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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
SelectAdaptivePool2d | # 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.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
def adaptive_avgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_pool2d(x, output_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
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn im... | BigFishMaster/tnt | SelectAdaptivePool2d | false | 17,482 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
def adaptive_avgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_pool2d(x, output_size... |
SpatialAttention | # 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 SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv3d(2, 1, kernel_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | JiehuaYang/DLCA | SpatialAttention | false | 17,483 | [
"MIT"
] | 5 | 9f06fe171f6b66e88767a8a9e2246a56373dfe12 | https://github.com/JiehuaYang/DLCA/tree/9f06fe171f6b66e88767a8a9e2246a56373dfe12 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, kernel_size=7):
super().__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv3d(2, 1, kernel_size, padding=padding, bias=False)
... |
UpsamplingBlock | # 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 UpsamplingBlock(nn.Module):
def __init__(self, input_nc, output_nc, kernel, stride, pad):
"""
Single block of upsampling operation
Input:
- int input_nc : Input number of channels
- int output_nc : Output number of channels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Jay2020-01/TextureGAN--Flask | UpsamplingBlock | false | 17,484 | [
"MIT"
] | 5 | cddea505b0d66b58d58fb24435f8bae42fd5a852 | https://github.com/Jay2020-01/TextureGAN--Flask/tree/cddea505b0d66b58d58fb24435f8bae42fd5a852 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_nc, output_nc, kernel, stride, pad):
"""
Single block of upsampling operation
Input:
- int input_nc : Input number of channels
- int output_nc : Output number of channels
- in... |
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.nn as nn
import torch.utils.data
from torch.nn import init
class MLP(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, act=nn.ReLU(),
normalize_input=True):
super(MLP, self).__init__()
self.linear_1 = nn.Linear(input_dim, hidden_dim)
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.... | JiaxuanYou/graph-pooling | MLP | false | 17,485 | [
"MIT"
] | 5 | e6237f03a72ac55d8a10192ca36fa596973461f5 | https://github.com/JiaxuanYou/graph-pooling/tree/e6237f03a72ac55d8a10192ca36fa596973461f5 | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import init
class Model(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, act=nn.ReLU(),
normalize_input=True):
super().__init__()
self.linear_1 = nn.Linear(input_dim, hidden_dim)
self.linear_... |
FM | # 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 FM(nn.Module):
"""Factorization Machine models pairwise (order-2) feature interactions
without linear term and bias.
Input shape
- 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
Output shape
- 2D tensor with shape: ``(batc... | 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... | Jie-Yuan/Deeps | FM | false | 17,486 | [
"MIT"
] | 4 | b4acbb8e16b8ff5d181e70c3b549df0d818d0d76 | https://github.com/Jie-Yuan/Deeps/tree/b4acbb8e16b8ff5d181e70c3b549df0d818d0d76 | import torch
from torch import nn
class Model(nn.Module):
"""Factorization Machine models pairwise (order-2) feature interactions
without linear term and bias.
Input shape
- 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
Output shape
- 2D tensor with shape: ``(b... |
GaussianPolicy | # 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 as tor
from torch import nn
from torch.distributions import Normal
def gauss_weights_init(mu, std):
def init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(mu, std)
return init
class SaveableModel(object):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JimmyMVP/plain_rl | GaussianPolicy | false | 17,487 | [
"MIT"
] | 10 | 4780f05fffb62533a339197b49de487cdc9d9954 | https://github.com/JimmyMVP/plain_rl/tree/4780f05fffb62533a339197b49de487cdc9d9954 | import torch
import torch as tor
from torch import nn
from torch.distributions import Normal
def gauss_weights_init(mu, std):
def init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(mu, std)
return init
class SaveableModel(object):
... |
MultiheadAttention | # 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
import torch.nn as nn
class MultiheadAttention(nn.Module):
def __init__(self, num_heads=4):
super().__init__()
self.num_heads = num_heads
def forward(self, key, query, value):
b, d, n = key.size()
_, _, m = query.size()
_, do, _ = value.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.... | Jiayuan-Gu/policy-refactorization | MultiheadAttention | false | 17,488 | [
"MIT"
] | 6 | c626c598d735d4c08c2c0553da34196b3fba0b6d | https://github.com/Jiayuan-Gu/policy-refactorization/tree/c626c598d735d4c08c2c0553da34196b3fba0b6d | import math
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_heads=4):
super().__init__()
self.num_heads = num_heads
def forward(self, key, query, value):
b, d, n = key.size()
_, _, m = query.size()
_, do, _ = value.size()
key ... |
ActorCriticPPO | # 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 as tor
from torch import nn
from torch.distributions import Normal
def gauss_weights_init(mu, std):
def init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(mu, std)
return init
class SaveableModel(object):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | JimmyMVP/plain_rl | ActorCriticPPO | false | 17,489 | [
"MIT"
] | 10 | 4780f05fffb62533a339197b49de487cdc9d9954 | https://github.com/JimmyMVP/plain_rl/tree/4780f05fffb62533a339197b49de487cdc9d9954 | import torch
import torch as tor
from torch import nn
from torch.distributions import Normal
def gauss_weights_init(mu, std):
def init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(mu, std)
return init
class SaveableModel(object):
... |
ECA | # 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 ECA(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, channel, k_size=3):
super(ECA, self).__init__()
self.avg_poo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Jiannan-Liu/nCoVSegNet | ECA | false | 17,490 | [
"MIT"
] | 5 | 7543e68edff011a7f7b694c97cf0f185d441fd6b | https://github.com/Jiannan-Liu/nCoVSegNet/tree/7543e68edff011a7f7b694c97cf0f185d441fd6b | import torch
import torch.nn as nn
class Model(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, channel, k_size=3):
super().__init__()
self.avg_pool = nn.... |
GraphConvolution | # 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.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.modules.loss
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | JinmiaoChenLab/SEDR | GraphConvolution | false | 17,491 | [
"MIT"
] | 5 | 18616dfe2ecb56e22225ffefe949d353e819a7d8 | https://github.com/JinmiaoChenLab/SEDR/tree/18616dfe2ecb56e22225ffefe949d353e819a7d8 | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.modules.loss
class Model(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_fe... |
InnerProductDecoder | # 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.modules.loss
class InnerProductDecoder(nn.Module):
"""Decoder for using inner product for prediction."""
def __init__(self, dropout, act=torch.sigmoid):
super(InnerProductDecoder, self).__init__()
self.dropout ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.modules.loss
assert_size_stride = torch._C... | JinmiaoChenLab/SEDR | InnerProductDecoder | false | 17,492 | [
"MIT"
] | 5 | 18616dfe2ecb56e22225ffefe949d353e819a7d8 | https://github.com/JinmiaoChenLab/SEDR/tree/18616dfe2ecb56e22225ffefe949d353e819a7d8 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.modules.loss
class Model(nn.Module):
"""Decoder for using inner product for prediction."""
def __init__(self, dropout, act=torch.sigmoid):
super().__init__()
self.dropout = dropout
self.act = act
d... |
CE | # 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 CE(nn.Module):
def __init__(self):
super(CE, self).__init__()
def forward(self, mat1, mat2):
return -torch.mean(mat2 * torch.log(mat1 + 1e-10) + (1 - mat2) *
torch.log(1 - mat1 + 1e-10))
def get_inputs():
return [torch.rand([4, 4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Jiangtong-Li/ZHSIR | CE | false | 17,493 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, mat1, mat2):
return -torch.mean(mat2 * torch.log(mat1 + 1e-10) + (1 - mat2) *
torch.log(1 - mat1 + 1e-10))
def get_inputs():
return [torch.rand([4, 4, 4, 4]),... |
MSE | # 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 MSE(nn.Module):
def __init__(self):
super(MSE, self).__init__()
def forward(self, x_true, x_pred):
return torch.sqrt(torch.mean(torch.pow(x_pred - x_true, 2), dim=-1))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Jiangtong-Li/ZHSIR | MSE | false | 17,494 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x_true, x_pred):
return torch.sqrt(torch.mean(torch.pow(x_pred - x_true, 2), dim=-1))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def ... |
PolicyAHG | # 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 numpy as np
import torch as tor
from torch import nn
class SaveableModel(object):
def save(self, path):
tor.save(self, path)
@classmethod
def load(cls, path):
return tor.load(path)
@classmethod
def load_best(cls, path):
assert os.path.isdir(path)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JimmyMVP/plain_rl | PolicyAHG | false | 17,495 | [
"MIT"
] | 10 | 4780f05fffb62533a339197b49de487cdc9d9954 | https://github.com/JimmyMVP/plain_rl/tree/4780f05fffb62533a339197b49de487cdc9d9954 | import torch
import numpy as np
import torch as tor
from torch import nn
class SaveableModel(object):
def save(self, path):
tor.save(self, path)
@classmethod
def load(cls, path):
return tor.load(path)
@classmethod
def load_best(cls, path):
assert os.path.isdir(path)
... |
PolicySPG | # 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 numpy as np
import torch as tor
from torch import nn
class SaveableModel(object):
def save(self, path):
tor.save(self, path)
@classmethod
def load(cls, path):
return tor.load(path)
@classmethod
def load_best(cls, path):
assert os.path.isdir(path)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JimmyMVP/plain_rl | PolicySPG | false | 17,496 | [
"MIT"
] | 10 | 4780f05fffb62533a339197b49de487cdc9d9954 | https://github.com/JimmyMVP/plain_rl/tree/4780f05fffb62533a339197b49de487cdc9d9954 | import torch
import numpy as np
import torch as tor
from torch import nn
class SaveableModel(object):
def save(self, path):
tor.save(self, path)
@classmethod
def load(cls, path):
return tor.load(path)
@classmethod
def load_best(cls, path):
assert os.path.isdir(path)
... |
_CMT_loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class _CMT_loss(nn.Module):
def __init__(self):
super(_CMT_loss, self).__init__()
self.d = nn.PairwiseDistance()
def forward(self, feat, sematics):
"""
:param feat: features of images or images. bs * d. d is the length of word vector.
