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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
MemoryEfficientMish | # 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 MemoryEfficientMish(nn.Module):
class F(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return x.mul(torch.tanh(F.softplus(x)))
@staticmethod
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
assert_s... | AkshayGanesh/yolov5processor | MemoryEfficientMish | false | 4,805 | [
"MIT"
] | 1 | 788accfa93798729c002b2c9b4f943284ff97cad | https://github.com/AkshayGanesh/yolov5processor/tree/788accfa93798729c002b2c9b4f943284ff97cad | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
class F(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return x.mul(torch.tanh(F.softplus(x)))
@staticmethod
def backward(ct... |
EqualLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo
class EqualLinear(nn.Module):
def __init__(self, in_dim, out_dim, lr_mul=1, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_dim, in_dim))
if bias:
self.bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.model_zoo
assert_size_stride = torch._C... | Aitical/ADspeech2face | EqualLinear | false | 4,806 | [
"MIT"
] | 1 | 2e811ff8cc7333729f4b77d1b1067296253e8e38 | https://github.com/Aitical/ADspeech2face/tree/2e811ff8cc7333729f4b77d1b1067296253e8e38 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo
class Model(nn.Module):
def __init__(self, in_dim, out_dim, lr_mul=1, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_dim, in_dim))
if bias:
self.bias = nn... |
BCEBlurWithLogitsLoss | # 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 BCEBlurWithLogitsLoss(nn.Module):
def __init__(self, alpha=0.05):
super(BCEBlurWithLogitsLoss, self).__init__()
self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')
self.alpha = alpha
def forward(self, pred, true):
loss = self.loss_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... | AkshayGanesh/yolov5processor | BCEBlurWithLogitsLoss | false | 4,807 | [
"MIT"
] | 1 | 788accfa93798729c002b2c9b4f943284ff97cad | https://github.com/AkshayGanesh/yolov5processor/tree/788accfa93798729c002b2c9b4f943284ff97cad | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, alpha=0.05):
super().__init__()
self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')
self.alpha = alpha
def forward(self, pred, true):
loss = self.loss_fcn(pred, true)
pred = torch.sigmoid... |
Sum | # 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 Sum(nn.Module):
def __init__(self, n, weight=False):
super(Sum, self).__init__()
self.weight = weight
self.iter = range(n - 1)
if weight:
self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True
)
... | 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... | AkshayGanesh/yolov5processor | Sum | false | 4,808 | [
"MIT"
] | 1 | 788accfa93798729c002b2c9b4f943284ff97cad | https://github.com/AkshayGanesh/yolov5processor/tree/788accfa93798729c002b2c9b4f943284ff97cad | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n, weight=False):
super().__init__()
self.weight = weight
self.iter = range(n - 1)
if weight:
self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True
)
def fo... |
StyleBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
from typing import List
from typing import Optional
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight"></a>
## Learning-rate... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Aarsh2001/annotated_deep_learning_paper_implementations | StyleBlock | false | 4,809 | [
"MIT"
] | 1 | ff0d5c065da1a46769f5f66fddc252c178f8fa37 | https://github.com/Aarsh2001/annotated_deep_learning_paper_implementations/tree/ff0d5c065da1a46769f5f66fddc252c178f8fa37 | import math
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
from typing import List
from typing import Optional
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight"></a>
## Learning-rate... |
MultiHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import nn
from torch.nn import functional as F
class Attention(nn.Module):
def __init__(self, d_key, drop_ratio, causal):
super(Attention, self).__init__()
self.scale = math.sqrt(d_key)
self.dropout = nn.Dropout(drop_ratio)
self.causal = causal
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Adelashl6/mask_transformers | MultiHead | false | 4,810 | [
"MIT"
] | 1 | 2a2e4d1b40ae3ed546cb850d041af246806b63e7 | https://github.com/Adelashl6/mask_transformers/tree/2a2e4d1b40ae3ed546cb850d041af246806b63e7 | import math
import torch
from torch import nn
from torch.nn import functional as F
class Attention(nn.Module):
def __init__(self, d_key, drop_ratio, causal):
super().__init__()
self.scale = math.sqrt(d_key)
self.dropout = nn.Dropout(drop_ratio)
self.causal = causal
def forwar... |
Hardswish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Hardswish(nn.Module):
@staticmethod
def forward(x):
return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | AkshayGanesh/yolov5processor | Hardswish | false | 4,811 | [
"MIT"
] | 1 | 788accfa93798729c002b2c9b4f943284ff97cad | https://github.com/AkshayGanesh/yolov5processor/tree/788accfa93798729c002b2c9b4f943284ff97cad | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
@staticmethod
def forward(x):
return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
SEModule | # 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
from torch.nn import Conv2d
from torch.nn import ReLU
from torch.nn import Sigmoid
from torch.nn import AdaptiveAvgPool2d
import torch.utils.model_zoo
class SEModule(Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.av... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
f... | Aitical/ADspeech2face | SEModule | false | 4,812 | [
"MIT"
] | 1 | 2e811ff8cc7333729f4b77d1b1067296253e8e38 | https://github.com/Aitical/ADspeech2face/tree/2e811ff8cc7333729f4b77d1b1067296253e8e38 | from torch.nn import Module
import torch
from torch.nn import Conv2d
from torch.nn import ReLU
from torch.nn import Sigmoid
from torch.nn import AdaptiveAvgPool2d
import torch.utils.model_zoo
class Model(Module):
def __init__(self, channels, reduction):
super().__init__()
self.avg_pool = Adaptive... |
Classify | # 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 autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Flatten(nn.Module):
@staticmethod
def forward(x):
return x.view(x.size(0), -1)
class Classify(nn.Module):
def __init__(self, c1,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | AkshayGanesh/yolov5processor | Classify | false | 4,813 | [
"MIT"
] | 1 | 788accfa93798729c002b2c9b4f943284ff97cad | https://github.com/AkshayGanesh/yolov5processor/tree/788accfa93798729c002b2c9b4f943284ff97cad | import torch
import torch.nn as nn
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Flatten(nn.Module):
@staticmethod
def forward(x):
return x.view(x.size(0), -1)
class Model(nn.Module):
def __init__(self, c1, c2... |
VAE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class Decoder(nn.Module):
""" VAE decoder """
def __init__(self, in_channels, latent_size):
super(Decoder, self).__init__()
self.latent_size = latent_size
self.in_channels = in_channels
... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | Adwaver4157/WorldModel_for_FinRL | VAE | false | 4,814 | [
"MIT"
] | 1 | 0aa0a984aadffe0f6f2e83e55678c0e9304fba05 | https://github.com/Adwaver4157/WorldModel_for_FinRL/tree/0aa0a984aadffe0f6f2e83e55678c0e9304fba05 | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class Decoder(nn.Module):
""" VAE decoder """
def __init__(self, in_channels, latent_size):
super().__init__()
self.latent_size = latent_size
self.in_channels = in_channels
self.fc_d... |
MLPNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
class MLPNetwork(nn.Module):
"""
MLP network (can be used as value or policy)
"""
def __init__(self, input_dim, out_dim, hidden_dim=64, nonlin=F.relu,
constrain_out=False, norm_in=False, discrete_action=True):
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn.functional as... | Aks-Dmv/maddpg-pytorch | MLPNetwork | false | 4,815 | [
"MIT"
] | 1 | 8afe2448875824cf5aee69c5d0314a3e00777b6f | https://github.com/Aks-Dmv/maddpg-pytorch/tree/8afe2448875824cf5aee69c5d0314a3e00777b6f | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
"""
MLP network (can be used as value or policy)
"""
def __init__(self, input_dim, out_dim, hidden_dim=64, nonlin=F.relu,
constrain_out=False, norm_in=False, discrete_action=True):
"""
I... |
Fp32LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Fp32LayerNorm(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
