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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
MetaAconC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 MetaAconC(nn.Module):
""" ACON activation (activate or not).
MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | IanVzs/labelImg | MetaAconC | false | 11,513 | [
"MIT"
] | 0 | 3d3dfbf9cf385f38c60376826fdce1f178f563a6 | https://github.com/IanVzs/labelImg/tree/3d3dfbf9cf385f38c60376826fdce1f178f563a6 | import torch
import torch.nn as nn
class Model(nn.Module):
""" ACON activation (activate or not).
MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"""
... |
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.nn.parallel
import torch.utils.data
import torch.onnx
import torch.optim
import torch.utils.data.distributed
import torch.nn.functional as F
import torch.autograd
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Li... | 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... | HolyLow/examples | VAE | false | 11,514 | [
"BSD-3-Clause"
] | 0 | 23b0cb1022cf7a21428883e95fded01d74a059bf | https://github.com/HolyLow/examples/tree/23b0cb1022cf7a21428883e95fded01d74a059bf | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.optim
import torch.utils.data.distributed
import torch.nn.functional as F
import torch.autograd
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(78... |
OutlookAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
import torch.nn.parallel
class OutlookAttention(nn.Module):
"""
Implementation of outlook attention
--dim: hidden dim
--num_heads: number of heads
--kernel_size: kernel size in each window for outlook attention
retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Inch-Z/volo | OutlookAttention | false | 11,515 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class Model(nn.Module):
"""
Implementation of outlook attention
--dim: hidden dim
--num_heads: number of heads
--kernel_size: kernel size in each window for outlook attention
return: token f... |
PatchEmbed | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
class PatchEmbed(nn.Module):
"""
Image to Patch Embedding.
Different with ViT use 1 conv layer, we use 4 conv layers to do patch embedding
"""
def __init__(self, img_size=224, stem_conv=False, stem_stride=1,
patch_size=8, in_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dy... | Inch-Z/volo | PatchEmbed | false | 11,516 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | import torch
import torch.nn as nn
import torch.nn.parallel
class Model(nn.Module):
"""
Image to Patch Embedding.
Different with ViT use 1 conv layer, we use 4 conv layers to do patch embedding
"""
def __init__(self, img_size=224, stem_conv=False, stem_stride=1,
patch_size=8, in_chans=3, ... |
PELU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 as th
import torch.nn as nn
class PELU(nn.Module):
def __init__(self, a=None, b=None):
super().__init__()
default_val = math.sqrt(0.1)
a = default_val if a is None else a
b = default_val if b is None else b
self.a = nn.Parameter(th.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.triton_helpers import math as tl_math
import math
import torch as th
import torch.nn as nn
assert_size_stride =... | InzamamRahaman/PELU | PELU | false | 11,517 | [
"MIT"
] | 0 | ee2598c32f3596f18d957417c97c03e8862086bf | https://github.com/InzamamRahaman/PELU/tree/ee2598c32f3596f18d957417c97c03e8862086bf | import math
import torch
import torch as th
import torch.nn as nn
class Model(nn.Module):
def __init__(self, a=None, b=None):
super().__init__()
default_val = math.sqrt(0.1)
a = default_val if a is None else a
b = default_val if b is None else b
self.a = nn.Parameter(th.te... |
AdjMSELoss | # 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 AdjMSELoss(nn.Module):
def __init__(self):
super(AdjMSELoss, self).__init__()
def forward(self, outputs, labels):
loss = torch.abs(outputs - labels)
adj_fact = torch.mean(torch.abs(labels)) ** 2
adj = torch.exp(-outputs * labels / adj_... | 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
... | JDE65/Adjusted-MAE-loss-function | AdjMSELoss | false | 11,518 | [
"MIT"
] | 0 | e0b54c41a499f68791b731e29e31b5e0f410ac5c | https://github.com/JDE65/Adjusted-MAE-loss-function/tree/e0b54c41a499f68791b731e29e31b5e0f410ac5c | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, outputs, labels):
loss = torch.abs(outputs - labels)
adj_fact = torch.mean(torch.abs(labels)) ** 2
adj = torch.exp(-outputs * labels / adj_fact)
loss = ... |
Transformer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
class Mlp(nn.Module):
"""Implementation of MLP"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Inch-Z/volo | Transformer | false | 11,519 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | import torch
import torch.nn as nn
import torch.nn.parallel
class Mlp(nn.Module):
"""Implementation of MLP"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hi... |
ClassBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
class Mlp(nn.Module):
"""Implementation of MLP"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Inch-Z/volo | ClassBlock | false | 11,520 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | import torch
import torch.nn as nn
import torch.nn.parallel
class Mlp(nn.Module):
"""Implementation of MLP"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hi... |
DummyModelWithSharedSubmodule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class DummyDenseWithRelu(nn.Module):
def __init__(self, input_size, output... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Donfa1con/distiller | DummyModelWithSharedSubmodule | false | 11,521 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class DummyDenseWithRelu(nn.Module):
def __init__(self, input_size, output... |
LocalConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 LocalConv2d(nn.Module):
def __init__(self, num_rows, num_feats_in, num_feats_out, kernel=1,
padding=0):
super(LocalConv2d, self).__init__()
self.num_rows = num_rows
self.out_channels = num_feats_out
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | JSharpClone/M3D-RPN- | LocalConv2d | false | 11,522 | [
"Apache-2.0"
] | 0 | 5192b095e921b5c054a66fd0ce948e67aee957be | https://github.com/JSharpClone/M3D-RPN-/tree/5192b095e921b5c054a66fd0ce948e67aee957be | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_rows, num_feats_in, num_feats_out, kernel=1,
padding=0):
super().__init__()
self.num_rows = num_rows
self.out_channels = num_feats_out
self.kernel = kernel
... |
BahdanauAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
from torch.nn.parameter import Parameter
import torch.onnx
import torch.testing
class EltwiseAdd(nn.Module... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Donfa1con/distiller | BahdanauAttention | false | 11,523 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
from torch.nn.parameter import Parameter
import torch.onnx
import torch.testing
class EltwiseAdd(nn.Module... |
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.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class Actor(nn.Module):
def __init__(self, nb_states, nb_actions, hidden1=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Donfa1con/distiller | Actor | false | 11,524 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class Model(nn.Module):
