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 |
|---|---|---|---|---|---|---|---|---|---|---|
TransposeGatedConv2d | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super(SpectralNorm, self).__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.triton_helpers import libdevice
import torch.nn as ... | delldu/DeepFillv2 | TransposeGatedConv2d | false | 6,565 | [
"MIT"
] | 1 | a564b9589c1b42bcdddd3d7601f4059c4594a439 | https://github.com/delldu/DeepFillv2/tree/a564b9589c1b42bcdddd3d7601f4059c4594a439 |
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... | devaansh100/pytorch_connectomics | WSDiceLoss | false | 6,566 | [
"MIT"
] | 1 | b1e4b16b0480546ea806d14876208080815ed964 | https://github.com/devaansh100/pytorch_connectomics/tree/b1e4b16b0480546ea806d14876208080815ed964 |
QuantizableHSigmoid | import torch
import torch.nn as nn
import torch.quantization
class QuantizableHSigmoid(nn.Module):
"""Hard Sigmoid for quantization."""
def __init__(self, inplace: 'bool'=True) ->None:
"""Initialize."""
super(QuantizableHSigmoid, self).__init__()
self.relu6 = nn.ReLU6(inplace=inplace)... | 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.quantization
assert_size_stride = torch._C._dynamo.gua... | dhlee347/model_compression | QuantizableHSigmoid | false | 6,567 | [
"MIT"
] | 1 | 274b85ff56d81f0b7cf6907cbc1bd10e16cdb956 | https://github.com/dhlee347/model_compression/tree/274b85ff56d81f0b7cf6907cbc1bd10e16cdb956 |
HSigmoid | import torch
import torch.nn as nn
import torch.quantization
class HSigmoid(nn.Module):
"""Hard Sigmoid."""
def __init__(self, inplace: 'bool'=True) ->None:
"""Initialize."""
super(HSigmoid, self).__init__()
self.relu6 = nn.ReLU6(inplace=inplace)
def forward(self, x: 'torch.Tenso... | 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.quantization
assert_size_stride = torch._C._dynamo.gua... | dhlee347/model_compression | HSigmoid | false | 6,568 | [
"MIT"
] | 1 | 274b85ff56d81f0b7cf6907cbc1bd10e16cdb956 | https://github.com/dhlee347/model_compression/tree/274b85ff56d81f0b7cf6907cbc1bd10e16cdb956 |
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... | devaansh100/pytorch_connectomics | WeightedCE | false | 6,569 | [
"MIT"
] | 1 | b1e4b16b0480546ea806d14876208080815ed964 | https://github.com/devaansh100/pytorch_connectomics/tree/b1e4b16b0480546ea806d14876208080815ed964 |
QuantizableHSwish | import torch
import torch.nn as nn
import torch.quantization
class QuantizableHSigmoid(nn.Module):
"""Hard Sigmoid for quantization."""
def __init__(self, inplace: 'bool'=True) ->None:
"""Initialize."""
super(QuantizableHSigmoid, self).__init__()
self.relu6 = nn.ReLU6(inplace=inplace)... | 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.quantization
assert_size_stride = torch._C._dynamo.gua... | dhlee347/model_compression | QuantizableHSwish | false | 6,570 | [
"MIT"
] | 1 | 274b85ff56d81f0b7cf6907cbc1bd10e16cdb956 | https://github.com/dhlee347/model_compression/tree/274b85ff56d81f0b7cf6907cbc1bd10e16cdb956 |
SEModule | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import torch.utils.data
def make_divisible(v, divisor, min_val=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | dercaft/XNAS | SEModule | false | 6,571 | [
"MIT"
] | 1 | d6d0fde0d4475210a41607181939188b177e44b1 | https://github.com/dercaft/XNAS/tree/d6d0fde0d4475210a41607181939188b177e44b1 |
BinaryReg | import torch
import torch.nn as nn
import torch.utils.data
class BinaryReg(nn.Module):
"""Regularization for encouraging the outputs to be binary.
"""
def __init__(self, alpha=1.0):
super().__init__()
self.alpha = alpha
def forward(self, pred):
diff = pred - 0.5
diff ... | 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
... | divyam-goel/pytorch_connectomics | BinaryReg | false | 6,572 | [
"MIT"
] | 1 | a2c70a7cc60fd84d67be6f225c123ff11daadb83 | https://github.com/divyam-goel/pytorch_connectomics/tree/a2c70a7cc60fd84d67be6f225c123ff11daadb83 |
Attention | import torch
from torch import nn as nn
from torch.nn import functional as F
class Attention(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.decoder_proj = nn.Linear(hidden_size, hidden_size)
self.encoder_proj = nn.Linear(hidden_size, hidden_size)
nn.init.xavi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | devjwsong/dialogue-error-correction-pytorch | Attention | false | 6,573 | [
"MIT"
] | 1 | ee0fa1f27eb995893a5943181a1fd0099a9e9202 | https://github.com/devjwsong/dialogue-error-correction-pytorch/tree/ee0fa1f27eb995893a5943181a1fd0099a9e9202 |
MMTMBi | import torch
import torch.nn as nn
from typing import Sequence
class MMTMBi(nn.Module):
"""
bi moludal fusion
"""
def __init__(self, dim_tab, dim_img, ratio=4):
"""
Parameters
----------
dim_tab: feature dimension of tabular data
dim_img: feature dimension of ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ditannan/Multi-modal-Multi-instance-Learning | MMTMBi | false | 6,575 | [
"Apache-2.0"
] | 1 | 06aada1ff85784d5ed50aa528c506947c892d584 | https://github.com/ditannan/Multi-modal-Multi-instance-Learning/tree/06aada1ff85784d5ed50aa528c506947c892d584 |
JaccardLoss | import torch
import torch.nn as nn
import torch.utils.data
class JaccardLoss(nn.Module):
"""Jaccard loss.
"""
def __init__(self, size_average=True, reduce=True, smooth=1.0):
super(JaccardLoss, self).__init__()
self.smooth = smooth
self.reduce = reduce
def jaccard_loss(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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | divyam-goel/pytorch_connectomics | JaccardLoss | false | 6,576 | [
"MIT"
] | 1 | a2c70a7cc60fd84d67be6f225c123ff11daadb83 | https://github.com/divyam-goel/pytorch_connectomics/tree/a2c70a7cc60fd84d67be6f225c123ff11daadb83 |
MMTMTri | import torch
import torch.nn as nn
from typing import Sequence
class MMTMTri(nn.Module):
"""
tri-modal fusion
"""
def __init__(self, dim_img, ratio=4):
"""
Parameters
----------
dim_tab: feature dimension of tabular data
dim_img: feature dimension of MIL model... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ditannan/Multi-modal-Multi-instance-Learning | MMTMTri | false | 6,577 | [
"Apache-2.0"
] | 1 | 06aada1ff85784d5ed50aa528c506947c892d584 | https://github.com/ditannan/Multi-modal-Multi-instance-Learning/tree/06aada1ff85784d5ed50aa528c506947c892d584 |
Sine | import torch
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data.distributed
class Sine(nn.Module):
""" Applies the sine function element-wise.
