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 |
|---|---|---|---|---|---|---|---|---|---|---|
BhattacharyyaDistance | import torch
import torch.nn as nn
class BhattacharyyaDistance(nn.Module):
def __init__(self):
super(BhattacharyyaDistance, self).__init__()
def forward(self, hist1, hist2):
bh_dist = torch.sqrt(hist1 * hist2).sum()
return bh_dist
def get_inputs():
return [torch.rand([4, 4, 4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | tommy90191/Find_Tiny_but_Important_Image_Changes | BhattacharyyaDistance | false | 4,438 | [
"MIT"
] | 0 | 429d679606f96f32db4cddf167a9cfb963d3df26 | https://github.com/tommy90191/Find_Tiny_but_Important_Image_Changes/tree/429d679606f96f32db4cddf167a9cfb963d3df26 |
l1normalization | import torch
import torch.nn as nn
class l1normalization(nn.Module):
def __init__(self, scale):
super(l1normalization, self).__init__()
self.scale = scale
def forward(self, x, dim=1):
return self.scale * x * x.pow(1).sum(dim).clamp(min=1e-12).rsqrt(
).expand_as(x)
def 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 libdevice
import torch.nn as nn
assert... | tommy90191/Find_Tiny_but_Important_Image_Changes | l1normalization | false | 4,439 | [
"MIT"
] | 0 | 429d679606f96f32db4cddf167a9cfb963d3df26 | https://github.com/tommy90191/Find_Tiny_but_Important_Image_Changes/tree/429d679606f96f32db4cddf167a9cfb963d3df26 |
Conv2dWithConstraint | import torch
from torch import nn
class Conv2dWithConstraint(nn.Conv2d):
def __init__(self, *args, max_norm=1, **kwargs):
self.max_norm = max_norm
super(Conv2dWithConstraint, self).__init__(*args, **kwargs)
def forward(self, x):
self.weight.data = torch.renorm(self.weight.data, p=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.triton_helpers import libdevice
from torch import n... | tomMoral/braindecode | Conv2dWithConstraint | false | 4,440 | [
"BSD-3-Clause"
] | 0 | 09d63b7e32fdfcfbaac7569a003f2611721a78ca | https://github.com/tomMoral/braindecode/tree/09d63b7e32fdfcfbaac7569a003f2611721a78ca |
KLCoefficient | import torch
import torch.nn as nn
from torch.nn import functional as F
class KLCoefficient(nn.Module):
def __init__(self):
super(KLCoefficient, self).__init__()
def forward(self, hist1, hist2):
kl = F.kl_div(hist1, hist2)
dist = 1.0 / 1 + kl
return dist
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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | tommy90191/Find_Tiny_but_Important_Image_Changes | KLCoefficient | false | 4,441 | [
"MIT"
] | 0 | 429d679606f96f32db4cddf167a9cfb963d3df26 | https://github.com/tommy90191/Find_Tiny_but_Important_Image_Changes/tree/429d679606f96f32db4cddf167a9cfb963d3df26 |
ConstractiveLoss | import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
class ConstractiveLoss(nn.Module):
def __init__(self, margin=2.0, dist_flag='l2'):
super(ConstractiveLoss, self).__init__()
self.margin = margin
self.dist_flag = dist_flag
def various_distance(... | 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 numpy as np
import to... | tommy90191/Find_Tiny_but_Important_Image_Changes | ConstractiveLoss | false | 4,442 | [
"MIT"
] | 0 | 429d679606f96f32db4cddf167a9cfb963d3df26 | https://github.com/tommy90191/Find_Tiny_but_Important_Image_Changes/tree/429d679606f96f32db4cddf167a9cfb963d3df26 |
l2normalization | import torch
import torch.nn as nn
class l2normalization(nn.Module):
def __init__(self, scale):
super(l2normalization, self).__init__()
self.scale = scale
def forward(self, x, dim=1):
"""out = scale * x / sqrt(\\sum x_i^2)"""
return self.scale * x * x.pow(2).sum(dim).clamp(mi... | 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... | tommy90191/Find_Tiny_but_Important_Image_Changes | l2normalization | false | 4,443 | [
"MIT"
] | 0 | 429d679606f96f32db4cddf167a9cfb963d3df26 | https://github.com/tommy90191/Find_Tiny_but_Important_Image_Changes/tree/429d679606f96f32db4cddf167a9cfb963d3df26 |
scale_feature | import torch
import torch.nn as nn
class scale_feature(nn.Module):
def __init__(self, scale):
super(scale_feature, self).__init__()
self.scale = scale
def forward(self, x):
return self.scale * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | tommy90191/Find_Tiny_but_Important_Image_Changes | scale_feature | false | 4,444 | [
"MIT"
] | 0 | 429d679606f96f32db4cddf167a9cfb963d3df26 | https://github.com/tommy90191/Find_Tiny_but_Important_Image_Changes/tree/429d679606f96f32db4cddf167a9cfb963d3df26 |
DQFFN | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQFFN(nn.Module):
def __init__(self, n):
"""
Create Feed-forward Network with n dim input and n dim output
"""
super(DQFFN, self).__init__()
self.n = n
self.l1 = nn.Linear(n * (n + 1) // 2, 20... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | thomashopkins32/RedBlueGame | DQFFN | false | 4,445 | [
"MIT"
] | 0 | dd3e759123acc02375fdfcc504892e00e6b31ef1 | https://github.com/thomashopkins32/RedBlueGame/tree/dd3e759123acc02375fdfcc504892e00e6b31ef1 |
L2Norm | import torch
from math import sqrt as sqrt
from itertools import product as product
import torch.nn as nn
import torch.nn.init as init
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.gamma = scale or 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.triton_helpers import libdevice
from math import sqrt as sqrt
from itertools import product as product
import t... | tomgause/pytorch-ssd | L2Norm | false | 4,446 | [
"MIT"
] | 0 | e458d4319deb21c8970bcce13382e7ada70ea1a2 | https://github.com/tomgause/pytorch-ssd/tree/e458d4319deb21c8970bcce13382e7ada70ea1a2 |
FeatureCorrelation | import torch
import torch.nn as nn
class FeatureCorrelation(nn.Module):
def __init__(self, scale):
super(FeatureCorrelation, self).__init__()
self.scale = scale
def forward(self, feature_A, feature_B):
b, c, h, w = feature_A.size()
feature_A = feature_A.transpose(2, 3).contig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | tommy90191/Find_Tiny_but_Important_Image_Changes | FeatureCorrelation | false | 4,447 | [
"MIT"
] | 0 | 429d679606f96f32db4cddf167a9cfb963d3df26 | https://github.com/tommy90191/Find_Tiny_but_Important_Image_Changes/tree/429d679606f96f32db4cddf167a9cfb963d3df26 |
ConstractiveThresholdHingeLoss | import torch
import torch.nn as nn
from torch.nn import functional as F
class ConstractiveThresholdHingeLoss(nn.Module):
def __init__(self, hingethresh=0.0, margin=2.0):
super(ConstractiveThresholdHingeLoss, self).__init__()
self.threshold = hingethresh
self.margin = margin
def forwa... | 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... | tommy90191/Find_Tiny_but_Important_Image_Changes | ConstractiveThresholdHingeLoss | false | 4,448 | [
"MIT"
] | 0 | 429d679606f96f32db4cddf167a9cfb963d3df26 | https://github.com/tommy90191/Find_Tiny_but_Important_Image_Changes/tree/429d679606f96f32db4cddf167a9cfb963d3df26 |
ResNetBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResNetBlock(nn.Module):
def __init__(self, in_channels: 'int', out_channels: 'int',
hid_channels: 'int', bias: 'bool'):
super().__init__()
self.shortcut = in_channels != out_channels
self.conv_0 = nn.Conv2d(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... | tmralmeida/VGAN | ResNetBlock | false | 4,449 | [
"MIT"
] | 0 | 103d2e7ac0b84b08ff3c3a40e0ccb16390b1e008 | https://github.com/tmralmeida/VGAN/tree/103d2e7ac0b84b08ff3c3a40e0ccb16390b1e008 |
Affine | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Affine(nn.Module):
def __init__(self, dim):
super().__init__()
self.alpha = nn.Parameter(torch.ones((1, 1, dim)))
self.beta = nn.Parameter(torch.zeros((1, 1, dim)))
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
import torch.nn.parallel
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | tor4z/pytorch-image-models | Affine | false | 4,450 | [
"Apache-2.0"
] | 0 | d7bab8a6c52a72487d1bed0a28aad41e326d7622 | https://github.com/tor4z/pytorch-image-models/tree/d7bab8a6c52a72487d1bed0a28aad41e326d7622 |
L1 | import torch
import torch.nn as nn
class L1(nn.Module):
def __init__(self):
super(L1, self).__init__()
def forward(self, output, target):
lossvalue = torch.abs(output[:, None] - target).mean()
return lossvalue
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | tomrunia/flownet2-pytorch | L1 | false | 4,451 | [
"Apache-2.0"
] | 0 | 759b09c375348cf64f52f914cf3bf3e9095cc959 | https://github.com/tomrunia/flownet2-pytorch/tree/759b09c375348cf64f52f914cf3bf3e9095cc959 |
L2 | import torch
import torch.nn as nn
class L2(nn.Module):
def __init__(self):
super(L2, self).__init__()
def forward(self, output, target):
lossvalue = torch.norm(output[:, None] - target, p=2, dim=1).mean()
return lossvalue
def get_inputs():
return [torch.rand([4, 4, 4, 4]), tor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | tomrunia/flownet2-pytorch | L2 | false | 4,452 | [
"Apache-2.0"
] | 0 | 759b09c375348cf64f52f914cf3bf3e9095cc959 | https://github.com/tomrunia/flownet2-pytorch/tree/759b09c375348cf64f52f914cf3bf3e9095cc959 |
AvgConsensus | import torch
import torch.nn as nn
class AvgConsensus(nn.Module):
"""Average consensus module.
