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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
ImgSenRanking | # 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.utils.data
def l2norm(input, p=2.0, dim=1, eps=1e-12):
"""
Compute L2 norm, row-wise
"""
l2_inp = input / input.norm(p, dim, keepdim=True).clamp(min=eps)
return l2_inp.expand_as(input)
def xavier_weight(tensor):
nin, nout = tensor.size()[0], tenso... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ypxie/HDGan | ImgSenRanking | false | 16,770 | [
"MIT"
] | 160 | d98e2a85f7ae6ce7bfacd1c15e519558d97cb931 | https://github.com/ypxie/HDGan/tree/d98e2a85f7ae6ce7bfacd1c15e519558d97cb931 | import torch
import numpy as np
import torch.utils.data
def l2norm(input, p=2.0, dim=1, eps=1e-12):
"""
Compute L2 norm, row-wise
"""
l2_inp = input / input.norm(p, dim, keepdim=True).clamp(min=eps)
return l2_inp.expand_as(input)
def xavier_weight(tensor):
nin, nout = tensor.size()[0], tenso... |
Seedloss | # 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
from torch.nn import functional as F
class Seedloss(nn.Module):
def __init__(self, ignore_label=21):
super(Seedloss, self).__init__()
self.ignore_label = ignore_label
self.eps = 1e-05
def my_softmax(self, score, dim=1):
probs = torch.clamp(F... | 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... | yaoqi-zd/SGAN | Seedloss | false | 16,771 | [
"MIT"
] | 48 | 43d8a859b03967e2423a73ef1ba332ee71714ba4 | https://github.com/yaoqi-zd/SGAN/tree/43d8a859b03967e2423a73ef1ba332ee71714ba4 | import torch
import torch.nn as nn
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, ignore_label=21):
super().__init__()
self.ignore_label = ignore_label
self.eps = 1e-05
def my_softmax(self, score, dim=1):
probs = torch.clamp(F.softmax(score, d... |
PrimaryCaps | # 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 PrimaryCaps(nn.Module):
"""Creates a primary convolutional capsule layer
that outputs a pose matrix and an activation.
Note that for computation convenience, pose matrix
are stored in first part while the activations are
stored in the second part.
Arg... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | yl-1993/Matrix-Capsules-EM-PyTorch | PrimaryCaps | false | 16,772 | [
"MIT"
] | 97 | ca4cd7f45a4234ddf49efe9db34c9ff645378437 | https://github.com/yl-1993/Matrix-Capsules-EM-PyTorch/tree/ca4cd7f45a4234ddf49efe9db34c9ff645378437 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Creates a primary convolutional capsule layer
that outputs a pose matrix and an activation.
Note that for computation convenience, pose matrix
are stored in first part while the activations are
stored in the second part.
Args:
... |
MaskedMHA | # 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.utils.data
from torch.nn import functional as F
class MaskedMHA(nn.Module):
"""
Multi Head Attention with mask
Modified from https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
"""
def __init__(self, n_embd, n_head, attn_pdro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | yjh0410/actionformer_release | MaskedMHA | false | 16,773 | [
"MIT"
] | 61 | 7a97422111d3e29c8d2e14088c850c6975855ea7 | https://github.com/yjh0410/actionformer_release/tree/7a97422111d3e29c8d2e14088c850c6975855ea7 | import math
import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import functional as F
class Model(nn.Module):
"""
Multi Head Attention with mask
Modified from https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
"""
def __init__(self, n_embd, n_head, attn_pdrop=0.... |
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 numpy as np
import torch.utils.data
import torch
import torch.nn as nn
from torch.nn import functional as F
class FocalLoss(nn.Module):
def __init__(self, weight=None, gamma=1.0, num_classes=80):
super(FocalLoss, self).__init__()
assert gamma >= 0
self.gamma = gamma
... | 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 numpy as np
imp... | yulonghui/yingying_boss | FocalLoss | false | 16,774 | [
"MIT"
] | 306 | f9cf956cb6507ef43f8005c61027f6b54f418224 | https://github.com/yulonghui/yingying_boss/tree/f9cf956cb6507ef43f8005c61027f6b54f418224 | import torch
import numpy as np
import torch.utils.data
import torch
import torch.nn as nn
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, weight=None, gamma=1.0, num_classes=80):
super().__init__()
assert gamma >= 0
self.gamma = gamma
self.weight =... |
GCT | # 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 GCT(nn.Module):
def __init__(self, num_channels, epsilon=1e-05, mode='l2', after_relu=False
):
super(GCT, self).__init__()
self.alpha = nn.Parameter(torch.ones(1, num_channels, 1, 1))
self.gamma = nn.Parameter(torch.zeros(1, num_channels, 1... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | yoxu515/CFBI | GCT | false | 16,775 | [
"BSD-3-Clause"
] | 312 | 0bab1e3c9fc3e3ba0629f716d60221e8f8d9d586 | https://github.com/yoxu515/CFBI/tree/0bab1e3c9fc3e3ba0629f716d60221e8f8d9d586 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_channels, epsilon=1e-05, mode='l2', after_relu=False
):
super().__init__()
self.alpha = nn.Parameter(torch.ones(1, num_channels, 1, 1))
self.gamma = nn.Parameter(torch.zeros(1, num_channels, 1, 1))
... |
MyEntLoss | # 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 MyEntLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x = torch.nn.Softmax(dim=1)(x)
p = x / torch.repeat_interleave(x.sum(dim=1).unsqueeze(-1), repeats
=20, dim=1)
logp = torch.log2(p)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | yuantn/MI-AOD | MyEntLoss | false | 16,776 | [
"Apache-2.0"
] | 188 | e57114d60f9ce5e43839cdf7068a90ee58092ec8 | https://github.com/yuantn/MI-AOD/tree/e57114d60f9ce5e43839cdf7068a90ee58092ec8 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x = torch.nn.Softmax(dim=1)(x)
p = x / torch.repeat_interleave(x.sum(dim=1).unsqueeze(-1), repeats
=20, dim=1)
logp = torch.log2(p)
ent ... |
ActorNetwork | # 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 func
class ActorNetwork(torch.nn.Module):
def __init__(self, s_space, a_space):
super(ActorNetwork, self).__init__()
self.first_dense = torch.nn.Linear(s_space, 50)
self.second_dense = torch.nn.Linear(50, a_space)
def forward(self, 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
assert_size_stride = torch._C... | yutiansut/Personae | ActorNetwork | false | 16,777 | [
"MIT"
] | 1,046 | e5e89cbaaf2c4708952d25fdb25e99837aecdb4e | https://github.com/yutiansut/Personae/tree/e5e89cbaaf2c4708952d25fdb25e99837aecdb4e | import torch
import torch.nn.functional as func
class Model(torch.nn.Module):
def __init__(self, s_space, a_space):
super().__init__()
self.first_dense = torch.nn.Linear(s_space, 50)
self.second_dense = torch.nn.Linear(50, a_space)
def forward(self, s):
phi_s = func.relu(self... |
CriticNetwork | # 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 func
class CriticNetwork(torch.nn.Module):
def __init__(self, s_space, a_space):
super(CriticNetwork, self).__init__()
self.s_dense = torch.nn.Linear(s_space, 50)
self.a_dense = torch.nn.Linear(a_space, 50)
self.q_dense = torch.nn.Linear(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | yutiansut/Personae | CriticNetwork | false | 16,778 | [
"MIT"
] | 1,046 | e5e89cbaaf2c4708952d25fdb25e99837aecdb4e | https://github.com/yutiansut/Personae/tree/e5e89cbaaf2c4708952d25fdb25e99837aecdb4e | import torch
import torch.nn.functional as func
class Model(torch.nn.Module):
def __init__(self, s_space, a_space):
super().__init__()
self.s_dense = torch.nn.Linear(s_space, 50)
self.a_dense = torch.nn.Linear(a_space, 50)
self.q_dense = torch.nn.Linear(50, 1)
def forward(sel... |
GELU | # 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 numpy as np
import torch.nn as nn
class GELU(nn.Module):
def forward(self, x):
cdf = 0.5 * (1.0 + torch.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 *
torch.pow(x, 3))))
return x * cdf
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | yyht/Funnel_Transformer | GELU | false | 16,779 | [
"MIT"
] | 193 | 4b35a794d5e122a8054471863a52d4eac1c39dcd | https://github.com/yyht/Funnel_Transformer/tree/4b35a794d5e122a8054471863a52d4eac1c39dcd | import torch
import numpy as np
import torch.nn as nn
class Model(nn.Module):
def forward(self, x):
cdf = 0.5 * (1.0 + torch.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 *
torch.pow(x, 3))))
return x * cdf
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... |
DynamicPreHead | # 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 DynamicPreHead(nn.Module):
def __init__(self, in_dim=3, embed_dim=100, kernel_size=1):
super(DynamicPreHead, self).__init__()
self.conv = nn.Conv2d(in_dim, embed_dim, kernel_size=kernel_size,
stride=1, padding=int((kernel_size - 1) / 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.... | yoxu515/CFBI | DynamicPreHead | false | 16,780 | [
"BSD-3-Clause"
] | 312 | 0bab1e3c9fc3e3ba0629f716d60221e8f8d9d586 | https://github.com/yoxu515/CFBI/tree/0bab1e3c9fc3e3ba0629f716d60221e8f8d9d586 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_dim=3, embed_dim=100, kernel_size=1):
super().__init__()
self.conv = nn.Conv2d(in_dim, embed_dim, kernel_size=kernel_size,
stride=1, padding=int((kernel_size - 1) / 2))
self.bn = nn.GroupNorm(int(... |
Dense | # 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.nn as nn
def get_einsum_string(ndims, einsum_symbols=None):
if einsum_symbols is None:
einsum_symbols = ['u', 'v', 'w', 'x', 'y', 'z']
assert ndims <= len(einsum_symbols)
einsum_prefix = ''
for i in range(ndims):
einsum_prefix += einsum_symb... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | yyht/Funnel_Transformer | Dense | false | 16,781 | [
"MIT"
] | 193 | 4b35a794d5e122a8054471863a52d4eac1c39dcd | https://github.com/yyht/Funnel_Transformer/tree/4b35a794d5e122a8054471863a52d4eac1c39dcd | import torch
import numpy as np
import torch.nn as nn
def get_einsum_string(ndims, einsum_symbols=None):
if einsum_symbols is None:
einsum_symbols = ['u', 'v', 'w', 'x', 'y', 'z']
assert ndims <= len(einsum_symbols)
einsum_prefix = ''
for i in range(ndims):
einsum_prefix += einsum_symb... |
InteractionLayer | # 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 torchvision.transforms.functional as F
from torch import nn
import torch.nn.functional as F
class InteractionLayer(nn.Module):
def __init__(self, d_model, d_feature, dropout=0.1):
super().__init__()
self.d_feature = d_feature
self.det_tfm = nn.Linear(d_mode... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | yoyomimi/AS-Net | InteractionLayer | false | 16,782 | [
"MIT"
] | 49 | 85ce753707c6d1838c3983111ccbba4b1861f438 | https://github.com/yoyomimi/AS-Net/tree/85ce753707c6d1838c3983111ccbba4b1861f438 | import math
import torch
import torchvision.transforms.functional as F
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, d_model, d_feature, dropout=0.1):
super().__init__()
self.d_feature = d_feature
self.det_tfm = nn.Linear(d_model, d_featur... |
Biaffine | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Biaffine(nn.Module):
"""
Biaffine layer for first-order scoring :cite:`dozat-etal-2017-biaffine`.
