entry_point stringlengths 1 65 | original_triton_code stringlengths 4.5k 619k | python_code stringlengths 208 60.9k | 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 | pytorch_code stringlengths 200 4.05k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
EcaModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
from torch import optim as optim
class EcaModule(nn.Module):
"""Constructs an ECA module.
Args:
channels: Number of channels of the input feature map for use in adaptive kernel sizes
for actual... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.utils.data
import torch.nn as nn
import torch.nn.parall... | dumpmemory/NonDeepNetworks | EcaModule | false | 15,250 | [
"BSD-3-Clause"
] | 307 | 5513bf588f4e64c99583440507232675c2e21e34 | https://github.com/dumpmemory/NonDeepNetworks/tree/5513bf588f4e64c99583440507232675c2e21e34 | import math
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
from torch import optim as optim
class Model(nn.Module):
"""Constructs an ECA module.
Args:
channels: Number of channels of the input feature map for use in adaptive kernel sizes
for actual cal... |
DownConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_channels, out_channels, stride=1, padding=1, bias=True, groups=1
):
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=
stride, padding=padding, bias=bias, groups=groups)
class DownConv(nn.Module):
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | duchn92/transfer-object | DownConv | false | 15,251 | [
"MIT"
] | 80 | 4db96931545ac0d28891375fbca3c0a5a382fb32 | https://github.com/duchn92/transfer-object/tree/4db96931545ac0d28891375fbca3c0a5a382fb32 | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_channels, out_channels, stride=1, padding=1, bias=True, groups=1
):
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=
stride, padding=padding, bias=bias, groups=groups)
class Model(nn.Module):
"""
... |
LabelSmoothingCrossEntropy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class LabelSmoothingCrossEntropy(nn.Module):
def __init__(self, eps=0.1, reduction='mean', ignore_index=-100):
"""LabelSmoothingCrossEntropy, no-softmax-input
对logits进行smoothing, 即log_softmax后进行操作
args:
ignore_index: (int, optional): Specifies... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | dumpmemory/Pytorch-NLU | LabelSmoothingCrossEntropy | false | 15,252 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, eps=0.1, reduction='mean', ignore_index=-100):
"""LabelSmoothingCrossEntropy, no-softmax-input
对logits进行smoothing, 即log_softmax后进行操作
args:
ignore_index: (int, optional): Specifies a target value that ... |
lstm_cell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class lstm_cell(nn.Module):
def __init__(self, input_num, hidden_num):
super(lstm_cell, self).__init__()
self.input_num = input_num
self.hidden_num = hidden_num
self.Wxi = nn.Linear(self.input_num, self.hidden_num, bias=True)
self.Whi = 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.triton_helpers import libdevice
import torch.nn as ... | dreamer121121/action-recognition-models-pytorch | lstm_cell | false | 15,253 | [
"MIT"
] | 200 | 6a8a5e9678c359f795079d1f9f3cbdb9502b363d | https://github.com/dreamer121121/action-recognition-models-pytorch/tree/6a8a5e9678c359f795079d1f9f3cbdb9502b363d | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_num, hidden_num):
super().__init__()
self.input_num = input_num
self.hidden_num = hidden_num
self.Wxi = nn.Linear(self.input_num, self.hidden_num, bias=True)
self.Whi = nn.Linear(self.hidde... |
ConvSig | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
from torch import optim as optim
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class ConvSig(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torc... | dumpmemory/NonDeepNetworks | ConvSig | false | 15,254 | [
"BSD-3-Clause"
] | 307 | 5513bf588f4e64c99583440507232675c2e21e34 | https://github.com/dumpmemory/NonDeepNetworks/tree/5513bf588f4e64c99583440507232675c2e21e34 | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
from torch import optim as optim
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Model(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, g=... |
CPC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class CPC(nn.Module):
"""
Contrastive Predictive Coding: score computation. See https://arxiv.org/pdf/1807.03748.pdf.
Args:
x_size (int): embedding size of input modality representation x
y_size (int): embedding size of input modality rep... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dumpmemory/Multimodal-Infomax | CPC | false | 15,255 | [
"MIT"
] | 57 | 9a6dc8f2bfa861cd447ba65c6a037cd7dd24f473 | https://github.com/dumpmemory/Multimodal-Infomax/tree/9a6dc8f2bfa861cd447ba65c6a037cd7dd24f473 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Contrastive Predictive Coding: score computation. See https://arxiv.org/pdf/1807.03748.pdf.
Args:
x_size (int): embedding size of input modality representation x
y_size (int): embedding size of input modality r... |
GroupLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class GroupLinear(nn.Module):
"""
Group Linear operator
"""
def __init__(self, in_planes, out_channels, groups=1, bias=True):
super(GroupLinear, self).__init__()
assert in_planes % groups == 0
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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_si... | dumpmemory/TokenLabeling | GroupLinear | false | 15,256 | [
"Apache-2.0"
] | 367 | 9dbfd59aedecfe83f6f3253db4e99b82359d48ac | https://github.com/dumpmemory/TokenLabeling/tree/9dbfd59aedecfe83f6f3253db4e99b82359d48ac | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Model(nn.Module):
"""
Group Linear operator
"""
def __init__(self, in_planes, out_channels, groups=1, bias=True):
super().__init__()
assert in_planes % groups == 0
assert out_channels % ... |
Biaffine | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.autograd
import torch.nn as nn
class Biaffine(nn.Module):
def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
super(Biaffine, self).__init__()
self.n_in = n_in
self.n_out = n_out
self.bias_x = bias_x
self.bias_y = bias_y
weight = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.autograd
import torch.nn as nn
assert_size_stride = torch._C._dynam... | dumpmemory/W2NER | Biaffine | false | 15,257 | [
"MIT"
] | 128 | fb1b6eb1111eb001b1c965097d995244b840bdda | https://github.com/dumpmemory/W2NER/tree/fb1b6eb1111eb001b1c965097d995244b840bdda | import torch
import torch.autograd
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
super().__init__()
self.n_in = n_in
self.n_out = n_out
self.bias_x = bias_x
self.bias_y = bias_y
weight = torch.zeros((n_ou... |
LabelSmoothingCrossEntropyV1 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class LabelSmoothingCrossEntropyV1(nn.Module):
def __init__(self, eps=0.1, reduction='mean', ignore_index=-100):
"""【直接smooth输入logits效果不好】LabelSmoothingCrossEntropy, no-softmax-input
eps==0-1, 通过控制ce权重、新增后置项来处理来平滑
urls: [pytorch | labelSmooth](https://zhu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | dumpmemory/Pytorch-NLU | LabelSmoothingCrossEntropyV1 | false | 15,258 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, eps=0.1, reduction='mean', ignore_index=-100):
"""【直接smooth输入logits效果不好】LabelSmoothingCrossEntropy, no-softmax-input
eps==0-1, 通过控制ce权重、新增后置项来处理来平滑
urls: [pytorch | labelSmooth](https://zhuanlan.zhihu.com/p/26570... |
GroupNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class GroupNorm(nn.Module):
def __init__(self, num_groups, embed_dim, eps=1e-05, affine=True):
super().__init__()
self.gn = nn.GroupNorm(num_groups, embed_dim, eps, affine)
def forward(self, x):
B, T,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_s... | dumpmemory/TokenLabeling | GroupNorm | false | 15,259 | [
"Apache-2.0"
] | 367 | 9dbfd59aedecfe83f6f3253db4e99b82359d48ac | https://github.com/dumpmemory/TokenLabeling/tree/9dbfd59aedecfe83f6f3253db4e99b82359d48ac | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Model(nn.Module):
def __init__(self, num_groups, embed_dim, eps=1e-05, affine=True):
super().__init__()
self.gn = nn.GroupNorm(num_groups, embed_dim, eps, affine)
def forward(self, x):
B, T, C =... |
FCLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class FCLayer(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0.1, is_active=
True, is_dropout=True, active_type='mish'):
"""
FC-Layer, mostly last output of model
args:
input_dim: input dimension, 输入维度, eg. 768
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
fr... | dumpmemory/Pytorch-NLU | FCLayer | false | 15,260 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0.1, is_active=
True, is_dropout=True, active_type='mish'):
"""
FC-Layer, mostly last output of model
args:
input_dim: input dimension, 输入维度, eg. 768
... |
ClassifierHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
def adaptive_avgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 torchvision.transforms.functional as F
import tor... | dumpmemory/NonDeepNetworks | ClassifierHead | false | 15,261 | [
"BSD-3-Clause"
] | 307 | 5513bf588f4e64c99583440507232675c2e21e34 | https://github.com/dumpmemory/NonDeepNetworks/tree/5513bf588f4e64c99583440507232675c2e21e34 | import torch
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
def adaptive_avgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_... |
GroupNormAct | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
def swish(x, inplace: 'bool'=False):
"""Swish - Described in: https://arxiv.org/abs/1710.05941
"""
return x.mul... | 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.utils.data
impo... | dumpmemory/NonDeepNetworks | GroupNormAct | false | 15,262 | [
"BSD-3-Clause"
] | 307 | 5513bf588f4e64c99583440507232675c2e21e34 | https://github.com/dumpmemory/NonDeepNetworks/tree/5513bf588f4e64c99583440507232675c2e21e34 | import torch
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
def swish(x, inplace: 'bool'=False):
"""Swish - Described in: https://arxiv.org/abs/1710.05941
"""
return x.mul... |
CXLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
class CXLoss(nn.Module):
def __init__(self, sigma=0.1, b=1.0, similarity='consine'):
super(CXLoss, self).__init__()
self.similarity = similarity
self.sigma = sigma
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | drgripa1/deepvecfont | CXLoss | false | 15,263 | [
"MIT"