def __init__(self, nb_states, nb_actions, hidden1=... |
ModelWithDuplicates | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 collections import OrderedDict
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class ModelWithDuplicates(nn.Module):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Donfa1con/distiller | ModelWithDuplicates | false | 11,525 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 | import torch
from collections import OrderedDict
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class Model(nn.Module):
def __init__(s... |
Mean | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class Mean(nn.Module):
def __init__(self, *args, **kwargs):
super(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data... | Donfa1con/distiller | Mean | false | 11,526 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class Model(nn.Module):
def __init__(self, *args, **kwargs):
super... |
policy1 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 policy1(nn.Module):
def __init__(self):
super(policy1, self).__init__()
self.sm = nn.Softmax(dim=-1)
self.actor = nn.Parameter(torch.FloatTensor([-0.35, 0.4, 1]))
def forward(self):
mu = self.sm(self.actor)
return mu
def get_... | 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
... | JWongDude/FruitLoops | policy1 | false | 11,527 | [
"MIT"
] | 0 | f4346d9db16ba619d71ce5bb819f5da08a88a120 | https://github.com/JWongDude/FruitLoops/tree/f4346d9db16ba619d71ce5bb819f5da08a88a120 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.sm = nn.Softmax(dim=-1)
self.actor = nn.Parameter(torch.FloatTensor([-0.35, 0.4, 1]))
def forward(self):
mu = self.sm(self.actor)
return mu
def get_inputs():
r... |
AlexNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class AlexNet(nn.Module):
def __init__(self):
super(AlexNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, (11, 11), stride=(4, 4), padding=(2, 2))
self.conv2 = nn.Conv2d(64, 192, (5, 5), stride=(1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Fritingo/AlexNet_on_browser | AlexNet | false | 11,528 | [
"MIT"
] | 0 | 3e674dd84e25ee74f2efde77882b4faa788907c2 | https://github.com/Fritingo/AlexNet_on_browser/tree/3e674dd84e25ee74f2efde77882b4faa788907c2 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 64, (11, 11), stride=(4, 4), padding=(2, 2))
self.conv2 = nn.Conv2d(64, 192, (5, 5), stride=(1, 1), padding=(... |
Norm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class Norm(nn.Module):
"""
A module wrapper for vector/matrix norm
... | 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
import... | Donfa1con/distiller | Norm | false | 11,529 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class Model(nn.Module):
"""
A module wrapper for vector/matrix norm
... |
TwoMLPHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 TwoMLPHead(nn.Module):
"""
Standard heads for FPN-based models
Arguments:
in_channels (int): number of input channels
representation_size (int): size of the intermediate representation
"""
def __init__(self, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | GerardWalsh/DeepLabv3FineTuning | TwoMLPHead | false | 11,530 | [
"MIT"
] | 0 | 149d4b33a7dc94c56361f559ca67cb0fcf9ae9d5 | https://github.com/GerardWalsh/DeepLabv3FineTuning/tree/149d4b33a7dc94c56361f559ca67cb0fcf9ae9d5 | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Standard heads for FPN-based models
Arguments:
in_channels (int): number of input channels
representation_size (int): size of the intermediate representation
"""
def __init__(self, in_ch... |
ClippedLinearQuantization | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
def linear_dequantize(input, scale, zero_point, inplace=False):
if inplace:... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | Donfa1con/distiller | ClippedLinearQuantization | false | 11,531 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
def linear_dequantize(input, scale, zero_point, inplace=False):
if inplace:... |
Downsample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Jack000/improved-diffusion | Downsample | false | 11,532 | [
"MIT"
] | 0 | e2abfc8072f9007b558b697b79d2affdae0eca3b | https://github.com/Jack000/improved-diffusion/tree/e2abfc8072f9007b558b697b79d2affdae0eca3b | import torch
import torch.nn as nn
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
... |
Classifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.distributed
import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, hidden_size):
super(Classifier, self).__init__()
self.linear1 = nn.Linear(hidden_size, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x, mask_cls):
h = 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
import torch.distributed
import torch
import torch.nn as nn
assert_size_stride =... | JackInTaiwan/BertSum | Classifier | false | 11,533 | [
"Apache-2.0"
] | 0 | 5b6f372b13358473d17c49bfc45f1e15c80f9fce | https://github.com/JackInTaiwan/BertSum/tree/5b6f372b13358473d17c49bfc45f1e15c80f9fce | import torch
import torch.distributed
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.linear1 = nn.Linear(hidden_size, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x, mask_cls):
h = self.linear1(x).squeez... |
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):
"""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))
self.b = nn.Parame... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | HamoolNizar/RumorDetectionSystem | LayerNorm | false | 11,534 | [
"MIT"
] | 0 | 902ae4d705c0a6db470064f0e7f07f3c167d3eac | https://github.com/HamoolNizar/RumorDetectionSystem/tree/902ae4d705c0a6db470064f0e7f07f3c167d3eac | import torch
import torch.nn as nn
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.Parameter(torch.zeros(n_s... |
DilatedResidualLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 DilatedResidualLayer(nn.Module):
def __init__(self, dilation, in_channels, out_channels):
super(DilatedResidualLayer, self).__init__()
self.conv_dilated = nn.Conv1d(in_channels, out_channels, 3, padding
=dilation,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Jaakik/hydra-ml | DilatedResidualLayer | false | 11,535 | [
"MIT"
] | 0 | eae54fc478163130c94450a2a2ddea4f204c1ea9 | https://github.com/Jaakik/hydra-ml/tree/eae54fc478163130c94450a2a2ddea4f204c1ea9 | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, dilation, in_channels, out_channels):
super().__init__()
self.conv_dilated = nn.Conv1d(in_channels, out_channels, 3, padding
=dilation, dilation=dilation)
self.conv_1x1... |
BiDAFAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 masked_softmax(logits, mask, dim=-1, log_softmax=False):
"""Take the softmax of `logits` over given dimension, and set
entries to 0 wherever `mask` is 0.
Args:
logits (torch.Tensor): Inputs to the softmax function.
mas... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JNXSTJ/squad | BiDAFAttention | false | 11,536 | [
"MIT"
] | 0 | ed875a90b212e1fe2f05144edb5595cedb5dd42b | https://github.com/JNXSTJ/squad/tree/ed875a90b212e1fe2f05144edb5595cedb5dd42b | import torch
import torch.nn as nn
import torch.nn.functional as F
def masked_softmax(logits, mask, dim=-1, log_softmax=False):
"""Take the softmax of `logits` over given dimension, and set
entries to 0 wherever `mask` is 0.