This function has a tensor of weights :math:`W` and bias terms if needed.
The score :math:`s(x, y)` of the vector pair :math:`(x, y)` is computed as :math:`x^T W ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | yzhangcs/parser | Biaffine | false | 16,783 | [
"MIT"
] | 439 | 3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | https://github.com/yzhangcs/parser/tree/3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Biaffine layer for first-order scoring :cite:`dozat-etal-2017-biaffine`.
This function has a tensor of weights :math:`W` and bias terms if needed.
The score :math:`s(x, y)` of the vector pair :math:`(x, y)` is computed as :math:`x^T W y /... |
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
class SAN(nn.Module):
def __init__(self, d_model, nhead, dropout=0.1):
super(SAN, self).__init__()
self.d_model = d_model
self.nhead = nhead
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.dropout = nn.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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | yuriy-os/russian-reviews-bert-e2e-absa | SAN | false | 16,784 | [
"Apache-2.0"
] | 293 | 369a6179353e3bf28643e8e9347b624078e38bf4 | https://github.com/yuriy-os/russian-reviews-bert-e2e-absa/tree/369a6179353e3bf28643e8e9347b624078e38bf4 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, d_model, nhead, dropout=0.1):
super().__init__()
self.d_model = d_model
self.nhead = nhead
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.dropout = nn.Dropout(p=drop... |
Triaffine | # 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 Triaffine(nn.Module):
"""
Triaffine layer for second-order scoring :cite:`zhang-etal-2020-efficient,wang-etal-2019-second`.
This function has a tensor of weights :math:`W` and bias terms if needed.
The score :math:`s(x, y, z)` of the vector triple :math:`(x, y... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | yzhangcs/parser | Triaffine | false | 16,785 | [
"MIT"
] | 439 | 3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | https://github.com/yzhangcs/parser/tree/3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Triaffine layer for second-order scoring :cite:`zhang-etal-2020-efficient,wang-etal-2019-second`.
This function has a tensor of weights :math:`W` and bias terms if needed.
The score :math:`s(x, y, z)` of the vector triple :math:`(x, y, z)... |
GlobalMaxPooling | # 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 GlobalMaxPooling(nn.Module):
def __init__(self):
super(GlobalMaxPooling, self).__init__()
def forward(self, x):
res, _ = torch.max(x, dim=1)
return res
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
retu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | zake7749/Sequence-to-Sequence-101 | GlobalMaxPooling | false | 16,786 | [
"MIT"
] | 64 | f9e9a8e836dc1cb3b35d6e148f6378fcd2736951 | https://github.com/zake7749/Sequence-to-Sequence-101/tree/f9e9a8e836dc1cb3b35d6e148f6378fcd2736951 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
res, _ = torch.max(x, dim=1)
return res
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
PositionalEmbedding | # 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 PositionalEmbedding(nn.Module):
def __init__(self, n_model, max_len=1024):
super().__init__()
self.embed = nn.Embedding(max_len, n_model)
self.reset_parameters()
@torch.no_grad()
def reset_parameters(self):
w = self.embed.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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | yzhangcs/parser | PositionalEmbedding | false | 16,787 | [
"MIT"
] | 439 | 3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | https://github.com/yzhangcs/parser/tree/3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n_model, max_len=1024):
super().__init__()
self.embed = nn.Embedding(max_len, n_model)
self.reset_parameters()
@torch.no_grad()
def reset_parameters(self):
w = self.embed.weight
max_len,... |
KernelConv | # 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 KernelConv(nn.Module):
"""
the class of computing prediction
"""
def __init__(self, kernel_size=[5], sep_conv=False, core_bias=False):
super(KernelConv, self).__init__()
self.kernel_size = sorted(kernel_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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | xenbaloch/efficientderain | KernelConv | false | 16,788 | [
"MIT"
] | 109 | d5646815fd14a5a03c859102ecd2f298db7e53be | https://github.com/xenbaloch/efficientderain/tree/d5646815fd14a5a03c859102ecd2f298db7e53be | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
the class of computing prediction
"""
def __init__(self, kernel_size=[5], sep_conv=False, core_bias=False):
super().__init__()
self.kernel_size = sorted(kernel_size)
self.sep_conv = ... |
SinusoidPositionalEmbedding | # 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 SinusoidPositionalEmbedding(nn.Module):
def forward(self, x):
seq_len, n_model = x[0].shape
pos = x.new_tensor(range(seq_len)).unsqueeze(-1) / 10000 ** (x.
new_tensor(range(n_model)) // 2 * 2 / n_model)
pos[:, 0::2], pos[:, 1::2] = pos[... | 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... | yzhangcs/parser | SinusoidPositionalEmbedding | false | 16,789 | [
"MIT"
] | 439 | 3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | https://github.com/yzhangcs/parser/tree/3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x):
seq_len, n_model = x[0].shape
pos = x.new_tensor(range(seq_len)).unsqueeze(-1) / 10000 ** (x.
new_tensor(range(n_model)) // 2 * 2 / n_model)
pos[:, 0::2], pos[:, 1::2] = pos[:, 0::2].sin(), pos[:,... |
OfflineTripletLoss | # 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 OfflineTripletLoss(nn.Module):
"""
Triplet loss
Takes embeddings of an anchor sample, a positive sample and a negative sample
"""
def __init__(self, margin=0.1):
super(OfflineTripletLoss, self).__init__()
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 import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | zhangxinyu-tj/PAST | OfflineTripletLoss | false | 16,790 | [
"MIT"
] | 112 | 67f1f7a780e869aa7867167538edb03faa96dec5 | https://github.com/zhangxinyu-tj/PAST/tree/67f1f7a780e869aa7867167538edb03faa96dec5 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""
Triplet loss
Takes embeddings of an anchor sample, a positive sample and a negative sample
"""
def __init__(self, margin=0.1):
super().__init__()
self.margin = margin
def forward(sel... |
SinusoidRelativePositionalEmbedding | # 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 SinusoidRelativePositionalEmbedding(nn.Module):
def forward(self, x):
seq_len, n_model = x[0].shape
pos = x.new_tensor(range(seq_len))
pos = (pos - pos.unsqueeze(-1)).unsqueeze(-1) / 10000 ** (x.
new_tensor(range(n_model)) // 2 * 2 / n_... | 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... | yzhangcs/parser | SinusoidRelativePositionalEmbedding | false | 16,791 | [
"MIT"
] | 439 | 3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | https://github.com/yzhangcs/parser/tree/3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x):
seq_len, n_model = x[0].shape
pos = x.new_tensor(range(seq_len))
pos = (pos - pos.unsqueeze(-1)).unsqueeze(-1) / 10000 ** (x.
new_tensor(range(n_model)) // 2 * 2 / n_model)
pos[..., 0::2],... |
SoftCrossEntropy | # 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.functional as F
class SoftCrossEntropy(nn.Module):
def __init__(self):
super().__init__()
def forward(self, inputs, target):
log_likelihood = -F.log_softmax(inputs, dim=1)
sample_num, _class_num = target.shape
loss = torch.sum... | 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... | zake7749/WSDM-Cup-2019 | SoftCrossEntropy | false | 16,792 | [
"Apache-2.0"
] | 64 | 5e9c9ae4197a5dedf6dbccc712bb2bbaae99edee | https://github.com/zake7749/WSDM-Cup-2019/tree/5e9c9ae4197a5dedf6dbccc712bb2bbaae99edee | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, inputs, target):
log_likelihood = -F.log_softmax(inputs, dim=1)
sample_num, _class_num = target.shape
loss = torch.sum(torch.mul(... |
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.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):
return grad_output
c... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | yzxing87/Invertible-ISP | Quantization | false | 16,793 | [
"MIT"
] | 246 | 344dd333dd2a075f6a9e4ffc445dc387ca3014c4 | https://github.com/yzxing87/Invertible-ISP/tree/344dd333dd2a075f6a9e4ffc445dc387ca3014c4 | import torch
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):
return grad_output
c... |
SoftMarginTriplet | # 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.nn.modules.loss import _Loss
class SoftMarginTriplet(_Loss):
__constants__ = ['reduction']
"""
inputs `x1`, `x2`, two 1D mini-batch `Tensor`s,
and a label 1D mini-batch tensor `y` with values (`1` or `-1`).