] | 68 | a44d81ba19a22e43b4e576cd8ebc5c2fd961a621 | https://github.com/drgripa1/deepvecfont/tree/a44d81ba19a22e43b4e576cd8ebc5c2fd961a621 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
class Model(nn.Module):
def __init__(self, sigma=0.1, b=1.0, similarity='consine'):
super().__init__()
self.similarity = similarity
self.sigma = sigma
self.b = b
... |
CriticNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
class CriticNet(nn.Module):
def __init__(self, args):
super(CriticNet, self).__init__()
state_dim = args.state_dim
action_dim =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | doudoulaile/RL-GAN-Net | CriticNet | false | 15,264 | [
"MIT"
] | 112 | 9c221223d1878bc24f0f39ad34928c1bb2974ae3 | https://github.com/doudoulaile/RL-GAN-Net/tree/9c221223d1878bc24f0f39ad34928c1bb2974ae3 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, args):
super().__init__()
state_dim = args.state_dim
action_dim = args.z_dim
... |
LabelSmoothingCrossEntropyV2 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class LabelSmoothingCrossEntropyV2(nn.Module):
""" 平滑的交叉熵, LabelSommth-CrossEntropy
This is the autograd version, you can also try the LabelSmoothSoftmaxCEV2 that uses derived gradients
url: https://github.com/CoinCheung/pytorch-loss
examples:
>>> criteria = L... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | dumpmemory/Pytorch-NLU | LabelSmoothingCrossEntropyV2 | false | 15,265 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | import torch
from torch import nn
class Model(nn.Module):
""" 平滑的交叉熵, LabelSommth-CrossEntropy
This is the autograd version, you can also try the LabelSmoothSoftmaxCEV2 that uses derived gradients
url: https://github.com/CoinCheung/pytorch-loss
examples:
>>> criteria = LabelSmoothingCrossEntro... |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.autograd
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, n_in, n_out, dropout=0):
super().__init__()
self.linear = nn.Linear(n_in, n_out)
self.activation = nn.GELU()
self.dropout = nn.Dropout(dropout)
def forward(self, x):
x = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.autogr... | dumpmemory/W2NER | MLP | false | 15,266 | [
"MIT"
] | 128 | fb1b6eb1111eb001b1c965097d995244b840bdda | https://github.com/dumpmemory/W2NER/tree/fb1b6eb1111eb001b1c965097d995244b840bdda | import torch
import torch.autograd
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n_in, n_out, dropout=0):
super().__init__()
self.linear = nn.Linear(n_in, n_out)
self.activation = nn.GELU()
self.dropout = nn.Dropout(dropout)
def forward(self, x):
x ... |
ConvMLPStage | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
import torch.nn as nn
from torch.nn import Linear
from torch.nn import LayerNorm
from torch.nn import Conv2d
from torch.nn import GELU
from torch.nn import Identity
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""
Obtained from: github.com:rwightma... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | dumpmemory/Convolutional-MLPs | ConvMLPStage | false | 15,267 | [
"Apache-2.0"
] | 117 | 89008c686e48803c012038f21f97e56276aa84ad | https://github.com/dumpmemory/Convolutional-MLPs/tree/89008c686e48803c012038f21f97e56276aa84ad | from torch.nn import Module
import torch
import torch.nn as nn
from torch.nn import Linear
from torch.nn import LayerNorm
from torch.nn import Conv2d
from torch.nn import GELU
from torch.nn import Identity
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""
Obtained from: github.com:rwightma... |
ResNormLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch import optim as optim
class ResNormLayer(nn.Module):
def __init__(self, linear_size):
super(ResNormLayer, self).__init__()
self.l_size = linear_size
self.nonlin1 = nn.ReLU(inplace=True)
self.nonlin2 = nn.ReLU(inplace=True)
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dqshuai/MetaFormer | ResNormLayer | false | 15,268 | [
"MIT"
] | 67 | 669bf18c35fdb51e35b0a79fa86224a18cd38ac5 | https://github.com/dqshuai/MetaFormer/tree/669bf18c35fdb51e35b0a79fa86224a18cd38ac5 | import torch
from torch import nn
from torch import optim as optim
class Model(nn.Module):
def __init__(self, linear_size):
super().__init__()
self.l_size = linear_size
self.nonlin1 = nn.ReLU(inplace=True)
self.nonlin2 = nn.ReLU(inplace=True)
self.norm_fn1 = nn.LayerNorm(s... |
RegressionHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import abc
import torch
import torch.nn as nn
import torch.utils.data.dataset
class BaseHead(nn.Module, metaclass=abc.ABCMeta):
"""Absract class for task heads"""
@abc.abstractmethod
def __init__(self):
super().__init__()
class RegressionHead(BaseHead):
def __init__(self, task, 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.triton_helpers import libdevice
import abc
import t... | dumpmemory/jiant | RegressionHead | false | 15,269 | [
"MIT"
] | 1,108 | f9e0e7c9ecf88da0c26559c5f903aef0338c7bd9 | https://github.com/dumpmemory/jiant/tree/f9e0e7c9ecf88da0c26559c5f903aef0338c7bd9 | import abc
import torch
import torch.nn as nn
import torch.utils.data.dataset
class BaseHead(nn.Module, metaclass=abc.ABCMeta):
"""Absract class for task heads"""
@abc.abstractmethod
def __init__(self):
super().__init__()
class Model(BaseHead):
def __init__(self, task, hidden_size, hidden_... |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class DeepMind(nn.Module):
def __init__(self):
super(DeepMind, self).__init__()
self.conv1 = nn.Conv2d(4, 32, 8, stride=4)
self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
self.conv3 = nn.Conv2d(64, 32, 3, stride=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | TianhongDai/Self_Imitation_Learning | Net | false | 15,270 | [
"MIT"
] | 61 | e49003582fa3d875495d84682f2a3332d4922dbc | https://github.com/TianhongDai/Self_Imitation_Learning/tree/e49003582fa3d875495d84682f2a3332d4922dbc | import torch
import torch.nn as nn
import torch.nn.functional as F
class DeepMind(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(4, 32, 8, stride=4)
self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
self.conv3 = nn.Conv2d(64, 32, 3, stride=1)
self.fc1 ... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.autograd
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, input_dim, cond_dim=0, center=True, scale=True,
epsilon=None, conditional=False, hidden_units=None,
hidden_activation='linear', hidden_initializer='xaiver', **kwargs):
super(LayerNorm, ... | 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.autograd
import torch.nn as nn
assert_size_stride = torch._C._dyna... | dumpmemory/W2NER | LayerNorm | false | 15,271 | [
"MIT"
] | 128 | fb1b6eb1111eb001b1c965097d995244b840bdda | https://github.com/dumpmemory/W2NER/tree/fb1b6eb1111eb001b1c965097d995244b840bdda | import torch
import torch.autograd
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim, cond_dim=0, center=True, scale=True,
epsilon=None, conditional=False, hidden_units=None,
hidden_activation='linear', hidden_initializer='xaiver', **kwargs):
super().__init__()
... |
SoftTargetCrossEntropy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional as F
import torch.utils.data
class SoftTargetCrossEntropy(nn.Module):
def __init__(self):
super(SoftTargetCrossEntropy, self).__init__()
def forward(self, x, target):
N_rep = x.shape[0]
N = 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 as nn
... | dumpmemory/TokenLabeling | SoftTargetCrossEntropy | false | 15,272 | [
"Apache-2.0"
] | 367 | 9dbfd59aedecfe83f6f3253db4e99b82359d48ac | https://github.com/dumpmemory/TokenLabeling/tree/9dbfd59aedecfe83f6f3253db4e99b82359d48ac | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional as F
import torch.utils.data
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, target):
N_rep = x.shape[0]
N = target.shape[0]
if not N == N_rep:
... |
AddPositionEmb | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from typing import Sequence
import torch.nn as nn
import torch._C
import torch.serialization
import torch.nn.parallel
class AddPositionEmb(nn.Module):
"""Module to add position embedding to input features
"""
def __init__(self, dim=384, spatial_shape=[14, 14]):
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 typing import Sequence
import torch.nn as nn
import torch._C
import torch.serialization
import torch.nn.parallel
assert_size_stride = t... | dumpmemory/poolformer | AddPositionEmb | false | 15,273 | [
"Apache-2.0"
] | 677 | d108be054469da760141f4789bf87c915c4fd0b2 | https://github.com/dumpmemory/poolformer/tree/d108be054469da760141f4789bf87c915c4fd0b2 | import torch
from typing import Sequence
import torch.nn as nn
import torch._C
import torch.serialization
import torch.nn.parallel
class Model(nn.Module):
"""Module to add position embedding to input features
"""
def __init__(self, dim=384, spatial_shape=[14, 14]):
super().__init__()
if i... |
AFTFull | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class AFTFull(nn.Module):
def __init__(self, max_seqlen, dim, hidden_dim=64):
super().__init__()
"""
max_seqlen: the maximum number of timesteps (sequence length) to be fed in
dim: the embedding dimension of the tokens
hidden_dim: the hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | dumpmemory/aft-pytorch | AFTFull | false | 15,274 | [
"MIT"
] | 170 | 9a896966481f4042c2882f544d7bb1381e81dca1 | https://github.com/dumpmemory/aft-pytorch/tree/9a896966481f4042c2882f544d7bb1381e81dca1 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, max_seqlen, dim, hidden_dim=64):
super().__init__()
"""
max_seqlen: the maximum number of timesteps (sequence length) to be fed in
dim: the embedding dimension of the tokens
hidden_dim: the hidden... |
AFTSimple | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class AFTSimple(nn.Module):
def __init__(self, max_seqlen, dim, hidden_dim=64):
super().__init__()
"""
max_seqlen: the maximum number of timesteps (sequence length) to be fed in
dim: the embedding dimension of the tokens
hidden_dim: the hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dumpmemory/aft-pytorch | AFTSimple | false | 15,275 | [
"MIT"
] | 170 | 9a896966481f4042c2882f544d7bb1381e81dca1 | https://github.com/dumpmemory/aft-pytorch/tree/9a896966481f4042c2882f544d7bb1381e81dca1 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, max_seqlen, dim, hidden_dim=64):
super().__init__()
"""
max_seqlen: the maximum number of timesteps (sequence length) to be fed in
dim: the embedding dimension of the tokens
