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 |
|---|---|---|---|---|---|---|---|---|---|---|
NoiseInjection | import torch
from torch import nn
class NoiseInjection(nn.Module):
def __init__(self):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1))
def forward(self, image, noise=None):
if noise is None:
batch, _, height, width = image.shape
noise = image.new... | 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... | ArashVahabpour/encoder4editing-contrastive | NoiseInjection | false | 13,271 | [
"MIT"
] | 1,051 | 1b91afe1693e01a41118e1ce2451b7d14bec51f4 | https://github.com/ArashVahabpour/encoder4editing-contrastive/tree/1b91afe1693e01a41118e1ce2451b7d14bec51f4 |
SemanticComposite | import torch
import torch.nn as nn
class SemanticComposite(nn.Module):
"""
SemanticComposite module.
Apply a self-attention layer and a semantic composite fuse gate to compute the
encoding result of one tensor.
:param in_features: Feature size of input.
:param dropout_rate: The dropout rate.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ambitioner-c/MatchZoo-py | SemanticComposite | false | 13,272 | [
"Apache-2.0"
] | 468 | bb088edce8e01c2c2326ca1a8ac647f0d23f088d | https://github.com/Ambitioner-c/MatchZoo-py/tree/bb088edce8e01c2c2326ca1a8ac647f0d23f088d |
MatchingTensor | import torch
import torch.nn as nn
import torch.nn.functional as F
class MatchingTensor(nn.Module):
"""
Module that captures the basic interactions between two tensors.
:param matching_dims: Word dimension of two interaction texts.
:param channels: Number of word interaction tensor channels.
:par... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ambitioner-c/MatchZoo-py | MatchingTensor | false | 13,273 | [
"Apache-2.0"
] | 468 | bb088edce8e01c2c2326ca1a8ac647f0d23f088d | https://github.com/Ambitioner-c/MatchZoo-py/tree/bb088edce8e01c2c2326ca1a8ac647f0d23f088d |
EqualLinear | from torch.autograd import Function
import math
import torch
from torch import nn
import torch.nn.functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
class FusedLeakyReLUFunctionBackward(Function):
@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.autograd import Function
import math
from torch import nn
assert_size... | ArashVahabpour/encoder4editing-contrastive | EqualLinear | false | 13,274 | [
"MIT"
] | 1,051 | 1b91afe1693e01a41118e1ce2451b7d14bec51f4 | https://github.com/ArashVahabpour/encoder4editing-contrastive/tree/1b91afe1693e01a41118e1ce2451b7d14bec51f4 |
MatchModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class MatchModule(nn.Module):
"""
Computing the match representation for Match LSTM.
:param hidden_size: Size of hidden vectors.
:param dropout_rate: Dropout rate of the projection layer. Defaults to 0.
Examples:
>>> impo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ambitioner-c/MatchZoo-py | MatchModule | false | 13,275 | [
"Apache-2.0"
] | 468 | bb088edce8e01c2c2326ca1a8ac647f0d23f088d | https://github.com/Ambitioner-c/MatchZoo-py/tree/bb088edce8e01c2c2326ca1a8ac647f0d23f088d |
QKVAttentionLegacy | import math
import torch
import numpy as np
import torch as th
import torch.nn as nn
def count_flops_attn(model, _x, y):
"""
A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | AranKomat/Diff-DALLE | QKVAttentionLegacy | false | 13,276 | [
"MIT"
] | 53 | 9418e98e97b599c5c65f16ee168fedf76a29095f | https://github.com/AranKomat/Diff-DALLE/tree/9418e98e97b599c5c65f16ee168fedf76a29095f |
Greedy | import torch
import torch.nn as nn
class Greedy(nn.Module):
def __init__(self):
super().__init__()
def forward(self, log_p):
return torch.argmax(log_p, dim=1).long()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ArChiiii/TSP_DRL_PtrNet | Greedy | false | 13,277 | [
"MIT"
] | 59 | 8218a508c563d9641b341dff5a6241d90e4e031b | https://github.com/ArChiiii/TSP_DRL_PtrNet/tree/8218a508c563d9641b341dff5a6241d90e4e031b |
CentralizedCritic | import torch
import torch.nn as nn
import torch.nn.functional as F
class CentralizedCritic(nn.Module):
def __init__(self, obs_dim, action_dim):
super(CentralizedCritic, self).__init__()
self.obs_dim = obs_dim
self.action_dim = action_dim
self.linear1 = nn.Linear(self.obs_dim, 1024... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | AYUSHKABIRVERMA/Multi-agent-reinforcement-learning | CentralizedCritic | false | 13,278 | [
"MIT"
] | 62 | cd7c13d723cd74dc278939d81d5dd1b0906cee7c | https://github.com/AYUSHKABIRVERMA/Multi-agent-reinforcement-learning/tree/cd7c13d723cd74dc278939d81d5dd1b0906cee7c |
PixelNorm | import torch
from torch import nn
class PixelNorm(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=
True) + 1e-08)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ArashVahabpour/encoder4editing-contrastive | PixelNorm | false | 13,279 | [
"MIT"
] | 1,051 | 1b91afe1693e01a41118e1ce2451b7d14bec51f4 | https://github.com/ArashVahabpour/encoder4editing-contrastive/tree/1b91afe1693e01a41118e1ce2451b7d14bec51f4 |
BCELoss2d | import torch
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
class BCELoss2d(nn.Module):
"""
Binary Cross Entropy loss function
"""
def __init__(self):
super(BCELoss2d, self).__init__()
self.bce_loss = nn.BCEWithLogitsLoss()
def forward(self, logits, lab... | 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... | ArmenGhambaryan/kaggle_carvana_segmentation | BCELoss2d | false | 13,280 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
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 |
EqualConv2d | import math
import torch
from torch import nn
import torch.nn.functional as F
class EqualConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_channel, in_channel,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | ArashVahabpour/encoder4editing-contrastive | EqualConv2d | false | 13,282 | [
"MIT"
] | 1,051 | 1b91afe1693e01a41118e1ce2451b7d14bec51f4 | https://github.com/ArashVahabpour/encoder4editing-contrastive/tree/1b91afe1693e01a41118e1ce2451b7d14bec51f4 |
GeLU2 | import torch
import torch.nn as nn
class GeLU2(nn.Module):
def forward(self, x):
return (1.702 * x).sigmoid() * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AshBT/VideoGPT | GeLU2 | false | 13,283 | [
"MIT"