Args:
logits (torch.Tensor): Inputs to the softmax function.
mas... |
Upsample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Jack000/improved-diffusion | Upsample | false | 11,537 | [
"MIT"
] | 0 | e2abfc8072f9007b558b697b79d2affdae0eca3b | https://github.com/Jack000/improved-diffusion/tree/e2abfc8072f9007b558b697b79d2affdae0eca3b | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
re... |
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
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self, input_size=50, hidden_size=256, dropout=0,
kernel_size=3, padding=1, activation_function=F.relu):
"""
Args:
input_size: dimention of input embedding
kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | JanKalo/OpenNRE | CNN | false | 11,538 | [
"MIT"
] | 0 | 2842903e5b66c88311820adac50a16ee3dc8ff77 | https://github.com/JanKalo/OpenNRE/tree/2842903e5b66c88311820adac50a16ee3dc8ff77 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_size=50, hidden_size=256, dropout=0,
kernel_size=3, padding=1, activation_function=F.relu):
"""
Args:
input_size: dimention of input embedding
kernel... |
TVLoss | # 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 TVLoss(nn.Module):
def __init__(self, strength):
super(TVLoss, self).__init__()
self.strength = strength
def forward(self, input):
self.x_diff = input[:, :, 1:, :] - input[:, :, :-1, :]
self.y_diff = input[:, :, :, 1:] - 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 math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | JaledMC/neural-style-pt | TVLoss | false | 11,539 | [
"MIT"
] | 0 | ce205c867761e251e86c89722df81c74dad7a221 | https://github.com/JaledMC/neural-style-pt/tree/ce205c867761e251e86c89722df81c74dad7a221 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, strength):
super().__init__()
self.strength = strength
def forward(self, input):
self.x_diff = input[:, :, 1:, :] - input[:, :, :-1, :]
self.y_diff = input[:, :, :, 1:] - input[:, :, :, :-1]
... |
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 torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, ignore_target=-1):
super().__init__()
self.ignore_target = ignore_target
def forward(self, input, target):
"""
:param input: (N), logit
:param target: (N), {0, 1}
: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... | JamesWang007/PointRCNN | DiceLoss | false | 11,540 | [
"MIT"
] | 0 | ea0812c52e6767b976fc50fed61e6b72fa6cdf81 | https://github.com/JamesWang007/PointRCNN/tree/ea0812c52e6767b976fc50fed61e6b72fa6cdf81 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, ignore_target=-1):
super().__init__()
self.ignore_target = ignore_target
def forward(self, input, target):
"""
:param input: (N), logit
:param target: (N), {0, 1}
:return:
""... |
SigmoidFocalClassificationLoss | # 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 _sigmoid_cross_entropy_with_logits(logits, labels):
loss = torch.clamp(logits, min=0) - logits * labels.type_as(logits)
loss += torch.log1p(torch.exp(-torch.abs(logits)))
return loss
class SigmoidFocalClassificationLoss(nn.Module):
"""Sigmoid focal cross entrop... | 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... | JamesWang007/PointRCNN | SigmoidFocalClassificationLoss | false | 11,541 | [
"MIT"
] | 0 | ea0812c52e6767b976fc50fed61e6b72fa6cdf81 | https://github.com/JamesWang007/PointRCNN/tree/ea0812c52e6767b976fc50fed61e6b72fa6cdf81 | import torch
import torch.nn as nn
def _sigmoid_cross_entropy_with_logits(logits, labels):
loss = torch.clamp(logits, min=0) - logits * labels.type_as(logits)
loss += torch.log1p(torch.exp(-torch.abs(logits)))
return loss
class Model(nn.Module):
"""Sigmoid focal cross entropy loss.
Focal loss ... |
GlobalAvgPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
import torch.nn.functional as F
class GlobalAvgPool2d(nn.Module):
def __init__(self):
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
return F.avg_pool2d(x, kernel_size=x.size()[2:])
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | JessyLee/Jessy_Dive_into_DL_Pytorch | GlobalAvgPool2d | false | 11,542 | [
"MIT"
] | 0 | 40b7921637b13507057f41485d928f3b59cc6f6a | https://github.com/JessyLee/Jessy_Dive_into_DL_Pytorch/tree/40b7921637b13507057f41485d928f3b59cc6f6a | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return F.avg_pool2d(x, kernel_size=x.size()[2:])
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
retur... |
PSNRLoss | # 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.functional import mse_loss as mse
def psnr(input: 'torch.Tensor', target: 'torch.Tensor', max_val: 'float'
) ->torch.Tensor:
"""Creates a function that calculates the PSNR between 2 images.
PSNR is Peek Signal to Noise Ratio, which is similar to mean squar... | 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
from t... | JoanFM/kornia | PSNRLoss | false | 11,543 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 | import torch
import torch.nn as nn
from torch.nn.functional import mse_loss as mse
def psnr(input: 'torch.Tensor', target: 'torch.Tensor', max_val: 'float'
) ->torch.Tensor:
"""Creates a function that calculates the PSNR between 2 images.
PSNR is Peek Signal to Noise Ratio, which is similar to mean squar... |
Conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from torch.nn import functional as F
class Conv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2d, self).__init__(in_channels, out_channels, kernel_size,
strid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | JassiGhuman/backgroundSubtraction | Conv2d | false | 11,544 | [
"MIT"
] | 0 | 351a380b34f9d84548bea734a69842227e373e65 | https://github.com/JassiGhuman/backgroundSubtraction/tree/351a380b34f9d84548bea734a69842227e373e65 | import torch
import torch.nn as nn
from torch.nn import functional as F
class Model(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super().__init__(in_channels, out_channels, kernel_size,
stride, padding, d... |
Rot180 | # 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 rot180(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-2, -1])
class Rot180(nn.Module):
"""Rotate a tensor image or a batch of tensor images
180 degrees. Input must be a tensor of shape (C, H, W)
or a batch of tensors :math:`(*, C, H, W)`.
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | JoanFM/kornia | Rot180 | false | 11,545 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 | import torch
import torch.nn as nn
def rot180(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-2, -1])
class Model(nn.Module):
"""Rotate a tensor image or a batch of tensor images
180 degrees. Input must be a tensor of shape (C, H, W)
or a batch of tensors :math:`(*, C, H, W)`.
... |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
def conv3x3(in_planes, out_planes, stride=1, dilation=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation, bias=False)
class BasicBlock(nn.Module):
expansion ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | JiazeWang/6-PACK | BasicBlock | false | 11,546 | [
"MIT"
] | 0 | bce910213cfbf89b4ed7b59ff6c70a59a7c19b99 | https://github.com/JiazeWang/6-PACK/tree/bce910213cfbf89b4ed7b59ff6c70a59a7c19b99 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
def conv3x3(in_planes, out_planes, stride=1, dilation=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation, bias=False)
class Model(nn.Module):
expansion = 1
... |
LastLevelMaxPool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
from torchvision.transforms import functional as F
from torch import nn
import torch.nn.functional as F
class LastLevelMaxPool(nn.Module):
def forward(self, x):
return [F.max_pool2d(x, 1, 2, 0)]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | Amir4g/maskrcnn-benchmark | LastLevelMaxPool | false | 11,547 | [
"MIT"
] | 0 | c734fef962c3a2782e0055cfb6f825505a4b0c26 | https://github.com/Amir4g/maskrcnn-benchmark/tree/c734fef962c3a2782e0055cfb6f825505a4b0c26 | import torch
import torch.utils.data
from torchvision.transforms import functional as F
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, x):
return [F.max_pool2d(x, 1, 2, 0)]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... |
Fire | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class Fire(nn.Module):
def __init__(self, inplanes, squeeze_planes, expand1x1_planes,
expand3x3_planes):
super(Fire, self).__init__()
self.inplanes = inplanes
self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
self.squeeze_a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | GerardWalsh/DeepLabv3FineTuning | Fire | false | 11,548 | [
"MIT"
] | 0 | 149d4b33a7dc94c56361f559ca67cb0fcf9ae9d5 | https://github.com/GerardWalsh/DeepLabv3FineTuning/tree/149d4b33a7dc94c56361f559ca67cb0fcf9ae9d5 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, inplanes, squeeze_planes, expand1x1_planes,
expand3x3_planes):
super().__init__()
self.inplanes = inplanes
self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
self.squeeze_activation... |
RgbaToRgb | # 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 rgba_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
"""Convert an image from RGBA to RGB.