Args:
dim (int): Decide which dim consensus function to apply.
Default: 1.
"""
def __init__(self, dim=1):
super().__init__()
self.dim = dim
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... | scenarios/dev | AvgConsensus | false | 4,453 | [
"Apache-2.0"
] | 0 | 9f91ebc142cea1c31231d233571ad59460ab6fba | https://github.com/scenarios/dev/tree/9f91ebc142cea1c31231d233571ad59460ab6fba |
WeightNet | import torch
import torch.nn as nn
class WeightNet(nn.Module):
"""WeightNet in Temporal interlace module.
The WeightNet consists of two parts: one convolution layer
and a sigmoid function. Following the convolution layer, the sigmoid
function and rescale module can scale our output to the range (0, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | scenarios/dev | WeightNet | false | 4,454 | [
"Apache-2.0"
] | 0 | 9f91ebc142cea1c31231d233571ad59460ab6fba | https://github.com/scenarios/dev/tree/9f91ebc142cea1c31231d233571ad59460ab6fba |
BinaryLogisticRegressionLoss | import torch
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > threshold).float()
num_positive... | 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
... | scenarios/dev | BinaryLogisticRegressionLoss | false | 4,455 | [
"Apache-2.0"
] | 0 | 9f91ebc142cea1c31231d233571ad59460ab6fba | https://github.com/scenarios/dev/tree/9f91ebc142cea1c31231d233571ad59460ab6fba |
GCN | import torch
from torchvision.datasets import *
import torch.nn as nn
from torchvision.transforms import *
class GCN(nn.Module):
def __init__(self, num_state, num_node, bias=False):
super(GCN, self).__init__()
self.conv1 = nn.Conv1d(num_node, num_node, kernel_size=1, padding=0,
stride... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torchvision.datasets imp... | tousifulhaque/DANet | GCN | false | 4,456 | [
"MIT"
] | 0 | 1a0c91f0e551a071b5e335b4157313780a8a1b1a | https://github.com/tousifulhaque/DANet/tree/1a0c91f0e551a071b5e335b4157313780a8a1b1a |
Normalize | import torch
from torchvision.datasets import *
import torch.nn as nn
import torch.nn.functional as F
from torchvision.transforms import *
class Normalize(nn.Module):
"""Performs :math:`L_p` normalization of inputs over specified dimension.
Does:
.. math::
v = \\frac{v}{\\max(\\lVert v \\rVert_p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torchvision.datasets im... | tousifulhaque/DANet | Normalize | false | 4,457 | [
"MIT"
] | 0 | 1a0c91f0e551a071b5e335b4157313780a8a1b1a | https://github.com/tousifulhaque/DANet/tree/1a0c91f0e551a071b5e335b4157313780a8a1b1a |
OffsetNet | import torch
import torch.nn as nn
class OffsetNet(nn.Module):
"""OffsetNet in Temporal interlace module.
The OffsetNet consists of one convolution layer and two fc layers
with a relu activation following with a sigmoid function. Following
the convolution layer, two fc layers and relu are applied to ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | scenarios/dev | OffsetNet | false | 4,458 | [
"Apache-2.0"
] | 0 | 9f91ebc142cea1c31231d233571ad59460ab6fba | https://github.com/scenarios/dev/tree/9f91ebc142cea1c31231d233571ad59460ab6fba |
TwoPartSimpleModel | import torch
import torch.nn as nn
import torch.utils.data
class SimpleModel(nn.Module):
def forward(self, x):
return 2 * x
def prepare_for_export(self, cfg, inputs, predictor_type):
return PredictorExportConfig(model=self, data_generator=lambda x: (x,))
class TwoPartSimpleModel(nn.Module)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | tsubauaaa/d2go | TwoPartSimpleModel | false | 4,459 | [
"Apache-2.0"
] | 0 | 9f746159ebf78ce79f644c405ca8695bc29d1075 | https://github.com/tsubauaaa/d2go/tree/9f746159ebf78ce79f644c405ca8695bc29d1075 |
CPAMDec | from torch.nn import Module
import torch
from torchvision.datasets import *
from torch.nn import Parameter
from torch.nn import Conv2d
from torch.nn import Linear
from torch.nn import Softmax
from torchvision.transforms import *
class CPAMDec(Module):
"""
CPAM decoding module
"""
def __init__(self, i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | tousifulhaque/DANet | CPAMDec | false | 4,460 | [
"MIT"
] | 0 | 1a0c91f0e551a071b5e335b4157313780a8a1b1a | https://github.com/tousifulhaque/DANet/tree/1a0c91f0e551a071b5e335b4157313780a8a1b1a |
SplitAndConcat | import torch
import torch.nn as nn
import torch.utils.data
class SplitAndConcat(nn.Module):
"""Split the data from split_dim and concatenate in concat_dim.