... | 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_... | Jiangtong-Li/ZHSIR | _CMT_loss | false | 17,497 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.d = nn.PairwiseDistance()
def forward(self, feat, sematics):
"""
:param feat: features of images or images. bs * d. d is the length of word vector.
:param sematics: ... |
_D3Shape_loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class _D3Shape_loss(nn.Module):
def __init__(self, cp=0.2, cn=10):
super(_D3Shape_loss, self).__init__()
self.alpha = 1 / cp
self.beta = cn
self.gamma = -2.77 / cn
def _d(self, feat1, feat2):
return torch.sum(torch.abs(feat1 - feat2)... | 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... | Jiangtong-Li/ZHSIR | _D3Shape_loss | false | 17,498 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, cp=0.2, cn=10):
super().__init__()
self.alpha = 1 / cp
self.beta = cn
self.gamma = -2.77 / cn
def _d(self, feat1, feat2):
return torch.sum(torch.abs(feat1 - feat2), 1)
def _l(self, d, i... |
GraphConvolution | # 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
from torch.nn.parameter import Parameter
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
from torch.nn.parameter import Parameter
asser... | Jiangtong-Li/ZHSIR | GraphConvolution | false | 17,499 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class Model(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, bias=True):
super().__init__()
self.in_features = in_feat... |
CNN_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
class CNN_attention(nn.Module):
def __init__(self, channel_size):
super(CNN_attention, self).__init__()
self.attention = nn.Conv2d(channel_size, channel_size, kernel_size=1)
self.softmax = nn.Softmax(dim=-1)
self._initialize_weights()
def fo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Jiangtong-Li/ZHSIR | CNN_attention | false | 17,500 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, channel_size):
super().__init__()
self.attention = nn.Conv2d(channel_size, channel_size, kernel_size=1)
self.softmax = nn.Softmax(dim=-1)
self._initialize_weights()
def forward(self, conv_feature):
... |
InnerProductDecoder | # 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.fx
import torch.utils.data
class InnerProductDecoder(torch.nn.Module):
"""The inner product decoder from the `"Variational Graph Auto-Encoders"
<https://arxiv.org/abs/1611.07308>`_ paper
.. math::
\\sigma(\\mathbf{Z}\\mathbf{Z}^{\\top})
where :math:`\\mathbf{Z} \\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
import torch.fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | JinheonBaek/pytorch_geometric | InnerProductDecoder | false | 17,501 | [
"MIT"
] | 4 | dfd32d08a3d8191d6290e53458d4eda515d04fd6 | https://github.com/JinheonBaek/pytorch_geometric/tree/dfd32d08a3d8191d6290e53458d4eda515d04fd6 | import torch
import torch.fx
import torch.utils.data
class Model(torch.nn.Module):
"""The inner product decoder from the `"Variational Graph Auto-Encoders"
<https://arxiv.org/abs/1611.07308>`_ paper
.. math::
\\sigma(\\mathbf{Z}\\mathbf{Z}^{\\top})
where :math:`\\mathbf{Z} \\in \\mathbb{R}^{... |
L2Normalization | # 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 L2Normalization(nn.Module):
def __init__(self):
super(L2Normalization, self).__init__()
def forward(self, x):
div = torch.sqrt(torch.sum(x * x, 1))
x = (x.T / (div + 1e-10)).T
return x
def get_inputs():
return [torch.rand([4, 4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Jiangtong-Li/ZHSIR | L2Normalization | false | 17,502 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
div = torch.sqrt(torch.sum(x * x, 1))
x = (x.T / (div + 1e-10)).T
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs()... |
_DSH_loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class _DSH_loss(nn.Module):
def __init__(self, gamma=1):
super(_DSH_loss, self).__init__()
self.gamma = gamma
self.d = nn.PairwiseDistance()
def forward(self, sk_feat, im_feat, bs, bi):
"""
:param sk_feat: features of sketches. bs * ... | 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_... | Jiangtong-Li/ZHSIR | _DSH_loss | false | 17,503 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, gamma=1):
super().__init__()
self.gamma = gamma
self.d = nn.PairwiseDistance()
def forward(self, sk_feat, im_feat, bs, bi):
"""
:param sk_feat: features of sketches. bs * m.
:param i... |
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 tnn
class Net(tnn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = tnn.Conv2d(3, 6, 5)
self.pool = tnn.MaxPool2d(2, 2)
self.conv2 = tnn.Conv2d(6, 16, 5)
self.fc1 = tnn.Linear(16 * 5 * 5, 120)
self.fc2 = tnn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as tnn
assert... | Jittor/Jittor | Net | false | 17,504 | [
"Apache-2.0"
] | 4 | bc945bae94bded917214b0afe12be6bf5b919dbe | https://github.com/Jittor/Jittor/tree/bc945bae94bded917214b0afe12be6bf5b919dbe | import torch
import torch.nn as tnn
class Model(tnn.Module):
def __init__(self):
super().__init__()
self.conv1 = tnn.Conv2d(3, 6, 5)
self.pool = tnn.MaxPool2d(2, 2)
self.conv2 = tnn.Conv2d(6, 16, 5)
self.fc1 = tnn.Linear(16 * 5 * 5, 120)
self.fc2 = tnn.Linear(120, ... |
IdentityMessage | # 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.fx
import torch.utils.data
class IdentityMessage(torch.nn.Module):
def __init__(self, raw_msg_dim: 'int', memory_dim: 'int', time_dim: 'int'):
super(IdentityMessage, self).__init__()
self.out_channels = raw_msg_dim + 2 * memory_dim + time_dim
def forward(self, z_src... | 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.fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | JinheonBaek/pytorch_geometric | IdentityMessage | false | 17,505 | [
"MIT"
] | 4 | dfd32d08a3d8191d6290e53458d4eda515d04fd6 | https://github.com/JinheonBaek/pytorch_geometric/tree/dfd32d08a3d8191d6290e53458d4eda515d04fd6 | import torch
import torch.fx
import torch.utils.data
class Model(torch.nn.Module):
def __init__(self, raw_msg_dim: 'int', memory_dim: 'int', time_dim: 'int'):
super().__init__()
self.out_channels = raw_msg_dim + 2 * memory_dim + time_dim
def forward(self, z_src, z_dst, raw_msg, t_enc):
... |
HardSwish | # 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.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
def hard_swish(x, inplace: 'bool'=False):
inner = F.relu6(x + 3.0).div_(6.0)
return x.mul_(inner) if inplace else x.mul(inner)
class H... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torc... | BigFishMaster/tnt | HardSwish | false | 17,506 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
def hard_swish(x, inplace: 'bool'=False):
inner = F.relu6(x + 3.0).div_(6.0)
return x.mul_(inner) if inplace else x.mul(inner)
class M... |
MessageNorm | # 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 Tensor
import torch.nn.functional as F
from torch.nn import Parameter
import torch.fx
import torch.utils.data
from inspect import Parameter
from torch.nn.parameter import Parameter
class MessageNorm(torch.nn.Module):
"""Applies message normalization over the aggregated messages as d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Paramet... | JinheonBaek/pytorch_geometric | MessageNorm | false | 17,507 | [
"MIT"
] | 4 | dfd32d08a3d8191d6290e53458d4eda515d04fd6 | https://github.com/JinheonBaek/pytorch_geometric/tree/dfd32d08a3d8191d6290e53458d4eda515d04fd6 | import torch
from torch import Tensor
import torch.nn.functional as F
from torch.nn import Parameter
import torch.fx
import torch.utils.data
from inspect import Parameter
from torch.nn.parameter import Parameter
class Model(torch.nn.Module):
"""Applies message normalization over the aggregated messages as describ... |
Attention | # 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
import torch.nn.functional as F
import torch.fx
import torch.utils.data
def restricted_softmax(src, dim: 'int'=-1, margin: 'float'=0.0):
src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0.0)
out = (src - src_max).exp()
out = out / (out.sum(dim=dim, keepdim=True) + (mar... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JinheonBaek/pytorch_geometric | Attention | false | 17,508 | [
"MIT"
] | 4 | dfd32d08a3d8191d6290e53458d4eda515d04fd6 | https://github.com/JinheonBaek/pytorch_geometric/tree/dfd32d08a3d8191d6290e53458d4eda515d04fd6 | import math
import torch
import torch.nn.functional as F
import torch.fx
import torch.utils.data
def restricted_softmax(src, dim: 'int'=-1, margin: 'float'=0.0):
src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0.0)
out = (src - src_max).exp()
out = out / (out.sum(dim=dim, keepdim=True) + (mar... |
MaxPool2dDynamicSamePadding | # 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
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
class MaxPool2dDynamicSamePadding(nn.MaxPool2d):
"""2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image 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 import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | BigFishMaster/tnt | MaxPool2dDynamicSamePadding | false | 17,509 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
class Model(nn.MaxPool2d):
"""2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size.