Args:
image: RGBA Image to be converted to RGB of shape :math:`(*,4,H,W)`.
Returns:
RGB version of the image with shape :math:`(*,3,H,W)`.
Example:
... | 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... | JoanFM/kornia | RgbaToRgb | false | 11,549 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 | import torch
import torch.nn as nn
def rgba_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
"""Convert an image from RGBA to RGB.
Args:
image: RGBA Image to be converted to RGB of shape :math:`(*,4,H,W)`.
Returns:
RGB version of the image with shape :math:`(*,3,H,W)`.
Example:
... |
ExtractTensorPatches | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import Union
from torch.nn.modules.utils import _pair
def _extract_tensor_patchesnd(input: 'torch.Tensor', window_sizes:
'Tuple[int, ...]', strides: 'Tuple[int, ...]') ->torch.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 typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import Union
from tor... | JoanFM/kornia | ExtractTensorPatches | false | 11,550 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 | import torch
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import Union
from torch.nn.modules.utils import _pair
def _extract_tensor_patchesnd(input: 'torch.Tensor', window_sizes:
'Tuple[int, ...]', strides: 'Tuple[int, ...]') ->torch.Tensor... |
InverseDepthSmoothnessLoss | # 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 _gradient_x(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :, :-1] - img[:, :, :, 1:]
def _gradient_y(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :-1, :] - img[:, :, 1:... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | JoanFM/kornia | InverseDepthSmoothnessLoss | false | 11,551 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 | import torch
import torch.nn as nn
def _gradient_x(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :, :-1] - img[:, :, :, 1:]
def _gradient_y(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :-1, :] - img[:, :, 1:... |
RgbaToBgr | # 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 bgr_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
"""Convert a BGR image to RGB.
Args:
image: BGR Image to be converted to BGR of shape :math:`(*,3,H,W)`.
Returns:
RGB version of the image with shape of shape :math:`(*,3,H,W)`.
Example:
... | 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... | JoanFM/kornia | RgbaToBgr | false | 11,552 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 | import torch
import torch.nn as nn
def bgr_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
"""Convert a BGR image to RGB.
Args:
image: BGR Image to be converted to BGR of shape :math:`(*,3,H,W)`.
Returns:
RGB version of the image with shape of shape :math:`(*,3,H,W)`.
Example:
... |
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 Encoder(nn.Module):
def __init__(self, out_dim=64):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | JanSoltysik/SimCLR | Encoder | false | 11,553 | [
"MIT"
] | 0 | 34ea6d17a630382b65a00aa445d82876754ee679 | https://github.com/JanSoltysik/SimCLR/tree/34ea6d17a630382b65a00aa445d82876754ee679 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, out_dim=64):
super().__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.c... |
InvDepth | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 InvDepth(nn.Module):
def __init__(self, height, width, min_depth=0.5, max_depth=25.0):
super(InvDepth, self).__init__()
self._min_range = 1.0 / max_depth
self._max_range = 1.0 / min_depth
self.w = nn.Parameter(self._init_weights(height, wid... | 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... | JoanFM/kornia | InvDepth | false | 11,554 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, height, width, min_depth=0.5, max_depth=25.0):
super().__init__()
self._min_range = 1.0 / max_depth
self._max_range = 1.0 / min_depth
self.w = nn.Parameter(self._init_weights(height, width))
def _in... |
PoseNetFeat | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
import torch.nn.functional as F
class PoseNetFeat(nn.Module):
def __init__(self, num_points):
super(PoseNetFeat, self).__init__()
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.conv2 = torch.nn.Conv1d(64, 128... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | JiazeWang/6-PACK | PoseNetFeat | false | 11,555 | [
"MIT"
] | 0 | bce910213cfbf89b4ed7b59ff6c70a59a7c19b99 | https://github.com/JiazeWang/6-PACK/tree/bce910213cfbf89b4ed7b59ff6c70a59a7c19b99 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_points):
super().__init__()
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.conv2 = torch.nn.Conv1d(64, 128, 1)
self.e_con... |
Hflip | # 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 hflip(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-1])
class Hflip(nn.Module):
"""Horizontally flip a tensor image or a batch of tensor images. Input must
be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
... | 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... | JoanFM/kornia | Hflip | false | 11,556 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 | import torch
import torch.nn as nn
def hflip(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-1])
class Model(nn.Module):
"""Horizontally flip a tensor image or a batch of tensor images. Input must
be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
... |
BinaryFocalLossWithLogits | # 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 binary_focal_loss_with_logits(input: 'torch.Tensor', target:
'torch.Tensor', alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
'str'='none', eps: 'float'=1e-08) ->torch.Tensor:
"""Function that computes Binary Focal loss.
.. math::
\\text{FL}(p_t) = -... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | JoanFM/kornia | BinaryFocalLossWithLogits | false | 11,557 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 | import torch
import torch.nn as nn
def binary_focal_loss_with_logits(input: 'torch.Tensor', target:
'torch.Tensor', alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
'str'='none', eps: 'float'=1e-08) ->torch.Tensor:
"""Function that computes Binary Focal loss.
.. math::
\\text{FL}(p_t) = -... |
TotalVariation | # 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 total_variation(img: 'torch.Tensor') ->torch.Tensor:
"""Function that computes Total Variation according to [1].
Args:
img (torch.Tensor): the input image with shape :math:`(N, C, H, W)` or :math:`(C, H, W)`.
Return:
torch.Tensor: a scalar with the ... | 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... | JoanFM/kornia | TotalVariation | false | 11,558 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 | import torch
import torch.nn as nn
def total_variation(img: 'torch.Tensor') ->torch.Tensor:
"""Function that computes Total Variation according to [1].
Args:
img (torch.Tensor): the input image with shape :math:`(N, C, H, W)` or :math:`(C, H, W)`.
