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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
BasicModel_ConvNet_MaxPool1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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
import torch.nn as nn
from typing import no_type_check
class BasicModel_ConvNet_MaxPool1d(nn.Module):
"""Same as above, but with the MaxPool2d replaced
with a MaxPool1d. This is useful because the MaxPool modules
behave differently to other modules from the perspectiv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | YNNEKUW/captum | BasicModel_ConvNet_MaxPool1d | false | 12,015 | [
"BSD-3-Clause"
] | 0 | c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | https://github.com/YNNEKUW/captum/tree/c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | import torch
from torch import Tensor
import torch.nn as nn
from typing import no_type_check
class Model(nn.Module):
"""Same as above, but with the MaxPool2d replaced
with a MaxPool1d. This is useful because the MaxPool modules
behave differently to other modules from the perspective
of the DeepLift A... |
BasicModel3 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel3(nn.Module):
"""
Example model two from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) - ReLU(x2))
"""
def __init__(self) ->None:
super().__init__()
def forward(self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | YNNEKUW/captum | BasicModel3 | false | 12,016 | [
"BSD-3-Clause"
] | 0 | c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | https://github.com/YNNEKUW/captum/tree/c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Example model two from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) - ReLU(x2))
"""
def __init__(self) ->None:
super().__init__()
def forward(self, inpu... |
make_style | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class make_style(nn.Module):
def __init__(self):
super().__init__()
self.flatten = nn.Flatten()
def forward(self, x0):
style = F.avg_pool2d(x0, kernel_size=(x0.shape[-2], x0.shape[-1]))
style = self.flatten(st... | 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_... | YinuoJin/cellpose | make_style | false | 12,017 | [
"BSD-3-Clause"
] | 0 | eb8df70f295ac8465633f468d487aee1dd13a181 | https://github.com/YinuoJin/cellpose/tree/eb8df70f295ac8465633f468d487aee1dd13a181 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.flatten = nn.Flatten()
def forward(self, x0):
style = F.avg_pool2d(x0, kernel_size=(x0.shape[-2], x0.shape[-1]))
style = self.flatten(style)
... |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 LayerNormalization(nn.Module):
""" Layer normalization module """
def __init__(self, d_hid, eps=0.001):
super(LayerNormalization, self).__init__()
self.eps = eps
self.a_2 = nn.Parameter(torch.ones(d_hid), requires_grad=True)
self.b_2 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | YunjieJi/attention-is-all-you-need-pytorch | PositionwiseFeedForward | false | 12,018 | [
"MIT"
] | 0 | 636117b438d584ccba0ae5d6998fc02f3888f46e | https://github.com/YunjieJi/attention-is-all-you-need-pytorch/tree/636117b438d584ccba0ae5d6998fc02f3888f46e | import torch
import torch.nn as nn
class LayerNormalization(nn.Module):
""" Layer normalization module """
def __init__(self, d_hid, eps=0.001):
super().__init__()
self.eps = eps
self.a_2 = nn.Parameter(torch.ones(d_hid), requires_grad=True)
self.b_2 = nn.Parameter(torch.zeros... |
NormLayer | # 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 NormLayer(nn.Module):
def __init__(self, mean, std, n=None, eps=1e-08) ->None:
super().__init__()
self.mean = mean
self.std = std
self.eps = eps
def forward(self, x):
return (x - self.mean) / (self.std + self.eps)
def get_inp... | 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... | YNNEKUW/captum | NormLayer | false | 12,019 | [
"BSD-3-Clause"
] | 0 | c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | https://github.com/YNNEKUW/captum/tree/c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, mean, std, n=None, eps=1e-08) ->None:
super().__init__()
self.mean = mean
self.std = std
self.eps = eps
def forward(self, x):
return (x - self.mean) / (self.std + self.eps)
def get_inputs(... |
WingLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import math
import torch
import torch.nn as nn
class WingLoss(nn.Module):
"""Wing Loss 'Wing Loss for Robust Facial Landmark Localisation with
Convolutional Neural Networks' Feng et al. CVPR'2018.