The padding is oper... |
StdConv2d | # 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
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
class StdConv2d(nn.Conv2d):
def forward(self, x):
w = self.weight
s = w.std(dim=[1, 2, 3], keepdim=True)
m = w.mean... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | BigFishMaster/tnt | StdConv2d | false | 17,510 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
class Model(nn.Conv2d):
def forward(self, x):
w = self.weight
s = w.std(dim=[1, 2, 3], keepdim=True)
m = w.mean(dim... |
ShiftedSoftplus | # 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
import torch.fx
import torch.utils.data
class ShiftedSoftplus(torch.nn.Module):
def __init__(self):
super(ShiftedSoftplus, self).__init__()
self.shift = torch.log(torch.tensor(2.0)).item()
def forward(self, x):
return F.softplus(x) - 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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.fx
import torch.utils.data
assert_size_stride = t... | JinheonBaek/pytorch_geometric | ShiftedSoftplus | false | 17,511 | [
"MIT"
] | 4 | dfd32d08a3d8191d6290e53458d4eda515d04fd6 | https://github.com/JinheonBaek/pytorch_geometric/tree/dfd32d08a3d8191d6290e53458d4eda515d04fd6 | import torch
import torch.nn.functional as F
import torch.fx
import torch.utils.data
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.shift = torch.log(torch.tensor(2.0)).item()
def forward(self, x):
return F.softplus(x) - self.shift
def get_inputs():
r... |
Hidden2Normal | # 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
class Hidden2Normal(torch.nn.Module):
def __init__(self, hidden_dim):
super(Hidden2Normal, self).__init__()
self.linear = torch.nn.Linear(hidden_dim, 5)
def forward(self, hidden_state):
normal = self.linear(hidden_state)
normal[:, 2] = 0.01 + 0.2 * torch.sigmoid(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | JosephGesnouin/Asymmetrical-Bi-RNNs-to-encode-pedestrian-trajectories | Hidden2Normal | false | 17,512 | [
"MIT"
] | 9 | 488924e938fc1674b5a0d2cb9f05178cad8de561 | https://github.com/JosephGesnouin/Asymmetrical-Bi-RNNs-to-encode-pedestrian-trajectories/tree/488924e938fc1674b5a0d2cb9f05178cad8de561 | import torch
class Model(torch.nn.Module):
def __init__(self, hidden_dim):
super().__init__()
self.linear = torch.nn.Linear(hidden_dim, 5)
def forward(self, hidden_state):
normal = self.linear(hidden_state)
normal[:, 2] = 0.01 + 0.2 * torch.sigmoid(normal[:, 2])
norma... |
Envelope | # 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.fx
import torch.utils.data
class Envelope(torch.nn.Module):
def __init__(self, exponent):
super(Envelope, self).__init__()
self.p = exponent + 1
self.a = -(self.p + 1) * (self.p + 2) / 2
self.b = self.p * (self.p + 2)
self.c = -self.p * (self.p + ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | JinheonBaek/pytorch_geometric | Envelope | false | 17,513 | [
"MIT"
] | 4 | dfd32d08a3d8191d6290e53458d4eda515d04fd6 | https://github.com/JinheonBaek/pytorch_geometric/tree/dfd32d08a3d8191d6290e53458d4eda515d04fd6 | import torch
import torch.fx
import torch.utils.data
class Model(torch.nn.Module):
def __init__(self, exponent):
super().__init__()
self.p = exponent + 1
self.a = -(self.p + 1) * (self.p + 2) / 2
self.b = self.p * (self.p + 2)
self.c = -self.p * (self.p + 1) / 2
def f... |
LinearSQ | # 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
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import functional as F
class LinearSQ(nn.Module):
__constants__ = ['in_features', 'out_features']
in_features: 'int'
out_features: 'int'
weight: 'Tensor'
def __init__(sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import Tensor
import torch.nn as nn
from torch.nn.paramet... | June01/WFSAL-icmr21 | LinearSQ | false | 17,514 | [
"MIT"
] | 9 | 86fd6e9e34483ea17e088e4c1ee8f66edf3aecce | https://github.com/June01/WFSAL-icmr21/tree/86fd6e9e34483ea17e088e4c1ee8f66edf3aecce | import math
import torch
from torch import Tensor
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import functional as F
class Model(nn.Module):
__constants__ = ['in_features', 'out_features']
in_features: 'int'
out_features: 'int'
weight: 'Tensor'
def __init__(self, ... |
MyAdd | # 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.nn.parallel
import torch.optim
import torch.utils.data
class MyAdd(nn.Module):
def __init__(self, size):
super(MyAdd, self).__init__()
self.weight = nn.Parameter(torch.rand(size))
def forward(self, x):
out = x + self.weight
retur... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert... | JurijsNazarovs/bayesian_nn | MyAdd | false | 17,515 | [
"MIT"
] | 6 | 936bf55e0a1e620504d5159c100a74493bd16399 | https://github.com/JurijsNazarovs/bayesian_nn/tree/936bf55e0a1e620504d5159c100a74493bd16399 | import torch
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class Model(nn.Module):
def __init__(self, size):
super().__init__()
self.weight = nn.Parameter(torch.rand(size))
def forward(self, x):
out = x + self.weight
return out
def... |
MetricCELoss | # 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
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
class MetricCELoss(nn.Module):
""" Cross-entropy loss for metric learning with a specified feature size.
In addition, there exists a ReL... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | BigFishMaster/tnt | MetricCELoss | false | 17,516 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
class Model(nn.Module):
""" Cross-entropy loss for metric learning with a specified feature size.