`"Implicit Neural Representations with Periodic Activation Functions" <https://arxiv.org/pdf/2006.09661.pdf>`_
Exa... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel... | doansangg/CGAN-PyTorch | Sine | false | 6,578 | [
"Apache-2.0"
] | 1 | 941f5bd75102bed7f2eccd7feb9af8e6134af0e4 | https://github.com/doansangg/CGAN-PyTorch/tree/941f5bd75102bed7f2eccd7feb9af8e6134af0e4 |
SimpleCNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleCNN(nn.Module):
def __init__(self, num_channels, num_classes):
super(SimpleCNN, self).__init__()
C = num_channels
self.conv1 = nn.Conv2d(in_channels=C, out_channels=C * 8,
kernel_size=3, stride=2, 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
import torch.nn as nn
assert_... | diogo149/doo | SimpleCNN | false | 6,579 | [
"MIT"
] | 1 | d83a1715fb9d4e5eac9f5d3d384a45cfc26fec2f | https://github.com/diogo149/doo/tree/d83a1715fb9d4e5eac9f5d3d384a45cfc26fec2f |
HSigmoid | import torch
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data.distributed
class HSigmoid(nn.Module):
""" Applies the Hard-Sigmoid function element-wise.
`"Searching for MobileNetV3" <https://arxiv.org/pdf/1905.02244.pdf>`_
Examples:
>>> m = Mish()... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.ut... | doansangg/CGAN-PyTorch | HSigmoid | false | 6,580 | [
"Apache-2.0"
] | 1 | 941f5bd75102bed7f2eccd7feb9af8e6134af0e4 | https://github.com/doansangg/CGAN-PyTorch/tree/941f5bd75102bed7f2eccd7feb9af8e6134af0e4 |
MyInstanceNorm2d | import torch
from torch import nn
class AffineChannelwise(nn.Module):
def __init__(self, num_channels):
super().__init__()
self.num_channels = num_channels
self.register_parameter('weight', nn.Parameter(torch.ones(
num_channels)))
self.register_parameter('bias', nn.Par... | 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... | dniku/dl-norms | MyInstanceNorm2d | false | 6,581 | [
"MIT"
] | 1 | 0f1eef942bd318ac988ec7dfa9caea300d17e82a | https://github.com/dniku/dl-norms/tree/0f1eef942bd318ac988ec7dfa9caea300d17e82a |
TSAFusion | import torch
import torch.nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
class TSAFusion(nn.Module):
"""Temporal Spatial Attention (TSA) fusion module.
Temporal: Calculate the correlation between center frame and
neighboring... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | cyysc1998/EDVRDarts | TSAFusion | false | 6,582 | [
"MIT"
] | 1 | 201badbc8c6469b519647a8869c3782ebe1176cf | https://github.com/cyysc1998/EDVRDarts/tree/201badbc8c6469b519647a8869c3782ebe1176cf |
MyGroupNorm | import torch
from torch import nn
class AffineChannelwise(nn.Module):
def __init__(self, num_channels):
super().__init__()
self.num_channels = num_channels
self.register_parameter('weight', nn.Parameter(torch.ones(
num_channels)))
self.register_parameter('bias', nn.Par... | 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... | dniku/dl-norms | MyGroupNorm | false | 6,583 | [
"MIT"
] | 1 | 0f1eef942bd318ac988ec7dfa9caea300d17e82a | https://github.com/dniku/dl-norms/tree/0f1eef942bd318ac988ec7dfa9caea300d17e82a |
HSwish | import torch
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data.distributed
class HSwish(nn.Module):
""" Applies the Hard-Swish function element-wise.
`"Searching for MobileNetV3" <https://arxiv.org/pdf/1905.02244.pdf>`_
Examples:
>>> m = Mish()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.ut... | doansangg/CGAN-PyTorch | HSwish | false | 6,584 | [
"Apache-2.0"
] | 1 | 941f5bd75102bed7f2eccd7feb9af8e6134af0e4 | https://github.com/doansangg/CGAN-PyTorch/tree/941f5bd75102bed7f2eccd7feb9af8e6134af0e4 |
AffineChannelwise | import torch
from torch import nn
class AffineChannelwise(nn.Module):
def __init__(self, num_channels):
super().__init__()
self.num_channels = num_channels
self.register_parameter('weight', nn.Parameter(torch.ones(
num_channels)))
self.register_parameter('bias', nn.Par... | 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... | dniku/dl-norms | AffineChannelwise | false | 6,585 | [
"MIT"
] | 1 | 0f1eef942bd318ac988ec7dfa9caea300d17e82a | https://github.com/dniku/dl-norms/tree/0f1eef942bd318ac988ec7dfa9caea300d17e82a |
Model | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as f
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv = nn.Conv2d(1, 16, 5)
self.pool = nn.MaxPool2d(2, 2)
self.fc = nn.Linear(2304, 10)
def forward(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 ... | dohmatob/adversarial-robustness-toolbox | Model | false | 6,586 | [
"MIT"
] | 1 | 7d3ba7d2d6690be69c08754fbc632947c2d10a97 | https://github.com/dohmatob/adversarial-robustness-toolbox/tree/7d3ba7d2d6690be69c08754fbc632947c2d10a97 |
PowerPropLinear | import torch
import torch.nn as nn
import torch.nn.functional as F
class PowerPropLinear(nn.Linear):
"""Powerpropagation Linear module."""