hidden_dim: the hidden... |
PixelNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.utils.cpp_extension
class PixelNorm(nn.Module):
"""pixel normalization"""
def forward(self, x):
x = x / x.pow(2).mean(dim=1, keepdim=True).sqrt().add(1e-08)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_stride = tor... | STomoya/animeface | PixelNorm | false | 15,276 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 | import torch
import torch.nn as nn
import torch.utils.cpp_extension
class Model(nn.Module):
"""pixel normalization"""
def forward(self, x):
x = x / x.pow(2).mean(dim=1, keepdim=True).sqrt().add(1e-08)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... |
SpanFCLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class SpanFCLayer(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0.1, is_active=
True, is_dropout=True, active_type='mish'):
"""SpanFCLayer
Span-FC-Layer, mostly last output of span of model, 新增LayerNorm(条件层标准化)
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, math as tl_math
fr... | dumpmemory/Pytorch-NLU | SpanFCLayer | false | 15,277 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0.1, is_active=
True, is_dropout=True, active_type='mish'):
"""SpanFCLayer
Span-FC-Layer, mostly last output of span of model, 新增LayerNorm(条件层标准化)
args:
inp... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class LayerNorm(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1, 1))
def forward(self, x):
std = torch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | dumpmemory/uniformer-pytorch | LayerNorm | false | 15,278 | [
"MIT"
] | 71 | 756c4edb7ab0947dc202c145f7c95571848e0594 | https://github.com/dumpmemory/uniformer-pytorch/tree/756c4edb7ab0947dc202c145f7c95571848e0594 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1, 1))
def forward(self, x):
std = torch.var... |
h_swish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class h_swish(nn.Module):
def __init__(self, inplace=True):
super(h_swish, self).__init__()
self.inplace = inplace
def forward(self, x):
out = F.relu6(x + 3.0, self.inplace) / 6.0
return out * x
def get_inpu... | 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... | dx9527/MobileNetV3-pytorch | h_swish | false | 15,279 | [
"MIT"
] | 291 | 7812dbcedd5db4e3bbfc21122b82205848f742cf | https://github.com/dx9527/MobileNetV3-pytorch/tree/7812dbcedd5db4e3bbfc21122b82205848f742cf | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, inplace=True):
super().__init__()
self.inplace = inplace
def forward(self, x):
out = F.relu6(x + 3.0, self.inplace) / 6.0
return out * x
def get_inputs():
retur... |
MultiplyLearned | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.fft
import torch.nn
class MultiplyLearned(torch.nn.Module):
def __init__(self, omega_0: 'float'):
"""
out = omega_0 * x, with a learned omega_0
"""
super().__init__()
self.omega_0 = torch.nn.Parameter(torch.Tensor(1))
with torch.no_grad():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.fft
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guard... | dwromero/ckconv | MultiplyLearned | false | 15,280 | [
"MIT"
] | 74 | d44c6441a98792477d6259368c210089bb33fe7a | https://github.com/dwromero/ckconv/tree/d44c6441a98792477d6259368c210089bb33fe7a | import torch
import torch.fft
import torch.nn
class Model(torch.nn.Module):
def __init__(self, omega_0: 'float'):
"""
out = omega_0 * x, with a learned omega_0
"""
super().__init__()
self.omega_0 = torch.nn.Parameter(torch.Tensor(1))
with torch.no_grad():
... |
MultiLabelCircleLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class MultiLabelCircleLoss(nn.Module):
def __init__(self, reduction='mean', inf=1000000000000.0):
"""CircleLoss of MultiLabel, 多个目标类的多标签分类场景，希望“每个目标类得分都不小于每个非目标类的得分”
多标签分类的交叉熵(softmax+crossentropy推广, N选K问题), LSE函数的梯度恰好是softmax函数
让同类相似度与非同类相似度之间拉开一定的margin... | 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... | dumpmemory/Pytorch-NLU | MultiLabelCircleLoss | false | 15,281 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, reduction='mean', inf=1000000000000.0):
"""CircleLoss of MultiLabel, 多个目标类的多标签分类场景，希望“每个目标类得分都不小于每个非目标类的得分”
多标签分类的交叉熵(softmax+crossentropy推广, N选K问题), LSE函数的梯度恰好是softmax函数
让同类相似度与非同类相似度之间拉开一定的margin。
- 使... |
DotProductLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class DotProductLoss(nn.Module):
def __init__(self):
super(DotProductLoss, self).__init__()
def forward(self, output, target):
return -torch.dot(target.view(-1), output.view(-1)) / target.nelement()
def get_inputs():
return [torch.rand([4, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ehsanik/dogTorch | DotProductLoss | false | 15,282 | [
"MIT"
] | 74 | 3a898862f6283e6603833991eeb62427216f2af7 | https://github.com/ehsanik/dogTorch/tree/3a898862f6283e6603833991eeb62427216f2af7 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, output, target):
return -torch.dot(target.view(-1), output.view(-1)) / target.nelement()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
d... |
ContrastiveLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn.functional as F
class ContrastiveLoss(torch.nn.Module):
def __init__(self, margin=2):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, output1, output2, label):
euclidean_distance = F.pairwise_distance(output1, output2)
... | 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
assert_size_stride = torch._... | e-Neural/OfflineSignatureVerification | ContrastiveLoss | false | 15,283 | [
"MIT"
] | 51 | ea11009a3b2ac82c7091075466c505602a50817a | https://github.com/e-Neural/OfflineSignatureVerification/tree/ea11009a3b2ac82c7091075466c505602a50817a | import torch
import torch.nn.functional as F
class Model(torch.nn.Module):
def __init__(self, margin=2):
super().__init__()
self.margin = margin
def forward(self, output1, output2, label):
euclidean_distance = F.pairwise_distance(output1, output2)
loss_contrastive = torch.mea... |
ImageToSequence | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from typing import NamedTuple
from torch.nn.utils.rnn import pack_padded_sequence
def image_to_sequence(x, columnwise=True, return_packed=False):
x, xs = (x.data, x.sizes) if isinstance(x, PaddedTensor) else (x, None)
if x.dim() == 2:
x = x.view(1, 1, x.size(0), x.size(1))
elif x.dim(... | 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 typing import NamedTuple
from torch.nn.utils.rnn import pack_padded_sequence
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | eivtho/PyLaia | ImageToSequence | false | 15,284 | [
"MIT"
] | 89 | 2a7a6e2eeb9b5af68c0faed0c564b02063e72be0 | https://github.com/eivtho/PyLaia/tree/2a7a6e2eeb9b5af68c0faed0c564b02063e72be0 | import torch
from typing import NamedTuple
from torch.nn.utils.rnn import pack_padded_sequence
def image_to_sequence(x, columnwise=True, return_packed=False):
x, xs = (x.data, x.sizes) if isinstance(x, PaddedTensor) else (x, None)
if x.dim() == 2:
x = x.view(1, 1, x.size(0), x.size(1))
elif x.dim(... |
GaussianNoise | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class GaussianNoise(nn.Module):
"""A gaussian noise module.
Args:
stddev (float): The standard deviation of the normal distribution.
Default: 0.1.
Shape:
- Input: (batch, *)
- Output: (batch, *) (same shape as input)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | eezkni/UEGAN | GaussianNoise | false | 15,285 | [
"MIT"
] | 73 | a6616ac559819d487cae0f301d98cf2922a11a09 | https://github.com/eezkni/UEGAN/tree/a6616ac559819d487cae0f301d98cf2922a11a09 | import torch
import torch.nn as nn
class Model(nn.Module):
"""A gaussian noise module.
Args:
stddev (float): The standard deviation of the normal distribution.
Default: 0.1.
Shape:
- Input: (batch, *)
- Output: (batch, *) (same shape as input)
"""
... |
LR_PAD | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
def lr_pad(x, padding=1):
""" Pad left/right-most to each other instead of zero padding """
return torch.cat([x[..., -padding:], x, x[..., :padding]], dim=3)
class LR_PAD(nn.Module):
""" Pad left/right-most to each other instead of zero padding """
def __init__(se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ekbanasolutions/HorizonNet | LR_PAD | false | 15,286 | [
"MIT"
] | 254 | 4eff713f8d446c53c479d86b4d06af166b724a74 | https://github.com/ekbanasolutions/HorizonNet/tree/4eff713f8d446c53c479d86b4d06af166b724a74 | import torch
import torch.nn as nn
def lr_pad(x, padding=1):
""" Pad left/right-most to each other instead of zero padding """
return torch.cat([x[..., -padding:], x, x[..., :padding]], dim=3)
class Model(nn.Module):
""" Pad left/right-most to each other instead of zero padding """
def __init__(sel... |
fChannelAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.optim
import torch.utils.data
class fChannelAttention(torch.nn.Module):
def __init__(self, N_in, ratio=1):
super(fChannelAttention, self).__init__()
self.N_in = N_in
self.ratio = ratio
self.weight_fc1 = torch.nn.Parameter(torch.Tensor(self.N_i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dyn... | dwromero/att_gconvs | fChannelAttention | false | 15,287 | [
"MIT"
] | 53 | 872259cad49763fdcfa3e96e80b6b5c331adf084 | https://github.com/dwromero/att_gconvs/tree/872259cad49763fdcfa3e96e80b6b5c331adf084 | import math
import torch
import torch.optim
import torch.utils.data
class Model(torch.nn.Module):
def __init__(self, N_in, ratio=1):
super().__init__()
self.N_in = N_in
self.ratio = ratio
self.weight_fc1 = torch.nn.Parameter(torch.Tensor(self.N_in //
ratio, self.N_in))... |
MultiscaleRecLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class MultiscaleRecLoss(nn.Module):
def __init__(self, scale=3, rec_loss_type='l1', multiscale=True):
super(MultiscaleRecLoss, self).__init__()
self.multiscale = multiscale
if rec_loss_type == 'l1':
self.criterion = nn.L1Loss()
elif r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | eezkni/UEGAN | MultiscaleRecLoss | false | 15,288 | [
"MIT"
] | 73 | a6616ac559819d487cae0f301d98cf2922a11a09 | https://github.com/eezkni/UEGAN/tree/a6616ac559819d487cae0f301d98cf2922a11a09 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, scale=3, rec_loss_type='l1', multiscale=True):
super().__init__()
self.multiscale = multiscale
if rec_loss_type == 'l1':
self.criterion = nn.L1Loss()
elif rec_loss_type == 'smoothl1':
... |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
def _neg_loss(pred, gt):
""" Modified focal loss. Exactly the same as CornerNet.