In addition, there exists a ReLU layer... |
CosineLinear | # 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 math
import torch
from torch.nn.parameter import Parameter
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torch.nn.modules.module import Module
class CosineLinear(Module):
def __i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JosephKJ/class-incremental-learning | CosineLinear | false | 17,517 | [
"MIT"
] | 8 | 689271b84f2e553930ca6687d036ac99bd84b311 | https://github.com/JosephKJ/class-incremental-learning/tree/689271b84f2e553930ca6687d036ac99bd84b311 | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torch.nn.modules.module import Module
class Model(Module):
def __init__(s... |
Conv2dMtl | # 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 math
import torch
from torch.nn.parameter import Parameter
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torch.nn.modules.module import Module
from torch.nn.modules.utils import _pair
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import math
from torch.nn.parameter import Parameter... | JosephKJ/class-incremental-learning | Conv2dMtl | false | 17,518 | [
"MIT"
] | 8 | 689271b84f2e553930ca6687d036ac99bd84b311 | https://github.com/JosephKJ/class-incremental-learning/tree/689271b84f2e553930ca6687d036ac99bd84b311 | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torch.nn.modules.module import Module
from torch.nn.modules.utils import _pair
... |
MyMul | # 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.nn.parallel
import torch.optim
import torch.utils.data
class MyMul(nn.Module):
def __init__(self, size):
super(MyMul, self).__init__()
self.weight = nn.Parameter(torch.rand(1))
def forward(self, x):
out = x * torch.abs(self.weight)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.nn.parallel
import torch.optim
import t... | JurijsNazarovs/bayesian_nn | MyMul | false | 17,519 | [
"MIT"
] | 6 | 936bf55e0a1e620504d5159c100a74493bd16399 | https://github.com/JurijsNazarovs/bayesian_nn/tree/936bf55e0a1e620504d5159c100a74493bd16399 | import torch
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class Model(nn.Module):
def __init__(self, size):
super().__init__()
self.weight = nn.Parameter(torch.rand(1))
def forward(self, x):
out = x * torch.abs(self.weight)
return o... |
SplitCosineLinear | # 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 math
import torch
from torch.nn.parameter import Parameter
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torch.nn.modules.module import Module
class CosineLinear(Module):
def __i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JosephKJ/class-incremental-learning | SplitCosineLinear | false | 17,520 | [
"MIT"
] | 8 | 689271b84f2e553930ca6687d036ac99bd84b311 | https://github.com/JosephKJ/class-incremental-learning/tree/689271b84f2e553930ca6687d036ac99bd84b311 | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torch.nn.modules.module import Module
class CosineLinear(Module):
def __i... |
SimpleSSM | # 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
class MatrixMultiplication(nn.Module):
"""
batch operation supporting matrix multiplication layer
"""
def __init__(self, in_features: 'int', out_features: 'int'):
super(MatrixMultiplication, self).__init__()
self.in_features = in_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | Junyoungpark/2021-lg-AI-camp | SimpleSSM | false | 17,521 | [
"MIT"
] | 4 | 3c0e5dd689e8e3dd61cc80243ad90cab951c06de | https://github.com/Junyoungpark/2021-lg-AI-camp/tree/3c0e5dd689e8e3dd61cc80243ad90cab951c06de | import math
import torch
import torch.nn as nn
class MatrixMultiplication(nn.Module):
"""
batch operation supporting matrix multiplication layer
"""
def __init__(self, in_features: 'int', out_features: 'int'):
super().__init__()
self.in_features = in_features
self.out_feat... |
SPPblock | # 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 SPPblock(nn.Module):
def __init__(self, in_channels):
super(SPPblock, self).__init__()
self.pool1 = nn.MaxPool2d(kernel_size=[2, 2], stride=2)
self.pool2 = nn.MaxPool2d(kernel_size=[3, 3], stride=3)
self.pool... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | JACKYLUO1991/HybridNet | SPPblock | false | 17,522 | [
"Apache-2.0"
] | 6 | eb97d8a048ca4bb4087bc542360172e169a08dbf | https://github.com/JACKYLUO1991/HybridNet/tree/eb97d8a048ca4bb4087bc542360172e169a08dbf | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.pool1 = nn.MaxPool2d(kernel_size=[2, 2], stride=2)
self.pool2 = nn.MaxPool2d(kernel_size=[3, 3], stride=3)
self.pool3 = nn.MaxPool2d(... |
group | # 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 mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, type=1):
super(mfm, self).__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv2d(in_channels, 2 * out_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.nn as nn
assert_... | JunhongH/CP-GAN | group | false | 17,523 | [
"Apache-2.0"
] | 9 | 5ac129da8cf6d010dc0da03bb4637d20c822d50b | https://github.com/JunhongH/CP-GAN/tree/5ac129da8cf6d010dc0da03bb4637d20c822d50b | import torch
import torch.nn as nn
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, type=1):
super().__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv2d(in_channels, 2 * out_channels,
... |
resblock | # 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 mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, type=1):
super(mfm, self).__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv2d(in_channels, 2 * out_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.nn as nn
assert_... | JunhongH/CP-GAN | resblock | false | 17,524 | [
"Apache-2.0"
] | 9 | 5ac129da8cf6d010dc0da03bb4637d20c822d50b | https://github.com/JunhongH/CP-GAN/tree/5ac129da8cf6d010dc0da03bb4637d20c822d50b | import torch
import torch.nn as nn
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, type=1):
super().__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv2d(in_channels, 2 * out_channels,
... |
mfm | # 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 mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, type=1):
super(mfm, self).__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv2d(in_channels, 2 * out_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.nn as nn
assert_... | JunhongH/CP-GAN | mfm | false | 17,525 | [
"Apache-2.0"
] | 9 | 5ac129da8cf6d010dc0da03bb4637d20c822d50b | https://github.com/JunhongH/CP-GAN/tree/5ac129da8cf6d010dc0da03bb4637d20c822d50b | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, type=1):
super().__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv2d(in_channels, 2 * out_channels... |
Simulator | # 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
class MatrixMultiplication(nn.Module):
"""
batch operation supporting matrix multiplication layer
"""
def __init__(self, in_features: 'int', out_features: 'int'):
super(MatrixMultiplication, self).__init__()
self.in_features = in_feat... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = ... | Junyoungpark/2021-lg-AI-camp | Simulator | false | 17,526 | [
"MIT"
] | 4 | 3c0e5dd689e8e3dd61cc80243ad90cab951c06de | https://github.com/Junyoungpark/2021-lg-AI-camp/tree/3c0e5dd689e8e3dd61cc80243ad90cab951c06de | import math
import torch
import torch.nn as nn
class MatrixMultiplication(nn.Module):
"""
batch operation supporting matrix multiplication layer
"""
def __init__(self, in_features: 'int', out_features: 'int'):
super().__init__()
self.in_features = in_features
self.out_feat... |
GaussianLayer | # 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 GaussianLayer(nn.Module):
def __init__(self, input_dim, output_dim):
super(GaussianLayer, self).__init__()
self.z_mu = torch.nn.Linear(input_dim, output_dim)
self.z_sigma = torch.nn.Linear(input_dim, output_dim)
def forward(self, x):
m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Junyoungpark/2021-lg-AI-camp | GaussianLayer | false | 17,527 | [
"MIT"
] | 4 | 3c0e5dd689e8e3dd61cc80243ad90cab951c06de | https://github.com/Junyoungpark/2021-lg-AI-camp/tree/3c0e5dd689e8e3dd61cc80243ad90cab951c06de | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim, output_dim):
super().__init__()
self.z_mu = torch.nn.Linear(input_dim, output_dim)
self.z_sigma = torch.nn.Linear(input_dim, output_dim)
def forward(self, x):
mu = self.z_mu(x)
st... |
BiaffineScorer | # 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 BiaffineScorer(nn.Module):
def __init__(self, input1_size, input2_size, output_size):
super().__init__()
self.W_bilin = nn.Bilinear(input1_size + 1, input2_size + 1,
output_size)
self.W_bilin.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
import torch.nn as nn
import torch.cuda
import torch.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | KaijuML/dtt-multi-branch | BiaffineScorer | false | 17,528 | [
"Apache-2.0"
] | 8 | a49850a95034e58d387b9d48c647cfc2b83c45b5 | https://github.com/KaijuML/dtt-multi-branch/tree/a49850a95034e58d387b9d48c647cfc2b83c45b5 | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class Model(nn.Module):
def __init__(self, input1_size, input2_size, output_size):
super().__init__()
self.W_bilin = nn.Bilinear(input1_size + 1, input2_size + 1,
output_size)
self.W_bilin.weight.dat... |
FCDiscriminator | # 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 FCDiscriminator(nn.Module):
def __init__(self, num_classes, ndf=64):
super(FCDiscriminator, self).__init__()
self.conv1 = nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2,
padding=1)
self.conv2 = nn.Conv2d(ndf, ndf * 2, kernel_size=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 import nn
assert_s... | JohanVer/heatnet | FCDiscriminator | false | 17,529 | [
"MIT"
] | 7 | a2de9ec918fbbc6d9433aba344cbbcb2a2cdc85e | https://github.com/JohanVer/heatnet/tree/a2de9ec918fbbc6d9433aba344cbbcb2a2cdc85e | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, num_classes, ndf=64):
super().__init__()
self.conv1 = nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2,
padding=1)
self.conv2 = nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1
... |
_GLUBlock | # 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 _GLUBlock(nn.Module):
def __init__(self, n_c_in, n_c_out):
super(_GLUBlock, self).__init__()
self.pad = nn.ConstantPad1d((1, 2), 0)
self.conv_data = nn.Conv1d(n_c_in, n_c_out, 4, stride=1, bias=True)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | KaibinBao/neuralnilm-pytorch | _GLUBlock | false | 17,530 | [
"Apache-2.0"
] | 4 | 017b85fc921f0638f93a0e16f615028f60b7d279 | https://github.com/KaibinBao/neuralnilm-pytorch/tree/017b85fc921f0638f93a0e16f615028f60b7d279 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Model(nn.Module):
def __init__(self, n_c_in, n_c_out):
super().__init__()
self.pad = nn.ConstantPad1d((1, 2), 0)
self.conv_data = nn.Conv1d(n_c_in, n_c_out, 4, stride=1, bias=True)
self.conv_... |
LSTM | # 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 LSTM(nn.Module):
def __init__(self, input_size, cell_size, hidden_size):
"""
cell_size is the size of cell_state.
hidden_size is the size of hidden_state, or say the output_state of each step
"""
supe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Kelang-Tian/ST-MGAT | LSTM | false | 17,531 | [
"MIT"
] | 8 | f527cb5748d022d9c3b4eddd3481cf641bb0dae3 | https://github.com/Kelang-Tian/ST-MGAT/tree/f527cb5748d022d9c3b4eddd3481cf641bb0dae3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_size, cell_size, hidden_size):
"""
cell_size is the size of cell_state.
hidden_size is the size of hidden_state, or say the output_state of each step
"""
sup... |
AverageAttention | # 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 PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
Args:
d_model (int): the size of input for the first-layer of the FFN.
d_ff (int): the hidden layer size of th... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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_str... | KaijuML/PARENTing-rl | AverageAttention | false | 17,532 | [
"Apache-2.0"
] | 8 | 98d20e1899e0ff3a9a7a6bb3e50ec28ff0b3b700 | https://github.com/KaijuML/PARENTing-rl/tree/98d20e1899e0ff3a9a7a6bb3e50ec28ff0b3b700 | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
Args:
d_model (int): the size of input for the first-layer of the FFN.
d_ff (int): the hidden layer size of th... |
StableBCELoss | # 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
class StableBCELoss(torch.nn.modules.Module):
def __init__(self):
super(StableBCELoss, self).__init__()
def forward(self, input, target):
neg_abs = -input.abs()
loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
return loss.mean()
def get_in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | KeremTurgutlu/fast-kaggle | StableBCELoss | false | 17,533 | [
"Apache-2.0"
] | 8 | 0ea341b44a58da2dfb606a0ae32bac166985b49e | https://github.com/KeremTurgutlu/fast-kaggle/tree/0ea341b44a58da2dfb606a0ae32bac166985b49e | import torch
class Model(torch.nn.modules.Module):
def __init__(self):
super().__init__()
def forward(self, input, target):
neg_abs = -input.abs()
loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
return loss.mean()
def get_inputs():
return [torch.r... |
TimeBlock | # 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 TimeBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
"""
:param in_channels: Number of input features at each node in each time
step.