def __init__(self, in_features, out_fetaures, alpha, bias=True, *args,
**kwargs):
self._alpha = alpha
super(PowerPropLinear, self).__init__(in_fea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | dlpbc/powerpropagation-pytorch | PowerPropLinear | false | 6,587 | [
"MIT"
] | 1 | 99e29ce25ede9330cb8f624cb1fa7ffef6f82f03 | https://github.com/dlpbc/powerpropagation-pytorch/tree/99e29ce25ede9330cb8f624cb1fa7ffef6f82f03 |
AllReduceLinear | import torch
from torch import Tensor
import torch.distributed as dist
import torch.nn as nn
from torch.nn import Linear
class ParallelModule(nn.Module):
"""Parents of all parallel layer classes"""
def __init__(self):
super().__init__()
self.mp_group = None
def allreduce(self, outputs):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.distributed as dist
import torch.nn as nn
from torch.nn import Line... | dobbytk/parallelformers | AllReduceLinear | false | 6,588 | [
"Apache-2.0"
] | 1 | a05780b1d178b4ac5100e42c2b6eec7aedc7dd33 | https://github.com/dobbytk/parallelformers/tree/a05780b1d178b4ac5100e42c2b6eec7aedc7dd33 |
PredictTargets | import torch
from torch import nn
from torch.nn import functional as F
class PredictTargets(nn.Module):
def __init__(self, dim):
super(PredictTargets, self).__init__()
self.linear1 = nn.Linear(2 * dim, dim)
self.linear2 = nn.Linear(dim, 1)
def forward(self, targets, embeddings):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | dmcinerney/ehr-extraction-models | PredictTargets | false | 6,589 | [
"Apache-2.0"
] | 1 | c7e7e176f69a2558d420c607254ed7e98b5e836a | https://github.com/dmcinerney/ehr-extraction-models/tree/c7e7e176f69a2558d420c607254ed7e98b5e836a |
SimpleEncoder | import math
import torch
from torch import Tensor
import torch.nn as nn
class PositionalEncoding(nn.Module):
"""
Learnable position embeddings
Args:
pe_type (str): type of position embeddings,
which is chosen from ['fully_learnable', 'sinusoidal']
d_model (int): embed dim (req... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 Tensor
import torch.nn as nn
assert_size_stride = ... | doiken23/mccformers.pytorch | SimpleEncoder | false | 6,590 | [
"MIT"
] | 1 | 678bd9448e3a2f35bd408e8c8e510e0ea1f9a19f | https://github.com/doiken23/mccformers.pytorch/tree/678bd9448e3a2f35bd408e8c8e510e0ea1f9a19f |
MMTMQuad | import torch
import torch.nn as nn
from typing import Sequence
class MMTMQuad(nn.Module):
"""
quad modal fusion
"""
def __init__(self, dim_tab, dim_img, ratio=4):
"""
Parameters
----------
dim_tab: feature dimension of tabular data
dim_img: feature dimension o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ditannan/Multi-modal-Multi-instance-Learning | MMTMQuad | false | 6,591 | [
"Apache-2.0"
] | 1 | 06aada1ff85784d5ed50aa528c506947c892d584 | https://github.com/ditannan/Multi-modal-Multi-instance-Learning/tree/06aada1ff85784d5ed50aa528c506947c892d584 |
Discrete | import torch
import torch.nn as nn
class Discrete(nn.Module):
def __init__(self, num_outputs):
super(Discrete, self).__init__()
def forward(self, x):
probs = nn.functional.softmax(x, dim=0)
dist = torch.distributions.Categorical(probs=probs)
return dist.entropy()
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.nn as nn
... | dreamflasher/client | Discrete | false | 6,592 | [
"MIT"
] | 1 | c8267f1c6b8b6970172d622bb8fbf7cc773d78b2 | https://github.com/dreamflasher/client/tree/c8267f1c6b8b6970172d622bb8fbf7cc773d78b2 |
DiceLoss | import functools
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch._C
import torch.serialization
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | dkswxd/Swin-Spectral | DiceLoss | false | 6,593 | [
"Apache-2.0"
] | 1 | 5d8c364b0d89e4dd21590bb58f7a434a5b97254c | https://github.com/dkswxd/Swin-Spectral/tree/5d8c364b0d89e4dd21590bb58f7a434a5b97254c |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim, 400)
self.l2 = nn.Linear(400 + action_dim, 300)
self.l3 = nn.Linear(300, 1)
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
assert_... | dmund95/bcq | Critic | false | 6,594 | [
"MIT"
] | 1 | b1ae39ad7789443f02273aaa1a433c55c6836a5f | https://github.com/dmund95/bcq/tree/b1ae39ad7789443f02273aaa1a433c55c6836a5f |
SquareRoot | import torch
import torch.nn.functional
from torch import nn
class SquareRoot(nn.Module):
def forward(self, x):
return x.sqrt()
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.triton_helpers import libdevice
import torch.nn.functional
from torch import nn
assert_size_stride = torch._C._... | drivendataorg/DrivenData-2021-Geopose-Solution | SquareRoot | false | 6,595 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(4, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 2)
def forward(self, x):
x = torch.tanh(self.fc1(x))
x = torch.tanh(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.triton_helpers import libdevice
import torch.nn as ... | dongminlee94/supplement4deeprl | Net | false | 6,596 | [
"MIT"
] | 1 | 4db1a83f5dd3254abd8135fe94734a0d8d14a957 | https://github.com/dongminlee94/supplement4deeprl/tree/4db1a83f5dd3254abd8135fe94734a0d8d14a957 |
Value | import torch
import torch.nn as nn
import torch.nn.functional as F
class Value(nn.Module):
def __init__(self, num_inputs):
super(Value, self).__init__()
self.affine1 = nn.Linear(num_inputs, 64)
self.affine2 = nn.Linear(64, 64)
self.value_head = nn.Linear(64, 1)
self.value_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | dragen1860/TRPO-Pytorch | Value | false | 6,597 | [
"MIT"
] | 1 | c5a8e5ac890ec50e331db12fd5885dd4fb753a3b | https://github.com/dragen1860/TRPO-Pytorch/tree/c5a8e5ac890ec50e331db12fd5885dd4fb753a3b |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
class Policy(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(Policy, self).__init__()
self.affine1 = nn.Linear(num_inputs, 64)
self.affine2 = nn.Linear(64, 64)
self.action_mean = nn.Linear(64, num_ou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | dragen1860/TRPO-Pytorch | Policy | false | 6,598 | [
"MIT"
] | 1 | c5a8e5ac890ec50e331db12fd5885dd4fb753a3b | https://github.com/dragen1860/TRPO-Pytorch/tree/c5a8e5ac890ec50e331db12fd5885dd4fb753a3b |
GlobalWeightedAvgPool2d | import torch
import torch.nn as nn
class GlobalWeightedAvgPool2d(nn.Module):
"""
Global Weighted Average Pooling from paper "Global Weighted Average
Pooling Bridges Pixel-level Localization and Image-level Classification"
"""
def __init__(self, features: 'int', flatten=False):