] | 396 | a823bc734af3387129f3bd624caad3db270707f2 | https://github.com/AshBT/VideoGPT/tree/a823bc734af3387129f3bd624caad3db270707f2 |
Upsample | import torch
import torch.nn as nn
import torch.nn.functional as F
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:
re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AranKomat/Diff-DALLE | Upsample | false | 13,284 | [
"MIT"
] | 53 | 9418e98e97b599c5c65f16ee168fedf76a29095f | https://github.com/AranKomat/Diff-DALLE/tree/9418e98e97b599c5c65f16ee168fedf76a29095f |
SpectrogramMasker | import torch
import torch.nn as nn
import torch.nn.functional as F
class SpectrogramMasker(nn.Module):
"""
Helper class transforming wave-level mask to spectrogram-level mask
"""
def __init__(self, win_length: 'int', hop_length: 'int'):
super().__init__()
self.win_length = win_length
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | AppleHolic/pytorch_sound | SpectrogramMasker | false | 13,285 | [
"BSD-2-Clause"
] | 86 | 2320516d21d70c406d1dee74927e238972c96106 | https://github.com/AppleHolic/pytorch_sound/tree/2320516d21d70c406d1dee74927e238972c96106 |
TransformerFFN | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
return m
def gelu(x):
"""
GELU activation
https://arxiv.org/abs/1606.08415
https://github.com/huggingface/pytorch-op... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | AlexShypula/CodeGen | TransformerFFN | false | 13,286 | [
"MIT"
] | 241 | 2e5f8090c4369fd3f0ebec4a867503edc1362d5d | https://github.com/AlexShypula/CodeGen/tree/2e5f8090c4369fd3f0ebec4a867503edc1362d5d |
SEModule | from torch.nn import Module
import torch
from torch.nn import Conv2d
from torch.nn import ReLU
from torch.nn import Sigmoid
from torch.nn import AdaptiveAvgPool2d
class SEModule(Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool = AdaptiveAvgPool2d(1)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
f... | ArashVahabpour/encoder4editing-contrastive | SEModule | false | 13,287 | [
"MIT"
] | 1,051 | 1b91afe1693e01a41118e1ce2451b7d14bec51f4 | https://github.com/ArashVahabpour/encoder4editing-contrastive/tree/1b91afe1693e01a41118e1ce2451b7d14bec51f4 |
Conv3BN | import torch
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
def conv3x3(in_, out):
return nn.Conv2d(in_, out, 3, padding=1)
class Conv3BN(nn.Module):
def __init__(self, in_: 'int', out: 'int', bn=False):
super().__init__()
self.conv = conv3x3(in_, out)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ArmenGhambaryan/kaggle_carvana_segmentation | Conv3BN | false | 13,288 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
LayerNorm32 | import torch
import torch.nn as nn
class LayerNorm32(nn.LayerNorm):
def forward(self, x):
return super().forward(x.float().transpose(1, 2)).type(x.dtype
).transpose(1, 2)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'normalized_shape': 4}... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AranKomat/Diff-DALLE | LayerNorm32 | false | 13,289 | [
"MIT"
] | 53 | 9418e98e97b599c5c65f16ee168fedf76a29095f | https://github.com/AranKomat/Diff-DALLE/tree/9418e98e97b599c5c65f16ee168fedf76a29095f |
BCEDiceLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
def dice_loss(preds, trues, weight=None, is_average=True):
num = preds.size(0)
preds = preds.view(num, -1)
trues = trues.view(num, -1)
if weight is not None:
w = torch.autogra... | 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... | ArmenGhambaryan/kaggle_carvana_segmentation | BCEDiceLoss | false | 13,290 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
DiceLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
def dice_loss(preds, trues, weight=None, is_average=True):
num = preds.size(0)
preds = preds.view(num, -1)
trues = trues.view(num, -1)
if weight is not None:
w = torch.autogra... | 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.backends.cudnn
import torch.utils.data
assert_size_str... | ArmenGhambaryan/kaggle_carvana_segmentation | DiceLoss | false | 13,291 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
DiceScore | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
class DiceScore(nn.Module):
def __init__(self, threshold=0.5):
super(DiceScore, self).__init__()
self.threshold = threshold
def forward(self, logits, labels):
probs ... | 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.backends.cudnn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
em... | ArmenGhambaryan/kaggle_carvana_segmentation | DiceScore | false | 13,292 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
Expand | import torch
import torch.nn as nn
import torch.utils.data
class Expand(nn.Module):
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
N, C, H, W = x.size()
s = self.gain
x = x.view(N, s, s, C // s ** 2, H, W)
x = x.permute(0... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | Arui66/FPSAutomaticAiming | Expand | false | 13,293 | [
"Apache-2.0"
] | 129 | 87674385d42b065b984b38a2ff59e7f2d4f07dc9 | https://github.com/Arui66/FPSAutomaticAiming/tree/87674385d42b065b984b38a2ff59e7f2d4f07dc9 |
Hardswish | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Hardswish(nn.Module):
@staticmethod
def forward(x):
return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | Arui66/FPSAutomaticAiming | Hardswish | false | 13,294 | [
"Apache-2.0"
] | 129 | 87674385d42b065b984b38a2ff59e7f2d4f07dc9 | https://github.com/Arui66/FPSAutomaticAiming/tree/87674385d42b065b984b38a2ff59e7f2d4f07dc9 |
MemoryEfficientMish | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class MemoryEfficientMish(nn.Module):
class F(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return x.mul(torch.tanh(F.softplus(x)))
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
import t... | Arui66/FPSAutomaticAiming | MemoryEfficientMish | false | 13,295 | [
"Apache-2.0"
] | 129 | 87674385d42b065b984b38a2ff59e7f2d4f07dc9 | https://github.com/Arui66/FPSAutomaticAiming/tree/87674385d42b065b984b38a2ff59e7f2d4f07dc9 |
UNetModule | import torch
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
def conv3x3(in_, out):
return nn.Conv2d(in_, out, 3, padding=1)
class Conv3BN(nn.Module):
def __init__(self, in_: 'int', out: 'int', bn=False):
super().__init__()