If `y == 1` then it assumed the fi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.asse... | zhangxinyu-tj/PAST | SoftMarginTriplet | false | 16,794 | [
"MIT"
] | 112 | 67f1f7a780e869aa7867167538edb03faa96dec5 | https://github.com/zhangxinyu-tj/PAST/tree/67f1f7a780e869aa7867167538edb03faa96dec5 | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
class Model(_Loss):
__constants__ = ['reduction']
"""
inputs `x1`, `x2`, two 1D mini-batch `Tensor`s,
and a label 1D mini-batch tensor `y` with values (`1` or `-1`).
If `y == 1` then it assumed the first input sh... |
BCELoss | # 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.distributed
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def bce_loss(pred, target, use_sigmoid=True):
"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: ... | 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... | zhangzhengde0225/SwinTrack | BCELoss | false | 16,795 | [
"MIT"
] | 143 | 526be17f8ef266cb924c6939bd8dda23e9b73249 | https://github.com/zhangzhengde0225/SwinTrack/tree/526be17f8ef266cb924c6939bd8dda23e9b73249 | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def bce_loss(pred, target, use_sigmoid=True):
"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: ... |
DotAttention | # 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.init as init
import torch.nn.functional as F
class DotAttention(nn.Module):
def __init__(self, hidden_size):
super(DotAttention, self).__init__()
self.hidden_size = hidden_size
self.attn_vector = nn.Parameter(torch.Tensor(1, hidden_size),... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | zake7749/DeepToxic | DotAttention | false | 16,796 | [
"MIT"
] | 206 | 92710446c55fe60526099f808a7e1179402e199f | https://github.com/zake7749/DeepToxic/tree/92710446c55fe60526099f808a7e1179402e199f | import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.hidden_size = hidden_size
self.attn_vector = nn.Parameter(torch.Tensor(1, hidden_size),
requires_gra... |
IoULoss | # 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.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def fp16_clamp(x, min=None, max=None):
if not x.is_cuda and x.dtype == torch.float16:
return x.float().clamp(min, max).half()
r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.distribut... | zhangzhengde0225/SwinTrack | IoULoss | false | 16,797 | [
"MIT"
] | 143 | 526be17f8ef266cb924c6939bd8dda23e9b73249 | https://github.com/zhangzhengde0225/SwinTrack/tree/526be17f8ef266cb924c6939bd8dda23e9b73249 | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def fp16_clamp(x, min=None, max=None):
if not x.is_cuda and x.dtype == torch.float16:
return x.float().clamp(min, max).half()
r... |
DIoULoss | # 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.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def diou(pred, target, eps=1e-07):
lt = torch.max(pred[:, :2], target[:, :2])
rb = torch.min(pred[:, 2:], target[:, 2:])
wh = (rb -... | 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.distributed
import torch
import torch.nn as nn
import torch.nn.functional
im... | zhangzhengde0225/SwinTrack | DIoULoss | false | 16,798 | [
"MIT"
] | 143 | 526be17f8ef266cb924c6939bd8dda23e9b73249 | https://github.com/zhangzhengde0225/SwinTrack/tree/526be17f8ef266cb924c6939bd8dda23e9b73249 | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def diou(pred, target, eps=1e-07):
lt = torch.max(pred[:, :2], target[:, :2])
rb = torch.min(pred[:, 2:], target[:, 2:])
wh = (rb -... |
VarifocalLoss | # 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.distributed
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def varifocal_loss(pred, target, alpha=0.75, gamma=2.0, iou_weighted=True,
use_sigmoid=True):
"""`Varif... | 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... | zhangzhengde0225/SwinTrack | VarifocalLoss | false | 16,799 | [
"MIT"
] | 143 | 526be17f8ef266cb924c6939bd8dda23e9b73249 | https://github.com/zhangzhengde0225/SwinTrack/tree/526be17f8ef266cb924c6939bd8dda23e9b73249 | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def varifocal_loss(pred, target, alpha=0.75, gamma=2.0, iou_weighted=True,
use_sigmoid=True):
"""`Varif... |
CXLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
class CXLoss(nn.Module):
def __init__(self, sigma=0.1, b=1.0, similarity='consine'):
super(CXLoss, self).__init__()
self.similarity = similarity
self.sigma = sigma
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | yizhiwang96/deepvecfont | CXLoss | false | 16,800 | [
"MIT"
] | 68 | 3ba4adb0406f16a6f387c5e12dd12286c9c341e8 | https://github.com/yizhiwang96/deepvecfont/tree/3ba4adb0406f16a6f387c5e12dd12286c9c341e8 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
class Model(nn.Module):
def __init__(self, sigma=0.1, b=1.0, similarity='consine'):
super().__init__()
self.similarity = similarity
self.sigma = sigma
self.b = b
... |
MaskedMHCA | # 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.utils.data
from torch.nn import functional as F
class LayerNorm(nn.Module):
"""
LayerNorm that supports inputs of size B, C, T
"""
def __init__(self, num_channels, eps=1e-05, affine=True, device=None,
dtype=None):
super().__i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | yjh0410/actionformer_release | MaskedMHCA | false | 16,801 | [
"MIT"
] | 61 | 7a97422111d3e29c8d2e14088c850c6975855ea7 | https://github.com/yjh0410/actionformer_release/tree/7a97422111d3e29c8d2e14088c850c6975855ea7 | import math
import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import functional as F
class LayerNorm(nn.Module):
"""
LayerNorm that supports inputs of size B, C, T
"""
def __init__(self, num_channels, eps=1e-05, affine=True, device=None,
dtype=None):
super().__i... |
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.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = weight_norm(nn.Linear(in_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | zaynmi/semantic-equivalent-da-for-vqa | Attention | false | 16,802 | [
"MIT"
] | 298 | f121fb3e8fee8af5f1935a7526f19e0d884bd95b | https://github.com/zaynmi/semantic-equivalent-da-for-vqa/tree/f121fb3e8fee8af5f1935a7526f19e0d884bd95b | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super().__init__()
self.lin = weight_norm(nn.Linear(in_size, out_s... |
EltwiseProdScoring | # 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 EltwiseProdScoring(nn.Module):
"""
Linearly mapping h and v to the same dimension, and do a elementwise
multiplication and a linear scoring
"""
def __init__(self, h_dim, a_dim, dot_dim=256):
"""Initialize layer."""
super(EltwiseProdScoring,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | zhangybzbo/speaker_follower | EltwiseProdScoring | false | 16,803 | [
"BSD-2-Clause",
"MIT"
] | 117 | e4d109ee26b2f57066adc9720443abf842ee9a9d | https://github.com/zhangybzbo/speaker_follower/tree/e4d109ee26b2f57066adc9720443abf842ee9a9d | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Linearly mapping h and v to the same dimension, and do a elementwise
multiplication and a linear scoring
"""
def __init__(self, h_dim, a_dim, dot_dim=256):
"""Initialize layer."""
super().__init__()
self.linear... |
RelativePositionalEmbedding | # 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 RelativePositionalEmbedding(nn.Module):
def __init__(self, n_model, max_len=1024):
super().__init__()
self.embed = nn.Embedding(max_len, n_model)
self.reset_parameters()
@torch.no_grad()
def reset_parameters(self):
w = self.embed.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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | yzhangcs/parser | RelativePositionalEmbedding | false | 16,804 | [
"MIT"
] | 439 | 3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | https://github.com/yzhangcs/parser/tree/3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n_model, max_len=1024):
super().__init__()
self.embed = nn.Embedding(max_len, n_model)
self.reset_parameters()
@torch.no_grad()
def reset_parameters(self):
w = self.embed.weight
max_len,... |
Classifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | zaynmi/semantic-equivalent-da-for-vqa | Classifier | false | 16,805 | [
"MIT"
] | 298 | f121fb3e8fee8af5f1935a7526f19e0d884bd95b | https://github.com/zaynmi/semantic-equivalent-da-for-vqa/tree/f121fb3e8fee8af5f1935a7526f19e0d884bd95b | import torch
import torch.utils.data
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super().__init__()
self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
self.dro... |
GumbelSigmoid | # 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 GumbelSigmoid(nn.Module):
def __init__(self, max_T, decay_alpha):
super(GumbelSigmoid, self).__init__()
self.max_T = max_T
self.decay_alpha = decay_alpha
self.softmax = nn.Softmax(dim=1)
self.p_value = 1e-08
self.register_bu... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_ma... | zdaxie/SpatiallyAdaptiveInference-Detection | GumbelSigmoid | false | 16,806 | [
"Apache-2.0"
] | 55 | 323801deac6f0641d00ecb23f6885df8483cc447 | https://github.com/zdaxie/SpatiallyAdaptiveInference-Detection/tree/323801deac6f0641d00ecb23f6885df8483cc447 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, max_T, decay_alpha):
super().__init__()
self.max_T = max_T
self.decay_alpha = decay_alpha
self.softmax = nn.Softmax(dim=1)
self.p_value = 1e-08
self.register_buffer('cur_T', torch.tensor(... |
AdaptiveInstanceNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.sparse
class AdaptiveInstanceNorm(nn.Module):
def __init__(self, in_channel, style_dim):
super().__init__()
self.norm = nn.InstanceNorm2d(in_channel)
self.linear = nn.Linear(style_dim, in_channel * 2)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | zhengqili/Crowdsampling-the-Plenoptic-Function | AdaptiveInstanceNorm | false | 16,807 | [
"MIT"
] | 70 | 3164e9f9574d597690f83dfdfb34cc470d2dcb88 | https://github.com/zhengqili/Crowdsampling-the-Plenoptic-Function/tree/3164e9f9574d597690f83dfdfb34cc470d2dcb88 | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.sparse
class Model(nn.Module):
def __init__(self, in_channel, style_dim):
super().__init__()