impor... | AbhilashMathews/adahessian | Fp32LayerNorm | false | 4,819 | [
"MIT"
] | 1 | bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 | https://github.com/AbhilashMathews/adahessian/tree/bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Model(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input):
... |
GeneratorBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import numpy as np
from torch import nn
from typing import Tuple
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
from typing import List
from typing import Optional
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight">... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | Aarsh2001/annotated_deep_learning_paper_implementations | GeneratorBlock | false | 4,821 | [
"MIT"
] | 1 | ff0d5c065da1a46769f5f66fddc252c178f8fa37 | https://github.com/Aarsh2001/annotated_deep_learning_paper_implementations/tree/ff0d5c065da1a46769f5f66fddc252c178f8fa37 | import math
import torch
import numpy as np
from torch import nn
from typing import Tuple
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
from typing import List
from typing import Optional
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight">... |
HighwayLayer | # 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
import torch.jit
import torch.onnx.operators
class HighwayLayer(nn.Module):
def __init__(self, input_dim, transform_activation=F.relu,
gate_activation=F.softmax, gate_bias=-2):
super().__init__()
self.highway_transform_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
from torch._inductor.runtime.... | Acidburn0zzz/translate-1 | HighwayLayer | false | 4,822 | [
"BSD-3-Clause"
] | 1 | 8385a3c95de397fec8ca7a032fe1c215fa4e31f9 | https://github.com/Acidburn0zzz/translate-1/tree/8385a3c95de397fec8ca7a032fe1c215fa4e31f9 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.jit
import torch.onnx.operators
class Model(nn.Module):
def __init__(self, input_dim, transform_activation=F.relu,
gate_activation=F.softmax, gate_bias=-2):
super().__init__()
self.highway_transform_activation... |
SimilarityMatrix | # 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 SimilarityMatrix(torch.nn.Module):
def __init__(self, padding=0):
super().__init__()
self.padding = padding
def forward(self, query_embed, doc_embed, query_tok, doc_tok):
simmat = []
assert type(query_embed) == type(doc_embed)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | AlexWang000/capreolus | SimilarityMatrix | false | 4,823 | [
"Apache-2.0"
] | 1 | 00b0bf471ea0eb116ab973254ea61b0492405c54 | https://github.com/AlexWang000/capreolus/tree/00b0bf471ea0eb116ab973254ea61b0492405c54 | import torch
import torch.utils.data
class Model(torch.nn.Module):
def __init__(self, padding=0):
super().__init__()
self.padding = padding
def forward(self, query_embed, doc_embed, query_tok, doc_tok):
simmat = []
assert type(query_embed) == type(doc_embed)
if not is... |
Hswish | # 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 Hswish(nn.Module):
def __init__(self, inplace=True):
super(Hswish, self).__init__()
self.inplace = inplace
def forward(self, x):
return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.0
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Alterith/masters_code | Hswish | false | 4,824 | [
"MIT"
] | 1 | 65d0f2d26698cc8f7a5ffb564936113e2bbec201 | https://github.com/Alterith/masters_code/tree/65d0f2d26698cc8f7a5ffb564936113e2bbec201 | import torch
import torch.nn as nn
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, inplace=True):
super().__init__()
self.inplace = inplace
def forward(self, x):
return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.0
def get_inputs():
return [to... |
Hsigmoid | # 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 Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Alterith/Dense_Video_Captioning_Feature_Extraction_Model_Choice | Hsigmoid | false | 4,826 | [
"MIT"
] | 1 | 65d0f2d26698cc8f7a5ffb564936113e2bbec201 | https://github.com/Alterith/Dense_Video_Captioning_Feature_Extraction_Model_Choice/tree/65d0f2d26698cc8f7a5ffb564936113e2bbec201 | import torch
import torch.nn as nn
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, inplace=True):
super().__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
def get_inputs():
return [torch.... |
PACRRConvMax2dModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
class PACRRConvMax2dModule(torch.nn.Module):
def __init__(self, shape, n_filters, k, channels):
super().__init__()
self.shape = shape
if shape != 1:
self.pad = torch.nn.ConstantPad2d((0, shape - 1, 0, shape - 1), 0)
else:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
asser... | AlexWang000/capreolus | PACRRConvMax2dModule | false | 4,827 | [
"Apache-2.0"
] | 1 | 00b0bf471ea0eb116ab973254ea61b0492405c54 | https://github.com/AlexWang000/capreolus/tree/00b0bf471ea0eb116ab973254ea61b0492405c54 | import torch
import torch.utils.data
class Model(torch.nn.Module):
def __init__(self, shape, n_filters, k, channels):
super().__init__()
self.shape = shape
if shape != 1:
self.pad = torch.nn.ConstantPad2d((0, shape - 1, 0, shape - 1), 0)
else:
self.pad = No... |
MLP_Qnet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
class MLPNetwork(nn.Module):
"""
MLP network (can be used as value or policy)
"""
def __init__(self, input_dim, out_dim, hidden_dim=64, nonlin=F.relu,
constrain_out=False, norm_in=False, discrete_action=True):
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn.functional as... | Aks-Dmv/maddpg-pytorch | MLP_Qnet | false | 4,828 | [
"MIT"
] | 1 | 8afe2448875824cf5aee69c5d0314a3e00777b6f | https://github.com/Aks-Dmv/maddpg-pytorch/tree/8afe2448875824cf5aee69c5d0314a3e00777b6f | import torch
import torch.nn.functional as F
import torch.nn as nn
class MLPNetwork(nn.Module):
"""
MLP network (can be used as value or policy)
"""
def __init__(self, input_dim, out_dim, hidden_dim=64, nonlin=F.relu,
constrain_out=False, norm_in=False, discrete_action=True):
"""
... |
JointsMSELoss | # 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 JointsMSELoss(nn.Module):
def __init__(self, use_target_weight):
super(JointsMSELoss, self).__init__()
self.criterion = nn.MSELoss(size_average=True)
self.use_target_weight = use_target_weight
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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | AlongRide/Py3torch_HigherHRNet | JointsMSELoss | false | 4,829 | [
"MIT"
] | 1 | 62c455b62c0ac6d1de482fd3740dc947033e9e9a | https://github.com/AlongRide/Py3torch_HigherHRNet/tree/62c455b62c0ac6d1de482fd3740dc947033e9e9a | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, use_target_weight):
super().__init__()
self.criterion = nn.MSELoss(size_average=True)
self.use_target_weight = use_target_weight
def forward(self, output, target, target_weight):
... |
tofp16 | # 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 tofp16(nn.Module):
"""
Model wrapper that implements::
def forward(self, input):
return input.half()
"""
def __init__(self):
super(tofp16, self).__init__()
def forward(self, input):
return input.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | AlongRide/Py3torch_HigherHRNet | tofp16 | false | 4,830 | [
"MIT"
] | 1 | 62c455b62c0ac6d1de482fd3740dc947033e9e9a | https://github.com/AlongRide/Py3torch_HigherHRNet/tree/62c455b62c0ac6d1de482fd3740dc947033e9e9a | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""
Model wrapper that implements::
def forward(self, input):
return input.half()
"""
def __init__(self):
super().__init__()
def forward(self, input):
return input.half()
def ... |
MultiheadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch.jit
import torch.onnx.operators
def combine_heads(X):
"""
Combine heads (the inverse of split heads):
1) Transpose X from (batch size, nheads, sequence length, d_head) to
(batch size, seq... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Acidburn0zzz/translate-1 | MultiheadAttention | false | 4,831 | [
"BSD-3-Clause"
] | 1 | 8385a3c95de397fec8ca7a032fe1c215fa4e31f9 | https://github.com/Acidburn0zzz/translate-1/tree/8385a3c95de397fec8ca7a032fe1c215fa4e31f9 | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch.jit
import torch.onnx.operators
def combine_heads(X):
"""
Combine heads (the inverse of split heads):
1) Transpose X from (batch size, nheads, sequence length, d_head) to
(batch size, seq... |
SimCLRLoss | # 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
import torch.utils.model_zoo
class SimCLRLoss(nn.Module):
def __init__(self, temperature):
super(SimCLRLoss, self).__init__()
self.T = temperature
self.ce = nn.CrossEntropyLoss()