self.conv = conv3x3(in_, out)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ArmenGhambaryan/kaggle_carvana_segmentation | UNetModule | false | 13,296 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
WeightedSoftDiceLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
class WeightedSoftDiceLoss(nn.Module):
def __init__(self):
super(WeightedSoftDiceLoss, self).__init__()
def forward(self, logits, labels, weights):
probs = F.sigmoid(logits)... | 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.backends.cudnn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
em... | ArmenGhambaryan/kaggle_carvana_segmentation | WeightedSoftDiceLoss | false | 13,297 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
WeightedBCELoss2d | import torch
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
class WeightedBCELoss2d(nn.Module):
def __init__(self):
super(WeightedBCELoss2d, self).__init__()
def forward(self, logits, labels, weights):
w = weights.view(-1)
logits = logits.view(-1)
g... | 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
... | ArmenGhambaryan/kaggle_carvana_segmentation | WeightedBCELoss2d | false | 13,298 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
SoftDiceLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
class SoftDiceLoss(nn.Module):
def __init__(self):
super(SoftDiceLoss, self).__init__()
def forward(self, logits, labels):
probs = F.sigmoid(logits)
num = labels.siz... | 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.backends.cudnn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
em... | ArmenGhambaryan/kaggle_carvana_segmentation | SoftDiceLoss | false | 13,299 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
LocationLayer | import torch
import torch.utils.data
from torch import nn
class LinearNorm(torch.nn.Module):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
super(LinearNorm, self).__init__()
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
torch.nn.init.xavier_unifor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | AeroXi/Tacotron2-Mandarin | LocationLayer | false | 13,300 | [
"MIT"
] | 67 | b7bc213d1c1a9c3e2f2e11f69f586c2582010668 | https://github.com/AeroXi/Tacotron2-Mandarin/tree/b7bc213d1c1a9c3e2f2e11f69f586c2582010668 |
BCEBlurWithLogitsLoss | import torch
import torch.nn as nn
import torch.utils.data
class BCEBlurWithLogitsLoss(nn.Module):
def __init__(self, alpha=0.05):
super(BCEBlurWithLogitsLoss, self).__init__()
self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')
self.alpha = alpha
def forward(self, pred, true):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Arui66/FPSAutomaticAiming | BCEBlurWithLogitsLoss | false | 13,301 | [
"Apache-2.0"
] | 129 | 87674385d42b065b984b38a2ff59e7f2d4f07dc9 | https://github.com/Arui66/FPSAutomaticAiming/tree/87674385d42b065b984b38a2ff59e7f2d4f07dc9 |
Sum | import torch
import torch.nn as nn
import torch.utils.data
class Sum(nn.Module):
def __init__(self, n, weight=False):
super(Sum, self).__init__()
self.weight = weight
self.iter = range(n - 1)
if weight:
self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=Tru... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | Arui66/FPSAutomaticAiming | Sum | false | 13,302 | [
"Apache-2.0"
] | 129 | 87674385d42b065b984b38a2ff59e7f2d4f07dc9 | https://github.com/Arui66/FPSAutomaticAiming/tree/87674385d42b065b984b38a2ff59e7f2d4f07dc9 |
Contract | import torch
import torch.nn as nn
import torch.utils.data
class Contract(nn.Module):
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
N, C, H, W = x.size()
s = self.gain
x = x.view(N, C, H // s, s, W // s, s)
x = x.permute... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | Arui66/FPSAutomaticAiming | Contract | false | 13,303 | [
"Apache-2.0"
] | 129 | 87674385d42b065b984b38a2ff59e7f2d4f07dc9 | https://github.com/Arui66/FPSAutomaticAiming/tree/87674385d42b065b984b38a2ff59e7f2d4f07dc9 |
Classify | import torch
import torch.nn as nn
import torch.utils.data
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Classify(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
super(Classify, self).__init__()
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... | Arui66/FPSAutomaticAiming | Classify | false | 13,304 | [
"Apache-2.0"
] | 129 | 87674385d42b065b984b38a2ff59e7f2d4f07dc9 | https://github.com/Arui66/FPSAutomaticAiming/tree/87674385d42b065b984b38a2ff59e7f2d4f07dc9 |
BinaryReg | import torch
from typing import Optional
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
class BinaryReg(nn.Module):
"""Regularization for encouraging the outputs to be binary.
Args:
pred (torch.Tensor): foreground logits.
mask (Optional[torch.Tensor], optional): weight... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | Atharva-Peshkar/pytorch_connectomics | BinaryReg | false | 13,305 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
AddBroadcastPosEmbed | import torch
import torch.nn as nn
def tensor_slice(x, begin, size):
assert all([(b >= 0) for b in begin])
size = [(l - b if s == -1 else s) for s, b, l in zip(size, begin, x.shape)]
assert all([(s >= 0) for s in size])
slices = [slice(b, b + s) for b, s in zip(begin, size)]
return x[slices]
cla... | 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... | AshBT/VideoGPT | AddBroadcastPosEmbed | false | 13,306 | [
"MIT"
] | 396 | a823bc734af3387129f3bd624caad3db270707f2 | https://github.com/AshBT/VideoGPT/tree/a823bc734af3387129f3bd624caad3db270707f2 |
WeightedBCE | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class WeightedBCE(nn.Module):
"""Weighted binary cross-entropy.
"""
def __init__(self, size_average=True, reduce=True):
super().__init__()
self.size_average = size_average
... | 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... | Atharva-Peshkar/pytorch_connectomics | WeightedBCE | false | 13,307 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
ContourDTConsistency | import torch
from typing import Optional
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
class ContourDTConsistency(nn.Module):
"""Consistency regularization between the instance contour map and
signed distance transform.
Args:
pred1 (torch.Tensor): contour logits.