Args:
omega (float), epsilon (float) are hyper-parameters.
use_target_weight (bool): Option ... | 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.nn as nn
assert_size_stride = torch._C._dynamo.g... | ZephyrII/mmpose_charger | WingLoss | false | 12,020 | [
"Apache-2.0"
] | 0 | ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | https://github.com/ZephyrII/mmpose_charger/tree/ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | import math
import torch
import torch.nn as nn
class Model(nn.Module):
"""Wing Loss 'Wing Loss for Robust Facial Landmark Localisation with
Convolutional Neural Networks' Feng et al. CVPR'2018.
Args:
omega (float), epsilon (float) are hyper-parameters.
use_target_weight (bool): Option to ... |
ExtResNetBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 conv3d(in_channels, out_channels, kernel_size, bias, padding):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding):
"""
Create a list of... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | YinanZYN/pytorch-3dunet | ExtResNetBlock | false | 12,021 | [
"MIT"
] | 0 | d1494f421a836af54c3dde65c54e3e62d5c00800 | https://github.com/YinanZYN/pytorch-3dunet/tree/d1494f421a836af54c3dde65c54e3e62d5c00800 | import torch
from torch import nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding):
"""
Create a list of... |
Conv2dBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
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
import t... | YueZHOU0926/MUNIT_3D | Conv2dBlock | false | 12,022 | [
"MIT"
] | 0 | 5cb22b5f3cb127d5b2c4eea038254a7881bab372 | https://github.com/YueZHOU0926/MUNIT_3D/tree/5cb22b5f3cb127d5b2c4eea038254a7881bab372 | import torch
from torch import nn
import torch.nn.functional as F
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine... |
BCELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class BCELoss(nn.Module):
"""Binary Cross Entropy loss."""
def __init__(self, use_target_weight=False, loss_weight=1.0):
super().__init__()
self.criterion = F.binary_cross_entropy
self.use_target_weight = use_target_we... | 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... | ZephyrII/mmpose_charger | BCELoss | false | 12,023 | [
"Apache-2.0"
] | 0 | ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | https://github.com/ZephyrII/mmpose_charger/tree/ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Binary Cross Entropy loss."""
def __init__(self, use_target_weight=False, loss_weight=1.0):
super().__init__()
self.criterion = F.binary_cross_entropy
self.use_target_weight = use_target_weig... |
CombinedTargetMSELoss | # 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 CombinedTargetMSELoss(nn.Module):
"""MSE loss for combined target.
CombinedTarget: The combination of classification target
(response map) and regression target (offset map).
Paper ref: Huang et al. The Devil is in the Details: Delving into
... | 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... | ZephyrII/mmpose_charger | CombinedTargetMSELoss | false | 12,024 | [
"Apache-2.0"
] | 0 | ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | https://github.com/ZephyrII/mmpose_charger/tree/ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | import torch
import torch.nn as nn
class Model(nn.Module):
"""MSE loss for combined target.
CombinedTarget: The combination of classification target
(response map) and regression target (offset map).