self.norm = nn.InstanceNorm2d(in_channel)
self.linear = nn.Linear(style_dim, in_channel * 2)
self.linea... |
CIoULoss | # 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.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def ciou(pred, target, eps=1e-07):
lt = torch.max(pred[:, :2], target[:, :2])
rb = torch.min(pred[:, 2:], target[:, 2:])
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch.dis... | zhangzhengde0225/SwinTrack | CIoULoss | false | 16,808 | [
"MIT"
] | 143 | 526be17f8ef266cb924c6939bd8dda23e9b73249 | https://github.com/zhangzhengde0225/SwinTrack/tree/526be17f8ef266cb924c6939bd8dda23e9b73249 | import math
import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def ciou(pred, target, eps=1e-07):
lt = torch.max(pred[:, :2], target[:, :2])
rb = torch.min(pred[:, 2:], target[:, 2:])
... |
LWS | # 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 LWS(nn.Module):
def __init__(self, num_features, num_classes, bias=True):
super(LWS, self).__init__()
self.fc = nn.Linear(num_features, num_classes, bias=bias)
self.scales = nn.Parameter(torch.ones(num_classes))
for param_name, param in 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 torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | zhangyongshun/BagofTricks-LT | LWS | false | 16,809 | [
"MIT"
] | 115 | aec4d9a552236c32231374b7b00fa5bf4208dae3 | https://github.com/zhangyongshun/BagofTricks-LT/tree/aec4d9a552236c32231374b7b00fa5bf4208dae3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_features, num_classes, bias=True):
super().__init__()
self.fc = nn.Linear(num_features, num_classes, bias=bias)
self.scales = nn.Parameter(torch.ones(num_classes))
for param_name, param in self.fc.na... |
RNN | # 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 torch.autograd import Variable
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
self.i2o = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch.autograd import Variable
assert_size_stride = t... | zhiyongc/Graph_Convolutional_LSTM | RNN | false | 16,810 | [
"MIT"
] | 281 | a703b63e626b1e2563fe3f45d9714e468b1d4a0e | https://github.com/zhiyongc/Graph_Convolutional_LSTM/tree/a703b63e626b1e2563fe3f45d9714e468b1d4a0e | import torch
import torch.nn as nn
from torch.autograd import Variable
class Model(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super().__init__()
self.hidden_size = hidden_size
self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
self.i2o = nn.Line... |
CosineClassifier | # 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
import torch.nn.functional as F
def cosine_fully_connected_layer(x_in, weight, scale=None, bias=None,
normalize_x=True, normalize_w=True):
assert x_in.dim() == 2
assert weight.dim() == 2
assert x_in.size(1) == weight.size(0)
if normalize_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.... | zheang01/FACT | CosineClassifier | false | 16,811 | [
"MIT"
] | 65 | a877cc86acc4d29fb7589c8ac571c8aef09e5fd8 | https://github.com/zheang01/FACT/tree/a877cc86acc4d29fb7589c8ac571c8aef09e5fd8 | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
def cosine_fully_connected_layer(x_in, weight, scale=None, bias=None,
normalize_x=True, normalize_w=True):
assert x_in.dim() == 2
assert weight.dim() == 2
assert x_in.size(1) == weight.size(0)
if normalize_x:
... |
GIoULoss | # 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.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def fp16_clamp(x, min=None, max=None):
if not x.is_cuda and x.dtype == torch.float16:
return x.float().clamp(min, max).half()
r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional
im... | zhangzhengde0225/SwinTrack | GIoULoss | false | 16,812 | [
"MIT"
] | 143 | 526be17f8ef266cb924c6939bd8dda23e9b73249 | https://github.com/zhangzhengde0225/SwinTrack/tree/526be17f8ef266cb924c6939bd8dda23e9b73249 | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def fp16_clamp(x, min=None, max=None):
if not x.is_cuda and x.dtype == torch.float16:
return x.float().clamp(min, max).half()
r... |
BoundedIoULoss | # 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.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def bounded_iou_loss(pred, target, beta=0.2, eps=0.001):
"""BIoULoss.
This is an implementation of paper
`Improving Object Localiz... | 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.distribut... | zhangzhengde0225/SwinTrack | BoundedIoULoss | false | 16,813 | [
"MIT"
] | 143 | 526be17f8ef266cb924c6939bd8dda23e9b73249 | https://github.com/zhangzhengde0225/SwinTrack/tree/526be17f8ef266cb924c6939bd8dda23e9b73249 | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def bounded_iou_loss(pred, target, beta=0.2, eps=0.001):
"""BIoULoss.
This is an implementation of paper
`Improving Object Localiz... |
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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
from queue import *
from math import *
class Attention(nn.Module):
def __init__(self, hidden_size):
super(Attention, self).__init__()
self.attn = nn.Linear(hidden_size * 2, hidden_size)
self.v = nn.Parameter(to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | zhongerqiandan/OpenDialog | Attention | false | 16,814 | [
"MIT"
] | 98 | f478b2a912c8c742da5ced510ac40da59217ddb3 | https://github.com/zhongerqiandan/OpenDialog/tree/f478b2a912c8c742da5ced510ac40da59217ddb3 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from queue import *
from math import *
class Model(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.attn = nn.Linear(hidden_size * 2, hidden_size)
self.v = nn.Parameter(torch.randn(hidden_si... |
segmentation_layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class segmentation_layer(nn.Module):
def __init__(self, args):
super(segmentation_layer, self).__init__()
self.segm_layer = nn.Conv2d(32, args.snumclass, kernel_size=1)
def forward(self, featMap):
segm =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | zhenpeiyang/RelativePose | segmentation_layer | false | 16,815 | [
"BSD-3-Clause"
] | 144 | 2e9fdf5003c5952cf610f8c6d891519b9e9e014b | https://github.com/zhenpeiyang/RelativePose/tree/2e9fdf5003c5952cf610f8c6d891519b9e9e014b | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, args):
super().__init__()
self.segm_layer = nn.Conv2d(32, args.snumclass, kernel_size=1)
def forward(self, featMap):
segm = self.segm_layer(featMap)
ret... |
MyUpsample2 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
class MyUpsample2(nn.Module):
def forward(self, x):
return x[:, :, :, None, :, None].expand(-1, -1, -1, 2, -1, 2).reshape(x
.size(0), x.size(1), x.size(2) * 2, x.size(3) * 2)
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
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.... | zigonk/ReSC | MyUpsample2 | false | 16,816 | [
"MIT"
] | 57 | c816365b0410f521974060ef0cc6eaa1dd09b63a | https://github.com/zigonk/ReSC/tree/c816365b0410f521974060ef0cc6eaa1dd09b63a | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
class Model(nn.Module):
def forward(self, x):
return x[:, :, :, None, :, None].expand(-1, -1, -1, 2, -1, 2).reshape(x
.size(0), x.size(1), x.size(2) * 2, x.size(3) * 2)
def get... |
BCEFocalLoss | # 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 BCEFocalLoss(torch.nn.Module):
"""
二分类的Focalloss alpha 固定
"""
def __init__(self, gamma=2, alpha=0.25, reduction='sum', loss_weight=1.0):
super().__init__()
self.gamma = gamma
self.alpha = alpha
self.reduction = reduction
self.loss_weight = loss_w... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | zhiqi-li/Panoptic-SegFormer | BCEFocalLoss | false | 16,817 | [
"Apache-2.0"
] | 97 | cdb9b68059e9ef825a3f7079c37aa835b1711227 | https://github.com/zhiqi-li/Panoptic-SegFormer/tree/cdb9b68059e9ef825a3f7079c37aa835b1711227 | import torch
class Model(torch.nn.Module):
"""
二分类的Focalloss alpha 固定
"""
def __init__(self, gamma=2, alpha=0.25, reduction='sum', loss_weight=1.0):
super().__init__()
self.gamma = gamma
self.alpha = alpha
self.reduction = reduction
self.loss_weight = loss_weight
... |
LAM_Gconv | # 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 LAM_Gconv(nn.Module):
def __init__(self, in_features, out_features, activation=nn.ReLU(
inplace=True)):
super(LAM_Gconv, self).__init__()
self.fc = nn.Linear(in_features=in_features, out_features=out_features)
self.activation = activation
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | zhaoweixi/GraFormer | LAM_Gconv | false | 16,818 | [
"BSD-2-Clause"
] | 384 | 0a0a04014cdf157c11ab8e952862efa27c6a1980 | https://github.com/zhaoweixi/GraFormer/tree/0a0a04014cdf157c11ab8e952862efa27c6a1980 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_features, out_features, activation=nn.ReLU(
inplace=True)):
super().__init__()
self.fc = nn.Linear(in_features=in_features, out_features=out_features)
self.activation = activation
def laplacian(s... |
IRHead | # 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 queue import *
from math import *
class IRHead(nn.Module):
def __init__(self, hidden_size, dropout=0.5):
super(IRHead, self).__init__()
self.M = nn.Parameter(torch.randn(hidden_size, hidden_size))
self.hidden_layer = nn.Linear(hidden_size * 2 + 1, h... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | zhongerqiandan/OpenDialog | IRHead | false | 16,819 | [
"MIT"
] | 98 | f478b2a912c8c742da5ced510ac40da59217ddb3 | https://github.com/zhongerqiandan/OpenDialog/tree/f478b2a912c8c742da5ced510ac40da59217ddb3 | import torch
import torch.nn as nn
from queue import *
from math import *
class Model(nn.Module):
def __init__(self, hidden_size, dropout=0.5):
super().__init__()
self.M = nn.Parameter(torch.randn(hidden_size, hidden_size))
self.hidden_layer = nn.Linear(hidden_size * 2 + 1, hidden_size)
... |
DenseBlock | # 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.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.Conv2d):
init.kaiming_normal_(m.weight, a=0, mode='f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.init as init
assert_size_stride = torch._C... | yzxing87/Invertible-ISP | DenseBlock | false | 16,820 | [
"MIT"