Runs faster and costs a little bit more memory
(https://github.com/tianweiy/CenterPoint)
Arguments:
pred (batch x c x h x w)
gt (batch x c x h x w)
"""
pos_inds = gt.eq(1).floa... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | edwardzhou130/Panoptic-PolarNet | FocalLoss | false | 15,289 | [
"BSD-3-Clause"
] | 90 | 3a72f2380a4e505e191b69da596f521a9d9f1a71 | https://github.com/edwardzhou130/Panoptic-PolarNet/tree/3a72f2380a4e505e191b69da596f521a9d9f1a71 | import torch
def _neg_loss(pred, gt):
""" Modified focal loss. Exactly the same as CornerNet.
Runs faster and costs a little bit more memory
(https://github.com/tianweiy/CenterPoint)
Arguments:
pred (batch x c x h x w)
gt (batch x c x h x w)
"""
pos_inds = gt.eq(1).floa... |
Sine | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class Sine(nn.Module):
def __init__(self, w0=30):
super().__init__()
self.w0 = w0
def forward(self, input):
return torch.sin(self.w0 * input)
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | eliemichel/ACORN | Sine | false | 15,290 | [
"MIT"
] | 186 | ca1b776e585251bd20468038c343decbbd62abf3 | https://github.com/eliemichel/ACORN/tree/ca1b776e585251bd20468038c343decbbd62abf3 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, w0=30):
super().__init__()
self.w0 = w0
def forward(self, input):
return torch.sin(self.w0 * input)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
CoPredictor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.autograd
import torch.nn as nn
class Biaffine(nn.Module):
def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
super(Biaffine, self).__init__()
self.n_in = n_in
self.n_out = n_out
self.bias_x = bias_x
self.bias_y = bias_y
weight = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.autogr... | dumpmemory/W2NER | CoPredictor | false | 15,291 | [
"MIT"
] | 128 | fb1b6eb1111eb001b1c965097d995244b840bdda | https://github.com/dumpmemory/W2NER/tree/fb1b6eb1111eb001b1c965097d995244b840bdda | import torch
import torch.autograd
import torch.nn as nn
class Biaffine(nn.Module):
def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
super().__init__()
self.n_in = n_in
self.n_out = n_out
self.bias_x = bias_x
self.bias_y = bias_y
weight = torch.zeros((n... |
ConvBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import Tensor
from typing import List
from typing import Optional
from typing import Union
from typing import Any
from typing import Tuple
from typing import NamedTuple
import torch.nn as nn
import torch.nn.functional as F
class PaddedTensor(NamedTuple):
data: 'torch.Tensor'
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 typing import List
from typing import Optional
from typing impo... | eivtho/PyLaia | ConvBlock | false | 15,292 | [
"MIT"
] | 89 | 2a7a6e2eeb9b5af68c0faed0c564b02063e72be0 | https://github.com/eivtho/PyLaia/tree/2a7a6e2eeb9b5af68c0faed0c564b02063e72be0 | import math
import torch
from torch import Tensor
from typing import List
from typing import Optional
from typing import Union
from typing import Any
from typing import Tuple
from typing import NamedTuple
import torch.nn as nn
import torch.nn.functional as F
class PaddedTensor(NamedTuple):
data: 'torch.Tensor'
... |
NNAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class NNAttention(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.q_net = nn.Linear(in_dim, out_dim)
self.k_net = nn.Linear(in_dim, out_dim)
self.v_net = nn.Linear(in_dim, out_dim)
def forward(self, Q, K, V):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | eitin-infant/FinRL-Meta | NNAttention | false | 15,293 | [
"MIT"
] | 214 | 4c94011e58425796e7e2e5c1bf848afd65c828d6 | https://github.com/eitin-infant/FinRL-Meta/tree/4c94011e58425796e7e2e5c1bf848afd65c828d6 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.q_net = nn.Linear(in_dim, out_dim)
self.k_net = nn.Linear(in_dim, out_dim)
self.v_net = nn.Linear(in_dim, out_dim)
def forward(self, Q, K, V):
q = s... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.fft
import torch.nn
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, num_channels: 'int', eps: 'float'=1e-12):
"""Uses GroupNorm implementation with group=1 for speed."""
super().__init__()
self.layer_norm = torch.nn.GroupNorm(1, num_channels=... | 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.fft
import torch.nn
import torch.nn as nn
assert_size_stride = tor... | dwromero/ckconv | LayerNorm | false | 15,294 | [
"MIT"
] | 74 | d44c6441a98792477d6259368c210089bb33fe7a | https://github.com/dwromero/ckconv/tree/d44c6441a98792477d6259368c210089bb33fe7a | import torch
import torch.fft
import torch.nn
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_channels: 'int', eps: 'float'=1e-12):
"""Uses GroupNorm implementation with group=1 for speed."""
super().__init__()
self.layer_norm = torch.nn.GroupNorm(1, num_channels=num_... |
SelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class SelfAttention(nn.Module):
def __init__(self, *args, **kargs):
super().__init__()
self.attention = nn.MultiheadAttention(*args, **kargs)
def forward(self, x):
return self.attention(x, x, x)[0]
def get_inputs():
return [torch.rand([4, 4])]... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | eitin-infant/FinRL-Meta | SelfAttention | false | 15,295 | [
"MIT"
] | 214 | 4c94011e58425796e7e2e5c1bf848afd65c828d6 | https://github.com/eitin-infant/FinRL-Meta/tree/4c94011e58425796e7e2e5c1bf848afd65c828d6 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, *args, **kargs):
super().__init__()
self.attention = nn.MultiheadAttention(*args, **kargs)
def forward(self, x):
return self.attention(x, x, x)[0]
def get_inputs():
return [torch.rand([4, 4])]
def g... |
ILN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.cpp_extension
class ILN(nn.Module):
def __init__(self, channels, resl, eps=1e-08):
super().__init__()
self.rho = nn.Parameter(torch.Tensor(1, channels, 1, 1))
self.rho.data.fill_(0.0)
self.instance_norm = nn.InstanceNorm2d(chan... | 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
assert_size_stride = tor... | STomoya/animeface | ILN | false | 15,296 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 | import torch
import torch.nn as nn
import torch.utils.cpp_extension
class Model(nn.Module):
def __init__(self, channels, resl, eps=1e-08):
super().__init__()
self.rho = nn.Parameter(torch.Tensor(1, channels, 1, 1))
self.rho.data.fill_(0.0)
self.instance_norm = nn.InstanceNorm2d(ch... |
ScalePredictor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class ScalePredictor(nn.Module):
def __init__(self, nz, scale_lr_decay=0.2, scale_bias=1.0):
super(ScalePredictor, self).__init__()
self.pred_layer = nn.Linear(nz, 1)
self.scale_bias = scale_bias
self.scale_lr_decay = scale_lr_decay
def forw... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | eldar/acsm | ScalePredictor | false | 15,297 | [
"Apache-2.0"
] | 52 | 04069e8bb4c12185473dc10c3355e5367fa98968 | https://github.com/eldar/acsm/tree/04069e8bb4c12185473dc10c3355e5367fa98968 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, nz, scale_lr_decay=0.2, scale_bias=1.0):
super().__init__()
self.pred_layer = nn.Linear(nz, 1)
self.scale_bias = scale_bias
self.scale_lr_decay = scale_lr_decay
def forward(self, feat):
scal... |
SpatialAttention2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch._utils
class SpatialAttention2d(nn.Module):
def __init__(self, channel):
super(SpatialAttention2d, self).__init__()
self.squeeze = nn.Conv2d(channel, 1, kernel_size=1, bias=False)
self.sigmoid = nn.Sigmoid()
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
import torch.nn as nn
import torch._utils
assert_size_stride = torch._C._dynamo.... | elmajdma/seismic-deeplearning | SpatialAttention2d | false | 15,298 | [
"MIT"
] | 270 | bc084abe153509c40b45f8bf0f80dfda1049d7dc | https://github.com/elmajdma/seismic-deeplearning/tree/bc084abe153509c40b45f8bf0f80dfda1049d7dc | import torch
import torch.nn as nn
import torch._utils
class Model(nn.Module):
def __init__(self, channel):
super().__init__()
self.squeeze = nn.Conv2d(channel, 1, kernel_size=1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
z = self.squeeze(x)
z = sel... |
InputMapping | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.fft
import torch.nn
class InputMapping(torch.nn.Conv1d):
def __init__(self, in_channels: 'int', out_channels: 'int', omega_0:
'float', stride: 'int'=1, bias: 'bool'=True):
super().__init__(in_channels=in_channels, out_channels=out_channels,
kernel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
i... | dwromero/ckconv | InputMapping | false | 15,299 | [
"MIT"
] | 74 | d44c6441a98792477d6259368c210089bb33fe7a | https://github.com/dwromero/ckconv/tree/d44c6441a98792477d6259368c210089bb33fe7a | import math
import torch
import torch.fft
import torch.nn
class Model(torch.nn.Conv1d):
def __init__(self, in_channels: 'int', out_channels: 'int', omega_0:
'float', stride: 'int'=1, bias: 'bool'=True):
super().__init__(in_channels=in_channels, out_channels=out_channels,
kernel_size=1... |
CMVN | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.onnx
class CMVN(torch.nn.Module):
eps = 1e-05
@torch.no_grad()
def forward(self, feat):
mean = feat.mean(dim=2, keepdim=True)
std = feat.std(dim=2, keepdim=True)
feat = (feat - mean) / (std + CMVN.eps)
return feat
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.triton_helpers import libdevice
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | entn-at/Online-Speech-Recognition | CMVN | false | 15,300 | [
"Apache-2.0"
] | 201 | 75680cef38c57d0ac60f5e23c90d24bb3046e4e7 | https://github.com/entn-at/Online-Speech-Recognition/tree/75680cef38c57d0ac60f5e23c90d24bb3046e4e7 | import torch
import torch.onnx
class Model(torch.nn.Module):
eps = 1e-05
@torch.no_grad()
def forward(self, feat):
mean = feat.mean(dim=2, keepdim=True)
std = feat.std(dim=2, keepdim=True)
feat = (feat - mean) / (std + CMVN.eps)
return feat
def get_inputs():
return [... |
PatchEmbed3D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
from itertools import chain as chain
import torch.nn as nn
class PatchEmbed3D(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, temporal_resolution=4, in_chans=3,
patch_size=16, z_block_size=2, embed_dim=768, flatten=True):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from itertools import chain as chain
import torch.nn as ... | dylan-campbell/Motionformer | PatchEmbed3D | false | 15,301 | [
"Apache-2.0"
] | 153 | 6c860614a3b252c6163971ba20e61ea3184d5291 | https://github.com/dylan-campbell/Motionformer/tree/6c860614a3b252c6163971ba20e61ea3184d5291 | import torch
import torch.utils.data
from itertools import chain as chain
import torch.nn as nn
class Model(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, temporal_resolution=4, in_chans=3,
patch_size=16, z_block_size=2, embed_dim=768, flatten=True):
super()... |
fChannelAttentionGG | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import numpy as np
import torch.optim
import torch.utils.data
class fChannelAttentionGG(torch.nn.Module):
def __init__(self, N_h_in, N_in, ratio=1, group='SE2'):
super(fChannelAttentionGG, self).__init__()
self.N_in = N_in
self.ratio = ratio
self.N_h_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 math
import numpy as np
import torch.optim
import torch.utils.data
assert_size_str... | dwromero/att_gconvs | fChannelAttentionGG | false | 15,302 | [
"MIT"
] | 53 | 872259cad49763fdcfa3e96e80b6b5c331adf084 | https://github.com/dwromero/att_gconvs/tree/872259cad49763fdcfa3e96e80b6b5c331adf084 | import math
import torch
import numpy as np
import torch.optim
import torch.utils.data
class Model(torch.nn.Module):
def __init__(self, N_h_in, N_in, ratio=1, group='SE2'):
super().__init__()
self.N_in = N_in
self.ratio = ratio
self.N_h_in = N_h_in
self.N_h = N_h_in
... |
DurationMSELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
from torch.optim import *
from torch.optim.lr_scheduler import *
class DurationMSELoss(torch.nn.Module):
"""Loss function module for duration predictor.