... | 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... | Atharva-Peshkar/pytorch_connectomics | ContourDTConsistency | false | 13,308 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
outconv | import torch
import torch.nn as nn
class outconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(outconv, self).__init__()
self.conv = nn.Conv2d(in_ch, out_ch, 1)
def forward(self, x):
x = self.conv(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AnonymousAuthors444/VEC_VAD | outconv | false | 13,309 | [
"MIT"
] | 67 | 0072bf857030e621e2f9c12689407b81e45ed603 | https://github.com/AnonymousAuthors444/VEC_VAD/tree/0072bf857030e621e2f9c12689407b81e45ed603 |
ModulatedConv2d | from torch.autograd import Function
import math
import torch
from torch import nn
import torch.nn.functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
def make_kernel(k):
k = torch.tensor(k, dtype=torch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.autograd... | ArashVahabpour/encoder4editing-contrastive | ModulatedConv2d | false | 13,310 | [
"MIT"
] | 1,051 | 1b91afe1693e01a41118e1ce2451b7d14bec51f4 | https://github.com/ArashVahabpour/encoder4editing-contrastive/tree/1b91afe1693e01a41118e1ce2451b7d14bec51f4 |
LayerNorm | import torch
import torch.nn as nn
import torch.optim
class LayerNorm(nn.Module):
"""Construct a layernorm module in the OpenAI style (epsilon inside the square root)."""
def __init__(self, n_state, e=1e-05):
super(LayerNorm, self).__init__()
self.g = nn.Parameter(torch.ones(n_state))
... | 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.... | Arsenaut/comet-commonsense | LayerNorm | false | 13,311 | [
"Apache-2.0"
] | 521 | ffa4691ba6bfcb46ea2ed4ce91de5c6815f66e52 | https://github.com/Arsenaut/comet-commonsense/tree/ffa4691ba6bfcb46ea2ed4ce91de5c6815f66e52 |
SamePadConvTranspose3d | import torch
import torch.nn as nn
import torch.nn.functional as F
class SamePadConvTranspose3d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
bias=True):
super().__init__()
if isinstance(kernel_size, int):
kernel_size = (kernel_size,) * 3
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AshBT/VideoGPT | SamePadConvTranspose3d | false | 13,312 | [
"MIT"
] | 396 | a823bc734af3387129f3bd624caad3db270707f2 | https://github.com/AshBT/VideoGPT/tree/a823bc734af3387129f3bd624caad3db270707f2 |
SamePadConv3d | import torch
import torch.nn as nn
import torch.nn.functional as F
class SamePadConv3d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
bias=True):
super().__init__()
if isinstance(kernel_size, int):
kernel_size = (kernel_size,) * 3
if i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AshBT/VideoGPT | SamePadConv3d | false | 13,313 | [
"MIT"
] | 396 | a823bc734af3387129f3bd624caad3db270707f2 | https://github.com/AshBT/VideoGPT/tree/a823bc734af3387129f3bd624caad3db270707f2 |
NonoverlapReg | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
class NonoverlapReg(nn.Module):
"""Regularization to prevent overlapping prediction of pre- and post-synaptic
masks in synaptic polarity prediction ("1" in MODEL.TARGET_OPT).
Args:
fg_masked (bool): mask the regul... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | Atharva-Peshkar/pytorch_connectomics | NonoverlapReg | false | 13,314 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
ForegroundDTConsistency | import torch
from typing import Optional
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class ForegroundDTConsistency(nn.Module):
"""Consistency regularization between the binary foreground mask and
signed distance transform.
Args:
pred1 (to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Atharva-Peshkar/pytorch_connectomics | ForegroundDTConsistency | false | 13,315 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
DiaynDiscrimNet | import torch
import torch.nn as nn
from torch.nn.init import kaiming_uniform_
import torch.utils.data
def weight_init(m):
if m.__class__.__name__ == 'Linear':
m.weight.data.copy_(kaiming_uniform_(m.weight.data))
m.bias.data.fill_(0)
class DiaynDiscrimNet(nn.Module):
def __init__(self, f_spa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | AswinRetnakumar/Machina | DiaynDiscrimNet | false | 13,316 | [
"MIT"
] | 302 | 6519935ca4553192ac99fc1c7c1e7cab9dd72693 | https://github.com/AswinRetnakumar/Machina/tree/6519935ca4553192ac99fc1c7c1e7cab9dd72693 |
MultiHeadAttention | import torch
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
"""
Multi Head Attention module. https://arxiv.org/abs/1706.03762
This version has no normalization module and suppose self-attention
"""
def __init__(self, hidden_dim:... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AppleHolic/pytorch_sound | MultiHeadAttention | false | 13,317 | [
"BSD-2-Clause"
] | 86 | 2320516d21d70c406d1dee74927e238972c96106 | https://github.com/AppleHolic/pytorch_sound/tree/2320516d21d70c406d1dee74927e238972c96106 |
DiceLoss | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
class DiceLoss(nn.Module):
"""DICE loss.
"""
def __init__(self, reduce=True, smooth=100.0, power=1):
super(DiceLoss, self).__init__()
self.smooth = smooth
self.reduce = reduce
self.power = ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | Atharva-Peshkar/pytorch_connectomics | DiceLoss | false | 13,318 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
Fp32LayerNorm | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Fp32LayerNorm(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, 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.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
impor... | AppleHolic/fairseq | Fp32LayerNorm | false | 13,319 | [
"MIT"
] | 429 | c5b32cb2bde59a7bb7987b22864731fe927523d4 | https://github.com/AppleHolic/fairseq/tree/c5b32cb2bde59a7bb7987b22864731fe927523d4 |
TransformerLayer | import torch
import torch.nn as nn
import torch.utils.data
class TransformerLayer(nn.Module):
def __init__(self, c, num_heads):
super().__init__()
self.q = nn.Linear(c, c, bias=False)
self.k = nn.Linear(c, c, bias=False)
self.v = nn.Linear(c, c, bias=False)
self.ma = nn.Mu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Arui66/FPSAutomaticAiming | TransformerLayer | false | 13,320 | [
"Apache-2.0"
] | 129 | 87674385d42b065b984b38a2ff59e7f2d4f07dc9 | https://github.com/Arui66/FPSAutomaticAiming/tree/87674385d42b065b984b38a2ff59e7f2d4f07dc9 |
ZeroPad1d | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ZeroPad1d(nn.Module):
def __init__(self, pad_left, pad_right):
super().__init__()
self.pad_left = pad_left
self.p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
assert_size_str... | AppleHolic/fairseq | ZeroPad1d | false | 13,321 | [
"MIT"
] | 429 | c5b32cb2bde59a7bb7987b22864731fe927523d4 | https://github.com/AppleHolic/fairseq/tree/c5b32cb2bde59a7bb7987b22864731fe927523d4 |
ToRGB | from torch.autograd import Function
import math
import torch
from torch import nn
import torch.nn.functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
def make_kernel(k):
k = torch.tensor(k, dtype=torch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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
import math
from torch import nn
import torc... | ArashVahabpour/encoder4editing-contrastive | ToRGB | false | 13,322 | [
"MIT"
] | 1,051 | 1b91afe1693e01a41118e1ce2451b7d14bec51f4 | https://github.com/ArashVahabpour/encoder4editing-contrastive/tree/1b91afe1693e01a41118e1ce2451b7d14bec51f4 |
Fp32GroupNorm | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Fp32GroupNorm(nn.GroupNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, 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.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
impor... | AppleHolic/fairseq | Fp32GroupNorm | false | 13,323 | [
"MIT"
] | 429 | c5b32cb2bde59a7bb7987b22864731fe927523d4 | https://github.com/AppleHolic/fairseq/tree/c5b32cb2bde59a7bb7987b22864731fe927523d4 |
WeightedCE | import torch
from typing import Optional
from typing import List
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class WeightedCE(nn.Module):
"""Mask weighted multi-class cross-entropy (CE) loss.