Return:
torch.Tensor: a scalar with the ... |
Vflip | # 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 vflip(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-2])
class Vflip(nn.Module):
"""Vertically flip a tensor image or a batch of tensor images. Input must
be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
... | 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... | JoanFM/kornia | Vflip | false | 11,559 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 | import torch
import torch.nn as nn
def vflip(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-2])
class Model(nn.Module):
"""Vertically flip a tensor image or a batch of tensor images. Input must
be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
... |
LinearSum | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 LinearSum(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, activ_input=
'relu', activ_output='relu', normalize=False, dropout_input=0.0,
dropout_pre_lin=0.0, dropout_output=0.0):
super(LinearSum, s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | JoannaLXY/block.bootstrap.pytorch | LinearSum | false | 11,560 | [
"BSD-3-Clause"
] | 0 | 42c3e7616b704e05c6ff2376ff68b5b18044fe77 | https://github.com/JoannaLXY/block.bootstrap.pytorch/tree/42c3e7616b704e05c6ff2376ff68b5b18044fe77 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, activ_input=
'relu', activ_output='relu', normalize=False, dropout_input=0.0,
dropout_pre_lin=0.0, dropout_output=0.0):
super().__init__()
... |
MFB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 MFB(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
activ_input='relu', activ_output='relu', normalize=False,
dropout_input=0.0, dropout_pre_norm=0.0, dropout_output=0.0):
super(MFB, 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
import torch.nn as nn
assert_... | JoannaLXY/block.bootstrap.pytorch | MFB | false | 11,561 | [
"BSD-3-Clause"
] | 0 | 42c3e7616b704e05c6ff2376ff68b5b18044fe77 | https://github.com/JoannaLXY/block.bootstrap.pytorch/tree/42c3e7616b704e05c6ff2376ff68b5b18044fe77 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
activ_input='relu', activ_output='relu', normalize=False,
dropout_input=0.0, dropout_pre_norm=0.0, dropout_output=0.0):
super().__in... |
MFH | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 MFH(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
activ_input='relu', activ_output='relu', normalize=False,
dropout_input=0.0, dropout_pre_lin=0.0, dropout_output=0.0):
super(MFH, self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | JoannaLXY/block.bootstrap.pytorch | MFH | false | 11,562 | [
"BSD-3-Clause"
] | 0 | 42c3e7616b704e05c6ff2376ff68b5b18044fe77 | https://github.com/JoannaLXY/block.bootstrap.pytorch/tree/42c3e7616b704e05c6ff2376ff68b5b18044fe77 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
activ_input='relu', activ_output='relu', normalize=False,
dropout_input=0.0, dropout_pre_lin=0.0, dropout_output=0.0):
super().__ini... |
BinaryExpAbs | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import abc
import inspect
import warnings
import torch.nn as nn
import to... | Johnsonms/NNI_master | BinaryExpAbs | false | 11,563 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... |
BinaryMul | # 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 abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryMul | false | 11,564 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... |
NetVLAD | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from sklearn.neighbors import NearestNeighbors
class NetVLAD(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, num_clusters=64, dim=128, normalize_input=True,
vladv2=False):
"""
Args:... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AlessandroRigoli/project_vg | NetVLAD | false | 11,565 | [
"MIT"
] | 0 | cb1323bee60cdb4108fe0aab68791321c7974832 | https://github.com/AlessandroRigoli/project_vg/tree/cb1323bee60cdb4108fe0aab68791321c7974832 | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from sklearn.neighbors import NearestNeighbors
class Model(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, num_clusters=64, dim=128, normalize_input=True,
vladv2=False):
"""
Args:
... |
Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JetRunner/PaSST-EE | Block | false | 11,566 | [
"Apache-2.0"
] | 0 | 2ff8f4fd0e9c1868856d08147e6e3cf1c1bed68c | https://github.com/JetRunner/PaSST-EE/tree/2ff8f4fd0e9c1868856d08147e6e3cf1c1bed68c | import torch
from torch import nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is ... |
BinarySigmoid | # 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 abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinarySigmoid | false | 11,567 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... |
MLB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 MLB(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, activ_input=
'relu', activ_output='relu', normalize=False, dropout_input=0.0,
dropout_pre_lin=0.0, dropout_output=0.0):
super(MLB, self).__init_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | JoannaLXY/block.bootstrap.pytorch | MLB | false | 11,568 | [
"BSD-3-Clause"
] | 0 | 42c3e7616b704e05c6ff2376ff68b5b18044fe77 | https://github.com/JoannaLXY/block.bootstrap.pytorch/tree/42c3e7616b704e05c6ff2376ff68b5b18044fe77 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, activ_input=
'relu', activ_output='relu', normalize=False, dropout_input=0.0,
dropout_pre_lin=0.0, dropout_output=0.0):
super().__init__()
... |
PatchEmbed | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from itertools import chain as chain
import torch.utils.data
import torch.nn as nn
class PatchEmbed(nn.Module):
"""
PatchEmbed.
"""
def __init__(self, dim_in=3, dim_out=768, kernel=(1, 16, 16), stride=(1,
4, 4), padding=(1, 7, 7), conv_2d=False):
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from itertools import chain as chain
import torch.utils.data
import torch.nn as ... | JerryYLi/SlowFast | PatchEmbed | false | 11,569 | [
"Apache-2.0"
] | 0 | 70bbd8d917c49f86b41fdd7c2de5c1231e6d950c | https://github.com/JerryYLi/SlowFast/tree/70bbd8d917c49f86b41fdd7c2de5c1231e6d950c | import torch
from itertools import chain as chain
import torch.utils.data
import torch.nn as nn
class Model(nn.Module):
"""
PatchEmbed.