The loss value is Calculated in log domain to make it Gaussian.
"""
def __init__(self, offset=1.0, reduction=... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | entn-at/efficient_tts | DurationMSELoss | false | 15,303 | [
"MIT"
] | 111 | 5e6ea55d0c9694f7e30eecb5048976088f1a3c66 | https://github.com/entn-at/efficient_tts/tree/5e6ea55d0c9694f7e30eecb5048976088f1a3c66 | import torch
import torch.utils.data
from torch.optim import *
from torch.optim.lr_scheduler import *
class Model(torch.nn.Module):
"""Loss function module for duration predictor.
The loss value is Calculated in log domain to make it Gaussian.
"""
def __init__(self, offset=1.0, reduction='mean'):
... |
Classifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
class Classifier(nn.Module):
def __init__(self, dims):
"""
Single hidden layer classifier
with softmax output.
"""
super(Classifier, self).__init__()
[x_dim, h_dim, y_dim] = dims
self.dense =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | engdorm/semi-supervised-pytorch | Classifier | false | 15,304 | [
"MIT"
] | 700 | b149e06aa413dd426886149930c8c265fd9cc746 | https://github.com/engdorm/semi-supervised-pytorch/tree/b149e06aa413dd426886149930c8c265fd9cc746 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
def __init__(self, dims):
"""
Single hidden layer classifier
with softmax output.
"""
super().__init__()
[x_dim, h_dim, y_dim] = dims
self.dense = nn.Linear(x_dim, h_d... |
Gate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Gate(nn.Module):
def __init__(self, hidden_size):
super(Gate, self).__init__()
self.hidden_size = hidden_size
self.wrx = nn.Linear(hidden_size, hidden_size)
self.wrh = 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.triton_helpers import libdevice
import torch.nn as ... | elsehow/Writing-editing-Network | Gate | false | 15,305 | [
"MIT"
] | 79 | a8551cd224a4987a6eec3cf566bcf0793ad36dfd | https://github.com/elsehow/Writing-editing-Network/tree/a8551cd224a4987a6eec3cf566bcf0793ad36dfd | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.hidden_size = hidden_size
self.wrx = nn.Linear(hidden_size, hidden_size)
self.wrh = nn.Linear(hidden_size, hidden_size)
self.... |
SquaredModulus | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class SquaredModulus(nn.Module):
"""Squared modulus layer.
Returns a keras layer that implements a squared modulus operator.
To implement the squared modulus of C complex-valued channels, the expected
input dimension is N*1*W*(2*C) where channels role alternates betw... | 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... | entn-at/leaf-audio-pytorch | SquaredModulus | false | 15,306 | [
"Apache-2.0"
] | 72 | 33f4ba4c8bdf07f125033f8e706d0d0bc6816445 | https://github.com/entn-at/leaf-audio-pytorch/tree/33f4ba4c8bdf07f125033f8e706d0d0bc6816445 | import torch
from torch import nn
class Model(nn.Module):
"""Squared modulus layer.
Returns a keras layer that implements a squared modulus operator.
To implement the squared modulus of C complex-valued channels, the expected
input dimension is N*1*W*(2*C) where channels role alternates between
r... |
VariantSigmoid | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class VariantSigmoid(nn.Module):
def __init__(self, alpha):
super().__init__()
self.alpha = alpha
def forward(self, x):
y = 1 / (1 + torch.exp(-self.alpha * x))
return y
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_ini... | 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... | entn-at/AGAIN-VC | VariantSigmoid | false | 15,307 | [
"MIT"
] | 78 | dbf94bf55882f897c312c7760cd892c51c93c9ab | https://github.com/entn-at/AGAIN-VC/tree/dbf94bf55882f897c312c7760cd892c51c93c9ab | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, alpha):
super().__init__()
self.alpha = alpha
def forward(self, x):
y = 1 / (1 + torch.exp(-self.alpha * x))
return y
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs(... |
ClassificationTestModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
import torch.nn as nn
from typing import Any
from torch.nn.modules import Module
class ClassificationTestModel(Module):
def __init__(self, in_chans: 'int'=3, num_classes: 'int'=1000, **kwargs:
Any) ->None:
super().__init__()
self.conv1 = nn.Conv2d(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 torch.nn as nn
from typing import Any
from to... | ethanwhite/torchgeo | ClassificationTestModel | false | 15,308 | [
"MIT"
] | 678 | cb20e1abfd9213f9ee7700df972385db13568642 | https://github.com/ethanwhite/torchgeo/tree/cb20e1abfd9213f9ee7700df972385db13568642 | from torch.nn import Module
import torch
import torch.nn as nn
from typing import Any
from torch.nn.modules import Module
class Model(Module):
def __init__(self, in_chans: 'int'=3, num_classes: 'int'=1000, **kwargs:
Any) ->None:
super().__init__()
self.conv1 = nn.Conv2d(in_channels=in_cha... |
Upsample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.utils.data
class Upsample(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv = nn.ConvTranspose2d(dim, dim, 4, 2, 1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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
import torch.utils.data
assert_size_stride = torch._C._dyna... | entn-at/GradTTS | Upsample | false | 15,309 | [
"MIT"
] | 55 | d31cbf41211615a01fffc3812715e3f7f2be214d | https://github.com/entn-at/GradTTS/tree/d31cbf41211615a01fffc3812715e3f7f2be214d | import torch
from torch import nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv = nn.ConvTranspose2d(dim, dim, 4, 2, 1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def ge... |
SCse | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch._utils
class SpatialAttention2d(nn.Module):
def __init__(self, channel):
super(SpatialAttention2d, self).__init__()
self.squeeze = nn.Conv2d(channel, 1, kernel_size=1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | elmajdma/seismic-deeplearning | SCse | false | 15,310 | [
"MIT"
] | 270 | bc084abe153509c40b45f8bf0f80dfda1049d7dc | https://github.com/elmajdma/seismic-deeplearning/tree/bc084abe153509c40b45f8bf0f80dfda1049d7dc | import torch
import torch.nn as nn
import torch._utils
class SpatialAttention2d(nn.Module):
def __init__(self, channel):
super().__init__()
self.squeeze = nn.Conv2d(channel, 1, kernel_size=1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
z = self.squeeze(x)
... |
Model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.linear1 = nn.Linear(28 * 28, 32)
self.linear2 = nn.Linear(32, 10)
def forward(self, inputs):
x = inputs.view(-1, 28 * 28)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | emirojaseng/pytorch-meta-optimizer | Model | false | 15,311 | [
"MIT"
] | 298 | 3641981c990150ceb6c55d25a05ba76388f9ec69 | https://github.com/emirojaseng/pytorch-meta-optimizer/tree/3641981c990150ceb6c55d25a05ba76388f9ec69 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.linear1 = nn.Linear(28 * 28, 32)
self.linear2 = nn.Linear(32, 10)
def forward(self, inputs):
x = inputs.view(-1, 28 * 28)
... |
LayerNorm1D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class LayerNorm1D(nn.Module):
def __init__(self, num_outputs, eps=1e-05, affine=True):
super(LayerNorm1D, self).__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(1, num_outputs))
self.bias = nn.Parameter(torch.zeros(1, num_outputs))... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | emirojaseng/pytorch-meta-optimizer | LayerNorm1D | false | 15,312 | [
"MIT"
] | 298 | 3641981c990150ceb6c55d25a05ba76388f9ec69 | https://github.com/emirojaseng/pytorch-meta-optimizer/tree/3641981c990150ceb6c55d25a05ba76388f9ec69 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_outputs, eps=1e-05, affine=True):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(1, num_outputs))
self.bias = nn.Parameter(torch.zeros(1, num_outputs))
def forward(self,... |
QRLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | from torch.nn import Module
import torch
from typing import cast
from torch.nn.modules import Module
class QRLoss(Module):
"""The QR (forward) loss between class probabilities and predictions.