super().__i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | dong03/DogNoseLandmarks | GlobalWeightedAvgPool2d | false | 6,599 | [
"MIT"
] | 1 | ac5d1e0436e9e0835a6939f8d125f1d36007bc62 | https://github.com/dong03/DogNoseLandmarks/tree/ac5d1e0436e9e0835a6939f8d125f1d36007bc62 |
MSELossWithIgnore | import torch
import torch.nn.functional
from torch import nn
class MSELossWithIgnore(nn.Module):
def __init__(self, ignore_value: 'int', fraction: 'float'=1.0):
super().__init__()
self.ignore_value = ignore_value
self.fraction = fraction
def forward(self, output, target):
los... | 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.functional
from torch import nn
assert_size_stride = torch._C._dynamo.gua... | drivendataorg/DrivenData-2021-Geopose-Solution | MSELossWithIgnore | false | 6,600 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
ATTA | import torch
import torch.nn as nn
class ATTA(nn.Module):
def __init__(self):
super(ATTA, self).__init__()
self.conv1 = nn.Conv2d(3, 3, 16, padding='same', groups=1, bias=False)
self.lr = nn.LeakyReLU(0.2)
self.conv2 = nn.Conv2d(3, 3, 3, padding='same', groups=1, bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | dreamflake/ODI | ATTA | false | 6,601 | [
"MIT"
] | 1 | d58001b96821c8a74d6ebb5402bd2be2b524890a | https://github.com/dreamflake/ODI/tree/d58001b96821c8a74d6ebb5402bd2be2b524890a |
Exponent | import torch
import torch.nn.functional
from torch import nn
class Exponent(nn.Module):
def forward(self, x):
return x.exp()
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.triton_helpers import math as tl_math
import torch.nn.functional
from torch import nn
assert_size_stride = torc... | drivendataorg/DrivenData-2021-Geopose-Solution | Exponent | false | 6,602 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
LogCoshWithIgnore | import torch
import torch.nn.functional
from torch import nn
class LogCoshWithIgnore(nn.Module):
def __init__(self, ignore_value, fraction: 'float'=1.0):
super().__init__()
self.ignore_value = ignore_value
self.fraction = fraction
def forward(self, output, target):
r = 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 libdevice, math as tl_math
import torc... | drivendataorg/DrivenData-2021-Geopose-Solution | LogCoshWithIgnore | false | 6,603 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
GlobalAvgPool2d | import torch
from torch import nn
class GlobalAvgPool2d(nn.Module):
"""Performs global average pooling over the entire height and width of a batched 2D tensor
# Arguments
input: Input tensor
"""
def forward(self, input):
return nn.functional.avg_pool2d(input, kernel_size=input.size()... | 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... | drjosephliu/few-shot-learning | GlobalAvgPool2d | false | 6,604 | [
"MIT"
] | 1 | 707c7ce2a0b1813327fb4e39660415b9437b8ec1 | https://github.com/drjosephliu/few-shot-learning/tree/707c7ce2a0b1813327fb4e39660415b9437b8ec1 |
CosineSimilarityLoss | import torch
import torch.nn.functional
from torch import nn
class CosineSimilarityLoss(nn.Module):
def __init__(self, gamma=1):
super().__init__()
self.gamma = gamma
def forward(self, output, target):
loss = 1.0 - torch.clamp(torch.nn.functional.cosine_similarity(
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 libdevice
import torch.nn.functional
f... | drivendataorg/DrivenData-2021-Geopose-Solution | CosineSimilarityLoss | false | 6,605 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
HuberLossWithIgnore | import torch
from torch import Tensor
import torch.nn.functional
from torch import nn
class HuberLossWithIgnore(nn.Module):
def __init__(self, ignore_value: 'int', delta: 'float'=1, fraction:
'float'=1.0):
super().__init__()
self.ignore_value = ignore_value
self.delta = delta
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | drivendataorg/DrivenData-2021-Geopose-Solution | HuberLossWithIgnore | false | 6,606 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
SmoothL1LossWithIgnore | import torch
import torch.nn.functional
from torch import nn
class SmoothL1LossWithIgnore(nn.Module):
def __init__(self, ignore_value: 'int', fraction: 'float'=1.0):
super().__init__()
self.ignore_value = ignore_value
self.fraction = fraction
def forward(self, output, target):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | drivendataorg/DrivenData-2021-Geopose-Solution | SmoothL1LossWithIgnore | false | 6,607 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
MyLeakyReLU | import torch
import torch.nn as nn
class MyLeakyReLU(nn.Module):
def __init__(self, negative_slope=0.01):
super(MyLeakyReLU, self).__init__()
self.negative_slope = negative_slope
def forward(self, x):
return torch.clamp(x, min=0.0) + torch.clamp(x, max=0.0
) * self.negati... | 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... | dsarrut/gaga | MyLeakyReLU | false | 6,608 | [
"Apache-2.0"
] | 1 | 4b34210074f8f82acb12e0ffb38858e83c319dc3 | https://github.com/dsarrut/gaga/tree/4b34210074f8f82acb12e0ffb38858e83c319dc3 |
GlobalMaxPool1d | import torch
from torch import nn
class GlobalMaxPool1d(nn.Module):
"""Performs global max pooling over the entire length of a batched 1D tensor
# Arguments
input: Input tensor
"""
def forward(self, input):
return nn.functional.max_pool1d(input, kernel_size=input.size()[2:]
... | 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... | drjosephliu/few-shot-learning | GlobalMaxPool1d | false | 6,609 | [
"MIT"
] | 1 | 707c7ce2a0b1813327fb4e39660415b9437b8ec1 | https://github.com/drjosephliu/few-shot-learning/tree/707c7ce2a0b1813327fb4e39660415b9437b8ec1 |
LabelSmoothing | import torch
from torch import nn
class LabelSmoothing(nn.Module):
"""
Label Smoothing
Attributes
----------
criterion : torch.nn.KLDivLoss
padding_idx : int
eps : float
n_vocab : int
"""
def __init__(self, n_vocab, eps, padding_idx=0):
"""
Param... | 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
from torch import nn
assert_size_stride = torch._C._dynamo.gua... | dugusword/transformer | LabelSmoothing | false | 6,610 | [
"MIT"
] | 1 | 7aa10968f0e60d545bbd17f1f8c1dfb7ee88c62b | https://github.com/dugusword/transformer/tree/7aa10968f0e60d545bbd17f1f8c1dfb7ee88c62b |
ViTClassifierPipe | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class ViTClassifierPipe(nn.Module):
def __init__(self, config: 'ViTConfig'):
super().__init__()
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.