"""
def __init__(self, class_weight: 'Optional[List[fl... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from typing import Opt... | Atharva-Peshkar/pytorch_connectomics | WeightedCE | false | 13,324 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
DiscrimNet | import torch
import torch.nn as nn
from torch.nn.init import kaiming_uniform_
import torch.utils.data
def weight_init(m):
if m.__class__.__name__ == 'Linear':
m.weight.data.copy_(kaiming_uniform_(m.weight.data))
m.bias.data.fill_(0)
class DiscrimNet(nn.Module):
def __init__(self, observatio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | AswinRetnakumar/Machina | DiscrimNet | false | 13,325 | [
"MIT"
] | 302 | 6519935ca4553192ac99fc1c7c1e7cab9dd72693 | https://github.com/AswinRetnakumar/Machina/tree/6519935ca4553192ac99fc1c7c1e7cab9dd72693 |
VNet | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import kaiming_uniform_
import torch.utils.data
def weight_init(m):
if m.__class__.__name__ == 'Linear':
m.weight.data.copy_(kaiming_uniform_(m.weight.data))
m.bias.data.fill_(0)
class VNet(nn.Module):
def... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | AswinRetnakumar/Machina | VNet | false | 13,326 | [
"MIT"
] | 302 | 6519935ca4553192ac99fc1c7c1e7cab9dd72693 | https://github.com/AswinRetnakumar/Machina/tree/6519935ca4553192ac99fc1c7c1e7cab9dd72693 |
FocalLoss | import torch
from torch import nn
class FocalLoss(nn.Module):
"""Implementation of Focal Loss.
Focal loss was proposed in `Focal Loss for Dense Object Detection_.
<https://arxiv.org/abs/1708.02002>`_.
Args:
gamma : The focal parameter. Defaults to 0.
eps : Constant for comput... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | Atharva-Phatak/torchflare | FocalLoss | false | 13,327 | [
"Apache-2.0"
] | 86 | 945f4bee73a855edd8cb19cd646731155499a27f | https://github.com/Atharva-Phatak/torchflare/tree/945f4bee73a855edd8cb19cd646731155499a27f |
RSoftmax | import torch
from torch.nn import functional as F
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
class RSoftmax(nn.Module):
"""Radix Softmax module in ``SplitAttentionConv2d``.
Args:
radix (int): Radix of input.
groups (int): Groups of 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 math as tl_math
import torch.nn as nn
... | Atten4Vis/DemystifyLocalViT | RSoftmax | false | 13,328 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
WeightedBCEWithLogitsLoss | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class WeightedBCEWithLogitsLoss(nn.Module):
"""Weighted binary cross-entropy with logits.
"""
def __init__(self, size_average=True, reduce=True, eps=0.0):
super().__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Atharva-Peshkar/pytorch_connectomics | WeightedBCEWithLogitsLoss | false | 13,329 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
BCEFocalLoss | import torch
from torch import nn
class BCEFocalLoss(nn.Module):
"""Implementation of Focal Loss for Binary Classification Problems.
Focal loss was proposed in `Focal Loss for Dense Object Detection_.
<https://arxiv.org/abs/1708.02002>`_.
"""
def __init__(self, gamma=0, eps=1e-07, reduction='mea... | 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 ... | Atharva-Phatak/torchflare | BCEFocalLoss | false | 13,330 | [
"Apache-2.0"
] | 86 | 945f4bee73a855edd8cb19cd646731155499a27f | https://github.com/Atharva-Phatak/torchflare/tree/945f4bee73a855edd8cb19cd646731155499a27f |
QNet | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import kaiming_uniform_
from torch.nn.init import uniform_
import torch.utils.data
def mini_weight_init(m):
if m.__class__.__name__ == 'Linear':
m.weight.data.copy_(uniform_(m.weight.data, -0.003, 0.003))
m.bias.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | AswinRetnakumar/Machina | QNet | false | 13,331 | [
"MIT"
] | 302 | 6519935ca4553192ac99fc1c7c1e7cab9dd72693 | https://github.com/AswinRetnakumar/Machina/tree/6519935ca4553192ac99fc1c7c1e7cab9dd72693 |
WeightedBCEFocalLoss | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class WeightedBCEFocalLoss(nn.Module):
"""Weighted binary focal loss with logits.
"""
def __init__(self, gamma=2.0, alpha=0.25, eps=0.0):
super().__init__()
self.eps = eps
... | 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... | Atharva-Peshkar/pytorch_connectomics | WeightedBCEFocalLoss | false | 13,332 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
SSE | import torch
from torch import nn
class SSE(nn.Module):
"""SSE : Channel Squeeze and Spatial Excitation block.