Paper ref: Huang et al. The Devil is in the Details: Delving into
Unbiased Data Pr... |
FCDiscriminator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class FCDiscriminator(nn.Module):
def __init__(self, num_classes, ndf=64):
super(FCDiscriminator, self).__init__()
self.conv1 = nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2,
padding=1)
self.conv2 = nn.Conv2d(ndf, ndf * 2, kernel_size=4... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | YoNyeoSeok/AsymTri | FCDiscriminator | false | 12,025 | [
"MIT"
] | 0 | a5a9a4b92074d770ed57802ff26b149a301cf4a4 | https://github.com/YoNyeoSeok/AsymTri/tree/a5a9a4b92074d770ed57802ff26b149a301cf4a4 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_classes, ndf=64):
super().__init__()
self.conv1 = nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2,
padding=1)
self.conv2 = nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1
... |
VertexDirectEmbedder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
from torch import nn
def normalize_embeddings(embeddings: 'torch.Tensor', epsilon: 'float'=1e-06
) ->torch.Tensor:
"""
Normalize N D-dimensional embedding vectors arranged in a tensor [N, D]
Args:
embeddings (tensor [N, D]): N D-dimensional embedding vecto... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
from... | YutouTaro/detectron2 | VertexDirectEmbedder | false | 12,026 | [
"Apache-2.0"
] | 0 | 29f90062fa2978a35f1d599bb30768a2370378ca | https://github.com/YutouTaro/detectron2/tree/29f90062fa2978a35f1d599bb30768a2370378ca | import torch
import torch.utils.data
from torch import nn
def normalize_embeddings(embeddings: 'torch.Tensor', epsilon: 'float'=1e-06
) ->torch.Tensor:
"""
Normalize N D-dimensional embedding vectors arranged in a tensor [N, D]
Args:
embeddings (tensor [N, D]): N D-dimensional embedding vecto... |
Affine | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Affine(nn.Module):
def __init__(self, dim):
super().__init__()
self.alpha = nn.Parameter(torch.ones((1, 1, dim)))
self.beta = nn.Parameter(torch.zeros((1, 1, dim)))
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | Yuki-Tanaka-33937424/pytorch-image-models | Affine | false | 12,027 | [
"Apache-2.0"
] | 0 | 6c1da622dcb2a0421aeb6cdcadd03cc366331f66 | https://github.com/Yuki-Tanaka-33937424/pytorch-image-models/tree/6c1da622dcb2a0421aeb6cdcadd03cc366331f66 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Model(nn.Module):
def __init__(self, dim):
super().__init__()
self.alpha = nn.Parameter(torch.ones((1, 1, dim)))
self.beta = nn.Parameter(torch.zeros((1, 1, dim)))
def forward(self, x):
... |
ResizeTransform | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as nnf
class ResizeTransform(nn.Module):
"""
Resize a transform, which involves resizing the vector field *and* rescaling it.
"""
def __init__(self, vel_resize, ndims):
super().__init__()
self.factor = 1.0 / vel_resize
... | 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... | Zer0-00/voxelmorph | ResizeTransform | false | 12,028 | [
"Apache-2.0"
] | 0 | ed2e0384cf22d19f7e57bea5887fc197d55f60bc | https://github.com/Zer0-00/voxelmorph/tree/ed2e0384cf22d19f7e57bea5887fc197d55f60bc | import torch
import torch.nn as nn
import torch.nn.functional as nnf
class Model(nn.Module):
"""
Resize a transform, which involves resizing the vector field *and* rescaling it.
"""
def __init__(self, vel_resize, ndims):
super().__init__()
self.factor = 1.0 / vel_resize
self.m... |
MPJPELoss | # 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 MPJPELoss(nn.Module):
"""MPJPE (Mean Per Joint Position Error) loss.
Args:
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the loss. Default:... | 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_... | ZephyrII/mmpose_charger | MPJPELoss | false | 12,029 | [
"Apache-2.0"
] | 0 | ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | https://github.com/ZephyrII/mmpose_charger/tree/ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | import torch
import torch.nn as nn
class Model(nn.Module):
"""MPJPE (Mean Per Joint Position Error) loss.