self.norm = nn.functional.normalize
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._inductor.runtime.triton_helpers import libdevice, math as tl_math
import nump... | Aitical/ADspeech2face | SimCLRLoss | false | 4,832 | [
"MIT"
] | 1 | 2e811ff8cc7333729f4b77d1b1067296253e8e38 | https://github.com/Aitical/ADspeech2face/tree/2e811ff8cc7333729f4b77d1b1067296253e8e38 | import torch
import numpy as np
import torch.nn as nn
import torch.utils.model_zoo
class Model(nn.Module):
def __init__(self, temperature):
super().__init__()
self.T = temperature
self.ce = nn.CrossEntropyLoss()
self.norm = nn.functional.normalize
self.softmax = nn.functio... |
ZeroPad1d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ZeroPad1d(nn.Module):
def __init__(self, pad_left, pad_right):
super().__init__()
self.pad_left = pad_left
self.p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
assert_size_str... | AbhilashMathews/adahessian | ZeroPad1d | false | 4,833 | [
"MIT"
] | 1 | bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 | https://github.com/AbhilashMathews/adahessian/tree/bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Model(nn.Module):
def __init__(self, pad_left, pad_right):
super().__init__()
self.pad_left = pad_left
self.pad_r... |
CrossEntropyLoss | # 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
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
... | 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
... | AlphaLFC/mmdetection | CrossEntropyLoss | false | 4,834 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 | import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import numbers
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init as init
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class LayerNorm(nn.Module):
"""Applies Layer Normalization over a mini-batch of inputs as described ... | 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 numbers
import torch.nn as nn
import torch.utils.data
import torch.nn.in... | AbhilashMathews/adahessian | LayerNorm | false | 4,835 | [
"MIT"
] | 1 | bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 | https://github.com/AbhilashMathews/adahessian/tree/bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 | import numbers
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init as init
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Model(nn.Module):
"""Applies Layer Normalization over a mini-batch of inputs as described in
... |
Fp32GroupNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Fp32GroupNorm(nn.GroupNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
impor... | AbhilashMathews/adahessian | Fp32GroupNorm | false | 4,836 | [
"MIT"
] | 1 | bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 | https://github.com/AbhilashMathews/adahessian/tree/bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Model(nn.GroupNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input):
... |
Generator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as f
class Generator(nn.Module):
def __init__(self, nz):
super(Generator, self).__init__()
self.fc1 = nn.Linear(nz, 10)
self.fc2 = nn.Linear(10, 1)
def forward(self, x):
x = f.relu(self.fc1(x))
x = self.fc2(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Anas-Alamri/vegans | Generator | false | 4,837 | [
"MIT"
] | 1 | 2e8513c9cbebf18d0125cebdc7d924dd6345883a | https://github.com/Anas-Alamri/vegans/tree/2e8513c9cbebf18d0125cebdc7d924dd6345883a | import torch
from torch import nn
import torch.nn.functional as f
class Model(nn.Module):
def __init__(self, nz):
super().__init__()
self.fc1 = nn.Linear(nz, 10)
self.fc2 = nn.Linear(10, 1)
def forward(self, x):
x = f.relu(self.fc1(x))
x = self.fc2(x)
return x... |
Swish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn
import torch.nn as nn
class Swish(nn.Module):
"""Applies the element-wise function:
.. math::
\\text{Swish}(x) = x * \\text{Sigmoid}(\\alpha * x) for constant value alpha.
Citation: Searching for Activation Functions, Ramachandran et al., 2017, https://arxiv.org/abs/... | 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | Alxaline/MONAI | Swish | false | 4,838 | [
"Apache-2.0"
] | 1 | 6b8fdf9db7f13ed7d88d605155a0463840abcbf2 | https://github.com/Alxaline/MONAI/tree/6b8fdf9db7f13ed7d88d605155a0463840abcbf2 | import torch
import torch.nn
import torch.nn as nn
class Model(nn.Module):
"""Applies the element-wise function:
.. math::
\\text{Swish}(x) = x * \\text{Sigmoid}(\\alpha * x) for constant value alpha.
Citation: Searching for Activation Functions, Ramachandran et al., 2017, https://arxiv.org/abs/... |
BalancedL1Loss | # 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 functools
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tenso... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AlphaLFC/mmdetection | BalancedL1Loss | false | 4,839 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 | import functools
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tenso... |
Clump | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class Clump(nn.Module):
"""Clipping input tensor."""
def __init__(self, min_v: 'int'=-50, max_v: 'int'=50):
"""Class for preparing input for DL model with mixed data.
Args:
min_v: Min value.
max_v: Max value.
"""
supe... | 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... | Andrey-Nikitin/LightAutoML | Clump | false | 4,840 | [
"Apache-2.0"
] | 1 | fe58d98d1ab05e177f0b9dea918fef8b922ae922 | https://github.com/Andrey-Nikitin/LightAutoML/tree/fe58d98d1ab05e177f0b9dea918fef8b922ae922 | import torch
from torch import nn
class Model(nn.Module):
"""Clipping input tensor."""
def __init__(self, min_v: 'int'=-50, max_v: 'int'=50):
"""Class for preparing input for DL model with mixed data.
Args:
min_v: Min value.
max_v: Max value.
"""
supe... |
L2Norm | # 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 L2Norm(nn.Module):
def __init__(self, n_dims, scale=20.0, eps=1e-10):
super(L2Norm, self).__init__()
self.n_dims = n_dims
self.weight = nn.Parameter(torch.Tensor(self.n_dims))
self.eps = eps
self.scale = scale
def forward(self,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AlphaLFC/mmdetection | L2Norm | false | 4,841 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n_dims, scale=20.0, eps=1e-10):
super().__init__()
self.n_dims = n_dims
self.weight = nn.Parameter(torch.Tensor(self.n_dims))
self.eps = eps
self.scale = scale
def forward(self, x):
... |
EncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import nn
from torch.nn import functional as F
class LayerNorm(nn.Module):
def __init__(self, d_model, eps=1e-06):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(d_model))
self.beta = nn.Parameter(torch.zeros(d_model))
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
from torch._inductor.runtime.... | Adelashl6/mask_transformers | EncoderLayer | false | 4,842 | [
"MIT"
] | 1 | 2a2e4d1b40ae3ed546cb850d041af246806b63e7 | https://github.com/Adelashl6/mask_transformers/tree/2a2e4d1b40ae3ed546cb850d041af246806b63e7 | import math
import torch
from torch import nn
from torch.nn import functional as F
class LayerNorm(nn.Module):
def __init__(self, d_model, eps=1e-06):
super().__init__()
self.gamma = nn.Parameter(torch.ones(d_model))
self.beta = nn.Parameter(torch.zeros(d_model))
self.eps = eps
... |
WordPredictor | # 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
import torch.jit
import torch.onnx.operators
class WordPredictor(nn.Module):
def __init__(self, encoder_output_dim, hidden_dim, output_dim,
topk_labels_per_source_token=None, use_self_attention=False):
super().__init__()
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn.functional as... | Acidburn0zzz/translate-1 | WordPredictor | false | 4,843 | [
"BSD-3-Clause"
] | 1 | 8385a3c95de397fec8ca7a032fe1c215fa4e31f9 | https://github.com/Acidburn0zzz/translate-1/tree/8385a3c95de397fec8ca7a032fe1c215fa4e31f9 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.jit
import torch.onnx.operators
class Model(nn.Module):
def __init__(self, encoder_output_dim, hidden_dim, output_dim,
topk_labels_per_source_token=None, use_self_attention=False):
super().__init__()
self.enco... |
ConvWS2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv_ws_2d(input, weight, bias=None, stride=1, padding=0, dilation=1,
groups=1, eps=1e-05):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1)
mean = weight_flat.mean(dim=1, keepdim=True).view(c_in, 1, 1, 1)
std = weight... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AlphaLFC/mmdetection | ConvWS2d | false | 4,845 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv_ws_2d(input, weight, bias=None, stride=1, padding=0, dilation=1,