This loss is defined in `'Resolving label uncertainty with implicit generative
models' <https://openreview.net/f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn... | ethanwhite/torchgeo | QRLoss | false | 15,313 | [
"MIT"
] | 678 | cb20e1abfd9213f9ee7700df972385db13568642 | https://github.com/ethanwhite/torchgeo/tree/cb20e1abfd9213f9ee7700df972385db13568642 | from torch.nn import Module
import torch
from typing import cast
from torch.nn.modules import Module
class Model(Module):
"""The QR (forward) loss between class probabilities and predictions.
This loss is defined in `'Resolving label uncertainty with implicit generative
models' <https://openreview.net/fo... |
SelfAttn | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import functional as F
class SelfAttn(nn.Module):
"""
self-attention with learnable parameters
"""
def __init__(self, dhid):
super().__init__()
self.scorer = nn.Linear(dhid, 1)
def forward(self, inp):
scores = F.softmax(self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | etaoxing/crl_alfred | SelfAttn | false | 15,314 | [
"MIT"
] | 148 | cad500cf84f71e47f1191e7810dde0c74d295f08 | https://github.com/etaoxing/crl_alfred/tree/cad500cf84f71e47f1191e7810dde0c74d295f08 | import torch
from torch import nn
from torch.nn import functional as F
class Model(nn.Module):
"""
self-attention with learnable parameters
"""
def __init__(self, dhid):
super().__init__()
self.scorer = nn.Linear(dhid, 1)
def forward(self, inp):
scores = F.softmax(self.sc... |
RQLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | from torch.nn import Module
import torch
from typing import cast
from torch.nn.modules import Module
import torch.nn.functional as F
class RQLoss(Module):
"""The RQ (backwards) loss between class probabilities and predictions.
This loss is defined in `'Resolving label uncertainty with implicit generative
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ethanwhite/torchgeo | RQLoss | false | 15,315 | [
"MIT"
] | 678 | cb20e1abfd9213f9ee7700df972385db13568642 | https://github.com/ethanwhite/torchgeo/tree/cb20e1abfd9213f9ee7700df972385db13568642 | from torch.nn import Module
import torch
from typing import cast
from torch.nn.modules import Module
import torch.nn.functional as F
class Model(Module):
"""The RQ (backwards) loss between class probabilities and predictions.
This loss is defined in `'Resolving label uncertainty with implicit generative
... |
SegmentationTestModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
import torch.nn as nn
from typing import Any
from typing import cast
from torch.nn.modules import Module
class SegmentationTestModel(Module):
def __init__(self, in_channels: 'int'=3, classes: 'int'=1000, **kwargs: Any
) ->None:
super().__init__()
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.nn as nn
from typing import Any
from to... | ethanwhite/torchgeo | SegmentationTestModel | false | 15,316 | [
"MIT"
] | 678 | cb20e1abfd9213f9ee7700df972385db13568642 | https://github.com/ethanwhite/torchgeo/tree/cb20e1abfd9213f9ee7700df972385db13568642 | from torch.nn import Module
import torch
import torch.nn as nn
from typing import Any
from typing import cast
from torch.nn.modules import Module
class Model(Module):
def __init__(self, in_channels: 'int'=3, classes: 'int'=1000, **kwargs: Any
) ->None:
super().__init__()
self.conv1 = nn.C... |
InvConvNear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class InvConvNear(nn.Module):
def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
super().__init__()
assert n_split % 2 == 0
self.channels = channels
self.n_split = n_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 import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | entn-at/GradTTS | InvConvNear | false | 15,317 | [
"MIT"
] | 55 | d31cbf41211615a01fffc3812715e3f7f2be214d | https://github.com/entn-at/GradTTS/tree/d31cbf41211615a01fffc3812715e3f7f2be214d | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class Model(nn.Module):
def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
super().__init__()
assert n_split % 2 == 0
self.channels = channels
self.n_split = n_split
... |
GaborConstraint | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import math
import torch
from torch import nn
class GaborConstraint(nn.Module):
"""Constraint mu and sigma, in radians.
Mu is constrained in [0,pi], sigma s.t full-width at half-maximum of the
gaussian response is in [1,pi/2]. The full-width at half maximum of the
Gaussian response is 2*sqrt(2*log(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... | entn-at/leaf-audio-pytorch | GaborConstraint | false | 15,318 | [
"Apache-2.0"
] | 72 | 33f4ba4c8bdf07f125033f8e706d0d0bc6816445 | https://github.com/entn-at/leaf-audio-pytorch/tree/33f4ba4c8bdf07f125033f8e706d0d0bc6816445 | import math
import torch
from torch import nn
class Model(nn.Module):
"""Constraint mu and sigma, in radians.
Mu is constrained in [0,pi], sigma s.t full-width at half-maximum of the
gaussian response is in [1,pi/2]. The full-width at half maximum of the
Gaussian response is 2*sqrt(2*log(2))/sigma . ... |
CausalConv1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1):
super().__init__()
self.kernel_size = kernel_size
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size,
stride=stride, padding=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... | ex4sperans/freesound-classification | CausalConv1d | false | 15,319 | [
"Apache-2.0"
] | 55 | 71b9920ce0ae376aa7f1a3a2943f0f92f4820813 | https://github.com/ex4sperans/freesound-classification/tree/71b9920ce0ae376aa7f1a3a2943f0f92f4820813 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1):
super().__init__()
self.kernel_size = kernel_size
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size,
stride=stride, padding=kernel_size)
... |
Conv1dLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
from torch.optim import *
from torch.optim.lr_scheduler import *
class Conv1dLinear(torch.nn.Module):
"""Conv1D + Linear for Transformer block.
A variant of MultiLayeredConv1d, which replaces second conv-layer to linear.
"""
def __init__(self, in_chans, hidden_c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | entn-at/efficient_tts | Conv1dLinear | false | 15,320 | [
"MIT"
] | 111 | 5e6ea55d0c9694f7e30eecb5048976088f1a3c66 | https://github.com/entn-at/efficient_tts/tree/5e6ea55d0c9694f7e30eecb5048976088f1a3c66 | import torch
import torch.utils.data
from torch.optim import *
from torch.optim.lr_scheduler import *
class Model(torch.nn.Module):
"""Conv1D + Linear for Transformer block.
A variant of MultiLayeredConv1d, which replaces second conv-layer to linear.
"""
def __init__(self, in_chans, hidden_chans, k... |
BahdanauAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from random import *
class BahdanauAttention(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.hidden_size = hidden_size
self.w1 = nn.Linear(hidden_size, hidden_size)
self.w2 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | evinaybit/100-Days-of-NLP | BahdanauAttention | false | 15,321 | [
"MIT"
] | 239 | 81e08884dd31b7b99bef27f43a179cda09ab5732 | https://github.com/evinaybit/100-Days-of-NLP/tree/81e08884dd31b7b99bef27f43a179cda09ab5732 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from random import *
class Model(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.hidden_size = hidden_size
self.w1 = nn.Linear(hidden_size, hidden_size)
self.w2 = nn.Linear(hidden_s... |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from random import *
class Attention(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.hidden_size = hidden_size
self.w1 = nn.Linear(hidden_size, hidden_size)
self.w2 = nn.Linear(hidden_size, hidden_size)
self.v = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | evinaybit/100-Days-of-NLP | Attention | false | 15,322 | [
"MIT"
] | 239 | 81e08884dd31b7b99bef27f43a179cda09ab5732 | https://github.com/evinaybit/100-Days-of-NLP/tree/81e08884dd31b7b99bef27f43a179cda09ab5732 | import torch
import torch.nn as nn
from random import *
class Model(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.hidden_size = hidden_size
self.w1 = nn.Linear(hidden_size, hidden_size)
self.w2 = nn.Linear(hidden_size, hidden_size)
self.v = nn.Linear... |
ChannelAttentionGG | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.optim
import torch.utils.data
class ChannelAttention(torch.nn.Module):
def __init__(self, N_out, N_in, ratio=1):
super(ChannelAttention, self).__init__()
self.linear = torch.nn.functional.linear
self.avg_pool = torch.nn.AdaptiveAvgPool2d(1)
se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dyn... | dwromero/att_gconvs | ChannelAttentionGG | false | 15,323 | [
"MIT"
] | 53 | 872259cad49763fdcfa3e96e80b6b5c331adf084 | https://github.com/dwromero/att_gconvs/tree/872259cad49763fdcfa3e96e80b6b5c331adf084 | import math
import torch
import torch.optim
import torch.utils.data
class ChannelAttention(torch.nn.Module):
def __init__(self, N_out, N_in, ratio=1):
super().__init__()
self.linear = torch.nn.functional.linear
self.avg_pool = torch.nn.AdaptiveAvgPool2d(1)
self.max_pool = torch.nn... |
DepthL1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class DepthL1Loss(nn.Module):
def __init__(self, eps=1e-05):
super(DepthL1Loss, self).__init__()
self.eps = eps
def forward(self, pred, gt):
bs = pred.size()[0]
img1 = torch.zeros_like(pred)
img2 = torch.zeros_like(gt)
img1 =... | 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
... | ezxzeng/FFB6D | DepthL1Loss | false | 15,324 | [
"MIT"
] | 145 | fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | https://github.com/ezxzeng/FFB6D/tree/fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, eps=1e-05):
super().__init__()
self.eps = eps
def forward(self, pred, gt):
bs = pred.size()[0]
img1 = torch.zeros_like(pred)
img2 = torch.zeros_like(gt)
img1 = img1.copy_(pred)
... |
C3D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
def get_10x_lr_params(model):
"""
This generator returns all the parameters for the fc layer of the net.
"""
b = [model.linear]
for j in range(len(b)):
for k in b[j].parameters():
if k.requires_grad:
yield k
def get_1x_lr_para... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | datamllab/autovideo | C3D | false | 15,325 | [
"MIT"
] | 233 | 34a702fe9d3114e7128dcff12cb43369e4932919 | https://github.com/datamllab/autovideo/tree/34a702fe9d3114e7128dcff12cb43369e4932919 | import torch
from torch import nn
def get_10x_lr_params(model):
"""
This generator returns all the parameters for the fc layer of the net.