layer_norm_eps)
self.classifier = nn.L... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | drunkcoding/huggingface-utils | ViTClassifierPipe | false | 6,611 | [
"MIT"
] | 1 | 4baad306857c357d94607076c6ab0cb5d6350cbe | https://github.com/drunkcoding/huggingface-utils/tree/4baad306857c357d94607076c6ab0cb5d6350cbe |
MultiHeadAttention | import math
import torch
from torch import nn
class ScaledDotProductAttention(nn.Module):
"""
Scaled Dot-Product Attention Layer
Attributes
----------
softmax : nn.Functional
softmax function applied at the last dimension
"""
def __init__(self, dropout=0.1):
super(ScaledD... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | dugusword/transformer | MultiHeadAttention | false | 6,612 | [
"MIT"
] | 1 | 7aa10968f0e60d545bbd17f1f8c1dfb7ee88c62b | https://github.com/dugusword/transformer/tree/7aa10968f0e60d545bbd17f1f8c1dfb7ee88c62b |
Fusion | import torch
import torch.nn as nn
import torch.utils.checkpoint
class Fusion(nn.Module):
"""
The subnetwork that is used in TFN for video and audio in the pre-fusion stage
"""
def __init__(self, in_size, hidden_size, n_class, dropout, modal_name=
'text'):
"""
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | dumpmemory/MMSA | Fusion | false | 6,613 | [
"MIT"
] | 1 | 08b3a7f4529c380356eeb1cf6bf9a89e7c9701e7 | https://github.com/dumpmemory/MMSA/tree/08b3a7f4529c380356eeb1cf6bf9a89e7c9701e7 |
FeatureVolume | import torch
import torch.nn as nn
import torch.nn.functional as F
class FeatureVolume(nn.Module):
def __init__(self, fdim, fsize):
super().__init__()
self.fsize = fsize
self.fdim = fdim
var = 0.01
self.fmx = nn.Parameter(torch.randn(1, fdim, fsize, fsize) * var)
s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | drixs2050/nglod | FeatureVolume | false | 6,614 | [
"MIT"
] | 1 | 0f3627d3ece82464335b0fab89c2269fcb016308 | https://github.com/drixs2050/nglod/tree/0f3627d3ece82464335b0fab89c2269fcb016308 |
CoAttentionTransformerEncoderLayer | import torch
from torch import Tensor
from typing import Optional
import torch.nn as nn
import torch.nn.functional as F
def _get_activation_fn(activation):
if activation == 'relu':
return F.relu
elif activation == 'gelu':
return F.gelu
raise ValueError('activation should be relu/gelu, not ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | doiken23/mccformers.pytorch | CoAttentionTransformerEncoderLayer | false | 6,615 | [
"MIT"
] | 1 | 678bd9448e3a2f35bd408e8c8e510e0ea1f9a19f | https://github.com/doiken23/mccformers.pytorch/tree/678bd9448e3a2f35bd408e8c8e510e0ea1f9a19f |
Adversarial_Loss | import torch
import torch.nn as nn
from numpy import *
class Adversarial_Loss(nn.Module):
def __init__(self, lambda_adv):
super(Adversarial_Loss, self).__init__()
self.lambda_adv = lambda_adv
pass
def forward(self, input_p, input_h):
dis_p = input_p * torch.log(input_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 math as tl_math
import torch.nn as nn
... | ducviet00/HMER | Adversarial_Loss | false | 6,616 | [
"MIT"
] | 1 | 0fa322ed35412737a24ec3955c9a3d96d1989bd4 | https://github.com/ducviet00/HMER/tree/0fa322ed35412737a24ec3955c9a3d96d1989bd4 |
MixedPad | import torch
def mixed_pad(input, pad, mode='constant', value=0, reversed_axes=False):
"""Mixed mode padding.
:type input: tensor[B,C,D1,D2,...,DD]
:type pad: int or tuple of ints with 2*D length
:type mode: str or tuple
:type value: float or tuple
Dimension numbering: reverse... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | dvolgyes/highresnet | MixedPad | false | 6,617 | [
"MIT"
] | 1 | 12b8831ed52e2dc45d2e14cc6f2954c583c97a46 | https://github.com/dvolgyes/highresnet/tree/12b8831ed52e2dc45d2e14cc6f2954c583c97a46 |
NetworkDQN | import torch
import torch.nn as nn
import torch.nn.functional as F
class NetworkDQN(nn.Module):
def __init__(self, fs, input_dim, fc1, fc2, n_actions):
super(NetworkDQN, self).__init__()
self.conv1 = nn.Conv2d(fs, 64, 8, 4)
self.pool1 = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(64... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | doganjr/MarioDQN | NetworkDQN | false | 6,618 | [
"MIT"
] | 1 | 62daa390f8ee0b732275e71675a2b9eae85c43a4 | https://github.com/doganjr/MarioDQN/tree/62daa390f8ee0b732275e71675a2b9eae85c43a4 |
Loss_D | import torch
import torch.nn as nn
from numpy import *
class Loss_D(nn.Module):
"""docstring for Loss_D"""
def __init__(self):
super(Loss_D, self).__init__()
def forward(self, input_h):
return -input_h * torch.log(input_h)
pass
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.triton_helpers import math as tl_math
import torch.nn as nn
from numpy import *
assert_size_stride = torch._C._... | ducviet00/HMER | Loss_D | false | 6,619 | [
"MIT"
] | 1 | 0fa322ed35412737a24ec3955c9a3d96d1989bd4 | https://github.com/ducviet00/HMER/tree/0fa322ed35412737a24ec3955c9a3d96d1989bd4 |
Invertible1x1Conv | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import torch.utils.data
import torch.nn
class Invertible1x1Conv(torch.nn.Module):
"""
The layer outputs both the convolution, and the log determinant
of its weight matrix. If reverse=True it does convolution with
inverse... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
from torch.autograd import Variable
import torch... | drostifrosti/TensorRT | Invertible1x1Conv | false | 6,620 | [
"Apache-2.0"
] | 1 | 76d673366139538fcb47a67e08734ff429306162 | https://github.com/drostifrosti/TensorRT/tree/76d673366139538fcb47a67e08734ff429306162 |
InterpolationBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class InterpolationBlock(nn.Module):
"""
Interpolation block.
Parameters:
----------
scale_factor : float
Multiplier for spatial size.
"""
def __init__(self, scale_factor):
super(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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | earhian/imgclsmob | InterpolationBlock | false | 6,621 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
DiracConv | import torch
import torch.nn as nn
import torch.utils.data
class DiracConv(nn.Module):
"""
DiracNetV2 specific convolution block with pre-activation.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
kernel_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | earhian/imgclsmob | DiracConv | false | 6,622 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
MaxPoolBranch | import torch
import torch.nn as nn
import torch.utils.data
class MaxPoolBranch(nn.Module):
"""
PolyNet specific max pooling branch block.
"""
def __init__(self):
super(MaxPoolBranch, self).__init__()
self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)
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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | earhian/imgclsmob | MaxPoolBranch | false | 6,623 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
DiracInitBlock | import torch
import torch.nn as nn
import torch.utils.data
class DiracInitBlock(nn.Module):
"""
DiracNetV2 specific initial block.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
"""
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
import torch.nn as nn
import ... | earhian/imgclsmob | DiracInitBlock | false | 6,624 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
NasAvgPoolBlock | import torch
import torch.nn as nn
import torch.utils.data
class NasAvgPoolBlock(nn.Module):
"""
NASNet specific 3x3 Average pooling layer with extra padding.
Parameters:
----------
extra_padding : bool, default False
Whether to use extra padding.
"""
def __init__(self, extra_pad... | 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.... | earhian/imgclsmob | NasAvgPoolBlock | false | 6,625 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
IBNbConvBlock | import torch
import torch.nn as nn
import torch.utils.data
class IBNbConvBlock(nn.Module):
"""
IBN(b)-ResNet specific convolution block with Instance normalization and ReLU activation.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | earhian/imgclsmob | IBNbConvBlock | false | 6,626 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
Discriminator | import torch
import torch.nn as nn
from numpy import *
class Discriminator(nn.Module):
"""docstring for Discriminator"""
def __init__(self, in_dim, out_dim):
super(Discriminator, self).__init__()
self.Linear1 = nn.Linear(in_dim, out_dim)
self.Relu = nn.ReLU()
self.Linear2 = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from nu... | ducviet00/HMER | Discriminator | false | 6,627 | [
"MIT"
] | 1 | 0fa322ed35412737a24ec3955c9a3d96d1989bd4 | https://github.com/ducviet00/HMER/tree/0fa322ed35412737a24ec3955c9a3d96d1989bd4 |
NasPathBranch | import torch
import torch.nn as nn
import torch.utils.data
def conv1x1(in_channels, out_channels, stride=1, bias=False):
"""
Convolution 1x1 layer.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
stride : int... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | earhian/imgclsmob | NasPathBranch | false | 6,628 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
IBNbResInitBlock | import torch
import torch.nn as nn
import torch.utils.data
def ibnb_conv7x7_block(in_channels, out_channels, stride=1, padding=3, bias
=False, activate=True):
"""
7x7 version of the IBN(b)-ResNet specific convolution block.