@param split_dim from which axis the data will be chunk
@param concat_dim to which axis the data will be concatenated
@param chunk size of the da... | 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.... | tsubauaaa/d2go | SplitAndConcat | false | 4,461 | [
"Apache-2.0"
] | 0 | 9f746159ebf78ce79f644c405ca8695bc29d1075 | https://github.com/tsubauaaa/d2go/tree/9f746159ebf78ce79f644c405ca8695bc29d1075 |
GELU | import torch
import torch.nn as nn
class GELU(nn.Module):
def forward(self, x):
return torch.sigmoid(1.702 * x) * 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... | txsing/augmix | GELU | false | 4,462 | [
"Apache-2.0"
] | 0 | 9127809d8534ccb20a654f631833153e75a277fd | https://github.com/txsing/augmix/tree/9127809d8534ccb20a654f631833153e75a277fd |
InstanceNormLayer | import torch
from torch import nn
class InstanceNormLayer(nn.Module):
"""Implements instance normalization layer."""
def __init__(self, epsilon=1e-08):
super().__init__()
self.epsilon = epsilon
def forward(self, x):
if len(x.shape) != 4:
raise ValueError(
... | 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... | tylerwilliams/InterFaceGAN | InstanceNormLayer | false | 4,463 | [
"MIT"
] | 0 | 120babcc0dc777aa902ef0dcdeaec7c528369dbc | https://github.com/tylerwilliams/InterFaceGAN/tree/120babcc0dc777aa902ef0dcdeaec7c528369dbc |
CCAMDec | from torch.nn import Module
import torch
from torchvision.datasets import *
from torch.nn import Parameter
from torch.nn import Softmax
from torchvision.transforms import *
class CCAMDec(Module):
"""
CCAM decoding module
"""
def __init__(self):
super(CCAMDec, self).__init__()
self.sof... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tousifulhaque/DANet | CCAMDec | false | 4,464 | [
"MIT"
] | 0 | 1a0c91f0e551a071b5e335b4157313780a8a1b1a | https://github.com/tousifulhaque/DANet/tree/1a0c91f0e551a071b5e335b4157313780a8a1b1a |
Bandpass | import torch
import torch.nn as nn
class Bandpass(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.mean = nn.Parameter(torch.randn(1, input_dim, dtype=torch.float32)
)
self.icov = nn.Parameter(torch.eye(input_dim, input_dim, dtype=
torch.float32) ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tsumansapkota/Input-Invex-Neural-Network | Bandpass | false | 4,465 | [
"Apache-2.0"
] | 0 | 6a14ee12b33da1d231d231c8f9631851a7668997 | https://github.com/tsumansapkota/Input-Invex-Neural-Network/tree/6a14ee12b33da1d231d231c8f9631851a7668997 |
DQN | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN(nn.Module):
def __init__(self, obs_size, action_size, seed):
super(DQN, self).__init__()
self.fc1 = nn.Linear(obs_size, 128)
self.fc2 = nn.Linear(128, 128)
self.fc3 = nn.Linear(128, sum(action_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
assert_... | ulyssesdotcodes/ReaL-Crowds | DQN | false | 4,466 | [
"BSD-3-Clause"
] | 0 | 9da01fe4d1858c3c26d6387e34f4e76db5385d51 | https://github.com/ulyssesdotcodes/ReaL-Crowds/tree/9da01fe4d1858c3c26d6387e34f4e76db5385d51 |
TSA_Fusion | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class TSA_Fusion(nn.Module):
""" Temporal Spatial Attention fusion module
Temporal: correlation;
Spatial: 3 pyramid levels.
"""
def __init__(self, nf=64, nframes=5, center=2):
super(TSA_Fusion, self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | sutkarsh/EDVR | TSA_Fusion | false | 4,467 | [
"Apache-2.0"
] | 0 | cd9f2d46edbb00333d8ffb31aebc52cfbda4b6e3 | https://github.com/sutkarsh/EDVR/tree/cd9f2d46edbb00333d8ffb31aebc52cfbda4b6e3 |
LenCompLoss | import torch
import torch.utils.data
import torch
import torch.nn as nn
class LenCompLoss(nn.Module):
def __init__(self):
super(LenCompLoss, self).__init__()
self.loss = nn.L1Loss()
def forward(self, x, y):
loss = self.loss(torch.sum(x), torch.sum(y))
return loss
def get_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
import torch.utils.dat... | usmanwardag/pytorch-CycleGAN-and-pix2pix | LenCompLoss | false | 4,468 | [
"BSD-3-Clause"
] | 0 | 72f2050600e7821476c9e19fcf8f1973f6a6f78c | https://github.com/usmanwardag/pytorch-CycleGAN-and-pix2pix/tree/72f2050600e7821476c9e19fcf8f1973f6a6f78c |
FluidGravityForce | import torch
import torch.nn as nn
class FluidGravityForce(nn.Module):
def __init__(self, gravity, maxSpeed=3):
"""
Initializes a fluid gravity model.
Arguments:
gravity: Gravity vector in the global frame (same as particle l) for the simulation
maxSpeed: The maxi... | 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... | ucsdarclab/liquid_reconstruction | FluidGravityForce | false | 4,469 | [
"MIT"
] | 0 | 5559edbf71dba05d432d85e7dbbfe3634e650aeb | https://github.com/ucsdarclab/liquid_reconstruction/tree/5559edbf71dba05d432d85e7dbbfe3634e650aeb |
KLDivergence | import torch
import torch as th
class KLDivergence(th.nn.Module):
"""
Args:
min_value(float): the loss is clipped so that value below this
number don't affect the optimization.
"""
def __init__(self, min_value=0.2):
super(KLDivergence, self).__init__()
self.min_val... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch as th
ass... | v-a-s-a/diffvg | KLDivergence | false | 4,470 | [
"Apache-2.0"
] | 0 | 3685f3d47a5a4e5c76c68643ebf383f809ba59ed | https://github.com/v-a-s-a/diffvg/tree/3685f3d47a5a4e5c76c68643ebf383f809ba59ed |
BMNLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > thr... | import torch
from torch import device
import triton
import triton.language as tl
from 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_ma... | scenarios/dev | BMNLoss | false | 4,471 | [
"Apache-2.0"
] | 0 | 9f91ebc142cea1c31231d233571ad59460ab6fba | https://github.com/scenarios/dev/tree/9f91ebc142cea1c31231d233571ad59460ab6fba |
MaxPPVPool1d | from torch.nn import Module
import torch
import torch.multiprocessing
import torch
class MaxPPVPool1d(Module):
"""Drop-in replacement for AdaptiveConcatPool1d - multiplies nf by 2"""
def forward(self, x):
_max = x.max(dim=-1).values
_ppv = torch.gt(x, 0).sum(dim=-1).float() / x.shape[-1]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
import torch.multiprocessing
import torch
assert_size_stride ... | sjdlloyd/tsai | MaxPPVPool1d | false | 4,472 | [
"Apache-2.0"
] | 0 | 98d9c02b8429708819d373b475deb9e99f0ab7df | https://github.com/sjdlloyd/tsai/tree/98d9c02b8429708819d373b475deb9e99f0ab7df |
ScoringFunction | import torch
import torch.nn as nn
class Conv2dAct(nn.Module):
def __init__(self, in_channels, out_channels, ksize=1, activation='relu'):
super(Conv2dAct, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, ksize)
if activation == 'sigmoid':
self.act = nn.Sigmoid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | sunwhawhang/headpose-fsanet-pytorch | ScoringFunction | false | 4,473 | [
"MIT"
] | 0 | d37d39dbff649b2f607367f35d9eadba2fea18f7 | https://github.com/sunwhawhang/headpose-fsanet-pytorch/tree/d37d39dbff649b2f607367f35d9eadba2fea18f7 |
CrossEntropy | import torch
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