:param out_channels: Desired number of outp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Kelang-Tian/ST-MGAT | TimeBlock | false | 17,534 | [
"MIT"
] | 8 | f527cb5748d022d9c3b4eddd3481cf641bb0dae3 | https://github.com/Kelang-Tian/ST-MGAT/tree/f527cb5748d022d9c3b4eddd3481cf641bb0dae3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
"""
:param in_channels: Number of input features at each node in each time
step.
:param out_channels: Desired number of output c... |
AUXModule | # 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
import torch.nn as nn
class AUXModule(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.linear = nn.Linear(in_features, out_features)
def forward(self, x):
x = F.adaptive_max_pool2d(x, output_size=(1, 1))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | KeremTurgutlu/fast-kaggle | AUXModule | false | 17,535 | [
"Apache-2.0"
] | 8 | 0ea341b44a58da2dfb606a0ae32bac166985b49e | https://github.com/KeremTurgutlu/fast-kaggle/tree/0ea341b44a58da2dfb606a0ae32bac166985b49e | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.linear = nn.Linear(in_features, out_features)
def forward(self, x):
x = F.adaptive_max_pool2d(x, output_size=(1, 1))
... |
Generator | # 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 Generator(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, d_model, vocab):
super(Generator, self).__init__()
self.proj = nn.Linear(d_model, vocab)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | KimGroup/AQT | Generator | false | 17,536 | [
"MIT"
] | 4 | b3440f04c1fb4cb44c30569bc6bf07103ac2553c | https://github.com/KimGroup/AQT/tree/b3440f04c1fb4cb44c30569bc6bf07103ac2553c | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, d_model, vocab):
super().__init__()
self.proj = nn.Linear(d_model, vocab)
def forward(self, x):
return F.log_softm... |
RewardCriterion | # 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
import torch.nn.init
class RewardCriterion(nn.Module):
def __init__(self):
super(RewardCriterion, self).__init__()
def forward(self, input, seq, reward):
input = input.contiguous().view(-1)
reward = reward.contiguous().view(-1)
mask = (seq > ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyn... | KunpengLi1994/PsTuts | RewardCriterion | false | 17,537 | [
"Apache-2.0"
] | 4 | 2063bf0aac8d3fd13bf5a14b80ce05586b8365f9 | https://github.com/KunpengLi1994/PsTuts/tree/2063bf0aac8d3fd13bf5a14b80ce05586b8365f9 | import torch
from torch import nn
import torch.nn.init
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, seq, reward):
input = input.contiguous().view(-1)
reward = reward.contiguous().view(-1)
mask = (seq > 0).float()
mask = torch... |
LandmarkHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from itertools import product as product
import torch.nn as nn
class LandmarkHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(LandmarkHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=
(1, 1), stride=1, padd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from itertools import product as product
import torch.nn as nn
assert_size_strid... | Jung-Jun-Uk/mixface | LandmarkHead | false | 17,538 | [
"MIT"
] | 10 | cee17f99d5e22bf962d9bccbda44a57ab8493173 | https://github.com/Jung-Jun-Uk/mixface/tree/cee17f99d5e22bf962d9bccbda44a57ab8493173 | import torch
from itertools import product as product
import torch.nn as nn
class Model(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super().__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=
(1, 1), stride=1, padding=0)
def forward(s... |
ScaledDotProductAttention | # 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 numpy as np
from torch import nn
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries and keys... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Kilichbek/artemis-m2-transformer | ScaledDotProductAttention | false | 17,539 | [
"MIT"
] | 8 | 99f7e797965710bf2565283d6b5028a6fe32664c | https://github.com/Kilichbek/artemis-m2-transformer/tree/99f7e797965710bf2565283d6b5028a6fe32664c | import torch
import numpy as np
from torch import nn
class Model(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries and keys
:param d_v:... |
ClassHead | # 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 itertools import product as product
import torch.nn as nn
class ClassHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(ClassHead, self).__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from itertools import product as product
import torch.nn as nn
assert_size_strid... | Jung-Jun-Uk/UNPG | ClassHead | false | 17,540 | [
"Apache-2.0"
] | 7 | a6f9c1731a68fc035eb8fe8198f5a5c643825a5b | https://github.com/Jung-Jun-Uk/UNPG/tree/a6f9c1731a68fc035eb8fe8198f5a5c643825a5b | import torch
from itertools import product as product
import torch.nn as nn
class Model(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super().__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2,
kernel_size=(1, 1... |
TransformerNet | # 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.nn import functional as F
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JEF1056/Reconstruction-Style | TransformerNet | false | 17,541 | [
"MIT"
] | 6 | 3430d9e9f05c6980ae251cf15b619148a2c899d6 | https://github.com/JEF1056/Reconstruction-Style/tree/3430d9e9f05c6980ae251cf15b619148a2c899d6 | import torch
from torch.nn import functional as F
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super().__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.... |
BboxHead | # 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 itertools import product as product
import torch.nn as nn
class BboxHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(BboxHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(
1, 1), stride=1, padding=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
from itertools import product as product
import torch.nn as nn
assert_size_strid... | Jung-Jun-Uk/UNPG | BboxHead | false | 17,542 | [
"Apache-2.0"
] | 7 | a6f9c1731a68fc035eb8fe8198f5a5c643825a5b | https://github.com/Jung-Jun-Uk/UNPG/tree/a6f9c1731a68fc035eb8fe8198f5a5c643825a5b | import torch
from itertools import product as product
import torch.nn as nn
class Model(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super().__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(
1, 1), stride=1, padding=0)
def forward(se... |
TransformerNet | # 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 functools
import torch
def get_norm_layer(norm_type='instance', affine_state=True):
if norm_type == 'batch':
norm_layer = functools.partial(torch.nn.BatchNorm2d, affine=True)
elif norm_type == 'instance':
norm_layer = functools.partial(torch.nn.InstanceNorm2d, affine=
affine... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JunhongH/CP-GAN | TransformerNet | false | 17,543 | [
"Apache-2.0"
] | 9 | 5ac129da8cf6d010dc0da03bb4637d20c822d50b | https://github.com/JunhongH/CP-GAN/tree/5ac129da8cf6d010dc0da03bb4637d20c822d50b | import functools
import torch
def get_norm_layer(norm_type='instance', affine_state=True):
if norm_type == 'batch':
norm_layer = functools.partial(torch.nn.BatchNorm2d, affine=True)
elif norm_type == 'instance':
norm_layer = functools.partial(torch.nn.InstanceNorm2d, affine=
affine... |
Highway | # 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 Highway(nn.Module):
def __init__(self, in_features, out_features):
"""
inputs: [N, T, C]
outputs: [N, T, C]
"""
super().__init__()
self.linear1 = nn.Linear(in_features, out_features)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | KinglittleQ/Tacotron | Highway | false | 17,544 | [
"MIT"
] | 6 | d43c0c4e5b91029ffae0f96d69a1d3b3106d49c5 | https://github.com/KinglittleQ/Tacotron/tree/d43c0c4e5b91029ffae0f96d69a1d3b3106d49c5 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_features, out_features):
"""
inputs: [N, T, C]
outputs: [N, T, C]
"""
super().__init__()
self.linear1 = nn.Linear(in_features, out_features)
sel... |
Conv1d | # 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 Conv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding='same'):
"""
inputs: [N, T, C_in]
outputs: [N, T, C_out]
"""
super().__init__()
if paddi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | KinglittleQ/Tacotron | Conv1d | false | 17,545 | [
"MIT"
] | 6 | d43c0c4e5b91029ffae0f96d69a1d3b3106d49c5 | https://github.com/KinglittleQ/Tacotron/tree/d43c0c4e5b91029ffae0f96d69a1d3b3106d49c5 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding='same'):
"""
inputs: [N, T, C_in]
outputs: [N, T, C_out]
"""
super().__init__()
if paddin... |
LinearFeedforward | # 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 Linear(nn.Linear):
def forward(self, x):
size = x.size()
return super().forward(x.contiguous().view(-1, size[-1])).view(*
size[:-1], -1)
class Feedforward(nn.Module):