] | 246 | 344dd333dd2a075f6a9e4ffc445dc387ca3014c4 | https://github.com/yzxing87/Invertible-ISP/tree/344dd333dd2a075f6a9e4ffc445dc387ca3014c4 | import torch
import torch.nn as nn
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.Conv2d):
init.kaiming_normal_(m.weight, a=0, mode='f... |
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.functional as F
import torch.nn as nn
from torch.autograd import Variable
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 o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | zhiyongc/Graph_Convolutional_LSTM | LSTM | false | 16,821 | [
"MIT"
] | 281 | a703b63e626b1e2563fe3f45d9714e468b1d4a0e | https://github.com/zhiyongc/Graph_Convolutional_LSTM/tree/a703b63e626b1e2563fe3f45d9714e468b1d4a0e | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
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 ... |
BG_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
import torch.utils.data.distributed
class BG_loss(nn.Module):
def __init__(self):
super(BG_loss, self).__init__()
self.loss = nn.L1Loss()
def forward(self, real_imgs, fake_imgs, masks):
real_imgs_ = real_imgs.clone()
fake_imgs_ = fake_imgs.c... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ziqi-jin/OpenUnReID | BG_loss | false | 16,822 | [
"Apache-2.0"
] | 344 | 50eb516945c418398cac890029d1b366c27c0185 | https://github.com/ziqi-jin/OpenUnReID/tree/50eb516945c418398cac890029d1b366c27c0185 | import torch
import torch.nn as nn
import torch.utils.data.distributed
class Model(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.L1Loss()
def forward(self, real_imgs, fake_imgs, masks):
real_imgs_ = real_imgs.clone()
fake_imgs_ = fake_imgs.clone()
... |
SmoothSoftmax | # 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
from torch import nn
class SmoothSoftmax(nn.Module):
def forward(self, x: 'Tensor'):
logistic_value = torch.sigmoid(x)
return logistic_value / logistic_value.sum(dim=-1, keepdim=True)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | zsl24/voice-activity-detection | SmoothSoftmax | false | 16,823 | [
"MIT"
] | 74 | a034be23c6283121c6b72e778c6ff6711045cbe3 | https://github.com/zsl24/voice-activity-detection/tree/a034be23c6283121c6b72e778c6ff6711045cbe3 | import torch
from torch import Tensor
from torch import nn
class Model(nn.Module):
def forward(self, x: 'Tensor'):
logistic_value = torch.sigmoid(x)
return logistic_value / logistic_value.sum(dim=-1, keepdim=True)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... |
Quaternion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.utils.data
class Quaternion(nn.Module):
def __init__(self):
super(Quaternion, self).__init__()
def forward(self, rvec):
theta = torch.sqrt(1e-05 + torch.sum(rvec ** 2, dim=1))
rvec = rvec / theta[:, None]
return torch.stack((1.0... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | zhuhao-nju/mofanerf | Quaternion | false | 16,824 | [
"MIT"
] | 55 | 0206526e25aab3dd8f0cc789f290c7559642676b | https://github.com/zhuhao-nju/mofanerf/tree/0206526e25aab3dd8f0cc789f290c7559642676b | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, rvec):
theta = torch.sqrt(1e-05 + torch.sum(rvec ** 2, dim=1))
rvec = rvec / theta[:, None]
return torch.stack((1.0 - 2.0 * rvec[:, 1] *... |
Rodrigues | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.utils.data
class Rodrigues(nn.Module):
def __init__(self):
super(Rodrigues, self).__init__()
def forward(self, rvec):
theta = torch.sqrt(1e-05 + torch.sum(rvec ** 2, dim=1))
rvec = rvec / theta[:, None]
costh = torch.cos(theta)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.utils.data
assert_size_stri... | zhuhao-nju/mofanerf | Rodrigues | false | 16,825 | [
"MIT"
] | 55 | 0206526e25aab3dd8f0cc789f290c7559642676b | https://github.com/zhuhao-nju/mofanerf/tree/0206526e25aab3dd8f0cc789f290c7559642676b | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, rvec):
theta = torch.sqrt(1e-05 + torch.sum(rvec ** 2, dim=1))
rvec = rvec / theta[:, None]
costh = torch.cos(theta)
sinth = tor... |
ChebConv | # 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 torch.nn import init
class ChebConv(nn.Module):
"""
The ChebNet convolution operation.
:param in_c: int, number of input channels.
:param out_c: int, number of output channels.
:param K: int, the order of Chebyshev Polynomial.
"""
def __init__(self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | zhaoweixi/GraFormer | ChebConv | false | 16,826 | [
"BSD-2-Clause"
] | 384 | 0a0a04014cdf157c11ab8e952862efa27c6a1980 | https://github.com/zhaoweixi/GraFormer/tree/0a0a04014cdf157c11ab8e952862efa27c6a1980 | import torch
import torch.nn as nn
from torch.nn import init
class Model(nn.Module):
"""
The ChebNet convolution operation.
:param in_c: int, number of input channels.
:param out_c: int, number of output channels.
:param K: int, the order of Chebyshev Polynomial.
"""
def __init__(self, i... |
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 torch
from torch import Tensor
from torch import nn
class Attention(nn.Module):
def forward(self, selected_input: 'Tensor', attention: 'Tensor'):
attended_input = selected_input * attention.unsqueeze(-1)
return attended_input
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | zsl24/voice-activity-detection | Attention | false | 16,827 | [
"MIT"
] | 74 | a034be23c6283121c6b72e778c6ff6711045cbe3 | https://github.com/zsl24/voice-activity-detection/tree/a034be23c6283121c6b72e778c6ff6711045cbe3 | import torch
from torch import Tensor
from torch import nn
class Model(nn.Module):
def forward(self, selected_input: 'Tensor', attention: 'Tensor'):
attended_input = selected_input * attention.unsqueeze(-1)
return attended_input
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand... |
RerangeLayer | # 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 RerangeLayer(nn.Module):
def __init__(self):
super(RerangeLayer, self).__init__()
def forward(self, inp):
return (inp + 1.0) / 2.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | zvict/HyperRIM | RerangeLayer | false | 16,828 | [
"Apache-2.0"
] | 92 | f3800196b59ea0f94561efa88ec2e6675e4c8b00 | https://github.com/zvict/HyperRIM/tree/f3800196b59ea0f94561efa88ec2e6675e4c8b00 | import torch
import torch.utils.data
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, inp):
return (inp + 1.0) / 2.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
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
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, focusing_param=2, balance_param=0.25):
super(FocalLoss, self).__init__()
self.focusing_param = focusing_param
self.balance_param = balance_param
def forward(self, output,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | zwx8981/DBCNN-Pytorch | FocalLoss | false | 16,829 | [
"MIT"
] | 150 | 16c3156054a30a3eabb45dffcf538f42452a14f3 | https://github.com/zwx8981/DBCNN-Pytorch/tree/16c3156054a30a3eabb45dffcf538f42452a14f3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, focusing_param=2, balance_param=0.25):
super().__init__()
self.focusing_param = focusing_param
self.balance_param = balance_param
def forward(self, output, target):
c... |
cross_entropy_prob | # 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 cross_entropy_prob(nn.Module):
def __init__(self):
super(cross_entropy_prob, self).__init__()
def forward(self, pred, soft_targets):
pred = F.log_softmax(pred)
loss = torch.mean(torch.sum(-soft_targets * pred, 1... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | zwx8981/DBCNN-Pytorch | cross_entropy_prob | false | 16,830 | [
"MIT"
] | 150 | 16c3156054a30a3eabb45dffcf538f42452a14f3 | https://github.com/zwx8981/DBCNN-Pytorch/tree/16c3156054a30a3eabb45dffcf538f42452a14f3 | 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, pred, soft_targets):
pred = F.log_softmax(pred)
loss = torch.mean(torch.sum(-soft_targets * pred, 1))
return loss
def get_inpu... |
SelfAttentionBlock | # 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.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
class Mlp(nn.Module):
""" Multilayer perceptron."""
def __init__(self, in_features, hidden_features=None, out_features=None,
a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | zhangzhengde0225/SwinTrack | SelfAttentionBlock | false | 16,831 | [
"MIT"
] | 143 | 526be17f8ef266cb924c6939bd8dda23e9b73249 | https://github.com/zhangzhengde0225/SwinTrack/tree/526be17f8ef266cb924c6939bd8dda23e9b73249 | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
class Mlp(nn.Module):
""" Multilayer perceptron."""
def __init__(self, in_features, hidden_features=None, out_features=None,
a... |
A2Block | # 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 A2Block(nn.Module):
"""
Implementation of A2Block(NIPS 2018)
"""
def __init__(self, inplane, plane):
super(A2Block, self).__init__()
self.down = nn.Conv2d(inplane, plane, 1)
self.up = nn.Conv2d(plane, inplane, 1)
self.gather... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | zj1008/GALD-DGCNet | A2Block | false | 16,832 | [
"MIT"
] | 127 | be7ebfe2b3d28ea28a2b4714852999d4af2a785e | https://github.com/zj1008/GALD-DGCNet/tree/be7ebfe2b3d28ea28a2b4714852999d4af2a785e | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Implementation of A2Block(NIPS 2018)
"""
def __init__(self, inplane, plane):
super().__init__()
self.down = nn.Conv2d(inplane, plane, 1)
self.up = nn.Conv2d(plane, inplane, 1)
self.gather_down = nn.Conv... |
BoundedSingleVar | # 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 BoundedSingleVar(torch.nn.Module):
"""Wrapper a single parameter to represent an unknown coefficient in inverse problem with the upper and lower bound.
:param lower_bound: The lower bound for the parameter.
:type lower_bound: float
:param upper_bound: The upper bound for the parame... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | zweien/idrlnet | BoundedSingleVar | false | 16,833 | [
"Apache-2.0"
] | 66 | 3a19a3301d565c0906aac84ff31eefcff75726a8 | https://github.com/zweien/idrlnet/tree/3a19a3301d565c0906aac84ff31eefcff75726a8 | import torch
class Model(torch.nn.Module):
"""Wrapper a single parameter to represent an unknown coefficient in inverse problem with the upper and lower bound.