class CrossEntropy(nn.Module):
def forward(self, x, y):
return F.cross_entropy(x, torch.argmax(y, -1))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | tgxs002/1-stage-wseg | CrossEntropy | false | 4,474 | [
"Apache-2.0"
] | 0 | de16c51cc6cf8cd0ef248145980434d5f6104910 | https://github.com/tgxs002/1-stage-wseg/tree/de16c51cc6cf8cd0ef248145980434d5f6104910 |
Gaussian | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class Gaussian(nn.Module):
def __init__(self, in_dim, z_dim):
super(Gaussian, self).__init__()
self.mu = nn.Linear(in_dim, z_dim)
self.var = nn.Linear(in_dim, z_dim)
def reparameterize(self... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 libd... | userVector/GMVAE | Gaussian | false | 4,475 | [
"MIT"
] | 0 | 2d0330c4174aa614f3817888798f88798313e01f | https://github.com/userVector/GMVAE/tree/2d0330c4174aa614f3817888798f88798313e01f |
VarianceC | import torch
import torch.nn as nn
class VarianceC(nn.Module):
def __init__(self):
super(VarianceC, self).__init__()
def forward(self, x):
mean_x = torch.mean(x, dim=1, keepdim=True)
sub_x = x.sub(mean_x)
x = torch.mean(torch.mul(sub_x, sub_x), dim=1, keepdim=True)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | sunwhawhang/headpose-fsanet-pytorch | VarianceC | false | 4,476 | [
"MIT"
] | 0 | d37d39dbff649b2f607367f35d9eadba2fea18f7 | https://github.com/sunwhawhang/headpose-fsanet-pytorch/tree/d37d39dbff649b2f607367f35d9eadba2fea18f7 |
ToyRes | import torch
import torch.nn as nn
import torch.multiprocessing
class ToyResLayer(nn.Module):
""" Custom Linear layer but mimics a standard linear layer """
def __init__(self):
super().__init__()
aprime = torch.Tensor(1)
bprime = torch.Tensor(1)
self.aprime = nn.Parameter(apri... | 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.multiprocessing
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | suswei/RLCT | ToyRes | false | 4,477 | [
"MIT"
] | 0 | e9e04ca5e64250dfbb94134ec5283286dcdc4358 | https://github.com/suswei/RLCT/tree/e9e04ca5e64250dfbb94134ec5283286dcdc4358 |
Tanh | import torch
import torch.nn as nn
import torch.multiprocessing
class Tanh(nn.Module):
def __init__(self, input_dim, output_dim, H):
super(Tanh, self).__init__()
self.fc1 = nn.Linear(input_dim, H, bias=False)
self.fc2 = nn.Linear(H, output_dim, bias=False)
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.triton_helpers import libdevice
import torch.nn as ... | suswei/RLCT | Tanh | false | 4,478 | [
"MIT"
] | 0 | e9e04ca5e64250dfbb94134ec5283286dcdc4358 | https://github.com/suswei/RLCT/tree/e9e04ca5e64250dfbb94134ec5283286dcdc4358 |
GaussianMixtureReconstructionLoss | import torch
import numpy as np
import torch as th
def gaussian_pdfs(dx, dy, params):
"""Returns the pdf at (dx, dy) for each Gaussian in the mixture.
"""
dx = dx.unsqueeze(-1)
dy = dy.unsqueeze(-1)
mu_x = params[..., 0]
mu_y = params[..., 1]
sigma_x = params[..., 2].exp()
sigma_y = pa... | 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 nump... | v-a-s-a/diffvg | GaussianMixtureReconstructionLoss | false | 4,479 | [
"Apache-2.0"
] | 0 | 3685f3d47a5a4e5c76c68643ebf383f809ba59ed | https://github.com/v-a-s-a/diffvg/tree/3685f3d47a5a4e5c76c68643ebf383f809ba59ed |
PixelNorm | import torch
import torch.nn as nn
import torch.utils.cpp_extension
import torch.utils.data.distributed
class PixelNorm(nn.Module):
def __init__(self, dim):
super().__init__()
def forward(self, input):
return input * torch.rsqrt(torch.mean(input ** 2, dim=2, keepdim=
True) + 1e-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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.cpp_extension
import torch.utils.data.... | Pragyanstha/SummerCamp2021 | PixelNorm | false | 4,480 | [
"MIT"
] | 0 | caa8bba64020ba52bdef2b23a7a54de93e93b8af | https://github.com/Pragyanstha/SummerCamp2021/tree/caa8bba64020ba52bdef2b23a7a54de93e93b8af |
UpsampleConv2d | from torch.nn import Module
import math
import torch
from torchvision.datasets import *
import torch.nn.functional as F
from torch.nn import Parameter
from torch.nn.modules.utils import _pair
from torchvision.transforms import *
class UpsampleConv2d(Module):
"""
To avoid the checkerboard artifacts of standard... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 ... | tousifulhaque/DANet | UpsampleConv2d | false | 4,481 | [
"MIT"
] | 0 | 1a0c91f0e551a071b5e335b4157313780a8a1b1a | https://github.com/tousifulhaque/DANet/tree/1a0c91f0e551a071b5e335b4157313780a8a1b1a |
Quantize | import torch
import torch.nn as nn
import torch.nn.functional as F
class Quantize(nn.Module):
def __init__(self, emb_dim, emb_size, decay=0.99, eps=1e-05, ema_flag=
False, bdt_flag=False):
super().__init__()
self.emb_dim = emb_dim
self.emb_size = emb_size
self.ema_flag = e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | unilight/crank | Quantize | false | 4,482 | [
"MIT"
] | 0 | 0dc5d9df17f3186155b1c9583ab604ff218ad9a6 | https://github.com/unilight/crank/tree/0dc5d9df17f3186155b1c9583ab604ff218ad9a6 |
ConvPlus | import torch
import torch.nn as nn
import torch.utils.data
class ConvPlus(nn.Module):
def __init__(self, c1, c2, k=3, s=1, g=1, bias=True):
super(ConvPlus, self).__init__()
self.cv1 = nn.Conv2d(c1, c2, (k, 1), s, (k // 2, 0), groups=g, bias
=bias)
self.cv2 = nn.Conv2d(c1, c2, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | verchable/GenderDiversityCalc | ConvPlus | false | 4,483 | [
"Apache-2.0"
] | 0 | eb07fbc9d13e567de4efd8ea2a0aae793a06bf1d | https://github.com/verchable/GenderDiversityCalc/tree/eb07fbc9d13e567de4efd8ea2a0aae793a06bf1d |
Mean | import torch
from torchvision.datasets import *
import torch.nn as nn
from torchvision.transforms import *
class Mean(nn.Module):
def __init__(self, dim, keep_dim=False):
super(Mean, self).__init__()
self.dim = dim
self.keep_dim = keep_dim
def forward(self, input):
return inp... | 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 torchvision.datasets import *
import torch.nn as nn
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.a... | tousifulhaque/DANet | Mean | false | 4,484 | [
"MIT"
] | 0 | 1a0c91f0e551a071b5e335b4157313780a8a1b1a | https://github.com/tousifulhaque/DANet/tree/1a0c91f0e551a071b5e335b4157313780a8a1b1a |
cheap_cnn | import torch
import torch.nn as nn
import torch.nn.functional as F
class cheap_cnn(nn.Module):
def __init__(self):
super(cheap_cnn, self).__init__()
self.cnn1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3)
self.cnn2 = nn.Conv2d(in_channels=32, out_channels=64, 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | vaibhav117/sim2real4real | cheap_cnn | false | 4,485 | [
"MIT"
] | 0 | b1f253ef359eda0c7e3b594f89c8a35f0cf925bf | https://github.com/vaibhav117/sim2real4real/tree/b1f253ef359eda0c7e3b594f89c8a35f0cf925bf |
ZeroCenter | import torch
import torch.nn as nn
class ZeroCenter(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
"""x : [B, C, H, W]"""
return x.sub_(x.flatten(1).mean(1, keepdim=True).unsqueeze(-1).