groups=1, eps=1e-05):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1)
mean = weight_flat.mean(dim=1, keepdim=True).view(c_in, 1, 1, 1)
std = weight... |
ValueNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
import torch.nn
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class ValueNetwork(nn.Module):
def __init__(self, num_inputs, hidden_dim):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | AmmarFayad/Influence-based-Reinforcement-Learning-in-Intrinsically-motivated-Agents | ValueNetwork | false | 4,846 | [
"MIT"
] | 1 | e7cfa4121542312de641792288f7487f86971c1e | https://github.com/AmmarFayad/Influence-based-Reinforcement-Learning-in-Intrinsically-motivated-Agents/tree/e7cfa4121542312de641792288f7487f86971c1e | import torch
import torch.nn.functional as F
from torch import nn
import torch.nn
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class Model(nn.Module):
def __init__(self, num_inputs, hidden_dim):
... |
GDN | # 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.autograd import Function
import torch
import torch.nn.functional as F
import torch.nn as nn
class LowerBound(Function):
@staticmethod
def forward(ctx, inputs, bound):
ctx.save_for_backward(inputs, inputs.new_ones(1) * bound)
return inputs.clamp(min=bound)
@staticmethod
def... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AmigoLab/pytorch-msssim | GDN | false | 4,847 | [
"MIT"
] | 1 | 234fde137d8d1b4f9b7a2b94523ecc8f11f54c49 | https://github.com/AmigoLab/pytorch-msssim/tree/234fde137d8d1b4f9b7a2b94523ecc8f11f54c49 | from torch.autograd import Function
import torch
import torch.nn.functional as F
import torch.nn as nn
class LowerBound(Function):
@staticmethod
def forward(ctx, inputs, bound):
ctx.save_for_backward(inputs, inputs.new_ones(1) * bound)
return inputs.clamp(min=bound)
@staticmethod
def... |
MeanStd | # 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 MeanStd(nn.Module):
def __init__(self):
super(MeanStd, self).__init__()
def forward(self, x):
x = x.view(x.size(0), x.size(1), -1)
mean_x = torch.mean(x, dim=2)
var_x = torch.mean(x ** 2, dim=2) - mean_x * mean_x
return torch.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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Andribi/pytorch_GAN_zoo | MeanStd | false | 4,848 | [
"BSD-3-Clause"
] | 1 | b37c7268cbd4ec7dc61ba65a3ccf11af71247597 | https://github.com/Andribi/pytorch_GAN_zoo/tree/b37c7268cbd4ec7dc61ba65a3ccf11af71247597 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x = x.view(x.size(0), x.size(1), -1)
mean_x = torch.mean(x, dim=2)
var_x = torch.mean(x ** 2, dim=2) - mean_x * mean_x
return torch.cat([mean_x, var... |
GHMC | # 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
def _expand_binary_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero(labels >= 1).squeeze()
if inds.numel() > 0:
bin_labels[inds, labels[inds] - 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
... | AlphaLFC/mmdetection | GHMC | false | 4,849 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 | import torch
import torch.nn as nn
import torch.nn.functional as F
def _expand_binary_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero(labels >= 1).squeeze()
if inds.numel() > 0:
bin_labels[inds, labels[inds] - 1]... |
Critic | # 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
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | AnKra/deep-reinforcement-learning | Critic | false | 4,850 | [
"MIT"
] | 1 | fa906b0a3a21102b5085ce0c934185d2e50c3324 | https://github.com/AnKra/deep-reinforcement-learning/tree/fa906b0a3a21102b5085ce0c934185d2e50c3324 | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Model(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, f... |
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 numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AnKra/deep-reinforcement-learning | Actor | false | 4,851 | [
"MIT"
] | 1 | fa906b0a3a21102b5085ce0c934185d2e50c3324 | https://github.com/AnKra/deep-reinforcement-learning/tree/fa906b0a3a21102b5085ce0c934185d2e50c3324 | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Model(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... |
Network | # 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 Network(nn.Module):
def __init__(self, input_shape, output_shape, n_features, **kwargs):
super(Network, self).__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h1 = nn.Linear(n_input, n_features)
self._h2 = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AmmarFahmy/mushroom-rl | Network | false | 4,852 | [
"MIT"
] | 1 | 2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 | https://github.com/AmmarFahmy/mushroom-rl/tree/2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_shape, output_shape, n_features, **kwargs):
super().__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h1 = nn.Linear(n_input, n_features)
self._h2 = nn.Linear(n_features... |
SmoothL1Loss | # 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 functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AlphaLFC/mmdetection | SmoothL1Loss | false | 4,853 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... |
outconv | # 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 outconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(outconv, self).__init__()
self.conv = nn.Conv2d(in_ch, out_ch, 1)
def forward(self, x):
x = self.conv(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AntarSidgi/LiverTumorSegmentation | outconv | false | 4,854 | [
"MIT"
] | 1 | 9e8b1182541e011dc9f14218276ee9cb736ce479 | https://github.com/AntarSidgi/LiverTumorSegmentation/tree/9e8b1182541e011dc9f14218276ee9cb736ce479 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.conv = nn.Conv2d(in_ch, out_ch, 1)
def forward(self, x):
x = self.conv(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_in... |
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 as nn
import torch.nn.functional as F
class CriticNetwork(nn.Module):
def __init__(self, input_shape, output_shape, **kwargs):
super().__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h = nn.Linear(n_input, n_output)
nn.ini... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | AmmarFahmy/mushroom-rl | CriticNetwork | false | 4,855 | [
"MIT"
] | 1 | 2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 | https://github.com/AmmarFahmy/mushroom-rl/tree/2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_shape, output_shape, **kwargs):
super().__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h = nn.Linear(n_input, n_output)
nn.init.xavier... |
DiceLoss | # 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 collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... | 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 collections
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing impor... | Alxaline/MONAI | DiceLoss | false | 4,856 | [
"Apache-2.0"
] | 1 | 6b8fdf9db7f13ed7d88d605155a0463840abcbf2 | https://github.com/Alxaline/MONAI/tree/6b8fdf9db7f13ed7d88d605155a0463840abcbf2 | import collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... |
Sum | # 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
from torch import nn
from torch.autograd import Variable as Variable
class Sum(nn.Module):
def __init__(self, in_channels, in_features, out_channels, dropout=0.0):
"""
Create a Sum layer.
Args:
in_channels (int):... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
f... | AmurG/SPFlow | Sum | false | 4,857 | [
"Apache-2.0"
] | 1 | ab28dd4af9ed722ace69c6b290cf0a279bbda39e | https://github.com/AmurG/SPFlow/tree/ab28dd4af9ed722ace69c6b290cf0a279bbda39e | import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
from torch.autograd import Variable as Variable
class Model(nn.Module):
def __init__(self, in_channels, in_features, out_channels, dropout=0.0):
"""
Create a Sum layer.
Args:
in_channels (int... |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
class QNetwork(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=64,
fc2_units=64):
"""Initialize parameters and build model.
Params
======
state_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AmineKheldouni/Graphs-in-Machine-Learning | QNetwork | false | 4,858 | [
"MIT"
] | 1 | 003217495c624eaa33d44d679a0bc2164ca1f3d2 | https://github.com/AmineKheldouni/Graphs-in-Machine-Learning/tree/003217495c624eaa33d44d679a0bc2164ca1f3d2 | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=64,
fc2_units=64):
"""Initialize parameters and build model.