:param lower_bound: The lower bound for the parameter.
:type lower_bound: float
:param upper_bound: The upper bound for the parameter.
:t... |
FcCat | # 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 FcCat(nn.Module):
def __init__(self, nIn, nOut):
super(FcCat, self).__init__()
self.fc = nn.Linear(nIn, nOut, bias=False)
def forward(self, x):
out = torch.cat((x, self.fc(x)), 1)
return out
def get_inputs():
return [torch.rand([... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | zwh930712/densenet.pytorch | FcCat | false | 16,834 | [
"Apache-2.0"
] | 826 | d1cd5e1957975628286e516512c6d1c14430f810 | https://github.com/zwh930712/densenet.pytorch/tree/d1cd5e1957975628286e516512c6d1c14430f810 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, nIn, nOut):
super().__init__()
self.fc = nn.Linear(nIn, nOut, bias=False)
def forward(self, x):
out = torch.cat((x, self.fc(x)), 1)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4]... |
CrossAttentionBlock | # 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.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
class Mlp(nn.Module):
""" Multilayer perceptron."""
def __init__(self, in_features, hidden_features=None, out_features=None,
a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | zhangzhengde0225/SwinTrack | CrossAttentionBlock | false | 16,835 | [
"MIT"
] | 143 | 526be17f8ef266cb924c6939bd8dda23e9b73249 | https://github.com/zhangzhengde0225/SwinTrack/tree/526be17f8ef266cb924c6939bd8dda23e9b73249 | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
class Mlp(nn.Module):
""" Multilayer perceptron."""
def __init__(self, in_features, hidden_features=None, out_features=None,
a... |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class FcCat(nn.Module):
def __init__(self, nIn, nOut):
super(FcCat, self).__init__()
self.fc = nn.Linear(nIn, nOut, bias=False)
def forward(self, x):
out = torch.cat((x, self.fc(x)), 1)
return out
class Net(nn.Module):
def __init__(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | zwh930712/densenet.pytorch | Net | false | 16,836 | [
"Apache-2.0"
] | 826 | d1cd5e1957975628286e516512c6d1c14430f810 | https://github.com/zwh930712/densenet.pytorch/tree/d1cd5e1957975628286e516512c6d1c14430f810 | import torch
import torch.nn as nn
class FcCat(nn.Module):
def __init__(self, nIn, nOut):
super().__init__()
self.fc = nn.Linear(nIn, nOut, bias=False)
def forward(self, x):
out = torch.cat((x, self.fc(x)), 1)
return out
class Model(nn.Module):
def __init__(self, nFeat... |
SpatialSoftmaxBZ | # 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 numpy as np
import torch.nn.functional as F
class SpatialSoftmaxBZ(torch.nn.Module):
"""
IMPORTANT:
i in [0, 1], where 0 is at the bottom, 1 is at the top
j in [-1, 1]
"""
def __init__(self, height, width):
super().__init__()
self.height = height
se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
ass... | zwc662/SequentialAttack | SpatialSoftmaxBZ | false | 16,837 | [
"MIT"
] | 116 | 677b19c51ea76d794939ee126fccd75ffa0e6fe6 | https://github.com/zwc662/SequentialAttack/tree/677b19c51ea76d794939ee126fccd75ffa0e6fe6 | import torch
import numpy as np
import torch.nn.functional as F
class Model(torch.nn.Module):
"""
IMPORTANT:
i in [0, 1], where 0 is at the bottom, 1 is at the top
j in [-1, 1]
"""
def __init__(self, height, width):
super().__init__()
self.height = height
self.width = ... |
AttentionLayer | # 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.utils.data
import torch.distributed
import torch.nn as nn
import torch.optim
import torch.optim.lr_scheduler
def Linear(in_features, out_features, bias=True, dropout=0):
"""Weight-normalized Linear layer (input: N x T x C)"""
m = nn.Linear(in_features,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | zsquaredz/XSum | AttentionLayer | false | 16,838 | [
"MIT"
] | 235 | 10f2fac2e70801e7a3973c864b5a24b61d3f8bfe | https://github.com/zsquaredz/XSum/tree/10f2fac2e70801e7a3973c864b5a24b61d3f8bfe | import torch
import torch.nn.functional as F
import torch.utils.data
import torch.distributed
import torch.nn as nn
import torch.optim
import torch.optim.lr_scheduler
def Linear(in_features, out_features, bias=True, dropout=0):
"""Weight-normalized Linear layer (input: N x T x C)"""
m = nn.Linear(in_features,... |
PSNR | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch.nn.modules.loss import _Loss
class PSNR(_Loss):
def __init__(self):
super(PSNR, self).__init__()
self.val_range = 255
def _quantize(self, img):
img = img * self.val_range
img = img.clamp(0, self.val_range).round()
return img
def forward(se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn.modules.loss i... | zzh-tech/RSCD | PSNR | false | 16,839 | [
"MIT"
] | 57 | b287b1621121f8ca7ece6b27ebd4e28a5f8e6f5e | https://github.com/zzh-tech/RSCD/tree/b287b1621121f8ca7ece6b27ebd4e28a5f8e6f5e | import torch
from torch.nn.modules.loss import _Loss
class Model(_Loss):
def __init__(self):
super().__init__()
self.val_range = 255
def _quantize(self, img):
img = img * self.val_range
img = img.clamp(0, self.val_range).round()
return img
def forward(self, x, y)... |
DenseLayer | # 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 actFunc(act, *args, **kwargs):
act = act.lower()
if act == 'relu':
return nn.ReLU()
elif act == 'relu6':
return nn.ReLU6()
elif act == 'leakyrelu':
return nn.LeakyReLU(0.1)
elif act == 'prelu':
return nn.PReLU()
elif act ==... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | zzh-tech/RSCD | DenseLayer | false | 16,840 | [
"MIT"
] | 57 | b287b1621121f8ca7ece6b27ebd4e28a5f8e6f5e | https://github.com/zzh-tech/RSCD/tree/b287b1621121f8ca7ece6b27ebd4e28a5f8e6f5e | import torch
import torch.nn as nn
def actFunc(act, *args, **kwargs):
act = act.lower()
if act == 'relu':
return nn.ReLU()
elif act == 'relu6':
return nn.ReLU6()
elif act == 'leakyrelu':
return nn.LeakyReLU(0.1)
elif act == 'prelu':
return nn.PReLU()
elif act ==... |
TxtNet | # 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
class TxtNet(nn.Module):
def __init__(self, code_len, txt_feat_len):
super(TxtNet, self).__init__()
self.fc1 = nn.Linear(txt_feat_len, 4096)
self.fc2 = nn.Linear(4096, code_len)
self.alpha = 1.0
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | zzs1994/DJsRH | TxtNet | false | 16,841 | [
"MIT"
] | 53 | 6041c2df810723dd0052e2e5b7c6bd33033f0f21 | https://github.com/zzs1994/DJsRH/tree/6041c2df810723dd0052e2e5b7c6bd33033f0f21 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, code_len, txt_feat_len):
super().__init__()
self.fc1 = nn.Linear(txt_feat_len, 4096)
self.fc2 = nn.Linear(4096, code_len)
self.alpha = 1.0
def forward(sel... |
FeatureFusion | # 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.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
class Mlp(nn.Module):
""" Multilayer perceptron."""
def __init__(self, in_features, hidden_features=None, out_features=None,
a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | zhangzhengde0225/SwinTrack | FeatureFusion | false | 16,842 | [
"MIT"
] | 143 | 526be17f8ef266cb924c6939bd8dda23e9b73249 | https://github.com/zhangzhengde0225/SwinTrack/tree/526be17f8ef266cb924c6939bd8dda23e9b73249 | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
class Mlp(nn.Module):
""" Multilayer perceptron."""
def __init__(self, in_features, hidden_features=None, out_features=None,
a... |
TargetQueryDecoderLayer | # 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.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
class Mlp(nn.Module):
""" Multilayer perceptron."""
def __init__(self, in_features, hidden_features=None, out_features=None,
a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | zhangzhengde0225/SwinTrack | TargetQueryDecoderLayer | false | 16,843 | [
"MIT"
] | 143 | 526be17f8ef266cb924c6939bd8dda23e9b73249 | https://github.com/zhangzhengde0225/SwinTrack/tree/526be17f8ef266cb924c6939bd8dda23e9b73249 | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
class Mlp(nn.Module):
""" Multilayer perceptron."""