unsqueeze(-1))
def get_inputs():
return [torch.rand([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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_per_fused_mean_sub_0(in_ptr0,... | vinnamkim/segmentation_models.pytorch | ZeroCenter | false | 4,486 | [
"MIT"
] | 0 | f967ded34df6fb536e8e8cba9b6491ae63b939f5 | https://github.com/vinnamkim/segmentation_models.pytorch/tree/f967ded34df6fb536e8e8cba9b6491ae63b939f5 |
EnsembleDense | import math
import torch
from torch import nn
class EnsembleDense(nn.Module):
__constants__ = ['num_ensembles', 'in_features', 'out_features']
in_features: 'int'
out_features: 'int'
weight: 'torch.Tensor'
def __init__(self, num_ensembles: 'int', in_features: 'int',
out_features: 'int', bi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | vermouth1992/rlutils | EnsembleDense | false | 4,487 | [
"Apache-2.0"
] | 0 | a326373b9e39dbf147c6c4261b82a688d4dc3e78 | https://github.com/vermouth1992/rlutils/tree/a326373b9e39dbf147c6c4261b82a688d4dc3e78 |
FocalLoss | import torch
from torch import nn
from torchvision.datasets.folder import *
class FocalLoss(nn.Module):
def __init__(self, gamma=0, eps=1e-07):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.eps = eps
self.ce = torch.nn.CrossEntropyLoss()
def forward(self, input, t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
f... | tks1998/Pytorch-Face-recongition-state-of-the-art-Qmul-surveface- | FocalLoss | false | 4,488 | [
"MIT"
] | 0 | e4068db0c53a4c6b8e81127191687662806af8d8 | https://github.com/tks1998/Pytorch-Face-recongition-state-of-the-art-Qmul-surveface-/tree/e4068db0c53a4c6b8e81127191687662806af8d8 |
Simple_AUG | import torch
import torch.nn as nn
from torch import autograd as autograd
import torch.fft
from itertools import product as product
class Simple_AUG(nn.Module):
def __init__(self, in_nc=3, out_nc=3, nf=5):
super(Simple_AUG, self).__init__()
self.c1 = nn.Conv2d(in_nc, nf, 3, 1, 1, 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | varun-jois/KAIR | Simple_AUG | false | 4,489 | [
"MIT"
] | 0 | 90c04671c6eb32a6765edfec94f7db3ba1f53f1e | https://github.com/varun-jois/KAIR/tree/90c04671c6eb32a6765edfec94f7db3ba1f53f1e |
Normalize | import torch
import torch.nn as nn
import torch.nn.functional as functional
class Normalize(nn.Module):
def __init__(self, dim: 'int', p: 'int'):
super().__init__()
self.dim = dim
self.p = p
def forward(self, inputs):
outputs = functional.normalize(inputs, dim=self.dim, p=sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | uripatish/torchup | Normalize | false | 4,490 | [
"MIT"
] | 0 | 0b7bee031fc99e536342331ba567c523a790d742 | https://github.com/uripatish/torchup/tree/0b7bee031fc99e536342331ba567c523a790d742 |
ProteinBertPooler | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class ProteinBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.trainable_encoder = config.trainable_encoder
if self.trainable_encoder:
self.dense = nn.Linear(config.hidden... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | StephanHeijl/tape | ProteinBertPooler | false | 4,491 | [
"BSD-3-Clause"
] | 0 | ec631ca53217686605477cf31af4fb8846ff660f | https://github.com/StephanHeijl/tape/tree/ec631ca53217686605477cf31af4fb8846ff660f |
Q | import torch
import torch.nn.functional as F
import torch.nn as nn
class Q(nn.Module):
def __init__(self, state_dim, action_dim, hidden):
super(Q, self).__init__()
self.fc1 = nn.Linear(state_dim + action_dim, hidden)
self.fc2 = nn.Linear(hidden, hidden)
self.fc3 = nn.Linear(hidden... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | victorkich/agaragan | Q | false | 4,492 | [
"MIT"
] | 0 | 64e312fc4fa42f5952f3ce997bafe674306a9419 | https://github.com/victorkich/agaragan/tree/64e312fc4fa42f5952f3ce997bafe674306a9419 |
ActorSAC | import torch
import torch.nn.functional as F
import torch.nn as nn
class ActorSAC(nn.Module):
def __init__(self, state_dim, hidden, min_log_std=-20, max_log_std=2):
super(ActorSAC, self).__init__()
self.fc1 = nn.Linear(state_dim, hidden)
self.fc2 = nn.Linear(hidden, hidden)
self.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_... | victorkich/agaragan | ActorSAC | false | 4,493 | [
"MIT"
] | 0 | 64e312fc4fa42f5952f3ce997bafe674306a9419 | https://github.com/victorkich/agaragan/tree/64e312fc4fa42f5952f3ce997bafe674306a9419 |
PAM_Module | from torch.nn import Module
import torch
import torch.serialization
import torch
import torch.utils.data
from torch.nn import Conv2d
from torch.nn import Parameter
from torch.nn import Softmax
class PAM_Module(Module):
""" Position attention module"""
def __init__(self, in_dim):
super(PAM_Module, 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 import triton_helpers
from torch._inductor.runtime.... | vis-opt-group/GTANet | PAM_Module | false | 4,494 | [
"MIT"
] | 0 | 269ff4418ee5f0267987e1fa4c69bda13e5cb00d | https://github.com/vis-opt-group/GTANet/tree/269ff4418ee5f0267987e1fa4c69bda13e5cb00d |
SE_layer_3d | import torch
import torch.nn as nn
import torch.multiprocessing
class SE_layer_3d(nn.Module):
def __init__(self, num_channels, reduction_ratio=2):
super(SE_layer_3d, self).__init__()
num_channels_reduced = num_channels // reduction_ratio
self.reduction_ratio = reduction_ratio
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | vinbigdata-medical/abdomen-phases | SE_layer_3d | false | 4,495 | [
"MIT"
] | 0 | 4adf5b8bf13aec85247d74e3cd3789c52cb88b92 | https://github.com/vinbigdata-medical/abdomen-phases/tree/4adf5b8bf13aec85247d74e3cd3789c52cb88b92 |
Mean | import torch
from torch import nn
class Mean(nn.Module):
def __init__(self, *args):
super(Mean, self).__init__()
self.shape = args
def forward(self, x):
return x.mean(self.shape)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | vitskvara/shape-guided-anomaly-detection | Mean | false | 4,496 | [
"MIT"
] | 0 | 6685b2e0b97968a6d0f478d2920486da107b277f | https://github.com/vitskvara/shape-guided-anomaly-detection/tree/6685b2e0b97968a6d0f478d2920486da107b277f |
HighwayLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx.operators
class HighwayLayer(nn.Module):
def __init__(self, input_dim, transform_activation=F.relu,
gate_activation=F.softmax, gate_bias=-2):
super().__init__()
self.highway_transform_activation = transfo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | vincentLiangBerkeley/translate | HighwayLayer | false | 4,497 | [
"BSD-3-Clause"
] | 0 | 734ae1ad9dfb778935e4825b5ce2687e2df559ea | https://github.com/vincentLiangBerkeley/translate/tree/734ae1ad9dfb778935e4825b5ce2687e2df559ea |
Patch2Image | import torch
from torch import nn
class Patch2Image(nn.Module):
""" take in patch and copy n_up times to form the full image"""
def __init__(self, patch_sz, n_up):
super(Patch2Image, self).__init__()
self.patch_sz = patch_sz
self.n_up = n_up
def forward(self, x):
assert 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | vitskvara/shape-guided-anomaly-detection | Patch2Image | false | 4,498 | [
"MIT"
] | 0 | 6685b2e0b97968a6d0f478d2920486da107b277f | https://github.com/vitskvara/shape-guided-anomaly-detection/tree/6685b2e0b97968a6d0f478d2920486da107b277f |
Feature | import torch
import torch.serialization
import torch
import torch.utils.data
class ResBlock(torch.nn.Module):
def __init__(self):
super(ResBlock, self).__init__()
self.conv1 = torch.nn.Conv2d(in_channels=64, out_channels=64,
kernel_size=3, stride=1, padding=1)
self.conv2 = tor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.serialization
im... | vis-opt-group/GTANet | Feature | false | 4,499 | [
"MIT"
] | 0 | 269ff4418ee5f0267987e1fa4c69bda13e5cb00d | https://github.com/vis-opt-group/GTANet/tree/269ff4418ee5f0267987e1fa4c69bda13e5cb00d |
DuRB_p | import torch
import numpy as np
import torch.serialization
import torch
import torch.nn as nn
import torch.utils.data
class ConvLayer(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size, stride, dilation=1):
super(ConvLayer, self).__init__()
self.dilation = dilation
if dilation ==... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | vis-opt-group/GTANet | DuRB_p | false | 4,500 | [
"MIT"
] | 0 | 269ff4418ee5f0267987e1fa4c69bda13e5cb00d | https://github.com/vis-opt-group/GTANet/tree/269ff4418ee5f0267987e1fa4c69bda13e5cb00d |
Accuracy | from torch.nn import Module
import torch
from torch import Tensor
class Accuracy(Module):
"""
Class for calculating the accuracy for a given prediction and the labels
for comparison.