def __init__(self, d_in, d_out, activation=Non... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Krish-sysadmin/genienlp | LinearFeedforward | false | 17,546 | [
"BSD-3-Clause"
] | 6 | 3586e4368eb0b0756a772294daedc043ce55454c | https://github.com/Krish-sysadmin/genienlp/tree/3586e4368eb0b0756a772294daedc043ce55454c | import torch
import torch.nn as nn
import torch.utils.data
class Linear(nn.Linear):
def forward(self, x):
size = x.size()
return super().forward(x.contiguous().view(-1, size[-1])).view(*
size[:-1], -1)
class Feedforward(nn.Module):
def __init__(self, d_in, d_out, activation=Non... |
vgg11_modified | # 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 vgg11_modified(nn.Module):
def __init__(self, num_classes=20):
super(vgg11_modified, self).__init__()
self.num_classes = num_classes
self.pad = nn.ReflectionPad2d((1, 1, 1, 1))
self.pool = nn.MaxPool2d((2, 2)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JonGant/FoveatedTextureTransform | vgg11_modified | false | 17,547 | [
"MIT"
] | 4 | a3bad4abdb0a61e038cfe3602ef568dfea1a6127 | https://github.com/JonGant/FoveatedTextureTransform/tree/a3bad4abdb0a61e038cfe3602ef568dfea1a6127 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_classes=20):
super().__init__()
self.num_classes = num_classes
self.pad = nn.ReflectionPad2d((1, 1, 1, 1))
self.pool = nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=T... |
SFCN | # 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 SFCN(nn.Module):
def __init__(self):
super(SFCN, self).__init__()
cnn = nn.Sequential()
input_c = [3, 18, 18]
padding = [3, 3, 6]
dilation = [1, 1, 2]
for i in range(3):
cnn.add_module('sfcn{}'.format(i), nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | L597383845/row-col-table-recognition | SFCN | false | 17,548 | [
"MIT"
] | 7 | 617718751861b3f4e35a4b34dde4c898575e6818 | https://github.com/L597383845/row-col-table-recognition/tree/617718751861b3f4e35a4b34dde4c898575e6818 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
cnn = nn.Sequential()
input_c = [3, 18, 18]
padding = [3, 3, 6]
dilation = [1, 1, 2]
for i in range(3):
cnn.add_module('sfcn{}'.format(i), nn.Conv2d(input_... |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.init
class Attention(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
"""
def __init__(self, dim):
super(Attention, self).__init__()
self.dim = dim
self.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.... | KunpengLi1994/PsTuts | Attention | false | 17,549 | [
"Apache-2.0"
] | 4 | 2063bf0aac8d3fd13bf5a14b80ce05586b8365f9 | https://github.com/KunpengLi1994/PsTuts/tree/2063bf0aac8d3fd13bf5a14b80ce05586b8365f9 | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.init
class Model(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
"""
def __init__(self, dim):
super().__init__()
self.dim = dim
self.linear1 = nn.Linear(di... |
ScaledDotProductAttentionMemory | # 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 numpy as np
from torch import nn
class ScaledDotProductAttentionMemory(nn.Module):
"""
Scaled dot-product attention with memory
"""
def __init__(self, d_model, d_k, d_v, h, m):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionalit... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Kilichbek/artemis-m2-transformer | ScaledDotProductAttentionMemory | false | 17,550 | [
"MIT"
] | 8 | 99f7e797965710bf2565283d6b5028a6fe32664c | https://github.com/Kilichbek/artemis-m2-transformer/tree/99f7e797965710bf2565283d6b5028a6fe32664c | import torch
import numpy as np
from torch import nn
class Model(nn.Module):
"""
Scaled dot-product attention with memory
"""
def __init__(self, d_model, d_k, d_v, h, m):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries and keys
... |
CNN_Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
class CNN_Net(nn.Module):
def __init__(self, device=None):
super(CNN_Net, self).__init__()
self.conv1 = nn.Conv2d(1, 64, 3, 1)
self.conv2 = nn.Conv2d(64, 16, 7, 1)
self.fc1 = nn.Linear(4 * 4 * 16, 200)
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.... | Koukyosyumei/NAIST-Experiments | CNN_Net | false | 17,551 | [
"Apache-2.0"
] | 4 | 2795f6d7f59e7881ba4fe08a37881b8c2b7b4498 | https://github.com/Koukyosyumei/NAIST-Experiments/tree/2795f6d7f59e7881ba4fe08a37881b8c2b7b4498 | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, device=None):
super().__init__()
self.conv1 = nn.Conv2d(1, 64, 3, 1)
self.conv2 = nn.Conv2d(64, 16, 7, 1)
self.fc1 = nn.Linear(4 * 4 * 16, 200)
self.fc2 = nn.Linear... |
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 Tensor
from torch.nn import Parameter
from torch.nn import LayerNorm
from typing import Optional
import torch.fx
from typing import Any
import torch.utils.data
from inspect import Parameter
from torch.nn.parameter import Parameter
def maybe_num_nodes(edge_index, num_nodes=None):
if ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import Tensor
fro... | JinheonBaek/pytorch_geometric | LayerNorm | false | 17,552 | [
"MIT"
] | 4 | dfd32d08a3d8191d6290e53458d4eda515d04fd6 | https://github.com/JinheonBaek/pytorch_geometric/tree/dfd32d08a3d8191d6290e53458d4eda515d04fd6 | import torch
from torch import Tensor
from torch.nn import Parameter
from torch.nn import LayerNorm
from typing import Optional
import torch.fx
from typing import Any
import torch.utils.data
from inspect import Parameter
from torch.nn.parameter import Parameter
def maybe_num_nodes(edge_index, num_nodes=None):
if ... |
SEBlock | # 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 SEBlock(nn.Module):
def __init__(self, num_channels):
super(SEBlock, self).__init__()
self.lin1 = nn.Conv2d(num_channels, num_channels, 1)
self.lin2 = nn.Conv2d(num_channels, num_channels, 1)
def forward(self, x):
h = nn.functional.avg_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Keleas/Wafer_maps | SEBlock | false | 17,553 | [
"MIT"
] | 7 | ee555cafab213a86baf2d9e3b7fb392e1b89a832 | https://github.com/Keleas/Wafer_maps/tree/ee555cafab213a86baf2d9e3b7fb392e1b89a832 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, num_channels):
super().__init__()
self.lin1 = nn.Conv2d(num_channels, num_channels, 1)
self.lin2 = nn.Conv2d(num_channels, num_channels, 1)
def forward(self, x):
h = nn.functional.avg_pool2d(x, int(x... |
ConvTranspose2d | # 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 torch.nn.functional import pad
from torch.nn.modules.utils import _pair
from torch.nn.parameter import Parameter
def convtranspose2d_same_padding(input, weight, bias=None, stride=1,
padding=1, dilation=1, groups=1):
input_rows... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.... | Koukyosyumei/secure_ml | ConvTranspose2d | false | 17,554 | [
"MIT"
] | 10 | 9da24f4ce4782ec2f6dd63b0437f657a0e190e40 | https://github.com/Koukyosyumei/secure_ml/tree/9da24f4ce4782ec2f6dd63b0437f657a0e190e40 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.functional import pad
from torch.nn.modules.utils import _pair
from torch.nn.parameter import Parameter
def convtranspose2d_same_padding(input, weight, bias=None, stride=1,
padding=1, dilation=1, groups=1):
input_rows... |
GCN | # 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 math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.functional as F
from torch.nn import Parameter
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | LEAP-WS/CGPN | GCN | false | 17,555 | [
"MIT"
] | 9 | 28564d9ec7cc7342ff53f3f5a1d36ca5985c11a9 | https://github.com/LEAP-WS/CGPN/tree/28564d9ec7cc7342ff53f3f5a1d36ca5985c11a9 | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.functional as F
from torch.nn import Parameter
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
... |
Conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.functional import pad
from torch.nn.modules.utils import _pair
from torch.nn.parameter import Parameter
def conv2d_same_padding(input, weight, bias=None, stride=1, padding=1,
dilation=1, groups=1):
input_rows = input.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.... | Koukyosyumei/secure_ml | Conv2d | false | 17,556 | [
"MIT"
] | 10 | 9da24f4ce4782ec2f6dd63b0437f657a0e190e40 | https://github.com/Koukyosyumei/secure_ml/tree/9da24f4ce4782ec2f6dd63b0437f657a0e190e40 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.functional import pad
from torch.nn.modules.utils import _pair
from torch.nn.parameter import Parameter
def conv2d_same_padding(input, weight, bias=None, stride=1, padding=1,
dilation=1, groups=1):
input_rows = input.... |
Highway | # 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.utils
class Highway(nn.Module):
def __init__(self, conv_out_dim, e_word):
super().__init__()
self.conv_out_dim = conv_out_dim
self.e_word = e_word
self.linear_proj = nn.Linear(conv_out_dim, self.e_word)
self.linear_gate = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | LFhase/Learning_CS224N | Highway | false | 17,557 | [
"MIT"
] | 5 | 21af6dd4f7b9dcb3f34aac9c2cebf4a02a17176f | https://github.com/LFhase/Learning_CS224N/tree/21af6dd4f7b9dcb3f34aac9c2cebf4a02a17176f | import torch