"""
b = [model.linear]
for j in range(len(b)):
for k in b[j].parameters():
if k.requires_grad:
yield k
def get_1x_lr_para... |
OFLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch.nn.modules.loss import _Loss
def of_l1_loss(pred_ofsts, kp_targ_ofst, labels, sigma=1.0, normalize=True,
reduce=False):
"""
:param pred_ofsts: [bs, n_kpts, n_pts, c]
:param kp_targ_ofst: [bs, n_pts, n_kpts, c]
:param labels: [bs, n_pts, 1]
"""
w = (... | 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.nn.modules.loss import _Loss
assert_size_stride = torch._C._dy... | ezxzeng/FFB6D | OFLoss | false | 15,326 | [
"MIT"
] | 145 | fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | https://github.com/ezxzeng/FFB6D/tree/fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | import torch
from torch.nn.modules.loss import _Loss
def of_l1_loss(pred_ofsts, kp_targ_ofst, labels, sigma=1.0, normalize=True,
reduce=False):
"""
:param pred_ofsts: [bs, n_kpts, n_pts, c]
:param kp_targ_ofst: [bs, n_pts, n_kpts, c]
:param labels: [bs, n_pts, 1]
"""
w = (... |
MinibatchStd | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.utils.tensorboard
class MinibatchStd(nn.Module):
"""
Adds the aveage std of each data point over a
slice of the minibatch to that slice as a new
feature map. This gives an output with one extra
channel.
Arguments:
group_size (int): Number... | 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.tensorboard
assert_size_stride = torch... | andoleg/stylegan2_pytorch | MinibatchStd | false | 15,327 | [
"MIT"
] | 121 | 27a367d00d35742cf66587f1bd1b1263469a8101 | https://github.com/andoleg/stylegan2_pytorch/tree/27a367d00d35742cf66587f1bd1b1263469a8101 | import torch
import torch.nn as nn
import torch.utils.tensorboard
class Model(nn.Module):
"""
Adds the aveage std of each data point over a
slice of the minibatch to that slice as a new
feature map. This gives an output with one extra
channel.
Arguments:
group_size (int): Number of ent... |
CosLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch.nn.modules.loss import _Loss
class CosLoss(_Loss):
def __init__(self, eps=1e-05):
super(CosLoss, self).__init__(True)
self.eps = eps
def forward(self, pred_ofsts, kp_targ_ofst, labels, normalize=True):
"""
:param pred_ofsts: [bs, n_kpts, n_pts, c]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.g... | ezxzeng/FFB6D | CosLoss | false | 15,328 | [
"MIT"
] | 145 | fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | https://github.com/ezxzeng/FFB6D/tree/fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | import torch
from torch.nn.modules.loss import _Loss
class Model(_Loss):
def __init__(self, eps=1e-05):
super().__init__(True)
self.eps = eps
def forward(self, pred_ofsts, kp_targ_ofst, labels, normalize=True):
"""
:param pred_ofsts: [bs, n_kpts, n_pts, c]
:param... |
TestPointLSTM | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class PointLSTMCell(nn.Module):
def __init__(self, pts_num, in_channels, hidden_dim, offset_dim, bias):
super(PointLSTMCell, self).__init__()
self.bias = bias
self.pts_num = pts_num
self.in_channels = in_channels
self.hidden_dim = 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
from torch._inductor.runtime.... | evanfebrianto/pointlstm_gesture_recognition_pytorch | TestPointLSTM | false | 15,329 | [
"Apache-2.0"
] | 69 | 797ccdc7da5a859e28f2a8cc7ef7118358b82cb4 | https://github.com/evanfebrianto/pointlstm_gesture_recognition_pytorch/tree/797ccdc7da5a859e28f2a8cc7ef7118358b82cb4 | import torch
import torch.nn as nn
class PointLSTMCell(nn.Module):
def __init__(self, pts_num, in_channels, hidden_dim, offset_dim, bias):
super().__init__()
self.bias = bias
self.pts_num = pts_num
self.in_channels = in_channels
self.hidden_dim = hidden_dim
self.of... |
ResidualBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, padding=1,
stride=1):
super(ResidualBlock, self).__init__()
self.padding1 = nn.ReflectionPad2d(padding)
self.conv1 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | eungbean/CoCosNet | ResidualBlock | false | 15,330 | [
"MIT"
] | 319 | f8007d9369cc11bc04709ef02dedbbf718d74414 | https://github.com/eungbean/CoCosNet/tree/f8007d9369cc11bc04709ef02dedbbf718d74414 | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, padding=1,
stride=1):
super().__init__()
self.padding1 = nn.ReflectionPad2d(padding)
self.conv1 = nn.Conv2d(in_channels, out... |
PointLSTMCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class PointLSTMCell(nn.Module):
def __init__(self, pts_num, in_channels, hidden_dim, offset_dim, bias):
super(PointLSTMCell, self).__init__()
self.bias = bias
self.pts_num = pts_num
self.in_channels = in_channels
self.hidden_dim = 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
from torch._inductor.runtime.... | evanfebrianto/pointlstm_gesture_recognition_pytorch | PointLSTMCell | false | 15,331 | [
"Apache-2.0"
] | 69 | 797ccdc7da5a859e28f2a8cc7ef7118358b82cb4 | https://github.com/evanfebrianto/pointlstm_gesture_recognition_pytorch/tree/797ccdc7da5a859e28f2a8cc7ef7118358b82cb4 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, pts_num, in_channels, hidden_dim, offset_dim, bias):
super().__init__()
self.bias = bias
self.pts_num = pts_num
self.in_channels = in_channels
self.hidden_dim = hidden_dim
self.offset_dim... |
BerHuLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class BerHuLoss(nn.Module):
def __init__(self, scale=0.5, eps=1e-05):
super(BerHuLoss, self).__init__()
self.scale = scale
self.eps = eps
def forward(self, pred, gt):
img1 = torch.zeros_like(pred)
img2 = torch.zeros_like(gt)
... | 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... | ezxzeng/FFB6D | BerHuLoss | false | 15,332 | [
"MIT"
] | 145 | fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | https://github.com/ezxzeng/FFB6D/tree/fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, scale=0.5, eps=1e-05):
super().__init__()
self.scale = scale
self.eps = eps
def forward(self, pred, gt):
img1 = torch.zeros_like(pred)
img2 = torch.zeros_like(gt)
img1 = img1.copy_(p... |
LogDepthL1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class LogDepthL1Loss(nn.Module):
def __init__(self, eps=1e-05):
super(LogDepthL1Loss, self).__init__()
self.eps = eps
def forward(self, pred, gt):
pred = pred.view(-1)
gt = gt.view(-1)
mask = gt > self.eps
diff = torch.abs(to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ezxzeng/FFB6D | LogDepthL1Loss | false | 15,333 | [
"MIT"
] | 145 | fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | https://github.com/ezxzeng/FFB6D/tree/fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, eps=1e-05):
super().__init__()
self.eps = eps
def forward(self, pred, gt):
pred = pred.view(-1)
gt = gt.view(-1)
mask = gt > self.eps
diff = torch.abs(torch.log(gt[mask]) - pred[mask... |
_Multiply | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import abc
import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn import MSELoss
import torch.nn
from torch import rand
class ConverterModule(Module, abc.ABC):
"""Interface class for test modules for converter."""
@abc.abstractmethod
def input_fn(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.nn import Module
import abc
from torch import Tensor
from torch.nn im... | f-dangel/backpack | _Multiply | false | 15,334 | [
"MIT"
] | 395 | 1da7e53ebb2c490e2b7dd9f79116583641f3cca1 | https://github.com/f-dangel/backpack/tree/1da7e53ebb2c490e2b7dd9f79116583641f3cca1 | from torch.nn import Module
import abc
import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn import MSELoss
import torch.nn
from torch import rand
class ConverterModule(Module, abc.ABC):
"""Interface class for test modules for converter."""
@abc.abstractmethod
def input_fn(self)... |
FactorizedReduce | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchvision.models import *
from torchvision.datasets import *
def get_norm_layer(norm, C):
if norm in [None, '', 'none']:
norm_layer = nn.Identity()
elif ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | evdcush/ppuda | FactorizedReduce | false | 15,335 | [
"MIT"
] | 262 | 22783ac92207da6730ee618c953af230c5c39f28 | https://github.com/evdcush/ppuda/tree/22783ac92207da6730ee618c953af230c5c39f28 | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchvision.models import *
from torchvision.datasets import *
def get_norm_layer(norm, C):
if norm in [None, '', 'none']:
norm_layer = nn.Identity()
elif ... |
OfstMapL1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class OfstMapL1Loss(nn.Module):
def __init__(self, eps=1e-05):
super().__init__()
self.eps = eps
def forward(self, rgb_labels, pred, gt, normalize=True, reduce=True):
wgt = (rgb_labels > 1e-08).float()
bs, n_kpts, c, h, w = pred.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._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ezxzeng/FFB6D | OfstMapL1Loss | false | 15,336 | [
"MIT"
] | 145 | fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | https://github.com/ezxzeng/FFB6D/tree/fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, eps=1e-05):
super().__init__()
self.eps = eps
def forward(self, rgb_labels, pred, gt, normalize=True, reduce=True):
wgt = (rgb_labels > 1e-08).float()
bs, n_kpts, c, h, w = pred.size()
wgt =... |
WeightNormConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
class WeightNormConv2d(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size, stride=1, padding=0,
bias=True, weight_norm=True, scale=False):
"""Intializes a Conv2d augmented with weight normalization.
(See torch.nn.utils.w... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | eyalbetzalel/GlowGAN | WeightNormConv2d | false | 15,337 | [
"MIT"
] | 54 | 144b8fef60d9dc38ca66c178a18c0c9a2a17c23e | https://github.com/eyalbetzalel/GlowGAN/tree/144b8fef60d9dc38ca66c178a18c0c9a2a17c23e | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size, stride=1, padding=0,
bias=True, weight_norm=True, scale=False):
"""Intializes a Conv2d augmented with weight normalization.