Expects the inputs to be from a range of 0 to 1 and sets a crossing threshold at 0.5
the labels are similarly round... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
from torch import Tensor
assert_size_stride = torch._C._dynam... | vixadd/sparseml | Accuracy | false | 4,501 | [
"Apache-2.0"
] | 0 | e2dcb66bad713542158dfe54cba113a0cc02ed39 | https://github.com/vixadd/sparseml/tree/e2dcb66bad713542158dfe54cba113a0cc02ed39 |
PureUpsampling | import torch
import torch.nn as nn
import torch.nn.functional as F
class PureUpsampling(nn.Module):
def __init__(self, scale=2, mode='bilinear'):
super(PureUpsampling, self).__init__()
assert isinstance(scale, int)
self.scale = scale
self.mode = mode
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | vlbthambawita/polyp-inpainting | PureUpsampling | false | 4,502 | [
"MIT"
] | 0 | f1d754f8ffb3f6d991206b2a661933ff32de0d7a | https://github.com/vlbthambawita/polyp-inpainting/tree/f1d754f8ffb3f6d991206b2a661933ff32de0d7a |
RandomCrop | import torch
from torch import nn
def choose_rand_patches(x, patch_sz, dim):
assert dim == 2 or dim == 3
batch_sz = x.shape[0]
patches = x.unfold(dim, patch_sz, 1)
n_patches = patches.shape[2]
idx = torch.randint(0, n_patches, (batch_sz,))
if dim == 2:
patches = patches[torch.arange(ba... | import torch
from torch import device
import triton
import 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.... | vitskvara/shape-guided-anomaly-detection | RandomCrop | false | 4,503 | [
"MIT"
] | 0 | 6685b2e0b97968a6d0f478d2920486da107b277f | https://github.com/vitskvara/shape-guided-anomaly-detection/tree/6685b2e0b97968a6d0f478d2920486da107b277f |
TVLoss | import torch
import torch.nn as nn
class TVLoss(nn.Module):
def __init__(self):
super(TVLoss, self).__init__()
def forward(self, x):
h_x, w_x = x.size()[2:]
h_tv = torch.abs(x[:, :, 1:, :] - x[:, :, :h_x - 1, :])
w_tv = torch.abs(x[:, :, :, 1:] - x[:, :, :, :w_x - 1])
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | vlbthambawita/polyp-inpainting | TVLoss | false | 4,504 | [
"MIT"
] | 0 | f1d754f8ffb3f6d991206b2a661933ff32de0d7a | https://github.com/vlbthambawita/polyp-inpainting/tree/f1d754f8ffb3f6d991206b2a661933ff32de0d7a |
conv_head_pooling | import torch
import torch.nn as nn
import torch.utils.data
class conv_head_pooling(nn.Module):
def __init__(self, in_feature, out_feature, stride, conv_type,
padding_mode='zeros', dilation=1):
super(conv_head_pooling, self).__init__()
if conv_type == 'depthwise':
_groups = 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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | tsubauaaa/d2go | conv_head_pooling | false | 4,505 | [
"Apache-2.0"
] | 0 | 9f746159ebf78ce79f644c405ca8695bc29d1075 | https://github.com/tsubauaaa/d2go/tree/9f746159ebf78ce79f644c405ca8695bc29d1075 |
ShortcutLayer | import torch
import torch.nn as nn
class ShortcutLayer(nn.Module):
def __init__(self, idx):
super(ShortcutLayer, self).__init__()
self.idx = idx
def forward(self, x, outputs):
return x + outputs[self.idx]
def get_inputs():
return [torch.rand([5, 4, 4, 4]), torch.rand([5, 4, 4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | vrindaprabhu/solofy | ShortcutLayer | false | 4,506 | [
"MIT"
] | 0 | d5e26ff20d293c200485c70be6dcd6481afba396 | https://github.com/vrindaprabhu/solofy/tree/d5e26ff20d293c200485c70be6dcd6481afba396 |
CustomGroupNorm | import torch
class CustomGroupNorm(torch.nn.Module):
"""
Custom Group Norm which adds n_groups=2 as default parameter
"""
def __init__(self, n_features, n_groups=2):
"""
Parameters
----------
n_features : int
number of input features
n_groups : int... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | vuamitom/shapenet | CustomGroupNorm | false | 4,507 | [
"BSD-2-Clause"
] | 0 | 9eb3dadc91801756cb3460707c37146c8176643e | https://github.com/vuamitom/shapenet/tree/9eb3dadc91801756cb3460707c37146c8176643e |
PairwiseNorm | import torch
from torch import nn
class PairwiseNorm(nn.Module):
def __init__(self, order=1, size_average=True):
super().__init__()
self.order = order
self.average = size_average
def forward(self, inp, target=None):
inp = inp.flatten(1)
assert len(inp) % 2 == 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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | vzinche/inferno | PairwiseNorm | false | 4,508 | [
"Apache-2.0"
] | 0 | 91b22dfcd1b6a9ec415f0bbb6ae66caea42f4034 | https://github.com/vzinche/inferno/tree/91b22dfcd1b6a9ec415f0bbb6ae66caea42f4034 |
Norm | import torch
from torch import nn
class Norm(nn.Module):
def __init__(self, order=1, size_average=True):
super().__init__()
self.order = order
self.average = size_average
def forward(self, inp, target=None):
if target is not None:
inp = inp - target
inp = ... | 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... | vzinche/inferno | Norm | false | 4,509 | [
"Apache-2.0"
] | 0 | 91b22dfcd1b6a9ec415f0bbb6ae66caea42f4034 | https://github.com/vzinche/inferno/tree/91b22dfcd1b6a9ec415f0bbb6ae66caea42f4034 |
ContrastiveLoss | import torch
from torch import nn
class ContrastiveLoss(nn.Module):
def __init__(self, margin=1.0, reduction='mean'):
super().__init__()
self.m = margin
assert reduction in ['mean', 'sum', 'none']
self.reduction = reduction
def forward(self, dist, class_):
dist = dist... | 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... | vzinche/inferno | ContrastiveLoss | false | 4,510 | [
"Apache-2.0"
] | 0 | 91b22dfcd1b6a9ec415f0bbb6ae66caea42f4034 | https://github.com/vzinche/inferno/tree/91b22dfcd1b6a9ec415f0bbb6ae66caea42f4034 |
WordPredictor | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx.operators
class WordPredictor(nn.Module):
def __init__(self, encoder_output_dim, hidden_dim, output_dim):
super().__init__()
self.encoder_output_dim = encoder_output_dim
self.hidden_dim = 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.... | vincentLiangBerkeley/translate | WordPredictor | false | 4,511 | [
"BSD-3-Clause"
] | 0 | 734ae1ad9dfb778935e4825b5ce2687e2df559ea | https://github.com/vincentLiangBerkeley/translate/tree/734ae1ad9dfb778935e4825b5ce2687e2df559ea |
PairwiseCrossCorrelation | import torch
from torch import nn
class PairwiseCrossCorrelation(nn.Module):
def __init__(self, lambd=1):
super().__init__()
self.lambd = lambd
def off_diagonal(self, x):
n, m = x.shape
assert n == m
return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | vzinche/inferno | PairwiseCrossCorrelation | false | 4,512 | [
"Apache-2.0"
] | 0 | 91b22dfcd1b6a9ec415f0bbb6ae66caea42f4034 | https://github.com/vzinche/inferno/tree/91b22dfcd1b6a9ec415f0bbb6ae66caea42f4034 |
Linear | import torch
from torch import Tensor
from warnings import warn
from torch.nn import functional as F
from torch.nn import Linear as normal_linear