def __init__(self, in_features, hidden_features=None, out_features=None,
a... |
Actor | # 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 Actor(nn.Module):
def __init__(self, kernel_size):
super(Actor, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=kernel_size)
self.conv2 = nn.Conv2d(16, 4, kernel_size=kernel_size)
self.pool1 = nn.M... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | zwc662/SequentialAttack | Actor | false | 16,844 | [
"MIT"
] | 116 | 677b19c51ea76d794939ee126fccd75ffa0e6fe6 | https://github.com/zwc662/SequentialAttack/tree/677b19c51ea76d794939ee126fccd75ffa0e6fe6 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, kernel_size):
super().__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=kernel_size)
self.conv2 = nn.Conv2d(16, 4, kernel_size=kernel_size)
self.pool1 = nn.MaxPool2d(2,... |
StdConv2dSame | # 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 torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.utils.data.distributed
def get_same_padding(x: 'int', k: 'int', s: 'int', d: 'int'):
return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0)
def pad_same(x, k, s, d=(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | ziniuwan/maed | StdConv2dSame | false | 16,845 | [
"MIT"
] | 145 | 9e1f1c37eba81da86c8d9c62dc9be41a01abff5b | https://github.com/ziniuwan/maed/tree/9e1f1c37eba81da86c8d9c62dc9be41a01abff5b | import math
import torch
import torch.nn as nn
import torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.utils.data.distributed
def get_same_padding(x: 'int', k: 'int', s: 'int', d: 'int'):
return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0)
def pad_same(x, k, s, d=(... |
MDNHead | # 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
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
from torch.distributions import Categorical
from torch.nn.utils import vector_to_parameters
from torch.nn.utils import parameters_to_vector
def ortho_init(module, no... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn... | zuoxingdong/lagom | MDNHead | false | 16,846 | [
"MIT"
] | 383 | 3b6710804dbc79c6dffb369ac87c68f4055ab6cd | https://github.com/zuoxingdong/lagom/tree/3b6710804dbc79c6dffb369ac87c68f4055ab6cd | from torch.nn import Module
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
from torch.distributions import Categorical
from torch.nn.utils import vector_to_parameters
from torch.nn.utils import parameters_to_vector
def ortho_init(module, no... |
_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 GCT(nn.Module):
def __init__(self, num_channels, epsilon=1e-05, mode='l2', after_relu=False
):
super(GCT, self).__init__()
self.alpha = nn.Parameter(torch.ones(1, num_channels, 1, 1))
self.gamma = nn.Parameter(torch.zeros(1, num_channels, 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.... | yoxu515/CFBI | _ASPPModule | false | 16,847 | [
"BSD-3-Clause"
] | 312 | 0bab1e3c9fc3e3ba0629f716d60221e8f8d9d586 | https://github.com/yoxu515/CFBI/tree/0bab1e3c9fc3e3ba0629f716d60221e8f8d9d586 | import torch
import torch.nn as nn
class GCT(nn.Module):
def __init__(self, num_channels, epsilon=1e-05, mode='l2', after_relu=False
):
super().__init__()
self.alpha = nn.Parameter(torch.ones(1, num_channels, 1, 1))
self.gamma = nn.Parameter(torch.zeros(1, num_channels, 1, 1))
... |
Gaussian | # 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.utils.tensorboard
import torch.utils.data
class Gaussian(torch.nn.Module):
"""Gaussian activation"""
def forward(self, x: 'Tensor') ->Tensor:
return torch.exp(-x * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.tensorboard
import torch.utils.data
assert_size_stride... | yangyinuo823/torchani | Gaussian | false | 16,848 | [
"MIT"
] | 305 | b0cd62eda59829d197b3c37f2215ba1af64f1c8d | https://github.com/yangyinuo823/torchani/tree/b0cd62eda59829d197b3c37f2215ba1af64f1c8d | import torch
from torch import Tensor
import torch.utils.tensorboard
import torch.utils.data
class Model(torch.nn.Module):
"""Gaussian activation"""
def forward(self, x: 'Tensor') ->Tensor:
return torch.exp(-x * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... |
waspIntrinsicComposer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class waspIntrinsicComposer(nn.Module):
def __init__(self, opt):
super(waspIntrinsicComposer, self).__init__()
self.ngpu = opt.ngpu
self.nc = opt.nc
def f... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | zhixinshu/DeformingAutoencoders-pytorch | waspIntrinsicComposer | false | 16,849 | [
"BSD-2-Clause"
] | 112 | 72996c5d11ae25dd0051bb51df353fef88e65742 | https://github.com/zhixinshu/DeformingAutoencoders-pytorch/tree/72996c5d11ae25dd0051bb51df353fef88e65742 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Model(nn.Module):
def __init__(self, opt):
super().__init__()
self.ngpu = opt.ngpu
self.nc = opt.nc
def forward(self, shading, albedo):
self... |
VGG16 | # 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 torch.nn import functional as F
class VGG16(nn.Module):
def __init__(self, conv5_dilation=1):
super(VGG16, self).__init__()
None
self.conv1_1 = nn.Conv2d(3, 64, 3, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
self.pool1 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | yaoqi-zd/SGAN | VGG16 | false | 16,850 | [
"MIT"
] | 48 | 43d8a859b03967e2423a73ef1ba332ee71714ba4 | https://github.com/yaoqi-zd/SGAN/tree/43d8a859b03967e2423a73ef1ba332ee71714ba4 | import torch
import torch.nn as nn
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, conv5_dilation=1):
super().__init__()
None
self.conv1_1 = nn.Conv2d(3, 64, 3, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
self.pool1 = nn.MaxPoo... |
BridgeConnection | # 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 torch.utils import tensorboard as tensorboard
class BridgeConnection(nn.Module):
def __init__(self, in_dim, out_dim, dout_p):
super(BridgeConnection, self).__init__()
self.norm = nn.LayerNorm(in_dim)
self.linear = nn.Linear(in_dim, out_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | valterlej/CustomBMT | BridgeConnection | false | 16,851 | [
"MIT"
] | 157 | c9326752d1355c81f845f2caab9c047be76067de | https://github.com/valterlej/CustomBMT/tree/c9326752d1355c81f845f2caab9c047be76067de | import torch
import torch.nn as nn
from torch.utils import tensorboard as tensorboard
class Model(nn.Module):
def __init__(self, in_dim, out_dim, dout_p):
super().__init__()
self.norm = nn.LayerNorm(in_dim)
self.linear = nn.Linear(in_dim, out_dim)
self.dropout = nn.Dropout(dout_p)... |
FeatureEmbedder | # 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.nn as nn
from torch.utils import tensorboard as tensorboard
class FeatureEmbedder(nn.Module):
def __init__(self, d_feat, d_model):
super(FeatureEmbedder, self).__init__()
self.d_model = d_model
self.embedder = nn.Linear(d_feat, d_model)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | valterlej/CustomBMT | FeatureEmbedder | false | 16,852 | [
"MIT"
] | 157 | c9326752d1355c81f845f2caab9c047be76067de | https://github.com/valterlej/CustomBMT/tree/c9326752d1355c81f845f2caab9c047be76067de | import torch
import numpy as np
import torch.nn as nn
from torch.utils import tensorboard as tensorboard
class Model(nn.Module):
def __init__(self, d_feat, d_model):
super().__init__()
self.d_model = d_model
self.embedder = nn.Linear(d_feat, d_model)
self.activation = nn.ReLU()
... |
SpatialCGNL | # 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 SpatialCGNL(nn.Module):
"""Spatial CGNL block with dot production kernel for image classfication.
"""
def __init__(self, inplanes, planes, use_scale=False, groups=8):
self.use_scale = use_scale
self.groups = groups
super(SpatialCGNL, 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.... | zj1008/GALD-DGCNet | SpatialCGNL | false | 16,853 | [
"MIT"
] | 127 | be7ebfe2b3d28ea28a2b4714852999d4af2a785e | https://github.com/zj1008/GALD-DGCNet/tree/be7ebfe2b3d28ea28a2b4714852999d4af2a785e | import torch
import torch.nn as nn
class Model(nn.Module):
"""Spatial CGNL block with dot production kernel for image classfication.
"""
def __init__(self, inplanes, planes, use_scale=False, groups=8):
self.use_scale = use_scale
self.groups = groups
super().__init__()
self... |
MultiheadedAttention | # 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.nn.functional as F
import torch.nn as nn
from torch.utils import tensorboard as tensorboard
def attention(Q, K, V, mask, dropout=None):
d_k = Q.size(-1)
QKt = Q.matmul(K.transpose(-1, -2))
sm_input = QKt / np.sqrt(d_k)
if mask is not None:
sm_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | valterlej/CustomBMT | MultiheadedAttention | false | 16,854 | [
"MIT"
] | 157 | c9326752d1355c81f845f2caab9c047be76067de | https://github.com/valterlej/CustomBMT/tree/c9326752d1355c81f845f2caab9c047be76067de | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
from torch.utils import tensorboard as tensorboard
def attention(Q, K, V, mask, dropout=None):
d_k = Q.size(-1)
QKt = Q.matmul(K.transpose(-1, -2))
sm_input = QKt / np.sqrt(d_k)
if mask is not None:
sm_input ... |
SinkhornDistance | # 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
class SinkhornDistance(torch.nn.Module):
"""
Given two empirical measures each with :math:`P_1` locations
:math:`x\\in\\mathbb{R}^{D_1}` and :math:`P_2` locations :math:`y\\in\\mathbb{R}^{D_2}`,
outputs an approximation of the regularized OT cost for po... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | yjh0410/actionformer_release | SinkhornDistance | false | 16,855 | [
"MIT"
] | 61 | 7a97422111d3e29c8d2e14088c850c6975855ea7 | https://github.com/yjh0410/actionformer_release/tree/7a97422111d3e29c8d2e14088c850c6975855ea7 | import torch
import torch.utils.data
class Model(torch.nn.Module):
"""
Given two empirical measures each with :math:`P_1` locations
:math:`x\\in\\mathbb{R}^{D_1}` and :math:`P_2` locations :math:`y\\in\\mathbb{R}^{D_2}`,
outputs an approximation of the regularized OT cost for point clouds.... |
FCN8s | # 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.nn as nn
class FCN8s(nn.Module):
def __init__(self, n_class=3):
super(FCN8s, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, 3, padding=100)
self.relu1_1 = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | twni2016/OrganSegRSTN_PyTorch | FCN8s | false | 16,856 | [
"MIT"
] | 100 | bf571320e718c8f138e04d48645e3b4dfe75801d | https://github.com/twni2016/OrganSegRSTN_PyTorch/tree/bf571320e718c8f138e04d48645e3b4dfe75801d | import torch
import numpy as np
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n_class=3):
super().__init__()
self.conv1_1 = nn.Conv2d(3, 64, 3, padding=100)
self.relu1_1 = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
self.relu1_2 ... |
LayoutNet | # 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 LayoutNet(nn.Module):
def __init__(self):
super(LayoutNet, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1, stride=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | wellowdata/pytorch-layoutnet | LayoutNet | false | 16,857 | [
"MIT"
] | 155 | 3d4352f94ed00d3c37890e9119452811d4f0893f | https://github.com/wellowdata/pytorch-layoutnet/tree/3d4352f94ed00d3c37890e9119452811d4f0893f | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1, stride=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=1)
self.conv3 = nn.Co... |
ClassNetVideoConv | # 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
class Unit3D(nn.Module):
"""Basic unit containing Conv3D + BatchNorm + non-linearity."""