import torch.nn as nn
import torch.nn.utils
class Model(nn.Module):
def __init__(self, conv_out_dim, e_word):
super().__init__()
self.conv_out_dim = conv_out_dim
self.e_word = e_word
self.linear_proj = nn.Linear(conv_out_dim, self.e_word)
self.linear_gate = nn... |
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
import torch.nn as nn
def log_minus_sigmoid(x):
return torch.clamp(-x, max=0) - torch.log(1 + torch.exp(-torch.abs(x))
) + 0.5 * torch.clamp(x, min=0, max=0)
def log_sigmoid(x):
return torch.clamp(x, max=0) - torch.log(1 + torch.exp(-torch.abs(x))
) + 0.5 * torch.clamp(x, min=0,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | LIANGKE23/Siamese-FC-KF-CF | FocalLoss | false | 17,558 | [
"MIT"
] | 10 | 3d9db19c0f39f0588a5061cd182bfbfc37dca76f | https://github.com/LIANGKE23/Siamese-FC-KF-CF/tree/3d9db19c0f39f0588a5061cd182bfbfc37dca76f | import torch
import torch.nn as nn
def log_minus_sigmoid(x):
return torch.clamp(-x, max=0) - torch.log(1 + torch.exp(-torch.abs(x))
) + 0.5 * torch.clamp(x, min=0, max=0)
def log_sigmoid(x):
return torch.clamp(x, max=0) - torch.log(1 + torch.exp(-torch.abs(x))
) + 0.5 * torch.clamp(x, min=0,... |
Quantization | # 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 Quant(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
input = torch.clamp(input, 0, 1)
output = (input * 255.0).round() / 255.0
return output
@staticmethod
def backward(ctx, grad_output):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | LCM1999/VolumeRescaling | Quantization | false | 17,559 | [
"Apache-2.0"
] | 4 | 3eeabf057e68804ed945711b440f19e419c10d7a | https://github.com/LCM1999/VolumeRescaling/tree/3eeabf057e68804ed945711b440f19e419c10d7a | import torch
import torch.utils.data
import torch.nn as nn
class Quant(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
input = torch.clamp(input, 0, 1)
output = (input * 255.0).round() / 255.0
return output
@staticmethod
def backward(ctx, grad_output):
... |
MultiHeadAttention | # 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 numpy as np
from torch import nn
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimens... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Kilichbek/artemis-m2-transformer | MultiHeadAttention | false | 17,560 | [
"MIT"
] | 8 | 99f7e797965710bf2565283d6b5028a6fe32664c | https://github.com/Kilichbek/artemis-m2-transformer/tree/99f7e797965710bf2565283d6b5028a6fe32664c | from torch.nn import Module
import torch
import numpy as np
from torch import nn
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimens... |
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
from torchvision.transforms import functional as F
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
"""
定义了一个简单的三层全连接神经网络，每一层都是线性的
"""
def __init__(self, in_dim, n_hidden1, out_dim):
super().__init__()
self.layer1 = nn.Linear(in_dim, n_hidden1)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Event0511/curling-reid | Model | false | 17,561 | [
"Apache-2.0"
] | 3 | 1494d0faeed951e495573c694362f001df5bf6fd | https://github.com/Event0511/curling-reid/tree/1494d0faeed951e495573c694362f001df5bf6fd | import torch
from torchvision.transforms import functional as F
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
"""
定义了一个简单的三层全连接神经网络，每一层都是线性的
"""
def __init__(self, in_dim, n_hidden1, out_dim):
super().__init__()
self.layer1 = nn.Linear(in_dim, n_hidden1)... |
_ASPPModule | # 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 _ASPPModule(nn.Module):
"""Atrous Spatial Pyramid Pooling"""
def __init__(self, in_channels, out_channels, pyramids):
super(_ASPPModule, self).__init__()
self.stages = nn.Module()
for i, (dilation, padding) in enumerate(zip(pyramids, pyramids))... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | LcDog/APL | _ASPPModule | false | 17,562 | [
"MIT"
] | 7 | a4302b5d28d63672eda7eff35075b3bce3eccd68 | https://github.com/LcDog/APL/tree/a4302b5d28d63672eda7eff35075b3bce3eccd68 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Atrous Spatial Pyramid Pooling"""
def __init__(self, in_channels, out_channels, pyramids):
super().__init__()
self.stages = nn.Module()
for i, (dilation, padding) in enumerate(zip(pyramids, pyramids)):
self.stag... |
alpha | # 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
class alpha(nn.Module):
def __init__(self, alpha_val=0):
super(alpha, self).__init__()
self.alpha = nn.Parameter(torch.Tensor([alpha_val]))
self.alpha.requires_grad = True
def forward(self, x):
out = torch.mul(self.al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | LayerFolding/Layer-Folding | alpha | false | 17,563 | [
"BSD-3-Clause"
] | 7 | 9c010edc17b1a4a68b36a67cf00c94840d76b735 | https://github.com/LayerFolding/Layer-Folding/tree/9c010edc17b1a4a68b36a67cf00c94840d76b735 | import torch
import torch.utils.data
import torch.nn as nn
class Model(nn.Module):
def __init__(self, alpha_val=0):
super().__init__()
self.alpha = nn.Parameter(torch.Tensor([alpha_val]))
self.alpha.requires_grad = True
def forward(self, x):
out = torch.mul(self.alpha, x)
... |
Policy | # 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 Policy(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Policy, self).__init__()
self.affine1 = nn.Linear(input_size, hidden_size, bias=False)
self.affine2 = nn.Linear(hidden_size, output_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.... | LaRiffle/private-RL | Policy | false | 17,564 | [
"MIT"
] | 4 | 05fdcefbc0aa8bddcb5e2eaf64d203d0c0a38a58 | https://github.com/LaRiffle/private-RL/tree/05fdcefbc0aa8bddcb5e2eaf64d203d0c0a38a58 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super().__init__()
self.affine1 = nn.Linear(input_size, hidden_size, bias=False)
self.affine2 = nn.Linear(hidden_size, output_size, bias=Fal... |
Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
def projection_pooling_column(input):
b, c, _h, w = input.size()
input = input.permute(0, 1, 3, 2)
ave_v = input.mean(dim=3)
ave_v = ave_v.reshape(b, c, w, -1)
input[:, :, :, :] = ave_v[:, :, :]
input = input.permute(0, 1, 3, 2)
return input
def project... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | L597383845/row-col-table-recognition | Block | false | 17,565 | [
"MIT"
] | 7 | 617718751861b3f4e35a4b34dde4c898575e6818 | https://github.com/L597383845/row-col-table-recognition/tree/617718751861b3f4e35a4b34dde4c898575e6818 | import torch
import torch.nn as nn
def projection_pooling_column(input):
b, c, _h, w = input.size()
input = input.permute(0, 1, 3, 2)
ave_v = input.mean(dim=3)
ave_v = ave_v.reshape(b, c, w, -1)
input[:, :, :, :] = ave_v[:, :, :]
input = input.permute(0, 1, 3, 2)
return input
def project... |
BalancedLoss | # 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 BalancedLoss(nn.Module):
def __init__(self, neg_weight=1.0):
super(BalancedLoss, self).__init__()
self.neg_weight = neg_weight
def forward(self, input, target):
pos_mask = target == 1
neg_mask = 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 libdevice, math as tl_math
import torc... | LIANGKE23/Siamese-FC-KF-CF | BalancedLoss | false | 17,566 | [
"MIT"
] | 10 | 3d9db19c0f39f0588a5061cd182bfbfc37dca76f | https://github.com/LIANGKE23/Siamese-FC-KF-CF/tree/3d9db19c0f39f0588a5061cd182bfbfc37dca76f | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, neg_weight=1.0):
super().__init__()
self.neg_weight = neg_weight
def forward(self, input, target):
pos_mask = target == 1
neg_mask = target == 0
pos_num = pos... |
EqualLinear | # 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 nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
if input.ndim == 3:
return F.leaky_relu(input + bias.view(1, *rest_dim, bias.shape[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 math
from torch import nn
from torch.nn import functional as F
assert_siz... | Liamkuo/SAIR | EqualLinear | false | 17,567 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 | import math
import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
if input.ndim == 3:
return F.leaky_relu(input + bias.view(1, *rest_dim, bias.shape[0... |
SaN | # 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 collections import OrderedDict
class Flatten(nn.Module):
def __init__(self):
super(Flatten, self).__init__()
def forward(self, x):
x = x.view(x.size(0), -1)
return x
class L2Normalization(nn.Module):
def __init__(self):
super(L2N... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Jiangtong-Li/ZHSIR | SaN | false | 17,568 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | import torch
import torch.nn as nn
from collections import OrderedDict
class Flatten(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x = x.view(x.size(0), -1)
return x
class L2Normalization(nn.Module):
def __init__(self):
super().__init__()
... |
DenseCrossEntropy | # 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 DenseCrossEntropy(nn.Module):