import torch.utils.data
from torchvision import transforms as transforms
class Linear(normal_linear):
def __init__(self, *args, **kwargs):
super(Linear, 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 warnings import warn
from torch.nn import Linear as normal_linear
import to... | wang93/pytorch-cifar10 | Linear | false | 4,513 | [
"Apache-2.0"
] | 0 | 07a54dd575aad9b011114352d08fdd9f61e360a1 | https://github.com/wang93/pytorch-cifar10/tree/07a54dd575aad9b011114352d08fdd9f61e360a1 |
Mnist_CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Mnist_CNN(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = 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
import ... | voyageth/PyTorch-tutorials-kr | Mnist_CNN | false | 4,514 | [
"BSD-3-Clause"
] | 0 | 05d2dd5931abfca6ce1e0b297f4ceb7f4eae6239 | https://github.com/voyageth/PyTorch-tutorials-kr/tree/05d2dd5931abfca6ce1e0b297f4ceb7f4eae6239 |
FrequencyLoss | import torch
import torch.nn as nn
class FrequencyLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=0.001):
super(FrequencyLoss, self).__init__()
self.criterion = torch.nn.L1Loss()
def forward(self, x, y):
x_fft = torch.fft.rfft2(x, dim=(2, 3))
y_fft = t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | vztu/DebandingNet | FrequencyLoss | false | 4,515 | [
"MIT"
] | 0 | 4af8e83ffbfc70dc220dd6fea2827fb75796f10c | https://github.com/vztu/DebandingNet/tree/4af8e83ffbfc70dc220dd6fea2827fb75796f10c |
FocalLossSigmoid | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class FocalLossSigmoid(nn.Module):
"""
sigmoid version focal loss
"""
def __init__(self, alpha=0.25, gamma=2, size_average=False):
super(FocalLossSigmoid, self).__init__()
self.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
... | wangbingok1118/SSD_Pytorch | FocalLossSigmoid | false | 4,516 | [
"MIT"
] | 0 | 8d3f924671cec367c3c420eba2f002cc5b5181bb | https://github.com/wangbingok1118/SSD_Pytorch/tree/8d3f924671cec367c3c420eba2f002cc5b5181bb |
MedianPool2d | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from torch.nn.modules.utils import _quadruple
class MedianPool2d(nn.Module):
""" Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kernel, int ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from torch.nn.modules.utils import _pair
from torch... | vztu/DebandingNet | MedianPool2d | false | 4,517 | [
"MIT"
] | 0 | 4af8e83ffbfc70dc220dd6fea2827fb75796f10c | https://github.com/vztu/DebandingNet/tree/4af8e83ffbfc70dc220dd6fea2827fb75796f10c |
ParseL1loss | import torch
from torch import nn
import torch.nn.functional as F
class ParseL1loss(nn.Module):
def __init__(self):
super(ParseL1loss, self).__init__()
def forward(self, output, target, mask):
mask = (mask == 1).float()
loss = F.l1_loss(output * mask, target * mask, size_average=Fals... | 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... | weberhen/NonCuboidRoom | ParseL1loss | false | 4,518 | [
"MIT"
] | 0 | 871a77941697f1457cdae541b8ffcdce4f9134e3 | https://github.com/weberhen/NonCuboidRoom/tree/871a77941697f1457cdae541b8ffcdce4f9134e3 |
WassersteinLoss | import torch
from torch import nn
class WassersteinLoss(nn.Module):
"""For WGAN."""
def forward(self, real, fake):
return real.mean() - fake.mean()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | wegroupwolves/fastai | WassersteinLoss | false | 4,519 | [
"Apache-2.0"
] | 0 | df40df403e05e132411f0f7abc7ec33c86e58bb9 | https://github.com/wegroupwolves/fastai/tree/df40df403e05e132411f0f7abc7ec33c86e58bb9 |
SpatialAttn | import torch
import torch.nn as nn
class SpatialAttn(nn.Module):
"""Spatial Attention Layer"""
def __init__(self):
super(SpatialAttn, self).__init__()
def forward(self, x):
x = x.mean(1, keepdim=True)
h = x.size(2)
w = x.size(3)
x = x.view(x.size(0), -1)
z... | 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... | wangminjie920705/Part-reid | SpatialAttn | false | 4,520 | [
"MIT"
] | 0 | 34a1e968a2eab692ba810332f309e82b441793f6 | https://github.com/wangminjie920705/Part-reid/tree/34a1e968a2eab692ba810332f309e82b441793f6 |
FocalLoss | import torch
from torch import nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, focusing_param=2, balance_param=0.25):
super(FocalLoss, self).__init__()
self.focusing_param = focusing_param
self.balance_param = balance_param
def forward(self, output, ... | 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... | wanghao15536870732/plants_disease_classify | FocalLoss | false | 4,521 | [
"Apache-2.0"
] | 0 | 6d0d1d39f0ec15fc2bd523142c5c403a1577da84 | https://github.com/wanghao15536870732/plants_disease_classify/tree/6d0d1d39f0ec15fc2bd523142c5c403a1577da84 |
SigmoidRange | import torch
from torch import nn
def sigmoid_range(x, low, high):
"""Sigmoid function with range `(low, high)`"""
return torch.sigmoid(x) * (high - low) + low
class SigmoidRange(nn.Module):
"""Sigmoid module with range `(low,x_max)`"""
def __init__(self, low, high):
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | wegroupwolves/fastai | SigmoidRange | false | 4,522 | [
"Apache-2.0"
] | 0 | df40df403e05e132411f0f7abc7ec33c86e58bb9 | https://github.com/wegroupwolves/fastai/tree/df40df403e05e132411f0f7abc7ec33c86e58bb9 |
TemporalRelation | import torch
import torch.nn as nn
class TemporalRelation(nn.Module):
def __init__(self, feat_dim, time_window=1):
super(TemporalRelation, self).__init__()
self.time_window = time_window
self.feat_dim = feat_dim
self.WT = nn.Linear(self.feat_dim, self.feat_dim, bias=False)
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | weiyi1991/UA_Concurrent | TemporalRelation | false | 4,523 | [
"MIT"
] | 0 | 11238c778c60095abf326800d6e6a13a643bf071 | https://github.com/weiyi1991/UA_Concurrent/tree/11238c778c60095abf326800d6e6a13a643bf071 |
LinearNormalGamma | import torch
from torch import nn
class LinearNormalGamma(nn.Module):
def __init__(self, in_chanels, out_channels):
super().__init__()
self.linear = nn.Linear(in_chanels, out_channels * 4)
def evidence(self, x):
return torch.log(torch.exp(x) + 1)
def forward(self, x):
pr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | wanzysky/evidential-deep-learning | LinearNormalGamma | false | 4,524 | [
"Apache-2.0"
] | 0 | 71ebd59ab3a4b66c38d919e8aa9ad3711a416796 | https://github.com/wanzysky/evidential-deep-learning/tree/71ebd59ab3a4b66c38d919e8aa9ad3711a416796 |
GMM_Module | import math
import torch
import torch.nn as nn
import torch.utils.data
class GMM_Module(nn.Module):
"""
GMM Module
"""
def __init__(self, out_channel_M, k):
super(GMM_Module, self).__init__()
self.conv1 = nn.Conv2d(int(out_channel_M), k * out_channel_M,
kernel_size=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
import math
import torch.nn as nn
import torch.utils.data