Params
======
state_size ... |
GHMR | # 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 GHMR(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
"Gradient Harmonized Single-stage Detector"
https://arxiv.org/abs/1811.05181
Args:
mu (float): The parameter for the Authentic Smooth L1 loss.
bins ... | 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... | AlphaLFC/mmdetection | GHMR | false | 4,859 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 | import torch
import torch.nn as nn
class Model(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
"Gradient Harmonized Single-stage Detector"
https://arxiv.org/abs/1811.05181
Args:
mu (float): The parameter for the Authentic Smooth L1 loss.
bins... |
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 as nn
import torch.nn.functional as F
class ActorNetwork(nn.Module):
def __init__(self, input_shape, output_shape, **kwargs):
super(ActorNetwork, self).__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h = nn.Linear(n_input, n_outpu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AmmarFahmy/mushroom-rl | ActorNetwork | false | 4,860 | [
"MIT"
] | 1 | 2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 | https://github.com/AmmarFahmy/mushroom-rl/tree/2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_shape, output_shape, **kwargs):
super().__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h = nn.Linear(n_input, n_output)
nn.init.xavier... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_s... | AnonymousGFR/wbgan.pytorch | LayerNorm | false | 4,861 | [
"MIT"
] | 1 | d75cb6599852e901df0136db87520e3314f8ca71 | https://github.com/AnonymousGFR/wbgan.pytorch/tree/d75cb6599852e901df0136db87520e3314f8ca71 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Model(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
... |
AdaIN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
from numpy import prod
def getLayerNormalizationFactor(x):
"""
Get He's constant for the given layer
https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
"""
size = x.weight.size()
fan_in = pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Andribi/pytorch_GAN_zoo | AdaIN | false | 4,862 | [
"BSD-3-Clause"
] | 1 | b37c7268cbd4ec7dc61ba65a3ccf11af71247597 | https://github.com/Andribi/pytorch_GAN_zoo/tree/b37c7268cbd4ec7dc61ba65a3ccf11af71247597 | import math
import torch
import torch.nn as nn
from numpy import prod
def getLayerNormalizationFactor(x):
"""
Get He's constant for the given layer
https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
"""
size = x.weight.size()
fan_in = pro... |
CmapPafHead | # 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
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.optim
assert_size_stride = ... | Anqi-nus/trtpose | CmapPafHead | false | 4,863 | [
"MIT"
] | 1 | 723ec95df8b8414b9289af90fbfbc98756792a21 | https://github.com/Anqi-nus/trtpose/tree/723ec95df8b8414b9289af90fbfbc98756792a21 | import torch
import torch.utils.data
import torch.nn
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
import torch.nn
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class QNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | AmmarFayad/Influence-based-Reinforcement-Learning-in-Intrinsically-motivated-Agents | QNetwork | false | 4,864 | [
"MIT"
] | 1 | e7cfa4121542312de641792288f7487f86971c1e | https://github.com/AmmarFayad/Influence-based-Reinforcement-Learning-in-Intrinsically-motivated-Agents/tree/e7cfa4121542312de641792288f7487f86971c1e | import torch
import torch.nn.functional as F
from torch import nn
import torch.nn
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class Model(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_... |
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 math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
... | 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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.... | AsmitaBhat30/lxmert | GeLU | false | 4,865 | [
"MIT"
] | 1 | 90292dc36a25c04c4f76fe9119e3141d5dc05874 | https://github.com/AsmitaBhat30/lxmert/tree/90292dc36a25c04c4f76fe9119e3141d5dc05874 | import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamma = nn.Param... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AntiAegis/PyTorch-GAN | LayerNorm | false | 4,866 | [
"MIT"
] | 1 | 1cb951b3ad3a58b749c1802f84947b85f72c8367 | https://github.com/AntiAegis/PyTorch-GAN/tree/1cb951b3ad3a58b749c1802f84947b85f72c8367 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamma = nn.Parameter(torch.Tensor(n... |
SimpleCNN | # 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 SimpleCNN(nn.Module):
def __init__(self):
super(SimpleCNN, self).__init__()
self.fc1 = nn.Linear(28 * 28, 500)
self.fc2 = nn.Linear(500, 256)
self.fc3 = nn.Linear(256, 10)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AnweshCR7/autonomous_greenhouse | SimpleCNN | false | 4,867 | [
"MIT"
] | 1 | a29cfe37d0152001d2544216ed65c3472f572b4e | https://github.com/AnweshCR7/autonomous_greenhouse/tree/a29cfe37d0152001d2544216ed65c3472f572b4e | import torch
import torch.nn as nn
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(28 * 28, 500)
self.fc2 = nn.Linear(500, 256)
self.fc3 = nn.Linear(256, 10)
def forward(self, x):
x = x.view(-1, ... |
Pairer | # 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
from torch import Tensor
from torch.functional import Tensor
from typing import Union
class Pairer(torch.nn.Module):
"""
To predict links between segments we will find all possible pairs and estimate the probability that they are linked.
We do this by creating a matrix whe... | 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... | AxlAlm/SegNLP | Pairer | false | 4,868 | [
"Apache-2.0"
] | 1 | 89b8d077952397dfcea089376b373b117bcf6a65 | https://github.com/AxlAlm/SegNLP/tree/89b8d077952397dfcea089376b373b117bcf6a65 | import torch
import numpy as np
from torch import Tensor
from torch.functional import Tensor
from typing import Union
class Model(torch.nn.Module):
"""
To predict links between segments we will find all possible pairs and estimate the probability that they are linked.
We do this by creating a matrix wher... |
SourceContextGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(ContextGate, self).__init__()
input_size = embeddings_size + decod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | AngusGLChen/qg | SourceContextGate | false | 4,869 | [
"MIT"
] | 1 | 3ebc5b94348a4c313829a6c71705fbc9dadd8181 | https://github.com/AngusGLChen/qg/tree/3ebc5b94348a4c313829a6c71705fbc9dadd8181 | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super().__init__()
input_size = embeddings_size + decoder_size + attenti... |
BothContextGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(ContextGate, self).__init__()
input_size = embeddings_size + decod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | AngusGLChen/qg | BothContextGate | false | 4,870 | [
"MIT"
] | 1 | 3ebc5b94348a4c313829a6c71705fbc9dadd8181 | https://github.com/AngusGLChen/qg/tree/3ebc5b94348a4c313829a6c71705fbc9dadd8181 | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super().__init__()
input_size = embeddings_size + decoder_size + attenti... |
TargetContextGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(ContextGate, self).__init__()
input_size = embeddings_size + decod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | AngusGLChen/qg | TargetContextGate | false | 4,871 | [
"MIT"
] | 1 | 3ebc5b94348a4c313829a6c71705fbc9dadd8181 | https://github.com/AngusGLChen/qg/tree/3ebc5b94348a4c313829a6c71705fbc9dadd8181 | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super().__init__()
input_size = embeddings_size + decoder_size + attenti... |
ContextGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(ContextGate, self).__init__()
input_size = embeddings_size + decod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.cuda
assert_size_stride = torch._C._dynamo.gu... | AngusGLChen/qg | ContextGate | false | 4,872 | [
"MIT"
] | 1 | 3ebc5b94348a4c313829a6c71705fbc9dadd8181 | https://github.com/AngusGLChen/qg/tree/3ebc5b94348a4c313829a6c71705fbc9dadd8181 | import torch
import torch.nn as nn
import torch.cuda
class Model(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super().__init__()
input_size = embeddings_size + decoder_size + attention_siz... |
SimpleCNN | # 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
class SimpleCNN(torch.nn.Module):
def __init__(self, in_ch=1, out_ch=3):
super(SimpleCNN, self).__init__()
self.conv1 = torch.nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=1,
padding=1)
self.conv2 = torch.nn.Conv2d(out_ch, out_ch, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Arjun-Arora/CS348B_project | SimpleCNN | false | 4,873 | [
"BSD-2-Clause"
] | 1 | 000ced8edbc3554db74db36ebcd76042d17398ee | https://github.com/Arjun-Arora/CS348B_project/tree/000ced8edbc3554db74db36ebcd76042d17398ee | import torch
import torch.nn.functional as F
class Model(torch.nn.Module):
def __init__(self, in_ch=1, out_ch=3):
super().__init__()
self.conv1 = torch.nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=1,
padding=1)
self.conv2 = torch.nn.Conv2d(out_ch, out_ch, kernel_size=3, stri... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.optim
class LayerNorm(nn.Module):
"""Construct a layernorm module in the OpenAI style (epsilon inside the square root)."""
def __init__(self, n_state, e=1e-05):
super(LayerNorm, self).__init__()
self.g = nn.Parameter(torch.ones(n_state))
... | 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.optim
assert_size_stride = torch._C._dynamo.... | Arvindkrishna1997/comet-dataset | LayerNorm | false | 4,874 | [
"Apache-2.0"
] | 1 | 2cb42a4aefdea6d0e81f544f94830d44730e9853 | https://github.com/Arvindkrishna1997/comet-dataset/tree/2cb42a4aefdea6d0e81f544f94830d44730e9853 | import torch
import torch.nn as nn
import torch.optim
class Model(nn.Module):
"""Construct a layernorm module in the OpenAI style (epsilon inside the square root)."""