def __init__(self):
super(DenseCrossEntropy, self).__init__()
def forward(self, logits, labels):
logits = logits.float()
labels = labels.float()
logprobs = F.log_softmax(log... | 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
... | LichenYang-Jeffrey/DCL-with-Efficient-B7 | DenseCrossEntropy | false | 17,569 | [
"MIT"
] | 4 | 84940c96a8c7926c630a7a6d5bfd5c6e52a57c2e | https://github.com/LichenYang-Jeffrey/DCL-with-Efficient-B7/tree/84940c96a8c7926c630a7a6d5bfd5c6e52a57c2e | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits, labels):
logits = logits.float()
labels = labels.float()
logprobs = F.log_softmax(logits, dim=-1)
loss = -labels... |
FusedLeakyReLU | # 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
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
if input.ndim == 3:
return F.leaky_relu(input + bias.view(1, *rest_dim, bias.shape[0]),
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda... | Liamkuo/SAIR | FusedLeakyReLU | false | 17,570 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 | import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
if input.ndim == 3:
return F.leaky_relu(input + bias.view(1, *rest_dim, bias.shape[0]),
... |
DenseCrossEntropy_smooth | # 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 DenseCrossEntropy(nn.Module):
def __init__(self):
super(DenseCrossEntropy, self).__init__()
def forward(self, logits, labels):
logits = logits.float()
labels = labels.float()
logprobs = F.log_softmax(log... | 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
... | LichenYang-Jeffrey/DCL-with-Efficient-B7 | DenseCrossEntropy_smooth | false | 17,571 | [
"MIT"
] | 4 | 84940c96a8c7926c630a7a6d5bfd5c6e52a57c2e | https://github.com/LichenYang-Jeffrey/DCL-with-Efficient-B7/tree/84940c96a8c7926c630a7a6d5bfd5c6e52a57c2e | import torch
import torch.nn as nn
import torch.nn.functional as F
class DenseCrossEntropy(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits, labels):
logits = logits.float()
labels = labels.float()
logprobs = F.log_softmax(logits, dim=-1)
lo... |
ycbcr_to_rgb_jpeg | # 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 numpy as np
from torch import nn
class ycbcr_to_rgb_jpeg(nn.Module):
""" Converts YCbCr image to RGB JPEG
Input:
image(tensor): batch x height x width x 3
Outpput:
result(tensor): batch x 3 x height x width
"""
def __init__(self):
super(ycbcr_to_rgb_jpe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
from torch import nn
assert_size_stride = torch._C._dynamo.gu... | Liamkuo/SAIR | ycbcr_to_rgb_jpeg | false | 17,572 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 | import torch
import numpy as np
from torch import nn
class Model(nn.Module):
""" Converts YCbCr image to RGB JPEG
Input:
image(tensor): batch x height x width x 3
Outpput:
result(tensor): batch x 3 x height x width
"""
def __init__(self):
super().__init__()
matrix ... |
chroma_subsampling | # 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 chroma_subsampling(nn.Module):
""" Chroma subsampling on CbCv channels
Input:
image(tensor): batch x height x width x 3
Output:
y(tensor): batch x height x width
cb(tensor): batch x height/2 x width/2
cr(tensor): batch x height/2 x wi... | 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... | Liamkuo/SAIR | chroma_subsampling | false | 17,573 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 | import torch
from torch import nn
class Model(nn.Module):
""" Chroma subsampling on CbCv channels
Input:
image(tensor): batch x height x width x 3
Output:
y(tensor): batch x height x width
cb(tensor): batch x height/2 x width/2
cr(tensor): batch x height/2 x width/2
"""... |
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
from torch import nn
class PixelNorm(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=
True) + 1e-08)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Liamkuo/SAIR | PixelNorm | false | 17,574 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=
True) + 1e-08)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inp... |
ResidualBlock_noBN | # 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.functional as F
import torch.nn.init as init
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv3d):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | LCM1999/VolumeRescaling | ResidualBlock_noBN | false | 17,575 | [
"Apache-2.0"
] | 4 | 3eeabf057e68804ed945711b440f19e419c10d7a | https://github.com/LCM1999/VolumeRescaling/tree/3eeabf057e68804ed945711b440f19e419c10d7a | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv3d):
... |
idct_8x8 | # 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 itertools
import torch
import numpy as np
from torch import nn
class idct_8x8(nn.Module):
""" Inverse discrete Cosine Transformation
Input:
dcp(tensor): batch x height x width
Output:
image(tensor): batch x height x width
"""
def __init__(self):
super(idct_8x8, 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
import itertools
import numpy as np
from torch import nn
assert_size_stride = to... | Liamkuo/SAIR | idct_8x8 | false | 17,576 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 | import itertools
import torch
import numpy as np
from torch import nn
class Model(nn.Module):
""" Inverse discrete Cosine Transformation
Input:
dcp(tensor): batch x height x width
Output:
image(tensor): batch x height x width
"""
def __init__(self):
super().__init__()
... |
OHEMLoss | # 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
from torch import nn
class OHEMLoss(nn.Module):
def __init__(self, rate=0.8):
super(OHEMLoss, self).__init__()
None
self.rate = rate
def change_rate(self, new_rate):
None
self.rate = new_rate
def forward(self, cls_pred... | 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... | LightnessOfBeing/kaggle-bengali-classification | OHEMLoss | false | 17,577 | [
"MIT"
] | 5 | 342bc2a9bf57f9f03fa25f5271cb178ab8f7b4ff | https://github.com/LightnessOfBeing/kaggle-bengali-classification/tree/342bc2a9bf57f9f03fa25f5271cb178ab8f7b4ff | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
def __init__(self, rate=0.8):
super().__init__()
None
self.rate = rate
def change_rate(self, new_rate):
None
self.rate = new_rate
def forward(self, cls_pred, cls_target):
... |
LinModel | # 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 LinModel(nn.Module):
def __init__(self, in_dim, out_dim):
super(LinModel, self).__init__()
self.linear = nn.Linear(in_dim, out_dim)
def forward(self, x):
out = self.linear(x)
out = F.softmax(out, dim=-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.... | Lisennlp/distributed_train_pytorch | LinModel | false | 17,578 | [
"Apache-2.0"
] | 10 | da43ac6b5f4484b5f7bc92e3c778539b9017cb82 | https://github.com/Lisennlp/distributed_train_pytorch/tree/da43ac6b5f4484b5f7bc92e3c778539b9017cb82 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.linear = nn.Linear(in_dim, out_dim)
def forward(self, x):
out = self.linear(x)
out = F.softmax(out, dim=-1)
return o... |
ToRGB | # 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 nn
from torch.nn import functional as F
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0,
pad_x1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
from torch.nn import functional as F
assert_siz... | Liamkuo/SAIR | ToRGB | false | 17,579 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 | import math
import torch
from torch import nn
from torch.nn import functional as F
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0,
pad_x1... |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
class BasicBlock(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.triton_helpers import libdevice
import torch.nn as ... | Limingxing00/Retinal-Vessel-Segmentation-ISBI2022 | BasicBlock | false | 17,580 | [
"MIT"
] | 9 | 9480de5c17dc3665a5f6d6d0117596bc5ffc108e | https://github.com/Limingxing00/Retinal-Vessel-Segmentation-ISBI2022/tree/9480de5c17dc3665a5f6d6d0117596bc5ffc108e | import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
class Model(nn.Module):
... |
Quantization_Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class Quantization_Loss(nn.Module):
def __init__(self):
super(Quantization_Loss, self).__init__()
def forward(self, inputs):
loss = -(inputs * torch.log(inputs + 1e-20) + (1.0 - inputs) *
torch.log(1.0 - inputs + 1e-20))
return loss.mean... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | LiuChaoXD/Remote-Sensing-Image-Retrieval-Models | Quantization_Loss | false | 17,581 | [
"MIT"
] | 4 | c135562263102080716e35260f111dcff7762264 | https://github.com/LiuChaoXD/Remote-Sensing-Image-Retrieval-Models/tree/c135562263102080716e35260f111dcff7762264 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, inputs):
loss = -(inputs * torch.log(inputs + 1e-20) + (1.0 - inputs) *
torch.log(1.0 - inputs + 1e-20))
return loss.mean()
def get_inputs():
return [... |
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