Paper : https://arxiv.org/abs/1803.02579
Adapted from
https://www.kaggle.com/c/tgs-salt-identification-challenge/discussion/66178
"""
def __init__(self, in_channels):
"""Co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | Atharva-Phatak/torchflare | SSE | false | 13,333 | [
"Apache-2.0"
] | 86 | 945f4bee73a855edd8cb19cd646731155499a27f | https://github.com/Atharva-Phatak/torchflare/tree/945f4bee73a855edd8cb19cd646731155499a27f |
ModelNet | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import kaiming_uniform_
import torch.utils.data
def weight_init(m):
if m.__class__.__name__ == 'Linear':
m.weight.data.copy_(kaiming_uniform_(m.weight.data))
m.bias.data.fill_(0)
class ModelNet(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
import torch.nn as nn
from to... | AswinRetnakumar/Machina | ModelNet | false | 13,334 | [
"MIT"
] | 302 | 6519935ca4553192ac99fc1c7c1e7cab9dd72693 | https://github.com/AswinRetnakumar/Machina/tree/6519935ca4553192ac99fc1c7c1e7cab9dd72693 |
TVLoss | import torch
import torch.nn as nn
class TVLoss(nn.Module):
def __init__(self, TVLoss_weight=1):
super(TVLoss, self).__init__()
self.TVLoss_weight = TVLoss_weight
def forward(self, x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
count_h = (x.s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Axrid/cv_template | TVLoss | false | 13,335 | [
"MIT"
] | 69 | 5c344692a1fcfb08b75d7104bcc78307b5640ecf | https://github.com/Axrid/cv_template/tree/5c344692a1fcfb08b75d7104bcc78307b5640ecf |
WSDiceLoss | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
class WSDiceLoss(nn.Module):
def __init__(self, smooth=100.0, power=2.0, v2=0.85, v1=0.15):
super().__init__()
self.smooth = smooth
self.power = power
self.v2 = v2
self.v1 = v1
def dic... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | Atharva-Peshkar/pytorch_connectomics | WSDiceLoss | false | 13,336 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
InputInjection | import torch
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
class InputInjection(nn.Module):
"""Downsampling module for CGNet."""
def __init__(self, num_downsampling):
super(InputInjection, self).__init__()
self.pool = nn.ModuleList()
... | 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._C
import torch.serialization
from torch import optim as optim
assert_size_stride = torch._C._dynamo.guar... | Atten4Vis/DemystifyLocalViT | InputInjection | false | 13,337 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
MSELoss | import functools
import torch
from torch.nn import functional as F
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Op... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import functools
from torch.nn import functional as F
import torch.nn as nn
import torch.... | Atten4Vis/DemystifyLocalViT | MSELoss | false | 13,338 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
ConvEncoder | import torch
import torch.nn as nn
class ConvEncoder(nn.Module):
"""
A simple Convolutional Encoder Model
"""
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 16, (3, 3), padding=(1, 1))
self.relu1 = nn.ReLU(inplace=True)
self.maxpool1 = nn.MaxPool2d((2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Alexander-Minyushkin/image_similarity | ConvEncoder | false | 13,339 | [
"Apache-2.0"
] | 160 | 99bb68f0ccf226c068c43ad4feb47b76f7a5f180 | https://github.com/Alexander-Minyushkin/image_similarity/tree/99bb68f0ccf226c068c43ad4feb47b76f7a5f180 |
CrossEntropyLoss2d | import torch
from torch import nn
class CrossEntropyLoss2d(nn.Module):
"""This criterion combines nn.LogSoftmax() and nn.NLLLoss() in one single class."""
def __init__(self, weight=None, ignore_index=-100):
super().__init__()
self.CE = nn.CrossEntropyLoss(weight=weight, ignore_index=ignore_in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | AtlasGooo2/WoodScape | CrossEntropyLoss2d | false | 13,340 | [
"MIT"
] | 348 | 597d9dda472c09bafea58ea69853948d63197eca | https://github.com/AtlasGooo2/WoodScape/tree/597d9dda472c09bafea58ea69853948d63197eca |
Hsigmoid | import torch
import torch.nn as 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(1.2 * x + 3.0, inplace=self.inplace) / 6.0
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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | BHD233/PaddleOCR2Pytorch | Hsigmoid | false | 13,341 | [
"Apache-2.0"
] | 364 | f114069b3e2669c6adf0adf9596756205f184c9c | https://github.com/BHD233/PaddleOCR2Pytorch/tree/f114069b3e2669c6adf0adf9596756205f184c9c |
ExampleBackbone | import torch
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
class ExampleBackbone(nn.Module):
def __init__(self):
super(ExampleBackbone, self).__init__()
self.conv = nn.Conv2d(3, 3, 3)
def init_weights(self, pretrained=None):
pass
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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._C
import torch.serialization
from torch impo... | Atten4Vis/DemystifyLocalViT | ExampleBackbone | false | 13,342 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
ConvDecoder | import torch
import torch.nn as nn
class ConvDecoder(nn.Module):
"""
A simple Convolutional Decoder Model
"""
def __init__(self):
super().__init__()
self.deconv1 = nn.ConvTranspose2d(256, 128, (2, 2), stride=(2, 2))
self.relu1 = nn.ReLU(inplace=True)
self.deconv2 = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Alexander-Minyushkin/image_similarity | ConvDecoder | false | 13,343 | [
"Apache-2.0"
] | 160 | 99bb68f0ccf226c068c43ad4feb47b76f7a5f180 | https://github.com/Alexander-Minyushkin/image_similarity/tree/99bb68f0ccf226c068c43ad4feb47b76f7a5f180 |
CrossEntropyLoss | import torch
from torch.nn import functional as F
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
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
from torch.nn import f... | Atten4Vis/DemystifyLocalViT | CrossEntropyLoss | false | 13,344 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
SimpleModel | import torch
import torch.nn as nn
class SimpleModel(nn.Module):
def __init__(self):
super(SimpleModel, self).__init__()
def forward(self, x):
return x * 2
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AyushExel/tensorboardX | SimpleModel | false | 13,345 | [
"MIT"
] | 5,378 | 34552d52d9154013d36772e4c32e9b189a3b9217 | https://github.com/AyushExel/tensorboardX/tree/34552d52d9154013d36772e4c32e9b189a3b9217 |
SpatialGatherModule | import torch
from torch.nn import functional as F
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
class SpatialGatherModule(nn.Module):
"""Aggregate the context features according to the initial predicted
probability distribution.