Parameters:
----------
in_channels : int
Number of input chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | earhian/imgclsmob | IBNbResInitBlock | false | 6,629 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
BCEFocalLoss | import torch
import torch.nn as 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='mean'):
... | 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... | earlbabson/torchflare | BCEFocalLoss | false | 6,630 | [
"Apache-2.0"
] | 1 | 15db06d313a53a3ec4640869335ba87730562b28 | https://github.com/earlbabson/torchflare/tree/15db06d313a53a3ec4640869335ba87730562b28 |
LeNet | import torch
import torch.nn as nn
class LeNet(nn.Module):
def __init__(self):
super().__init__()
self.conv_1 = nn.Conv2d(3, 6, kernel_size=5, padding=2)
self.sigmoid = nn.Sigmoid()
self.avgpool = nn.AvgPool2d(kernel_size=5, stride=2)
self.conv_2 = nn.Conv2d(6, 16, kernel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ducnguyenhuynh/via-trafficsign-classification | LeNet | false | 6,631 | [
"MIT"
] | 1 | e65fccc1ee377603334453eacfc3f65619dc0714 | https://github.com/ducnguyenhuynh/via-trafficsign-classification/tree/e65fccc1ee377603334453eacfc3f65619dc0714 |
FocalLoss | import torch
import torch.nn as 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).
"""
def __init__(self, gamma=0, eps=1e-07, reduction='mean'):
"""Constructor Method for FocalL... | 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
... | earlbabson/torchflare | FocalLoss | false | 6,632 | [
"Apache-2.0"
] | 1 | 15db06d313a53a3ec4640869335ba87730562b28 | https://github.com/earlbabson/torchflare/tree/15db06d313a53a3ec4640869335ba87730562b28 |
EncoderUnit | import math
import torch
from torch import nn
class ScaledDotProductAttention(nn.Module):
"""
Scaled Dot-Product Attention Layer
Attributes
----------
softmax : nn.Functional
softmax function applied at the last dimension
"""
def __init__(self, dropout=0.1):
super(ScaledD... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dugusword/transformer | EncoderUnit | false | 6,633 | [
"MIT"
] | 1 | 7aa10968f0e60d545bbd17f1f8c1dfb7ee88c62b | https://github.com/dugusword/transformer/tree/7aa10968f0e60d545bbd17f1f8c1dfb7ee88c62b |
SqueezeInitBlock | import torch
import torch.nn as nn
import torch.utils.data
class SqueezeInitBlock(nn.Module):
"""
SqueezeNet specific initial block.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
kernel_size : int or tuple/... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | earhian/imgclsmob | SqueezeInitBlock | false | 6,634 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
WRNBottleneck | import torch
import torch.nn as nn
import torch.utils.data
def wrn_conv1x1(in_channels, out_channels, stride, activate):
"""
1x1 version of the WRN specific convolution block.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of outpu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | earhian/imgclsmob | WRNBottleneck | false | 6,635 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
SSE | import torch
import torch.nn as 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):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | earlbabson/torchflare | SSE | false | 6,636 | [
"Apache-2.0"
] | 1 | 15db06d313a53a3ec4640869335ba87730562b28 | https://github.com/earlbabson/torchflare/tree/15db06d313a53a3ec4640869335ba87730562b28 |
WRNInitBlock | import torch
import torch.nn as nn
import torch.utils.data
class WRNConv(nn.Module):
"""
WRN specific convolution block.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
kernel_size : int or tuple/list of 2 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
import torch.nn as nn
import ... | earhian/imgclsmob | WRNInitBlock | false | 6,637 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
TripletLoss | import torch
import torch.nn.functional as F
import torch.nn as 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 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | earlbabson/torchflare | TripletLoss | false | 6,638 | [
"Apache-2.0"
] | 1 | 15db06d313a53a3ec4640869335ba87730562b28 | https://github.com/earlbabson/torchflare/tree/15db06d313a53a3ec4640869335ba87730562b28 |
DiceLoss | import torch
import torch.nn as nn
def calculate_segmentation_statistics(outputs: 'torch.Tensor', targets:
'torch.Tensor', class_dim: 'int'=1, threshold=None):
"""Compute calculate segmentation statistics.
Args:
outputs: torch.Tensor.
targets: torch.Tensor.
threshold: threshold fo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | earlbabson/torchflare | DiceLoss | false | 6,639 | [
"Apache-2.0"
] | 1 | 15db06d313a53a3ec4640869335ba87730562b28 | https://github.com/earlbabson/torchflare/tree/15db06d313a53a3ec4640869335ba87730562b28 |
Classifier | import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, z_dim, hidden_dim, class_dim):
super().__init__()
self.fc1 = nn.Linear(z_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, class_dim)
self.softplus = nn.Softplus()
self.softmax = nn.Softmax(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | einbandi/samplednn | Classifier | false | 6,640 | [
"MIT"
] | 1 | 3525e46ab5096a569dde40e5a10d6ee05128ec7d | https://github.com/einbandi/samplednn/tree/3525e46ab5096a569dde40e5a10d6ee05128ec7d |
IOULoss | import torch
import torch.nn as nn
def calculate_segmentation_statistics(outputs: 'torch.Tensor', targets:
'torch.Tensor', class_dim: 'int'=1, threshold=None):
"""Compute calculate segmentation statistics.
Args:
outputs: torch.Tensor.
targets: torch.Tensor.
threshold: threshold fo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | earlbabson/torchflare | IOULoss | false | 6,641 | [
"Apache-2.0"
] | 1 | 15db06d313a53a3ec4640869335ba87730562b28 | https://github.com/earlbabson/torchflare/tree/15db06d313a53a3ec4640869335ba87730562b28 |
MeshEdgeEmbeddingLayer | import torch
import torch.utils.data
import torch
from torch import nn
class MeshEdgeEmbeddingLayer(nn.Module):
"""
Very important - who said that a-c is meaningfull at first layer...