def __init__(self, in_channels, output_channels, kernel_shape=(1, 1, 1),
stride=(1, 1, 1), padding=0, activation_fn=F.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
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
as... | SheffieldAI/pykale | ClassNetVideoConv | false | 16,858 | [
"MIT"
] | 324 | be7670941fb06835883c80477b26702d407017db | https://github.com/SheffieldAI/pykale/tree/be7670941fb06835883c80477b26702d407017db | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Unit3D(nn.Module):
"""Basic unit containing Conv3D + BatchNorm + non-linearity."""
def __init__(self, in_channels, output_channels, kernel_shape=(1, 1, 1),
stride=(1, 1, 1), padding=0, activation_fn=F.rel... |
single_param | # 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.distributions
class single_param(nn.Module):
def __init__(self, value):
super(single_param, self).__init__()
self.p = nn.Parameter(torch.FloatTensor([value]))
def forward(self):
return torch.abs(self.p)
def get_inputs():
return []... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.distributions
assert_size_stride = tor... | AaltoML/PeriodicBNN | single_param | false | 16,859 | [
"MIT"
] | 9 | 1638edb365641e7fe2ea2ab3c15b9439473f9cf3 | https://github.com/AaltoML/PeriodicBNN/tree/1638edb365641e7fe2ea2ab3c15b9439473f9cf3 | import torch
import torch.nn as nn
import torch.distributions
class Model(nn.Module):
def __init__(self, value):
super().__init__()
self.p = nn.Parameter(torch.FloatTensor([value]))
def forward(self):
return torch.abs(self.p)
def get_inputs():
return []
def get_init_inputs():... |
VertexDirectEmbedder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
from torch import nn
def normalize_embeddings(embeddings: 'torch.Tensor', epsilon: 'float'=1e-06
) ->torch.Tensor:
"""
Normalize N D-dimensional embedding vectors arranged in a tensor [N, D]
Args:
embeddings (tensor [N, D]): N D-dimensional embedding vecto... | 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
from... | AbirKhan96/facebook-detectron2 | VertexDirectEmbedder | false | 16,860 | [
"Apache-2.0"
] | 5 | 6a3bf813353d74bbeb8674e3566e7bbb33eb5c87 | https://github.com/AbirKhan96/facebook-detectron2/tree/6a3bf813353d74bbeb8674e3566e7bbb33eb5c87 | import torch
import torch.utils.data
from torch import nn
def normalize_embeddings(embeddings: 'torch.Tensor', epsilon: 'float'=1e-06
) ->torch.Tensor:
"""
Normalize N D-dimensional embedding vectors arranged in a tensor [N, D]
Args:
embeddings (tensor [N, D]): N D-dimensional embedding vecto... |
IIDIsotropicGaussianUVLoss | # 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.utils.data
import torch.nn.functional as F
from torch import nn
class IIDIsotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of iid residuals with isotropic covariance:
$Sigma_i = sigma_i^2 I$
The loss (negative log likelihood) is then:
$1/2 sum_{i=1}^n ... | 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 math... | AbirKhan96/facebook-detectron2 | IIDIsotropicGaussianUVLoss | false | 16,861 | [
"Apache-2.0"
] | 5 | 6a3bf813353d74bbeb8674e3566e7bbb33eb5c87 | https://github.com/AbirKhan96/facebook-detectron2/tree/6a3bf813353d74bbeb8674e3566e7bbb33eb5c87 | import math
import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""
Loss for the case of iid residuals with isotropic covariance:
$Sigma_i = sigma_i^2 I$
The loss (negative log likelihood) is then:
$1/2 sum_{i=1}^n (log(2 pi) + 2 log si... |
LastLevelMaxPool | # 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.functional as F
from torch import nn
class LastLevelMaxPool(nn.Module):
"""
This module is used in the original FPN to generate a downsampled
P6 feature from P5.
"""
def __init__(self):
super().__init__()
self.num_levels = 1
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | AbirKhan96/facebook-detectron2 | LastLevelMaxPool | false | 16,862 | [
"Apache-2.0"
] | 5 | 6a3bf813353d74bbeb8674e3566e7bbb33eb5c87 | https://github.com/AbirKhan96/facebook-detectron2/tree/6a3bf813353d74bbeb8674e3566e7bbb33eb5c87 | import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""
This module is used in the original FPN to generate a downsampled
P6 feature from P5.
"""
def __init__(self):
super().__init__()
self.num_levels = 1
self.i... |
HardSigmoid | # 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.utils.data.distributed
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
def hard_sigmoid(input_, inplace: 'bool'=False):
"""hard sigmoid function"""
if inplace:
return input_.add_(3.0).clamp_(0.0, 6.0).di... | 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.utils.data
import torch.utils.data.distributed
from torch import nn
import t... | Adlik/zen_nas | HardSigmoid | false | 16,863 | [
"Apache-2.0"
] | 7 | d820d5c7d5bbb6fd66a76d5f16513647d6ea7a57 | https://github.com/Adlik/zen_nas/tree/d820d5c7d5bbb6fd66a76d5f16513647d6ea7a57 | import torch
import torch.utils.data
import torch.utils.data.distributed
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
def hard_sigmoid(input_, inplace: 'bool'=False):
"""hard sigmoid function"""
if inplace:
return input_.add_(3.0).clamp_(0.0, 6.0).di... |
ResizeTransform | # 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 nnf
import torch.utils
class ResizeTransform(nn.Module):
"""
Resize a transform, which involves resizing the vector field *and* rescaling it.
"""
def __init__(self, vel_resize, ndims):
super().__init__()
self.factor = 1.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | Alison-brie/AutoReg | ResizeTransform | false | 16,864 | [
"MIT"
] | 10 | a23d45a6f7c6e47f61430e1565dda316452a4418 | https://github.com/Alison-brie/AutoReg/tree/a23d45a6f7c6e47f61430e1565dda316452a4418 | import torch
import torch.nn as nn
import torch.nn.functional as nnf
import torch.utils
class Model(nn.Module):
"""
Resize a transform, which involves resizing the vector field *and* rescaling it.
"""
def __init__(self, vel_resize, ndims):
super().__init__()
self.factor = 1.0 / vel_re... |
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 torch
import torch.utils.data
import torch.nn.functional as F
class Conv2d(torch.nn.Conv2d):
"""
A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features.
"""
def __init__(self, *args, **kwargs):
"""
Extra keyword arguments supported in addition to 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | AbirKhan96/facebook-detectron2 | Conv2d | false | 16,865 | [
"Apache-2.0"
] | 5 | 6a3bf813353d74bbeb8674e3566e7bbb33eb5c87 | https://github.com/AbirKhan96/facebook-detectron2/tree/6a3bf813353d74bbeb8674e3566e7bbb33eb5c87 | import torch
import torch.utils.data
import torch.nn.functional as F
class Model(torch.nn.Conv2d):
"""
A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features.
"""
def __init__(self, *args, **kwargs):
"""
Extra keyword arguments supported in addition to tho... |
Linear_softmax | # 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 Linear_softmax(nn.Module):
def __init__(self, inp, out):
super(Linear_softmax, self).__init__()
self.f1 = nn.Linear(inp, out)
def forward(self, x):
x = self.f1(x)
return F.softmax(x, dim=1)
def get_inp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Alfo5123/ConcreteDropout | Linear_softmax | false | 16,866 | [
"MIT"
] | 7 | c442871553e20a2de078c0fbac7fa52302d50abf | https://github.com/Alfo5123/ConcreteDropout/tree/c442871553e20a2de078c0fbac7fa52302d50abf | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, inp, out):
super().__init__()
self.f1 = nn.Linear(inp, out)
def forward(self, x):
x = self.f1(x)
return F.softmax(x, dim=1)
def get_inputs():
return [torch.rand... |
IndepAnisotropicGaussianUVLoss | # 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.utils.data
import torch.nn.functional as F
from torch import nn
class IndepAnisotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of independent residuals with anisotropic covariances:
$Sigma_i = sigma_i^2 I + r_i r_i^T$
The loss (negative log likelihood) is ... | 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 math... | AbirKhan96/facebook-detectron2 | IndepAnisotropicGaussianUVLoss | false | 16,867 | [
"Apache-2.0"
] | 5 | 6a3bf813353d74bbeb8674e3566e7bbb33eb5c87 | https://github.com/AbirKhan96/facebook-detectron2/tree/6a3bf813353d74bbeb8674e3566e7bbb33eb5c87 | import math
import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""
Loss for the case of independent residuals with anisotropic covariances:
$Sigma_i = sigma_i^2 I + r_i r_i^T$
The loss (negative log likelihood) is then:
$1/2 sum_{i=1}^... |
TrueDynamics | # 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 numpy as np
import torch.nn as nn
from torch.autograd import Variable
class TrueDynamics(nn.Module):
def __init__(self, env, hidden_size=200, drop_prob=0.0):
super().__init__()
self.env = env
self.hidden_size = hidden_size
self.drop_prob = drop_prob
sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Alfo5123/ConcreteDropout | TrueDynamics | false | 16,868 | [
"MIT"
] | 7 | c442871553e20a2de078c0fbac7fa52302d50abf | https://github.com/Alfo5123/ConcreteDropout/tree/c442871553e20a2de078c0fbac7fa52302d50abf | import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
class Model(nn.Module):
def __init__(self, env, hidden_size=200, drop_prob=0.0):
super().__init__()
self.env = env
self.hidden_size = hidden_size
self.drop_prob = drop_prob
self.mask1... |
EqualConv2d | # 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 math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | AaltoVision/balanced-pioneer | EqualConv2d | false | 16,869 | [
"MIT"
] | 5 | 51f58080fd2db3159de3e1ccb47f38e03220faf0 | https://github.com/AaltoVision/balanced-pioneer/tree/51f58080fd2db3159de3e1ccb47f38e03220faf0 | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... |
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