"""
def __init__(self, dim_in=3, dim_out=768, kernel=(1, 16, 16), stride=(1,
4, 4), padding=(1, 7, 7), conv_2d=False):
super().__init__()
if ... |
BinaryDivide | # 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 abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryDivide | false | 11,570 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... |
BinaryMinus | # 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 abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryMinus | false | 11,571 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... |
BinaryMin | # 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 abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
... | Johnsonms/NNI_master | BinaryMin | false | 11,572 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... |
BinaryParamAdd | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryParamAdd | false | 11,573 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... |
BinaryMax | # 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 abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
... | Johnsonms/NNI_master | BinaryMax | false | 11,574 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... |
DistillKL | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class DistillKL(nn.Module):
"""Distilling the Knowledge in a Neural Network"""
def __init__(self, T):
super(DistillKL, self).__init__()
s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Johnsonms/NNI_master | DistillKL | false | 11,575 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class Model(nn.Module):
"""Distilling the Knowledge in a Neural Network"""
def __init__(self, T):
super().__init__()
self.T = T
def ... |
PatchSequential | # 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 warnings
from typing import Dict
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import cast
from typing import List
from typing import Union
from torch.distributions import Bernoulli
from itertools import zip_longest... | 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 math
import warnings
from typing import Dict
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.... | JoanFM/kornia | PatchSequential | false | 11,576 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 | import math
import torch
import warnings
from typing import Dict
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import cast
from typing import List
from typing import Union
from torch.distributions import Bernoulli
from itertools import zip_longest... |
LinearCombine | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class LinearCombine(nn.Module):
def __init__(self, layers_num, trainable=True, input_aware=False,
word_level=False):
super(LinearCombine, sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import ... | Johnsonms/NNI_master | LinearCombine | false | 11,577 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class Model(nn.Module):
def __init__(self, layers_num, trainable=True, input_aware=False,
word_level=False):
super().__init__()
self.... |
ResidualConvUnit | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.fft
import torch.nn as nn
import torch.utils.cpp_extension
class ResidualConvUnit(nn.Module):
def __init__(self, cin, activation, bn):
super().__init__()
self.conv = nn.Conv2d(cin, cin, kernel_size=3, stride=1, padding=1,
bias=True)
self.skip_add = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.fft
import torch.nn as nn
import torch.utils.cpp_extension
assert_s... | CeciLyu/projected_gan | ResidualConvUnit | false | 11,578 | [
"MIT"
] | 0 | 5e86ee0c88d47164c30ede37448e7ba7f010fa7b | https://github.com/CeciLyu/projected_gan/tree/5e86ee0c88d47164c30ede37448e7ba7f010fa7b | import torch
import torch.fft
import torch.nn as nn
import torch.utils.cpp_extension
class Model(nn.Module):
def __init__(self, cin, activation, bn):
super().__init__()
self.conv = nn.Conv2d(cin, cin, kernel_size=3, stride=1, padding=1,
bias=True)
self.skip_add = nn.quantized.... |
Pooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class ReLUConvBN(nn.Module):
"""
Parameters
---
C_in: int
the number of input channels
C_out: int
the number of output channels
stride: int
stride... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
assert_size_stride = torch._C... | Johnsonms/NNI_master | Pooling | false | 11,579 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class ReLUConvBN(nn.Module):
"""
Parameters
---
C_in: int
the number of input channels
C_out: int
the number of output channels
stride: int
stride... |
Interpolate | # 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.fft
import torch.nn as nn
import torch.utils.cpp_extension
class Interpolate(nn.Module):
"""Interpolation module."""
def __init__(self, size, mode='bilinear', align_corners=False):
"""Init.
Args:
scale_factor (float): scaling
mode (str): inter... | 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.fft
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_strid... | CeciLyu/projected_gan | Interpolate | false | 11,580 | [
"MIT"
] | 0 | 5e86ee0c88d47164c30ede37448e7ba7f010fa7b | https://github.com/CeciLyu/projected_gan/tree/5e86ee0c88d47164c30ede37448e7ba7f010fa7b | import torch
import torch.fft
import torch.nn as nn
import torch.utils.cpp_extension
class Model(nn.Module):
"""Interpolation module."""
def __init__(self, size, mode='bilinear', align_corners=False):
"""Init.
Args:
scale_factor (float): scaling
mode (str): interpolati... |
Mask | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class Mask(nn.Module):
def forward(self, seq, mask):
seq_mask = torch.unsqueeze(mask, 2)
seq_mask = torch.transpose(seq_mask.repeat(1, 1, seq.size()[1]), 1, 2)
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
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
assert_size_stride = torch._C... | Johnsonms/NNI_master | Mask | false | 11,581 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class Model(nn.Module):
def forward(self, seq, mask):
seq_mask = torch.unsqueeze(mask, 2)
seq_mask = torch.transpose(seq_mask.repeat(1, 1, seq.size()[1]), 1, 2)
retu... |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class FC(nn.Module):
def __init__(self, in_size, out_size, dropout_r=0.0, use_relu=True):
super(FC, self).__init__()
self.dropout_r = dropout_r
self.use_relu = use_relu
self.linear = nn.Linear(in_size, out_size)
if use_relu:
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | JoonseoKang/mcan-cap | MLP | false | 11,582 | [
"Apache-2.0"
] | 0 | 788e21fc1bc712018166aa44cc3298264f493f3b | https://github.com/JoonseoKang/mcan-cap/tree/788e21fc1bc712018166aa44cc3298264f493f3b | import torch
import torch.nn as nn
class FC(nn.Module):
def __init__(self, in_size, out_size, dropout_r=0.0, use_relu=True):
super().__init__()
self.dropout_r = dropout_r
self.use_relu = use_relu
self.linear = nn.Linear(in_size, out_size)
if use_relu:
self.relu... |
InformedSender | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.nn.parallel
import torch.utils.data
import torch.distributions
class InformedSender(nn.Module):
def __init__(self, game_size, feat_size, embedding_size, hidden_size,
vocab_size=100, temp=1.0):
super(InformedSender, 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.... | IA3005/NLP_ens | InformedSender | false | 11,583 | [
"MIT"
] | 0 | 794ebbff46d5e6d5476f29b577b40bbb52991246 | https://github.com/IA3005/NLP_ens/tree/794ebbff46d5e6d5476f29b577b40bbb52991246 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class Model(nn.Module):
def __init__(self, game_size, feat_size, embedding_size, hidden_size,
vocab_size=100, temp=1.0):
super().__init__()
self.g... |
BinaryExpSquare | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import abc
import inspect
import warnings
import torch.nn as nn
import to... | Johnsonms/NNI_master | BinaryExpSquare | false | 11,584 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... |
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
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class Hsigmoid(nn.Module):
"""Hsigmoid activation function."""
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data... | Johnsonms/NNI_master | Hsigmoid | false | 11,585 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class Model(nn.Module):
"""Hsigmoid activation function."""
def __init__(self, inplace=True):
super().__init__()
self.inplace = inplace
... |
SymmSoftplus | # 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.utils.data import Dataset as Dataset
import torch.utils.data
def symm_softplus(x, softplus_=torch.nn.functional.softplus):
return softplus_(x) - 0.5 * x
class SymmSoftplus(torch.nn.Module):
def forward(self, x):
return symm_softplus(x)
def get_inputs():
return [torch.r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.utils.data import Dataset as Dataset
import torch.u... | JunLi-Galios/CP-Flow | SymmSoftplus | false | 11,586 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff | import torch
from torch.utils.data import Dataset as Dataset
import torch.utils.data
def symm_softplus(x, softplus_=torch.nn.functional.softplus):
return softplus_(x) - 0.5 * x
class Model(torch.nn.Module):
def forward(self, x):
return symm_softplus(x)
def get_inputs():
return [torch.rand([4,... |
InteractiveKLLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class InteractiveKLLoss(nn.Module):
def __init__(self, temperature):
super().__init__()
self.temperature = temperature
self.kl_loss =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Johnsonms/NNI_master | InteractiveKLLoss | false | 11,587 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class Model(nn.Module):
def __init__(self, temperature):
super().__init__()
self.temperature = temperature
self.kl_loss = nn.KLDivLos... |
GlobalAvgPool1d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from abc import abstractmethod
from torch.nn import functional
from typing import *
class AvgPool(nn.Module):
"""
AvgPool Module.
"""
def __init__(self):
super().__init__()
@abstractmet... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from abc import abstractmethod
from typing import ... | Johnsonms/NNI_master | GlobalAvgPool1d | false | 11,588 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from abc import abstractmethod
from torch.nn import functional
from typing import *
class AvgPool(nn.Module):
"""
AvgPool Module.