(See torch.nn.utils.weight_norm ... |
multi_scale_spatial | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class multi_scale_spatial(nn.Module):
def __init__(self, limb_blocks):
super(multi_scale_spatial, self).__init__()
(self.left_arm, self.right_arm, self.left_leg, self.right_leg, self
.head_spine) = limb_blocks
self.maxpool1 = nn.AdaptiveMaxPo... | 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... | fabro66/Online-Skeleton-based-Action-Recognition | multi_scale_spatial | false | 15,338 | [
"MIT"
] | 63 | de00cbf17ceea98a7d07f68bbbd966bfd02d3b40 | https://github.com/fabro66/Online-Skeleton-based-Action-Recognition/tree/de00cbf17ceea98a7d07f68bbbd966bfd02d3b40 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, limb_blocks):
super().__init__()
(self.left_arm, self.right_arm, self.left_leg, self.right_leg, self
.head_spine) = limb_blocks
self.maxpool1 = nn.AdaptiveMaxPool2d((1, 20))
self.maxpool2 = n... |
LayerNormGRUCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from typing import Optional
import torch.nn.functional as F
from torch import nn
import torch.utils.data
import torch.nn
from torch.nn import RNNCellBase
import torch.multiprocessing
from torch.nn import Identity
class LayerNormGRUCell(RNNCellBase):
"""
Implements GRUCell with layer normalisation... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | faz1993/InnerEye-DeepLearning | LayerNormGRUCell | false | 15,339 | [
"MIT"
] | 402 | fb258d5c9a3ba18565b5a67e7ac1f00127d9ecb9 | https://github.com/faz1993/InnerEye-DeepLearning/tree/fb258d5c9a3ba18565b5a67e7ac1f00127d9ecb9 | import torch
from typing import Optional
import torch.nn.functional as F
from torch import nn
import torch.utils.data
import torch.nn
from torch.nn import RNNCellBase
import torch.multiprocessing
from torch.nn import Identity
class Model(RNNCellBase):
"""
Implements GRUCell with layer normalisation and zone-o... |
LearnedPositionalEncoding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class LearnedPositionalEncoding(nn.Module):
def __init__(self, context_size, embedding_dim, dropout=0):
super(LearnedPositionalEncoding, self).__init__()
self.pe = nn.Embedding(context_size, embedding_dim)
self.... | 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.cuda
import torch.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | fangleai/encoder-agnostic-adaptation | LearnedPositionalEncoding | false | 15,340 | [
"MIT"
] | 70 | d917e654152df202dd35bba49c409c3ecd24eaf7 | https://github.com/fangleai/encoder-agnostic-adaptation/tree/d917e654152df202dd35bba49c409c3ecd24eaf7 | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class Model(nn.Module):
def __init__(self, context_size, embedding_dim, dropout=0):
super().__init__()
self.pe = nn.Embedding(context_size, embedding_dim)
self.dropout = nn.Dropout(p=dropout)
def forward(se... |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class MLP(nn.Module):
def __init__(self, n_embd, n_state, dropout):
super(MLP, 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 math
import ... | fangleai/encoder-agnostic-adaptation | MLP | false | 15,341 | [
"MIT"
] | 70 | d917e654152df202dd35bba49c409c3ecd24eaf7 | https://github.com/fangleai/encoder-agnostic-adaptation/tree/d917e654152df202dd35bba49c409c3ecd24eaf7 | import math
import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class Model(nn.Module):
def __init__(self, n_embd, n_state, dropout):
super().__init__()
... |
KnowledgeDistillationLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class KnowledgeDistillationLoss(nn.Module):
def __init__(self, reduction='mean', alpha=1.0):
super().__init__()
self.reduction = reduction
self.alpha = alpha
def forward(self, inputs, targets, mask=None):
inputs = inputs.narrow(1, 0, targets... | 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
... | fcdl94/ModelingTheBackground | KnowledgeDistillationLoss | false | 15,342 | [
"MIT"
] | 105 | 1c589833ce5c1a7446469d4602ceab2cdeac1b0e | https://github.com/fcdl94/ModelingTheBackground/tree/1c589833ce5c1a7446469d4602ceab2cdeac1b0e | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, reduction='mean', alpha=1.0):
super().__init__()
self.reduction = reduction
self.alpha = alpha
def forward(self, inputs, targets, mask=None):
inputs = inputs.narrow(1, 0, targets.shape[1])
o... |
ActNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
class ActNorm(torch.nn.Module):
def __init__(self, nsq, data_init=True):
super(ActNorm, self).__init__()
self.initialized = not data_init
self.m = torch.nn.Parameter(torch.zeros(1, nsq, 1))
self.logs = torch.nn.Parameter(torch.zeros(1, nsq, 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.utils.data
assert_size_stride = torch._C._dynamo.guards.asse... | entn-at/blow | ActNorm | false | 15,343 | [
"Apache-2.0"
] | 147 | b597286b24c7ea88c8d9408f9aa35aa8df2ebe11 | https://github.com/entn-at/blow/tree/b597286b24c7ea88c8d9408f9aa35aa8df2ebe11 | import torch
import torch.utils.data
class Model(torch.nn.Module):
def __init__(self, nsq, data_init=True):
super().__init__()
self.initialized = not data_init
self.m = torch.nn.Parameter(torch.zeros(1, nsq, 1))
self.logs = torch.nn.Parameter(torch.zeros(1, nsq, 1))
return... |
PosEnc | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchvision.models import *
from torchvision.datasets import *
class PosEnc(nn.Module):
def __init__(self, C, ks):
super().__init__()
self.weight = nn... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchv... | evdcush/ppuda | PosEnc | false | 15,344 | [
"MIT"
] | 262 | 22783ac92207da6730ee618c953af230c5c39f28 | https://github.com/evdcush/ppuda/tree/22783ac92207da6730ee618c953af230c5c39f28 | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchvision.models import *
from torchvision.datasets import *
class Model(nn.Module):
def __init__(self, C, ks):
super().__init__()
self.weight = nn.... |
LearnedUpsampling1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class LearnedUpsampling1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, bias=True):
super().__init__()
self.conv_t = nn.ConvTranspose1d(in_channels=in_channels,
out_channels=out_channels, kernel_size=kernel_size, 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | fdb/samplernn-pytorch | LearnedUpsampling1d | false | 15,345 | [
"MIT"
] | 259 | 87ce71cc2cf26601a271648597f198df33059f96 | https://github.com/fdb/samplernn-pytorch/tree/87ce71cc2cf26601a271648597f198df33059f96 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, bias=True):
super().__init__()
self.conv_t = nn.ConvTranspose1d(in_channels=in_channels,
out_channels=out_channels, kernel_size=kernel_size, stride=
kernel_... |
MinibatchStdDev | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.cpp_extension
class MinibatchStdDev(torch.nn.Module):
def __init__(self, group_size, num_channels=1):
super().__init__()
self.group_size = group_size
self.num_channels = num_channels
def forward(self, x):
N, C, H, W = x.shape
G = self.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.triton_helpers import libdevice
import torch.utils.cpp_extension
assert_size_stride = torch._C._dynamo.guards.a... | STomoya/animeface | MinibatchStdDev | false | 15,346 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 | import torch
import torch.utils.cpp_extension
class Model(torch.nn.Module):
def __init__(self, group_size, num_channels=1):
super().__init__()
self.group_size = group_size
self.num_channels = num_channels
def forward(self, x):
N, C, H, W = x.shape
G = self.group_size ... |
SimpleFusionGenerator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class SimpleFusionGenerator(nn.Module):
def __init__(self, decoder_input_size, lm_input_size, output_size):
super(SimpleFusionGenerator, self).__init__()
self.decoder_linear = nn.Linear(decoder_input_size, output_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.... | fangleai/encoder-agnostic-adaptation | SimpleFusionGenerator | false | 15,347 | [
"MIT"
] | 70 | d917e654152df202dd35bba49c409c3ecd24eaf7 | https://github.com/fangleai/encoder-agnostic-adaptation/tree/d917e654152df202dd35bba49c409c3ecd24eaf7 | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class Model(nn.Module):
def __init__(self, decoder_input_size, lm_input_size, output_size):
super().__init__()
self.decoder_linear = nn.Linear(decoder_input_size, output_size)
self.lm_linear = nn.Linear(lm_input... |
PointwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class PointwiseFeedForward(nn.Module):
"""
A two-feed-forward-layer module
"""
def __init__(self, d_hid, d_inner_hid=None, d_out=None, dropout=0):
super(PointwiseFeedForward, self).__init__()
if d_inner_hid is None:
d_inner_hid = d_hid
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | fhamborg/NewsMTSC | PointwiseFeedForward | false | 15,348 | [
"MIT"
] | 46 | 5a8f88d7fbb921090e984cc378b02d75524c1025 | https://github.com/fhamborg/NewsMTSC/tree/5a8f88d7fbb921090e984cc378b02d75524c1025 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
A two-feed-forward-layer module
"""
def __init__(self, d_hid, d_inner_hid=None, d_out=None, dropout=0):
super().__init__()
if d_inner_hid is None:
d_inner_hid = d_hid
if d_out is None:
d_out... |
Noise | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch.nn as nn
class Noise(nn.Module):
def __init__(self):
super(Noise, self).__init__()
def forward(self, input, train=False):
input = input * 255.0
if train:
noise = torch.nn.init.uniform_(torch.zeros_like(input), -0.5, 0.5)
... | 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.utils.data
impo... | felixcheng97/IICNet | Noise | false | 15,349 | [
"MIT"
] | 50 | 2648d7148c01a03226128c24a285c4a52e2b5aa0 | https://github.com/felixcheng97/IICNet/tree/2648d7148c01a03226128c24a285c4a52e2b5aa0 | import torch
import torch.utils.data
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, train=False):
input = input * 255.0
if train:
noise = torch.nn.init.uniform_(torch.zeros_like(input), -0.5, 0.5)
... |
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