def __init__(self, n_state, e=1e-05):
super().__init__()
self.g = nn.Parameter(torch.ones(n_state))
self.b = nn.Pa... |
ScaledDotProductAttention | # 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
from torch import nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
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
from torch._inductor.runtime.... | AutuanLiu/LeetCode2019 | ScaledDotProductAttention | false | 4,875 | [
"MIT"
] | 1 | 8efc7c5475fd888f7d86c3b08a3c1c9e55c1ac30 | https://github.com/AutuanLiu/LeetCode2019/tree/8efc7c5475fd888f7d86c3b08a3c1c9e55c1ac30 | import torch
import numpy as np
from torch import nn
class Model(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
self.softmax = nn.Soft... |
MyLayerNorm | # 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 MyLayerNorm(nn.Module):
def __init__(self, input_dim):
super(MyLayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(input_dim))
if True or use_bias:
self.beta = nn.Parameter(torch.ones(input_dim))
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Ar-Kareem/Sketch-RNN | MyLayerNorm | false | 4,876 | [
"MIT"
] | 1 | 350824040715ea281182de01bca467130f326566 | https://github.com/Ar-Kareem/Sketch-RNN/tree/350824040715ea281182de01bca467130f326566 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.gamma = nn.Parameter(torch.ones(input_dim))
if True or use_bias:
self.beta = nn.Parameter(torch.ones(input_dim))
def forward(self, x):
dims = 2
... |
ConvNet | # 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 ConvNet(nn.Module):
"""Standard convolutional net for baseline
Architecture: 2 convolutional layers, 3 fully connected layers.
"""
def __init__(self):
super(ConvNet, self).__init__()
args = {'stride': 1, 'padding': 1}
self.conv1 = nn.Co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Allen-Z-4230/MoCo-CIFAR10 | ConvNet | false | 4,877 | [
"MIT"
] | 1 | b2ade575b8ed1e05e32e4ec629acdfee55c8ff41 | https://github.com/Allen-Z-4230/MoCo-CIFAR10/tree/b2ade575b8ed1e05e32e4ec629acdfee55c8ff41 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Standard convolutional net for baseline
Architecture: 2 convolutional layers, 3 fully connected layers.
"""
def __init__(self):
super().__init__()
args = {'stride': 1, 'padding': 1}
self.conv1 = nn.Conv2d(3, 10, 3, ... |
HS | # 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 HS(nn.Module):
def __init__(self):
super(HS, self).__init__()
def forward(self, inputs):
clip = torch.clamp(inputs + 3, 0, 6) / 6
return inputs * clip
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
retur... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | BXuan694/basemodel-pytorch | HS | false | 4,878 | [
"MIT"
] | 1 | a36c96904580be902e323db17eebbe2ea1f54176 | https://github.com/BXuan694/basemodel-pytorch/tree/a36c96904580be902e323db17eebbe2ea1f54176 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, inputs):
clip = torch.clamp(inputs + 3, 0, 6) / 6
return inputs * clip
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
ConditionalBatchNorm2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.nn import Parameter
def l2normalize(v, eps=0.0001):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super(SpectralNorm, self).__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.triton_helpers import libdevice
import torch.nn as ... | AnonymousGFR/wbgan.pytorch | ConditionalBatchNorm2d | false | 4,879 | [
"MIT"
] | 1 | d75cb6599852e901df0136db87520e3314f8ca71 | https://github.com/AnonymousGFR/wbgan.pytorch/tree/d75cb6599852e901df0136db87520e3314f8ca71 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.nn import Parameter
def l2normalize(v, eps=0.0001):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super().__init__()
sel... |
GlobalAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.cuda
def aeq(base, *rest):
""" Assert the first arg equals to each of the rest."""
for a in rest[:]:
assert a == base, 'base(' + str(base
) + ") doesn't equals to each of " + str(rest)
class Bottle(nn.Module):
def forward(self, 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.... | AngusGLChen/qg | GlobalAttention | false | 4,880 | [
"MIT"
] | 1 | 3ebc5b94348a4c313829a6c71705fbc9dadd8181 | https://github.com/AngusGLChen/qg/tree/3ebc5b94348a4c313829a6c71705fbc9dadd8181 | import torch
import torch.nn as nn
import torch.cuda
def aeq(base, *rest):
""" Assert the first arg equals to each of the rest."""
for a in rest[:]:
assert a == base, 'base(' + str(base
) + ") doesn't equals to each of " + str(rest)
class Bottle(nn.Module):
def forward(self, input):... |
AdditiveAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import Tensor
from torch.functional import Tensor
import torch.nn as nn
class AdditiveAttention(nn.Module):
"""
Originally from:
https://arxiv.org/pdf/1409.0473v5.pdf
Also referenced to as Content Based Attention:
https://arxiv.org/pdf/1506.03134v1.pdf
Attenti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | AxlAlm/SegNLP | AdditiveAttention | false | 4,881 | [
"Apache-2.0"
] | 1 | 89b8d077952397dfcea089376b373b117bcf6a65 | https://github.com/AxlAlm/SegNLP/tree/89b8d077952397dfcea089376b373b117bcf6a65 | import torch
from torch import Tensor
from torch.functional import Tensor
import torch.nn as nn
class Model(nn.Module):
"""
Originally from:
https://arxiv.org/pdf/1409.0473v5.pdf
Also referenced to as Content Based Attention:
https://arxiv.org/pdf/1506.03134v1.pdf
Attention is learne... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
class LayerNorm(torch.nn.Module):
def __init__(self, dimensions, eps: 'float'=1e-06) ->None:
super().__init__()
self.gamma = torch.nn.Parameter(torch.ones(dimensions))
self.beta = torch.nn.Parameter(torch.zeros(dimensions))
self.eps = eps
def forward(self, tensor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | AutuanLiu/LeetCode2019 | LayerNorm | false | 4,882 | [
"MIT"
] | 1 | 8efc7c5475fd888f7d86c3b08a3c1c9e55c1ac30 | https://github.com/AutuanLiu/LeetCode2019/tree/8efc7c5475fd888f7d86c3b08a3c1c9e55c1ac30 | import torch
class Model(torch.nn.Module):
def __init__(self, dimensions, eps: 'float'=1e-06) ->None:
super().__init__()
self.gamma = torch.nn.Parameter(torch.ones(dimensions))
self.beta = torch.nn.Parameter(torch.zeros(dimensions))
self.eps = eps
def forward(self, tensor: 't... |
Dnn_net_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.utils.data
class Dnn_net_Loss(torch.nn.Module):
def __init__(self):
super(Dnn_net_Loss, self).__init__()
def forward(self, model_output, targ_input):
criterion = torch.nn.MSELoss(reduction='none')
criterion
targ_input = torch.cat((targ_input[:, :, 0]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | BaiYunLiu/newPLC | Dnn_net_Loss | false | 4,883 | [
"BSD-3-Clause"
] | 1 | 18245a14648bc28b7269ea1d6e444ca6021ac8d2 | https://github.com/BaiYunLiu/newPLC/tree/18245a14648bc28b7269ea1d6e444ca6021ac8d2 | import torch
import torch.utils.data
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, model_output, targ_input):
criterion = torch.nn.MSELoss(reduction='none')
criterion
targ_input = torch.cat((targ_input[:, :, 0], targ_input[:, :, 1]), 1... |
Similarity | # 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 Similarity(nn.Module):
"""
Dot product or cosine similarity
"""
def __init__(self, temp):
super().__init__()
self.temp = temp
self.cos = nn.CosineSimilarity(dim=-1)
def forward(self, x, y):
return self.cos(x, y) / self.temp... | 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... | BDBC-KG-NLP/MixCSE_AAAI2022 | Similarity | false | 4,884 | [
"MIT"
] | 1 | 884145e24a5258c044fedb658df9999f012df875 | https://github.com/BDBC-KG-NLP/MixCSE_AAAI2022/tree/884145e24a5258c044fedb658df9999f012df875 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Dot product or cosine similarity
"""
def __init__(self, temp):
super().__init__()
self.temp = temp
self.cos = nn.CosineSimilarity(dim=-1)
def forward(self, x, y):
return self.cos(x, y) / self.temp
de... |
VAE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import functional as F
class VAE(nn.Module):
def __init__(self, n_features=24, z_dim=15):
super(VAE, self).__init__()
self.en1 = nn.Linear(n_features, 200)
self.en2 = nn.Linear(200, 100)
self.en3 = nn.Linear(... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math... | Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow | VAE | false | 4,885 | [
"Apache-2.0"
] | 1 | 772ba547c2b7d5d90e79382bf4d8a50e4d733210 | https://github.com/Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow/tree/772ba547c2b7d5d90e79382bf4d8a50e4d733210 | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, n_features=24, z_dim=15):
super().__init__()
self.en1 = nn.Linear(n_features, 200)
self.en2 = nn.Linear(200, 100)
self.en3 = nn.Linear(100, 50... |
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
from torch import nn
class Attention(nn.Module):
"""A generic attention module for a decoder in seq2seq"""
def __init__(self, dim, use_tanh=False, C=10):