assert_size_stride = t... | wemozj/Image-Compression-based-GMM-and-Attention-Module | GMM_Module | false | 4,525 | [
"Apache-2.0"
] | 0 | 93f804dbcea8ffc1621456f3d104d0342c75373b | https://github.com/wemozj/Image-Compression-based-GMM-and-Attention-Module/tree/93f804dbcea8ffc1621456f3d104d0342c75373b |
MobileViTv2Attention | import torch
from torch import nn
from torch.nn import init
class MobileViTv2Attention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries and keys
:p... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | weihaoxie/External-Attention-pytorch | MobileViTv2Attention | false | 4,526 | [
"MIT"
] | 0 | 9bec70f4ed8dd858c815e9bad240ab2f95a91a9f | https://github.com/weihaoxie/External-Attention-pytorch/tree/9bec70f4ed8dd858c815e9bad240ab2f95a91a9f |
BitEstimator | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Bitparm(nn.Module):
"""
save params
"""
def __init__(self, channel, final=False):
super(Bitparm, self).__init__()
self.final = final
self.h = nn.Parameter(torch.nn.init.normal_(tor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
import t... | wemozj/Image-Compression-based-GMM-and-Attention-Module | BitEstimator | false | 4,527 | [
"Apache-2.0"
] | 0 | 93f804dbcea8ffc1621456f3d104d0342c75373b | https://github.com/wemozj/Image-Compression-based-GMM-and-Attention-Module/tree/93f804dbcea8ffc1621456f3d104d0342c75373b |
Prototype | import torch
import torch.nn
import torch.optim
import torch.utils.data
class Prototype(torch.nn.Module):
""""""
def __init__(self, prototype_num, latent_size) ->None:
super(Prototype, self).__init__()
self.latent_size = latent_size
self.prototype_num = prototype_num
self.prot... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | wenqiangxie/Prototype-Net | Prototype | false | 4,528 | [
"MIT"
] | 0 | a5ddd9976b78828d87806f9451a5092de3ff5c69 | https://github.com/wenqiangxie/Prototype-Net/tree/a5ddd9976b78828d87806f9451a5092de3ff5c69 |
Model | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(2, 2, 1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 2, 64, 64])]
def get_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_str... | whu-pzhang/mmsegmentation | Model | false | 4,529 | [
"Apache-2.0"
] | 0 | 46326f63ce411c794d237e986dd3924590d0e75e | https://github.com/whu-pzhang/mmsegmentation/tree/46326f63ce411c794d237e986dd3924590d0e75e |
MultiHeadAttention | import torch
from torch import Tensor
from typing import Any
import torch.nn.functional as F
from torch import nn
def ifnone(a: 'Any', b: 'Any') ->Any:
"""`a` if `a` is not None, otherwise `b`."""
return b if a is None else a
class MultiHeadAttention(nn.Module):
"""MutiHeadAttention."""
def __init_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | wegroupwolves/fastai | MultiHeadAttention | false | 4,530 | [
"Apache-2.0"
] | 0 | df40df403e05e132411f0f7abc7ec33c86e58bb9 | https://github.com/wegroupwolves/fastai/tree/df40df403e05e132411f0f7abc7ec33c86e58bb9 |
GDN | from torch.autograd import Function
import torch
import torch.nn as nn
import torch.utils.data
class LowerBound(Function):
@staticmethod
def forward(ctx, inputs, bound):
b = torch.ones_like(inputs) * bound
ctx.save_for_backward(inputs, b)
return torch.max(inputs, b)
@staticmethod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | wemozj/Image-Compression-based-GMM-and-Attention-Module | GDN | false | 4,531 | [
"Apache-2.0"
] | 0 | 93f804dbcea8ffc1621456f3d104d0342c75373b | https://github.com/wemozj/Image-Compression-based-GMM-and-Attention-Module/tree/93f804dbcea8ffc1621456f3d104d0342c75373b |
UFOAttention | import torch
from torch import nn
from torch.nn import init
def XNorm(x, gamma):
norm_tensor = torch.norm(x, 2, -1, True)
return x * gamma / norm_tensor
class UFOAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
... | 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.triton_helpers import libdevice
from torch import n... | weihaoxie/External-Attention-pytorch | UFOAttention | false | 4,532 | [
"MIT"
] | 0 | 9bec70f4ed8dd858c815e9bad240ab2f95a91a9f | https://github.com/weihaoxie/External-Attention-pytorch/tree/9bec70f4ed8dd858c815e9bad240ab2f95a91a9f |
NaiveGate | import torch
import torch.nn as nn
import torch.nn.functional as F
class NaiveGate(nn.Module):
"""
A naive gate implementation that defines the standard behavior of the gate
which determines which experts the tokens are going to.
Both the indecies and the score, or confidence, are output to the parent... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | whn09/fastmoe | NaiveGate | false | 4,533 | [
"Apache-2.0"
] | 0 | d0ffaffc6431abcd3ea6d0287dbf09f8cd727a0a | https://github.com/whn09/fastmoe/tree/d0ffaffc6431abcd3ea6d0287dbf09f8cd727a0a |
ActorNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class ActorNetwork(nn.Module):
def __init__(self, input_size, hidden_size, action_size):
super(ActorNetwork, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | whongyu/MA3C | ActorNetwork | false | 4,534 | [
"MIT"
] | 0 | d3b38cf42a909c0938624ba853119804efaf47eb | https://github.com/whongyu/MA3C/tree/d3b38cf42a909c0938624ba853119804efaf47eb |
ComplexBatchNormalize | import torch
import torch.nn as nn
def cylindricalToPolarConversion(input1, input2=None):
if input2 is None:
"""input1 is tensor of [B,C,H,W,D,2] contains both real and imaginary channels
in the last dims"""
ndims = input1.ndimension()
real_input = input1.narrow(ndims - 1, 0, 1).s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | wizofe/urus-mri-recon | ComplexBatchNormalize | false | 4,535 | [
"MIT"
] | 0 | eab8e48dca31d2b936ce69ccc251ec5a4a10facc | https://github.com/wizofe/urus-mri-recon/tree/eab8e48dca31d2b936ce69ccc251ec5a4a10facc |
Channel_mean | import torch
import torch.nn as nn
class Channel_mean(nn.Module):
def __init__(self) ->None:
super().__init__()
def forward(self, V):
"""
only V[0]
"""
return torch.sum(V[0], dim=0).squeeze()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inp... | 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... | wk989898/ARES-implement | Channel_mean | false | 4,536 | [
"MIT"
] | 0 | b2411be01124feaccbc89d74f6025fbfa584bb3f | https://github.com/wk989898/ARES-implement/tree/b2411be01124feaccbc89d74f6025fbfa584bb3f |
MnistFeatureExtractor | import torch
import torch.nn as nn
import torch.nn.functional as F
class MnistFeatureExtractor(nn.Module):
def __init__(self, activation=F.leaky_relu):
super(MnistFeatureExtractor, self).__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | wiatrak2/BScThesis | MnistFeatureExtractor | false | 4,537 | [
"MIT"
] | 0 | e5dd012fd9052e7088d8464b409dc055dbfcf840 | https://github.com/wiatrak2/BScThesis/tree/e5dd012fd9052e7088d8464b409dc055dbfcf840 |
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