Employ the soft-weighted... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Atten4Vis/DemystifyLocalViT | SpatialGatherModule | false | 13,346 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
AdaptiveAvgMaxPool2d | import torch
import torch.nn as nn
def pooling_factor(pool_type='avg'):
return 2 if pool_type == 'avgmaxc' else 1
class AdaptiveAvgMaxPool2d(torch.nn.Module):
"""Selectable global pooling layer with dynamic input kernel size
"""
def __init__(self, output_size=1, pool_type='avg'):
super(Adap... | 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... | BCV-Uniandes/DMS | AdaptiveAvgMaxPool2d | false | 13,347 | [
"MIT"
] | 66 | 9fa3a3a2ef5980dd17e21b73234a4cd0b3d00e16 | https://github.com/BCV-Uniandes/DMS/tree/9fa3a3a2ef5980dd17e21b73234a4cd0b3d00e16 |
TripletLoss | import torch
import torch.nn.functional as F
from torch import nn
def cosine_dist(x, y):
"""Computes Cosine Distance."""
x = F.normalize(x, dim=1)
y = F.normalize(y, dim=1)
dist = 2 - 2 * torch.mm(x, y.t())
return dist
def euclidean_dist(x, y):
"""Computes Euclidean distance."""
m, n = x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Atharva-Phatak/torchflare | TripletLoss | false | 13,348 | [
"Apache-2.0"
] | 86 | 945f4bee73a855edd8cb19cd646731155499a27f | https://github.com/Atharva-Phatak/torchflare/tree/945f4bee73a855edd8cb19cd646731155499a27f |
AttentionPool2d | import math
import torch
import numpy as np
import torch as th
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 dim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AranKomat/Diff-DALLE | AttentionPool2d | false | 13,349 | [
"MIT"
] | 53 | 9418e98e97b599c5c65f16ee168fedf76a29095f | https://github.com/AranKomat/Diff-DALLE/tree/9418e98e97b599c5c65f16ee168fedf76a29095f |
IoULoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class IoULoss(nn.Module):
"""
Creates a criterion that computes the Intersection over Union (IoU)
between a segmentation mask and its ground truth.
Rahman, M.A. and Wang, Y:
Optimizing Intersection-Over-Union in Deep Neural Networ... | 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... | BCV-Uniandes/DMS | IoULoss | false | 13,350 | [
"MIT"
] | 66 | 9fa3a3a2ef5980dd17e21b73234a4cd0b3d00e16 | https://github.com/BCV-Uniandes/DMS/tree/9fa3a3a2ef5980dd17e21b73234a4cd0b3d00e16 |
GHMR | import torch
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
class GHMR(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
`Gradient Harmonized Single-stage Detector
<https://arxiv.org/abs/1811.05181>`_.
Args:
... | 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... | Atten4Vis/DemystifyLocalViT | GHMR | false | 13,351 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
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 |
PixelShuffleICNR | import torch
from torch import nn
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
bias=False)
class PixelShuffleICNR(nn.Module):
def __init__(self, in_planes, out_planes, scale=2):
super().__init__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | AtlasGooo2/WoodScape | PixelShuffleICNR | false | 13,353 | [
"MIT"
] | 348 | 597d9dda472c09bafea58ea69853948d63197eca | https://github.com/AtlasGooo2/WoodScape/tree/597d9dda472c09bafea58ea69853948d63197eca |
Mlp | import torch
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Atten4Vis/DemystifyLocalViT | Mlp | false | 13,354 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
MonoLinearHyperNet | import torch
from abc import abstractmethod
from torch import nn
from torch.nn.utils import weight_norm
class HyperNet(nn.Module):
"""This module is responsible for taking the losses from all tasks and return a single loss term.
We can think of this as our learnable loss criterion
"""
def __init__(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 libdevice
from abc import abs... | AvivNavon/AuxiLearn | MonoLinearHyperNet | false | 13,355 | [
"MIT"
] | 58 | 2c32f5cb548714ad3efe5c804003a30d6f012e2b | https://github.com/AvivNavon/AuxiLearn/tree/2c32f5cb548714ad3efe5c804003a30d6f012e2b |
L2Norm | import torch
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
class L2Norm(nn.Module):
def __init__(self, n_dims, scale=20.0, eps=1e-10):
"""L2 normalization layer.