super(Attention, self).__init__()
self.use_tanh = use_tanh
self.project_query = nn.Linear(dim, 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.triton_helpers import libdevice
import math
from to... | BCHoagland/attention-learn-to-route | Attention | false | 4,886 | [
"MIT"
] | 1 | c411289c3b42be5b9c89240f665a029dfc51e034 | https://github.com/BCHoagland/attention-learn-to-route/tree/c411289c3b42be5b9c89240f665a029dfc51e034 | import math
import torch
from torch import nn
class Model(nn.Module):
"""A generic attention module for a decoder in seq2seq"""
def __init__(self, dim, use_tanh=False, C=10):
super().__init__()
self.use_tanh = use_tanh
self.project_query = nn.Linear(dim, dim)
self.project_ref ... |
ConvLayer | # 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 ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_s... | Bartolo1024/ignite | ConvLayer | false | 4,887 | [
"BSD-3-Clause"
] | 1 | b087fef0bc5f97cda415c1c56f1cd589383c54be | https://github.com/Bartolo1024/ignite/tree/b087fef0bc5f97cda415c1c56f1cd589383c54be | import torch
class Model(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super().__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv2d(in_channels, out... |
AE_4D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
class AE_4D(nn.Module):
def __init__(self, n_features=4):
super(AE_4D, self).__init__()
self.en1 = nn.Linear(n_features, 200)
self.en2 = nn.Linear(200, 100)
self.en3 = nn.Linear(100, 50)
self.en4 = nn.Linear(50, 3)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow | AE_4D | false | 4,888 | [
"Apache-2.0"
] | 1 | 772ba547c2b7d5d90e79382bf4d8a50e4d733210 | https://github.com/Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow/tree/772ba547c2b7d5d90e79382bf4d8a50e4d733210 | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, n_features=4):
super().__init__()
self.en1 = nn.Linear(n_features, 200)
self.en2 = nn.Linear(200, 100)
self.en3 = nn.Linear(100, 50)
self.en4 = nn.Linear(50, 3)
se... |
ActorMARL | # 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 ActorMARL(nn.Module):
def __init__(self, dim_observation, dim_action):
super(ActorMARL, self).__init__()
self.FC1 = nn.Linear(dim_observation, 500)
self.FC2 = nn.Linear(500, 128)
self.FC3 = nn.Linear(128, 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.... | BIT-UAV-JJJ/ElegantRL | ActorMARL | false | 4,889 | [
"Apache-2.0"
] | 1 | 5ce5c1030949bb862d0d56b0e78a9a1f47efe63a | https://github.com/BIT-UAV-JJJ/ElegantRL/tree/5ce5c1030949bb862d0d56b0e78a9a1f47efe63a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, dim_observation, dim_action):
super().__init__()
self.FC1 = nn.Linear(dim_observation, 500)
self.FC2 = nn.Linear(500, 128)
self.FC3 = nn.Linear(128, dim_action)
def f... |
eSEModule | # 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 Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
class eSEModule(nn.Modul... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | BXuan694/basemodel-pytorch | eSEModule | false | 4,890 | [
"MIT"
] | 1 | a36c96904580be902e323db17eebbe2ea1f54176 | https://github.com/BXuan694/basemodel-pytorch/tree/a36c96904580be902e323db17eebbe2ea1f54176 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super().__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
class Model(nn.Module):
def __ini... |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, n_classes):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 *... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ArWeHei/edflow | Net | false | 4,891 | [
"MIT"
] | 1 | 3383cfbc42a43e906bc7781ad05714fd4fc9616e | https://github.com/ArWeHei/edflow/tree/3383cfbc42a43e906bc7781ad05714fd4fc9616e | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, n_classes):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120... |
SE | # 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 SE(nn.Module):
"""Squeeze-and-Excitation block."""
def __init__(self, in_planes, se_planes):
super(SE, self).__init__()
self.se1 = nn.Conv2d(in_planes, se_planes, kernel_size=1, bias=True)
self.se2 = nn.Conv2d(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 torch.nn as nn
assert_... | BXuan694/basemodel-pytorch | SE | false | 4,892 | [
"MIT"
] | 1 | a36c96904580be902e323db17eebbe2ea1f54176 | https://github.com/BXuan694/basemodel-pytorch/tree/a36c96904580be902e323db17eebbe2ea1f54176 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Squeeze-and-Excitation block."""
def __init__(self, in_planes, se_planes):
super().__init__()
self.se1 = nn.Conv2d(in_planes, se_planes, kernel_size=1, bias=True)
self.se2 = nn.Conv2d(se_plan... |
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, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, action_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Barisimre/TD3-Generative | Actor | false | 4,893 | [
"MIT"
] | 1 | 434419b020b88010f09f194c40feac1d420b2086 | https://github.com/Barisimre/TD3-Generative/tree/434419b020b88010f09f194c40feac1d420b2086 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super().__init__()
self.l1 = nn.Linear(state_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, action_dim)
self.... |
GeneralizedDiceLoss | # 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 collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... | 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 collections
from typi... | Alxaline/MONAI | GeneralizedDiceLoss | false | 4,894 | [
"Apache-2.0"
] | 1 | 6b8fdf9db7f13ed7d88d605155a0463840abcbf2 | https://github.com/Alxaline/MONAI/tree/6b8fdf9db7f13ed7d88d605155a0463840abcbf2 | import collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... |
Critic | # 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 Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, 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
import ... | Barisimre/TD3-Generative | Critic | false | 4,895 | [
"MIT"
] | 1 | 434419b020b88010f09f194c40feac1d420b2086 | https://github.com/Barisimre/TD3-Generative/tree/434419b020b88010f09f194c40feac1d420b2086 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.l1 = nn.Linear(state_dim + action_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, 1)
self.l4 = nn.... |
DNNnet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
class DNNnet(torch.nn.Module):
def __init__(self, n_layer, n_in_channel, n_out_channel):
super(DNNnet, self).__init__()
self.n_layer = n_layer
self.fc_layers = torch.nn.ModuleList()
self.act_func = torch.nn.Sigmoid()
start_layer = torch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | BaiYunLiu/newPLC | DNNnet | false | 4,896 | [
"BSD-3-Clause"
] | 1 | 18245a14648bc28b7269ea1d6e444ca6021ac8d2 | https://github.com/BaiYunLiu/newPLC/tree/18245a14648bc28b7269ea1d6e444ca6021ac8d2 | import torch
import torch.utils.data
class Model(torch.nn.Module):
def __init__(self, n_layer, n_in_channel, n_out_channel):
super().__init__()
self.n_layer = n_layer
self.fc_layers = torch.nn.ModuleList()
self.act_func = torch.nn.Sigmoid()
start_layer = torch.nn.Linear(n_... |
SkipLastTargetChannelWrapper | # 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 MSELoss
class SkipLastTargetChannelWrapper(nn.Module):
"""
Loss wrapper which removes additional target channel
"""
def __init__(self, loss, squeeze_channel=False):
super(SkipLastTargetChannelWrapper, self).__init__()
self.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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | BioTrillion/pytorch-3dunet | SkipLastTargetChannelWrapper | false | 4,897 | [
"MIT"
] | 1 | 217781197dd94211ee7fe5d53a8b404f0b8391a6 | https://github.com/BioTrillion/pytorch-3dunet/tree/217781197dd94211ee7fe5d53a8b404f0b8391a6 | import torch
import torch.nn as nn
from torch.nn import MSELoss
class Model(nn.Module):
"""
Loss wrapper which removes additional target channel
"""
def __init__(self, loss, squeeze_channel=False):
super().__init__()
self.loss = loss
self.squeeze_channel = squeeze_channel
... |
WeightBCE | # 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 WeightBCE(nn.Module):
def __init__(self, epsilon: 'float'=1e-08) ->None:
super(WeightBCE, self).__init__()
self.epsilon = epsilon
def forward(self, x: 'Tensor', label: 'Tensor', weight: 'Tensor') ->Tensor:
"""
:... | 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... | BetterRaven/Transfer-Learning_vscode | WeightBCE | false | 4,898 | [
"MIT"
] | 1 | 90c9bce630f54fd2322cce8fab5fe1d074ff141c | https://github.com/BetterRaven/Transfer-Learning_vscode/tree/90c9bce630f54fd2322cce8fab5fe1d074ff141c | import torch
from torch import Tensor
from torch import nn
class Model(nn.Module):
def __init__(self, epsilon: 'float'=1e-08) ->None:
super().__init__()
self.epsilon = epsilon
def forward(self, x: 'Tensor', label: 'Tensor', weight: 'Tensor') ->Tensor:
"""
:param x: [N, 1]
... |
CNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
class CNN(torch.nn.Module):
"""Basic CNN architecture."""
