropout_prob):
"""
In the constructor we instantiate two nn.Linear modu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | sfox14/butterfly | Sinkhorn_Net | false | 16,387 | [
"Apache-2.0"
] | 52 | 13cc15cee5bdb7adaf376219aaf20fab0459e9ef | https://github.com/sfox14/butterfly/tree/13cc15cee5bdb7adaf376219aaf20fab0459e9ef |
Downsample | import torch
class Downsample(torch.nn.Module):
def __init__(self, s, use_max=False, batch_mode=False):
super(Downsample, self).__init__()
self.batch_mode = batch_mode
if use_max:
layer = torch.nn.MaxPool3d(s, stride=s)
else:
layer = torch.nn.Conv3d(1, 1, 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tens... | JasonQSY/Associative3D | Downsample | false | 8,347 | [
"MIT"
] | 25 | c50818b593ec48c38ed7ee3e109c23531089da32 | https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32 |
NaiveGroupNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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
from torch.nn import Parameter
from torch.nn import... | hav4ik/AdelaiDet | NaiveGroupNorm | false | 3,719 | [
"BSD-2-Clause"
] | 0 | 6ed9c1e1a25a3e25dddfa858ce0f219a30593ce2 | https://github.com/hav4ik/AdelaiDet/tree/6ed9c1e1a25a3e25dddfa858ce0f219a30593ce2 |
ConvSqu | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | JuliannaChaykina/social-distance | ConvSqu | false | 2,425 | [
"Apache-2.0"
] | 0 | 1c8ade043254b78de49a1244d438203ddb38c586 | https://github.com/JuliannaChaykina/social-distance/tree/1c8ade043254b78de49a1244d438203ddb38c586 |
PartialBCELoss | import torch
class PartialBCELoss(torch.nn.Module):
def __init__(self):
super(PartialBCELoss, self).__init__()
self.log_sigmoid = torch.nn.LogSigmoid()
def forward(self, logits, targets, targets_mask, weights=None):
pos_vals = -targets * self.log_sigmoid(logits)
neg_vals = -s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size... | HKUST-KnowComp/MLMET | PartialBCELoss | false | 8,169 | [
"MIT"
] | 10 | ae1188a929a5ca6a8e087bb091853b328ea2c7e7 | https://github.com/HKUST-KnowComp/MLMET/tree/ae1188a929a5ca6a8e087bb091853b328ea2c7e7 |
CDiceLoss | import torch
import torch._C
import torch.serialization
from torch import nn
import torch.nn.functional as F
from typing import *
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".... | 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._C
import... | shuaizzZ/mmsegmentation | CDiceLoss | false | 4,324 | [
"Apache-2.0"
] | 0 | a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c | https://github.com/shuaizzZ/mmsegmentation/tree/a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c |
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