"""
def __init__(self, input_size, embedding_size, bias=True):
super(MeshEdgeEmbeddingLayer, self).__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
import torch.utils.data
import torch
from torch import nn
assert_size_stride = t... | eldadp100/The-Mesh-Transformer | MeshEdgeEmbeddingLayer | false | 6,642 | [
"MIT"
] | 1 | b3ab18f774251feff1093040dfdcf7b836a43505 | https://github.com/eldadp100/The-Mesh-Transformer/tree/b3ab18f774251feff1093040dfdcf7b836a43505 |
Decoder | import torch
import torch.nn as nn
class Decoder(nn.Module):
def __init__(self, z_dim, hidden_dim, input_dim):
super().__init__()
self.fc1 = nn.Linear(z_dim, hidden_dim)
self.fc21 = nn.Linear(hidden_dim, input_dim)
self.softplus = nn.Softplus()
self.sigmoid = 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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | einbandi/samplednn | Decoder | false | 6,643 | [
"MIT"
] | 1 | 3525e46ab5096a569dde40e5a10d6ee05128ec7d | https://github.com/einbandi/samplednn/tree/3525e46ab5096a569dde40e5a10d6ee05128ec7d |
AuxiliaryConvolutions | import torch
from torch import nn
import torch.nn.functional as F
from itertools import product as product
import torch.optim
import torch.utils.data
class AuxiliaryConvolutions(nn.Module):
"""
Additional convolutions to produce higher-level feature maps.
"""
def __init__(self):
super(Auxilia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
from ite... | dee-walia20/SSD-Implementation-using-Pytorch | AuxiliaryConvolutions | false | 6,644 | [
"MIT"
] | 1 | 2a7dcdcea2787f4bffd45f335819f08af2b525dd | https://github.com/dee-walia20/SSD-Implementation-using-Pytorch/tree/2a7dcdcea2787f4bffd45f335819f08af2b525dd |
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... | endaaman/augmix | GELU | false | 6,645 | [
"Apache-2.0"
] | 1 | 11c86a126c7b261ca178a715763763ca22b20b81 | https://github.com/endaaman/augmix/tree/11c86a126c7b261ca178a715763763ca22b20b81 |
Noise_injector | import torch
import torch.nn as nn
def truncated_normal_(tensor, mean=0, std=1):
size = tensor.shape
tmp = tensor.new_empty(size + (4,)).normal_()
valid = (tmp < 2) & (tmp > -2)
ind = valid.max(-1, keepdim=True)[1]
tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1))
tensor.data.mul_(std).add_(m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | dkgupta90/CARMSS | Noise_injector | false | 6,646 | [
"Apache-2.0"
] | 1 | 1f397caa39b9f504951285eff150857f7d86a7c3 | https://github.com/dkgupta90/CARMSS/tree/1f397caa39b9f504951285eff150857f7d86a7c3 |
VoxelFeatureExtractor | import torch
from torch import nn
class VoxelFeatureExtractor(nn.Module):
"""Computes mean of non-zero points within voxel."""
def forward(self, feature, occupancy):
"""
:feature FloatTensor of shape (N, K, C)
:return FloatTensor of shape (N, C)
"""
denominator = occup... | 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... | eraofelix/PV-RCNN | VoxelFeatureExtractor | false | 6,647 | [
"MIT"
] | 1 | 6361ec99cc1c92120263ef56b2c2b003c2cd7264 | https://github.com/eraofelix/PV-RCNN/tree/6361ec99cc1c92120263ef56b2c2b003c2cd7264 |
QModReLU | import torch
import torch.nn.functional as F
import torch.fx
class QModReLU(torch.nn.Module):
"""
Quaternion ModeReLU
"""
def __init__(self, bias=0):
super().__init__()
self.bias = torch.nn.Parameter(torch.Tensor([bias]))
def forward(self, x):
norm = x.norm()
retu... | 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.fx
assert_size_... | eleGAN23/HI2I | QModReLU | false | 6,648 | [
"MIT"
] | 1 | 7730ee0963614290099b011c113048ef6d1b149c | https://github.com/eleGAN23/HI2I/tree/7730ee0963614290099b011c113048ef6d1b149c |
DecoderNet | import torch
import torch.nn.functional as F
import torch.nn as nn
class DecoderNet(nn.Module):
"""
The decoder takes an interpolated feature vector and turn it into the
output signal. This net is intended to be very lightweight, it has only one
hidden layer.
"""
def __init__(self, feature_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | eliemichel/ReACORN | DecoderNet | false | 6,649 | [
"MIT"
] | 1 | 74501551ecb387352271674efb2ed6240d234df6 | https://github.com/eliemichel/ReACORN/tree/74501551ecb387352271674efb2ed6240d234df6 |
AlexOutputBlock | import torch
import torch.nn as nn
import torch.utils.data
class AlexDense(nn.Module):
"""
AlexNet specific dense block.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
"""
def __init__(self, in_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 import triton_helpers
import torch.nn as nn
import ... | earhian/imgclsmob | AlexOutputBlock | false | 6,650 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
SelfAttentionLayer | import math
import torch
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F
class SelfAttentionLayer(nn.Module):
def __init__(self, elem_size, embd_size):
super(SelfAttentionLayer, self).__init__()
self.embd_size = embd_size
self.query_lin = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | eldadp100/The-Mesh-Transformer | SelfAttentionLayer | false | 6,651 | [
"MIT"
] | 1 | b3ab18f774251feff1093040dfdcf7b836a43505 | https://github.com/eldadp100/The-Mesh-Transformer/tree/b3ab18f774251feff1093040dfdcf7b836a43505 |
BatchNorm2D_noparam | import torch
import torch.nn as nn
class BatchNorm2D_noparam(nn.Module):
def __init__(self, eps=1e-08):
super(BatchNorm2D_noparam, self).__init__()
self.eps = eps
def forward(self, x):
_bs, _c, _h, _w = x.shape
mean = torch.mean(x, (0, 2, 3), keepdim=True)
var = torch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ericlearning/General-I2I | BatchNorm2D_noparam | false | 6,652 | [
"MIT"
] | 1 | ba7c5d6a582bdf2e7b53c0e20c31e9097b1883a9 | https://github.com/ericlearning/General-I2I/tree/ba7c5d6a582bdf2e7b53c0e20c31e9097b1883a9 |
CReLU | import torch
import torch.nn as nn
import torch.nn.functional as F
class CReLU(nn.ReLU):
def __init__(self):
super(CReLU, self).__init__()
def forward(self, input):
return torch.cat((F.relu(input, self.inplace), F.relu(-input, self.