def __init__(self, in_channels=1):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 64, 8, 1)
self.conv2 = nn.Conv2d(64, 128, 6, 2)
self.c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | AxelBohm/cleverhans | CNN | false | 4,899 | [
"MIT"
] | 1 | 35f44d686fa24a8d3a30218dc9ad2617859afbf0 | https://github.com/AxelBohm/cleverhans/tree/35f44d686fa24a8d3a30218dc9ad2617859afbf0 | import torch
from torch import nn
import torch.nn.functional as F
class Model(torch.nn.Module):
"""Basic CNN architecture."""
def __init__(self, in_channels=1):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, 64, 8, 1)
self.conv2 = nn.Conv2d(64, 128, 6, 2)
self.conv3 = ... |
Policy | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.affine1 = nn.Linear(4, 128)
self.affine2 = nn.Linear(128, 2)
self.saved_log_probs = []
self.rewards = []
def forward(sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Bartolo1024/ignite | Policy | false | 4,900 | [
"BSD-3-Clause"
] | 1 | b087fef0bc5f97cda415c1c56f1cd589383c54be | https://github.com/Bartolo1024/ignite/tree/b087fef0bc5f97cda415c1c56f1cd589383c54be | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.affine1 = nn.Linear(4, 128)
self.affine2 = nn.Linear(128, 2)
self.saved_log_probs = []
self.rewards = []
def forward(self, x):
... |
MAB | # 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 MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Behrouz-Babaki/NCG4CVRP | MAB | false | 4,901 | [
"MIT"
] | 1 | 87d63366c0b461f44ce8e982159a1e207af77b44 | https://github.com/Behrouz-Babaki/NCG4CVRP/tree/87d63366c0b461f44ce8e982159a1e207af77b44 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super().__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
self.fc... |
SAB | # 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 MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Behrouz-Babaki/NCG4CVRP | SAB | false | 4,902 | [
"MIT"
] | 1 | 87d63366c0b461f44ce8e982159a1e207af77b44 | https://github.com/Behrouz-Babaki/NCG4CVRP/tree/87d63366c0b461f44ce8e982159a1e207af77b44 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super().__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
self.fc_k... |
PointLoss | # 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.parallel
import torch.utils.data
import torch.nn as nn
def array2samples_distance(array1, array2):
"""
arguments:
array1: the array, size: (num_point, num_feature)
array2: the samples, size: (num_point, num_feature)
returns:
distances: each entry is th... | 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.parallel
import torch.utils.data
import torch.nn as nn
assert_size_stride... | AndyYuanC/VegPN | PointLoss | false | 4,903 | [
"MIT"
] | 1 | eb981d62ad854d3ca607240cc431a0870c1e95ba | https://github.com/AndyYuanC/VegPN/tree/eb981d62ad854d3ca607240cc431a0870c1e95ba | import torch
import torch.nn.parallel
import torch.utils.data
import torch.nn as nn
def array2samples_distance(array1, array2):
"""
arguments:
array1: the array, size: (num_point, num_feature)
array2: the samples, size: (num_point, num_feature)
returns:
distances: each entry is th... |
ContrastiveLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class ContrastiveLoss(nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
Loss is proportional to square distance when inputs are of the same type, and proportional to
the square of margin - dista... | 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... | BrunoKM/rhoana_graph_tools | ContrastiveLoss | false | 4,904 | [
"MIT"
] | 1 | 7150f4bc6337ecf51dd9123cf03561a57d655160 | https://github.com/BrunoKM/rhoana_graph_tools/tree/7150f4bc6337ecf51dd9123cf03561a57d655160 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
Loss is proportional to square distance when inputs are of the same type, and proportional to
the square of margin - distance when t... |
ResidualBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Bartolo1024/ignite | ResidualBlock | false | 4,905 | [
"BSD-3-Clause"
] | 1 | b087fef0bc5f97cda415c1c56f1cd589383c54be | https://github.com/Bartolo1024/ignite/tree/b087fef0bc5f97cda415c1c56f1cd589383c54be | import torch
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super().__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv2d(in_channels,... |
WeightedSmoothL1Loss | # 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 WeightedSmoothL1Loss(nn.SmoothL1Loss):
def __init__(self, threshold, initial_weight, apply_below_threshold=True):
super().__init__(reduction='none')
self.threshold = threshold
self.apply_below_threshold = apply_below_threshold
self.weight =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | BioTrillion/pytorch-3dunet | WeightedSmoothL1Loss | false | 4,906 | [
"MIT"
] | 1 | 217781197dd94211ee7fe5d53a8b404f0b8391a6 | https://github.com/BioTrillion/pytorch-3dunet/tree/217781197dd94211ee7fe5d53a8b404f0b8391a6 | import torch
import torch.nn as nn
class Model(nn.SmoothL1Loss):
def __init__(self, threshold, initial_weight, apply_below_threshold=True):
super().__init__(reduction='none')
self.threshold = threshold
self.apply_below_threshold = apply_below_threshold
self.weight = initial_weight... |
BCEDiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
transposed = te... | 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... | BioTrillion/pytorch-3dunet | BCEDiceLoss | false | 4,907 | [
"MIT"
] | 1 | 217781197dd94211ee7fe5d53a8b404f0b8391a6 | https://github.com/BioTrillion/pytorch-3dunet/tree/217781197dd94211ee7fe5d53a8b404f0b8391a6 | import torch
import torch.nn as nn
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
transposed = te... |
BatchLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
-----
Objects inherited from `MetaModule` are fully compa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Bunnycakes62/SIREN | BatchLinear | false | 4,908 | [
"MIT"
] | 1 | 87c2c9e28411fd6a83d1d0d1bc5141cce30e646b | https://github.com/Bunnycakes62/SIREN/tree/87c2c9e28411fd6a83d1d0d1bc5141cce30e646b | import torch
import torch.nn as nn
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
-----
Objects inherited from `MetaModule` are fully compa... |
PMA | # 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 MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Behrouz-Babaki/NCG4CVRP | PMA | false | 4,909 | [
"MIT"
] | 1 | 87d63366c0b461f44ce8e982159a1e207af77b44 | https://github.com/Behrouz-Babaki/NCG4CVRP/tree/87d63366c0b461f44ce8e982159a1e207af77b44 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super().__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
self.fc_k... |
ISAB | # 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 MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Behrouz-Babaki/NCG4CVRP | ISAB | false | 4,910 | [
"MIT"
] | 1 | 87d63366c0b461f44ce8e982159a1e207af77b44 | https://github.com/Behrouz-Babaki/NCG4CVRP/tree/87d63366c0b461f44ce8e982159a1e207af77b44 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super().__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
self.fc_k... |
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