Args:
n_dims (int): Number of dimensions to be normalized
... | 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._C
import torch.serialization
from torch imp... | Atten4Vis/DemystifyLocalViT | L2Norm | false | 13,356 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
Hswish | import torch
import torch.nn as nn
import torch.nn.functional as F
class Hswish(nn.Module):
def __init__(self, inplace=True):
super(Hswish, self).__init__()
self.inplace = inplace
def forward(self, x):
return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.0
def get_inputs():
re... | 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... | BHD233/PaddleOCR2Pytorch | Hswish | false | 13,357 | [
"Apache-2.0"
] | 364 | f114069b3e2669c6adf0adf9596756205f184c9c | https://github.com/BHD233/PaddleOCR2Pytorch/tree/f114069b3e2669c6adf0adf9596756205f184c9c |
ClsHead | import torch
import torch.nn as nn
import torch.nn.functional as F
class ClsHead(nn.Module):
"""
Class orientation
Args:
params(dict): super parameters for build Class network
"""
def __init__(self, in_channels, class_dim, **kwargs):
super(ClsHead, self).__init__()
self.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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BHD233/PaddleOCR2Pytorch | ClsHead | false | 13,358 | [
"Apache-2.0"
] | 364 | f114069b3e2669c6adf0adf9596756205f184c9c | https://github.com/BHD233/PaddleOCR2Pytorch/tree/f114069b3e2669c6adf0adf9596756205f184c9c |
FFN | import torch
import torch.nn as nn
import torch.nn.functional as F
class FFN(nn.Module):
"""
Feed-Forward Network
"""
def __init__(self, d_inner_hid, d_model, dropout_rate):
super(FFN, self).__init__()
self.dropout_rate = dropout_rate
self.fc1 = torch.nn.Linear(in_features=d_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 import triton_helpers
import torch.nn as nn
assert_... | BHD233/PaddleOCR2Pytorch | FFN | false | 13,359 | [
"Apache-2.0"
] | 364 | f114069b3e2669c6adf0adf9596756205f184c9c | https://github.com/BHD233/PaddleOCR2Pytorch/tree/f114069b3e2669c6adf0adf9596756205f184c9c |
LinearZeros | import torch
import torch.nn as 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
import torch.... | BQZic/glow-pytorch | LinearZeros | false | 13,360 | [
"MIT"
] | 479 | 4b43042326bbe644ccfda3c81a138375321808ed | https://github.com/BQZic/glow-pytorch/tree/4b43042326bbe644ccfda3c81a138375321808ed |
Conv2dWithFastWeight | import torch
from torch import Tensor
from typing import Tuple
from typing import Union
import torch.nn as nn
import torch.nn.functional as F
class Conv2dWithFastWeight(nn.Conv2d):
def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
'Union[int, Tuple]', stride: 'Union[int, Tuple]'=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 typing import Tuple
from typing import Union
import torch.nn as nn
assert_s... | BIGWangYuDong/mmfewshot | Conv2dWithFastWeight | false | 13,361 | [
"Apache-2.0"
] | 376 | dac097afc92df176bc2de76b7c90968584865197 | https://github.com/BIGWangYuDong/mmfewshot/tree/dac097afc92df176bc2de76b7c90968584865197 |
WShift | import torch
import torch.nn as nn
import torch.nn.parallel
class WShift(nn.Module):
def __init__(self, style_dim):
super().__init__()
self.w_shift = nn.Parameter(torch.zeros(1, style_dim))
def forward(self, input):
out = input + self.w_shift
return out
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | AyushExel/GANSketching | WShift | false | 13,362 | [
"MIT"
] | 598 | c72524ac4425de898087af7a4c554b777a4e2218 | https://github.com/AyushExel/GANSketching/tree/c72524ac4425de898087af7a4c554b777a4e2218 |
CTCHead | import torch
import torch.nn as nn
import torch.nn.functional as F
class CTCHead(nn.Module):
def __init__(self, in_channels, out_channels=6625, fc_decay=0.0004,
mid_channels=None, **kwargs):
super(CTCHead, self).__init__()
if mid_channels is None:
self.fc = nn.Linear(in_channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BHD233/PaddleOCR2Pytorch | CTCHead | false | 13,363 | [
"Apache-2.0"
] | 364 | f114069b3e2669c6adf0adf9596756205f184c9c | https://github.com/BHD233/PaddleOCR2Pytorch/tree/f114069b3e2669c6adf0adf9596756205f184c9c |
MultiHeadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
"""
Multi-Head Attention
"""
def __init__(self, d_key, d_value, d_model, n_head=1, dropout_rate=0.0):
super(MultiHeadAttention, self).__init__()
self.n_head = n_head
self.d_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BHD233/PaddleOCR2Pytorch | MultiHeadAttention | false | 13,364 | [
"Apache-2.0"
] | 364 | f114069b3e2669c6adf0adf9596756205f184c9c | https://github.com/BHD233/PaddleOCR2Pytorch/tree/f114069b3e2669c6adf0adf9596756205f184c9c |
Encoding | import torch
from torch.nn import functional as F
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
class Encoding(nn.Module):
"""Encoding Layer: a learnable residual encoder.
Input is of shape (batch_size, channels, height, width).
Output is of shape (bat... | 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
... | Atten4Vis/DemystifyLocalViT | Encoding | false | 13,365 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
BertLayerNorm | import torch
from torch import nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_si... | 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... | BIT-ENGD/eeqa | BertLayerNorm | false | 13,366 | [
"MIT"
] | 142 | 2995abbaff1fb47131246a247ee7ed62aa94f4c3 | https://github.com/BIT-ENGD/eeqa/tree/2995abbaff1fb47131246a247ee7ed62aa94f4c3 |
RelationCrossing | import torch
import torch.nn as nn
import torch.nn.functional as F
class RelationCrossing(nn.Module):
def __init__(self, in_feats: 'int', out_feats: 'int', num_heads: 'int',
dropout: 'float'=0.0, negative_slope: 'float'=0.2):
"""
Description
----------
Relation crossing l... | 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
... | BUPT-GAMMA/OpenHGNN | RelationCrossing | false | 13,367 | [
"Apache-2.0"
] | 235 | 5f218dad4ed1415aa6d842bc20785c61e74e5405 | https://github.com/BUPT-GAMMA/OpenHGNN/tree/5f218dad4ed1415aa6d842bc20785c61e74e5405 |
GHMC | import torch
from torch.nn import functional as F
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
def _expand_onehot_labels(labels, label_weights, target_shape, ignore_index):
"""Expand onehot labels to match the size of prediction."""
bin_labels = labels.new_... | 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
... | Atten4Vis/DemystifyLocalViT | GHMC | false | 13,368 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
AvgReadout | import torch
import torch.nn as nn
class AvgReadout(nn.Module):
"""
Considering the efficiency of the method, we simply employ average pooling, computing the average of the set of embedding matrices
.. math::
\\begin{equation}
\\mathbf{H}=\\mathcal{Q}\\left(\\left\\{\\mathbf{H}^{(r)} \\mid ... | 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... | BUPT-GAMMA/OpenHGNN | AvgReadout | false | 13,369 | [
"Apache-2.0"
] | 235 | 5f218dad4ed1415aa6d842bc20785c61e74e5405 | https://github.com/BUPT-GAMMA/OpenHGNN/tree/5f218dad4ed1415aa6d842bc20785c61e74e5405 |
GDL | import torch
import numpy as np
from torch import nn
import torch.nn.functional
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
from torch import nn
import torch.nn.functional
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | BRAIN-Lab-UNC/BrainExtraction-TissueSegmentation-Macaque | GDL | false | 13,370 | [
"MIT"
] | 770 | b5329035d9e32c8a27151cf2396eaf209396a334 | https://github.com/BRAIN-Lab-UNC/BrainExtraction-TissueSegmentation-Macaque/tree/b5329035d9e32c8a27151cf2396eaf209396a334 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.