"""
def __init__(self):
super().__init__()
@abstractmet... |
BinaryAdd | # 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 abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryAdd | false | 11,589 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... |
BackboneModel1 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.optim
import torch.utils.data
from typing import *
class BackboneModel1(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 1, 1, 1)
def forward(self, x):
return self.conv1(x)
def get_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.u... | Johnsonms/NNI_master | BackboneModel1 | false | 11,590 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 1, 1, 1)
def forward(self, x):
return self.conv1(x)
def get_inputs():... |
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 torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
def attention(query, key, value, mask=None, dropout=None):
d_k = query.size(-1)
logits = torch.matmul(query, key.transpose(-2, -1)) / math.sqr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Johnsonms/NNI_master | MultiHeadAttention | false | 11,591 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
def attention(query, key, value, mask=None, dropout=None):
d_k = query.size(-1)
logits = torch.matmul(query, key.transpose(-2, -1)) / math.sqr... |
PosLinear2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.utils.data import Dataset as Dataset
import torch.nn as nn
import torch.utils.data
class PosLinear2(torch.nn.Linear):
def forward(self, x: 'Tensor') ->Tensor:
return nn.functional.linear(x, torch.nn.functional.softmax(self.
weight, 1), 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JunLi-Galios/CP-Flow | PosLinear2 | false | 11,592 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff | import torch
from torch import Tensor
from torch.utils.data import Dataset as Dataset
import torch.nn as nn
import torch.utils.data
class Model(torch.nn.Linear):
def forward(self, x: 'Tensor') ->Tensor:
return nn.functional.linear(x, torch.nn.functional.softmax(self.
weight, 1), self.bias)
... |
ActorCritic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class ActorCritic(nn.Module):
def __init__(self, num_states, num_actions, hidden_size):
super(ActorCritic, self).__init__()
self.num_actions ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Johnsonms/NNI_master | ActorCritic | false | 11,593 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class Model(nn.Module):
def __init__(self, num_states, num_actions, hidden_size):
super().__init__()
self.num_actions = num_actions
s... |
MSELoss | # 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 MSELoss(nn.Module):
""" Mean-squared error loss """
def __init__(self, reduction='mean', eps=1e-08):
super().__init__()
if reduction not in ('mean', 'sum'):
raise ValueError(
'`reduction` not recognized. must be "mean" or "s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | KAGRA-TW-ML/deepclean-prod | MSELoss | false | 11,594 | [
"MIT"
] | 0 | 9fb834cb4027fd3b377bc0e763c237235c98eabd | https://github.com/KAGRA-TW-ML/deepclean-prod/tree/9fb834cb4027fd3b377bc0e763c237235c98eabd | import torch
import torch.nn as nn
class Model(nn.Module):
""" Mean-squared error loss """
def __init__(self, reduction='mean', eps=1e-08):
super().__init__()
if reduction not in ('mean', 'sum'):
raise ValueError(
'`reduction` not recognized. must be "mean" or "sum... |
PosLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.utils.data import Dataset as Dataset
import torch.nn as nn
import torch.utils.data
class PosLinear(torch.nn.Linear):
def forward(self, x: 'Tensor') ->Tensor:
gain = 1 / x.size(1)
return nn.functional.linear(x, torch.nn.functional.softplus(self.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | JunLi-Galios/CP-Flow | PosLinear | false | 11,595 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff | import torch
from torch import Tensor
from torch.utils.data import Dataset as Dataset
import torch.nn as nn
import torch.utils.data
class Model(torch.nn.Linear):
def forward(self, x: 'Tensor') ->Tensor:
gain = 1 / x.size(1)
return nn.functional.linear(x, torch.nn.functional.softplus(self.
... |
PFLDLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class PFLDLoss(nn.Module):
"""Weighted loss of L2 distance with the pose angle for PFLD."""
def __init__(self):
super(PFLDLoss, self).__init__()
def forward(self, landmark_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import ... | Johnsonms/NNI_master | PFLDLoss | false | 11,596 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class Model(nn.Module):
"""Weighted loss of L2 distance with the pose angle for PFLD."""
def __init__(self):
super().__init__()
def forward(self, landmark_gt, euler_angle_g... |
NoiseInjection | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class NoiseInjection(nn.Module):
def __init__(self, channel):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
def forward(self, image, noise):
return image + self.weight * noise
def get_inputs():
return [torch.rand(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | KUMartin77/AAA738_StyleGAN_pytorch | NoiseInjection | false | 11,597 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, channel):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
def forward(self, image, noise):
return image + self.weight * noise
def get_inputs():
return [torch.rand([4, 4, 4,... |
wide_basic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 get_norm(n_filters, norm):
if norm is None:
return Identity()
elif norm == 'batch':
return nn.BatchNorm2d(n_filters, momentum=0.9)
elif norm == 'instance':
return nn.InstanceNorm2d(n_filters, affine=True)
elif norm == 'layer':
retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | JunLi-Galios/JEM | wide_basic | false | 11,598 | [
"Apache-2.0"
] | 0 | dd4d33f64269d3999458f129ac83a3043ad7e63f | https://github.com/JunLi-Galios/JEM/tree/dd4d33f64269d3999458f129ac83a3043ad7e63f | import torch
import torch.nn as nn
def get_norm(n_filters, norm):
if norm is None:
return Identity()
elif norm == 'batch':
return nn.BatchNorm2d(n_filters, momentum=0.9)
elif norm == 'instance':
return nn.InstanceNorm2d(n_filters, affine=True)
elif norm == 'layer':
retu... |
Softplus | # 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.utils.data import Dataset as Dataset
import torch.nn as nn
import torch.utils.data
def activation_shifting(activation):
def shifted_activation(x):
return activation(x) - activation(torch.zeros_like(x))
return shifted_activation
def cauchy_softplus(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, math as tl_math
import numpy as np
from torch.utils.data import Dataset as Dat... | JunLi-Galios/CP-Flow | Softplus | false | 11,599 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff | import torch
import numpy as np
from torch.utils.data import Dataset as Dataset
import torch.nn as nn
import torch.utils.data
def activation_shifting(activation):
def shifted_activation(x):
return activation(x) - activation(torch.zeros_like(x))
return shifted_activation
def cauchy_softplus(x):
... |
PosConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.utils.data import Dataset as Dataset
import torch.nn.init as init
import torch.utils.data
class PosConv2d(torch.nn.Conv2d):
def reset_parameters(self) ->None:
super().reset_parameters()
self.fan_in, _ = init._calculate_fan_in_and_fan_out(self.weigh... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | JunLi-Galios/CP-Flow | PosConv2d | false | 11,600 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff | import torch
from torch import Tensor
from torch.utils.data import Dataset as Dataset
import torch.nn.init as init
import torch.utils.data
class Model(torch.nn.Conv2d):
def reset_parameters(self) ->None:
super().reset_parameters()
self.fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
... |
EqualConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | KUMartin77/AAA738_StyleGAN_pytorch | EqualConv2d | false | 11,601 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... |
SoftCrossEntropyLoss | # 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 SoftCrossEntropyLoss(torch.nn.Module):
"""SoftCrossEntropyLoss (useful for label smoothing and mixup).