inplace)), 1)
def get_inputs():
return [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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ethancaballero/multi-agent-reinforcement-learning-for-emergent-communication | CReLU | false | 6,653 | [
"MIT"
] | 1 | 426edaa1ee58b467dfc0f46fe1f83ceea26f2ed7 | https://github.com/ethancaballero/multi-agent-reinforcement-learning-for-emergent-communication/tree/426edaa1ee58b467dfc0f46fe1f83ceea26f2ed7 |
LigthSpeechLoss | import torch
from torch import nn
import torch.utils.data
class LigthSpeechLoss(nn.Module):
""" LigthSpeech Loss """
def __init__(self):
super(LigthSpeechLoss, self).__init__()
def forward(self, mel, padd_predicted, cemb_out, mel_tac2_target, D, cemb):
mel_loss = nn.MSELoss()(mel, mel_ta... | 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
i... | entn-at/LightSpeech | LigthSpeechLoss | false | 6,654 | [
"MIT"
] | 1 | 48250fbcede4b258ba13ab17e3e83afc5fe85a01 | https://github.com/entn-at/LightSpeech/tree/48250fbcede4b258ba13ab17e3e83afc5fe85a01 |
Net | import torch
from torch import nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=10, kernel_size=
(7, 3))
self.pool = nn.MaxPool2d(kernel_size=(1, 3))
self.conv2 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | elliottwaissbluth/tensor-hero | Net | false | 6,655 | [
"MIT"
] | 1 | be99ca4380a5ec59c0826e5fc8a87ec0f8956201 | https://github.com/elliottwaissbluth/tensor-hero/tree/be99ca4380a5ec59c0826e5fc8a87ec0f8956201 |
GaussianSample | import torch
import torch.nn as nn
class Stochastic(nn.Module):
"""
Base stochastic layer that uses the
reparametrization trick [Kingma 2013]
to draw a sample from a distribution
parametrised by mu and log_var.
"""
def reparametrize(self, mu, logvar):
epsilon = torch.randn(mu.size... | 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 math... | ericli0419/SCALEX | GaussianSample | false | 6,656 | [
"MIT"
] | 1 | 2fedbe4c3287cf86de7b786c98122fe45707416e | https://github.com/ericli0419/SCALEX/tree/2fedbe4c3287cf86de7b786c98122fe45707416e |
PatchedSelfAttentionLayer | import math
import torch
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F
class SelfAttentionLayer(nn.Module):
def __init__(self, elem_size, embd_size):
super(SelfAttentionLayer, self).__init__()
self.embd_size = embd_size
self.query_lin = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | eldadp100/The-Mesh-Transformer | PatchedSelfAttentionLayer | false | 6,657 | [
"MIT"
] | 1 | b3ab18f774251feff1093040dfdcf7b836a43505 | https://github.com/eldadp100/The-Mesh-Transformer/tree/b3ab18f774251feff1093040dfdcf7b836a43505 |
SpatialAttention | import torch
import torch.nn as nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv1d(2, 1, kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | esbgkannan/GT-CNN | SpatialAttention | false | 6,658 | [
"MIT"
] | 1 | 4f3828d7ed8f6c3ed796fa4e2e166ef5c16cb3d9 | https://github.com/esbgkannan/GT-CNN/tree/4f3828d7ed8f6c3ed796fa4e2e166ef5c16cb3d9 |
MyLinear | import torch
from torch import nn
from torch.nn import functional as F
class MyLinear(nn.Module):
def __init__(self, in_units, units):
super().__init__()
self.weight = nn.Parameter(torch.randn(in_units, units))
self.bias = nn.Parameter(torch.randn(units))
def forward(self, X):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | eunice012716/Intern-Training | MyLinear | false | 6,659 | [
"MIT"
] | 1 | c3bbf42448a0b41e96d88569b6cfd57d78338716 | https://github.com/eunice012716/Intern-Training/tree/c3bbf42448a0b41e96d88569b6cfd57d78338716 |
BCE_Dice | import torch
import torch.nn as nn
def IoU(logit, truth, smooth=1):
prob = torch.sigmoid(logit)
intersection = torch.sum(prob * truth)
union = torch.sum(prob + truth)
iou = (2 * intersection + smooth) / (union + smooth)
return iou
class DiceLoss(nn.Module):
def __init__(self, smooth=1):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | evilidol/kaggle-Steel-Defect-Detection | BCE_Dice | false | 6,660 | [
"MIT"
] | 1 | 41e3e360f49d706c8c79bcd442342c529648a736 | https://github.com/evilidol/kaggle-Steel-Defect-Detection/tree/41e3e360f49d706c8c79bcd442342c529648a736 |
PrimaryCaps | import torch
import torch.nn as nn
class PrimaryCaps(nn.Module):
"""Creates a primary convolutional capsule layer
that outputs a pose matrix and an activation.
Note that for computation convenience, pose matrix
are stored in first part while the activations are
stored in the second part.
Arg... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | esdrascosta/Matrix-Capsules | PrimaryCaps | false | 6,661 | [
"MIT"
] | 1 | ddf35dfa1acfb51a11a3ec27e15fe863a2ff6fa4 | https://github.com/esdrascosta/Matrix-Capsules/tree/ddf35dfa1acfb51a11a3ec27e15fe863a2ff6fa4 |
Highway | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils
import torch.onnx
class Highway(nn.Module):
def __init__(self, e_word):
super(Highway, self).__init__()
self.embed_size = e_word
self.w_proj = nn.Linear(self.embed_size, self.embed_size, 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
import ... | evazhang612/honygenerator | Highway | false | 6,662 | [
"MIT"
] | 1 | cafcf1736faba978ecaed624b949ebc1498477ee | https://github.com/evazhang612/honygenerator/tree/cafcf1736faba978ecaed624b949ebc1498477ee |
P2SActivationLayer | import torch
import torch.nn as torch_nn
from torch.nn import Parameter
import torch.utils
class P2SActivationLayer(torch_nn.Module):
""" Output layer that produces cos heta between activation vector x
and class vector w_j
in_dim: dimension of input feature vectors
output_dim: dimension of output ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | eurecom-asp/raw-pc-darts-anti-spoofing | P2SActivationLayer | false | 6,663 | [
"MIT"
] | 1 | f2dcb5a8fc0cb811328a341a9bd90ffb292adaa1 | https://github.com/eurecom-asp/raw-pc-darts-anti-spoofing/tree/f2dcb5a8fc0cb811328a341a9bd90ffb292adaa1 |
ChannelGate2d | import torch
import torch.nn as nn
class ChannelGate2d(nn.Module):
def __init__(self, channels, reduction=2):
super(ChannelGate2d, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1,
padding=0)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | evilidol/kaggle-Steel-Defect-Detection | ChannelGate2d | false | 6,664 | [
"MIT"
] | 1 | 41e3e360f49d706c8c79bcd442342c529648a736 | https://github.com/evilidol/kaggle-Steel-Defect-Detection/tree/41e3e360f49d706c8c79bcd442342c529648a736 |
SelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
from torch.nn import functional as F
class SelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
assert config.n_embd % config.n_head == 0
self.qkv = nn.Linear(config.n_embd, 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._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | evelynmitchell/rasp | SelfAttention | false | 6,665 | [
"MIT"
] | 1 | 9b33bbf911e6c4ff018c9883c39eb698c0abe803 | https://github.com/evelynmitchell/rasp/tree/9b33bbf911e6c4ff018c9883c39eb698c0abe803 |
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