Identical to torch.nn.CrossEntropyLoss if used with one-hot labels."""
def __init__(self):
super(SoftCrossEntropyLoss, self).__init__()
def forwar... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | KateHaeun/pycls | SoftCrossEntropyLoss | false | 11,602 | [
"MIT"
] | 0 | f3d87a36cb0a8adead31c7ad98f43facf7fe4c47 | https://github.com/KateHaeun/pycls/tree/f3d87a36cb0a8adead31c7ad98f43facf7fe4c47 | import torch
import torch.utils.data
class Model(torch.nn.Module):
"""SoftCrossEntropyLoss (useful for label smoothing and mixup).
Identical to torch.nn.CrossEntropyLoss if used with one-hot labels."""
def __init__(self):
super().__init__()
def forward(self, x, y):
loss = -y * torch.... |
FusedUpsample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
from math import sqrt
class FusedUpsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(in_channel, out_channel, kernel_size, kernel_size)
bias... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | KUMartin77/AAA738_StyleGAN_pytorch | FusedUpsample | false | 11,603 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd | import torch
from torch import nn
from torch.nn import functional as F
from math import sqrt
class Model(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(in_channel, out_channel, kernel_size, kernel_size)
bias = torch... |
AdaptiveInstanceNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | KUMartin77/AAA738_StyleGAN_pytorch | AdaptiveInstanceNorm | false | 11,604 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... |
ResHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn as nn
def gap2d(_w_in):
"""Helper for building a gap2d layer."""
return nn.AdaptiveAvgPool2d((1, 1))
def gap2d_cx(cx, _w_in):
"""Accumulates complexity of gap2d into cx = (h, w, flops, params, acts)."""
flops, params, a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.utils.data
import torch.nn as nn
assert... | KateHaeun/pycls | ResHead | false | 11,605 | [
"MIT"
] | 0 | f3d87a36cb0a8adead31c7ad98f43facf7fe4c47 | https://github.com/KateHaeun/pycls/tree/f3d87a36cb0a8adead31c7ad98f43facf7fe4c47 | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn as nn
def gap2d(_w_in):
"""Helper for building a gap2d layer."""
return nn.AdaptiveAvgPool2d((1, 1))
def gap2d_cx(cx, _w_in):
"""Accumulates complexity of gap2d into cx = (h, w, flops, params, acts)."""
flops, params, a... |
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
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | KUMartin77/AAA738_StyleGAN_pytorch | EqualLinear | false | 11,606 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd | import torch
from torch import nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
f... |
TransformerEncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.nn.parallel
import torch.utils.data
import torch.distributions
class TransformerEncoderLayer(nn.Module):
def __init__(self, embed_dim, num_heads, hidden_size, dropout=0.0,
attention_dropout=0.0, activation_dropout=0.0):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | IA3005/NLP_ens | TransformerEncoderLayer | false | 11,607 | [
"MIT"
] | 0 | 794ebbff46d5e6d5476f29b577b40bbb52991246 | https://github.com/IA3005/NLP_ens/tree/794ebbff46d5e6d5476f29b577b40bbb52991246 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class Model(nn.Module):
def __init__(self, embed_dim, num_heads, hidden_size, dropout=0.0,
attention_dropout=0.0, activation_dropout=0.0):
super().__init_... |
ConvInRelu | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
from torch import nn
import torch.onnx
class ConvInRelu(nn.Module):
def __init__(self, channels_in, channels_out, kernel_size, stride=1):
super(ConvInRelu, self).__init__()
self.n_params = 0
self.channels = channels_out
self.reflection_pad = nn.Refl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JuanFuriaz/donkey_share | ConvInRelu | false | 11,608 | [
"MIT"
] | 0 | caad831ca21094f05f9084f881ca3bbfa4168e4c | https://github.com/JuanFuriaz/donkey_share/tree/caad831ca21094f05f9084f881ca3bbfa4168e4c | import torch
import numpy as np
from torch import nn
import torch.onnx
class Model(nn.Module):
def __init__(self, channels_in, channels_out, kernel_size, stride=1):
super().__init__()
self.n_params = 0
self.channels = channels_out
self.reflection_pad = nn.ReflectionPad2d(int(np.fl... |
FCNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.optim
import torch.utils.data
from typing import *
class FCNet(nn.Module):
def __init__(self, input_size, output_size):
super().__init__()
self.l1 = nn.Linear(input_size, 5)
self.relu = nn.ReLU()
self.l2 = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Johnsonms/NNI_master | FCNet | false | 11,609 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class Model(nn.Module):
def __init__(self, input_size, output_size):
super().__init__()
self.l1 = nn.Linear(input_size, 5)
self.relu = nn.ReLU()
self.l2 = nn... |
Classifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, n_hid, n_out):
super(Classifier, self).__init__()
self.n_hid = n_hid
self.n_out = n_out
self.linear = nn.Linear(n_hid, n_out)
def forward(self, x):
tx = self.linear(x)
return to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | KathleenQ/context-aware-doc-analysis | Classifier | false | 11,610 | [
"MIT"
] | 0 | 93af994b2dee09f5fe6bfcc2e76e47e74708d3fe | https://github.com/KathleenQ/context-aware-doc-analysis/tree/93af994b2dee09f5fe6bfcc2e76e47e74708d3fe | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n_hid, n_out):
super().__init__()
self.n_hid = n_hid
self.n_out = n_out
self.linear = nn.Linear(n_hid, n_out)
def forward(self, x):
tx = self.linear(x)
return torch.log_softmax(tx.sq... |
AdaptiveCatAvgMaxPool2d | # 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
import torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
def adaptive_catavgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_m... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
import torchvision.transforms.functional as... | DifferentSC/pytorch-image-models | AdaptiveCatAvgMaxPool2d | false | 11,611 | [
"Apache-2.0"
] | 0 | ccfb5751abc70d80add4f197464190c4a2637c6c | https://github.com/DifferentSC/pytorch-image-models/tree/ccfb5751abc70d80add4f197464190c4a2637c6c | import torch
import torch.nn as nn
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
def adaptive_catavgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_m... |
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.distributed
import torch
import torch.nn as nn
import torch.nn.functional as F
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_le... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Katarina11/PreSumm | GlobalAttention | false | 11,612 | [
"MIT"
] | 0 | 616e72f038d512e9e9112af375d66a0b2e3db6cd | https://github.com/Katarina11/PreSumm/tree/616e72f038d512e9e9112af375d66a0b2e3db6cd | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional as F
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_le... |
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