Args:
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the loss. Default: 1.0... |
FocalLossBinary | # 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.jit
import torch.nn.functional as F
import torch.nn.functional
from functools import partial
from torch.nn.modules.loss import _Loss
def reduced_focal_loss(outputs: 'torch.Tensor', targets: 'torch.Tensor',
threshold: 'float'=0.5, gamma: 'float'=2.0, reduction='mean'):
"""
Compute... | 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... | ZhongYingMatrix/nnUNet | FocalLossBinary | false | 12,030 | [
"Apache-2.0"
] | 0 | c3f028e79d4d5c3f2eb58396ffd0ae54048c132b | https://github.com/ZhongYingMatrix/nnUNet/tree/c3f028e79d4d5c3f2eb58396ffd0ae54048c132b | import torch
import torch.jit
import torch.nn.functional as F
import torch.nn.functional
from functools import partial
from torch.nn.modules.loss import _Loss
def reduced_focal_loss(outputs: 'torch.Tensor', targets: 'torch.Tensor',
threshold: 'float'=0.5, gamma: 'float'=2.0, reduction='mean'):
"""
Compute... |
ArcMarginProduct | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torchvision.transforms.functional as F
from torch import nn
from torch.nn import functional as F
class ArcMarginProduct(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.weight = nn.Parameter(torch.FloatTensor(out_features, in_featu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | aaron276h/kaggle-rcic-1st | ArcMarginProduct | false | 12,031 | [
"MIT"
] | 0 | d35e97847df3c29f548e60bc936d3fec7a0a4c08 | https://github.com/aaron276h/kaggle-rcic-1st/tree/d35e97847df3c29f548e60bc936d3fec7a0a4c08 | import math
import torch
import torchvision.transforms.functional as F
from torch import nn
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.weight = nn.Parameter(torch.FloatTensor(out_features, in_features)
... |
LinearNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 LinearNet(nn.Module):
def __init__(self, board_width, board_height):
super(LinearNet, self).__init__()
self.board_width = board_width
self.board_height = board_height
self.model = nn.Linear(in_features=4 * se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ZiwenZhuang/AlphaZero_Gomoku | LinearNet | false | 12,032 | [
"MIT"
] | 0 | 72db1c3eda1f6133da24c924da6032ea3569076e | https://github.com/ZiwenZhuang/AlphaZero_Gomoku/tree/72db1c3eda1f6133da24c924da6032ea3569076e | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, board_width, board_height):
super().__init__()
self.board_width = board_width
self.board_height = board_height
self.model = nn.Linear(in_features=4 * self.board_width * se... |
ScaleLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch._utils
class ScaleLayer(nn.Module):
def __init__(self, init_value=1.0, lr_mult=1):
super().__init__()
self.lr_mult = lr_mult
self.scale = nn.Parameter(torch.full((1,), init_value / lr_mult,
dtype=torch.float32))
def forward(... | 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._utils
assert_size_stride = torch._C._... | aagaard/ritm_interactive_segmentation | ScaleLayer | false | 12,033 | [
"MIT"
] | 0 | c68b45a54e99eb5401f50e62f7e43a11e34964ee | https://github.com/aagaard/ritm_interactive_segmentation/tree/c68b45a54e99eb5401f50e62f7e43a11e34964ee | import torch
import torch.nn as nn
import torch._utils
class Model(nn.Module):
def __init__(self, init_value=1.0, lr_mult=1):
super().__init__()
self.lr_mult = lr_mult
self.scale = nn.Parameter(torch.full((1,), init_value / lr_mult,
dtype=torch.float32))
def forward(self,... |
SoftIoU | # 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
class SoftIoU(nn.Module):
def __init__(self, from_sigmoid=False, ignore_label=-1):
super().__init__()
self._from_sigmoid = from_sigmoid
self._ignore_label = ignore_label
def forward(self, pred, label):
label = label.view(... | 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
assert_size_stride = torch._C._dynamo.guards.as... | aagaard/ritm_interactive_segmentation | SoftIoU | false | 12,034 | [
"MIT"
] | 0 | c68b45a54e99eb5401f50e62f7e43a11e34964ee | https://github.com/aagaard/ritm_interactive_segmentation/tree/c68b45a54e99eb5401f50e62f7e43a11e34964ee | import torch
import torch.nn as nn
import torch._utils
class Model(nn.Module):
def __init__(self, from_sigmoid=False, ignore_label=-1):
super().__init__()
self._from_sigmoid = from_sigmoid
self._ignore_label = ignore_label
def forward(self, pred, label):
label = label.view(pr... |
PatchMerging | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
class PatchMerging(nn.Module):
""" Patch Merging Layer
Args:
dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self, dim, norm_layer=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.triton_helpers import libdevice
import torch.nn as ... | acewjh/Video-Swin-Transformer | PatchMerging | false | 12,035 | [
"Apache-2.0"
] | 0 | bfbc8dde12e991455b34b921ca45a978b4dbfdbc | https://github.com/acewjh/Video-Swin-Transformer/tree/bfbc8dde12e991455b34b921ca45a978b4dbfdbc | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
""" Patch Merging Layer
Args:
dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self, dim, norm_layer=nn.LayerN... |
IrisClassifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.onnx
class IrisClassifier(nn.Module):
def __init__(self):
super(IrisClassifier, self).__init__()
self.fc1 = nn.Linear(4, 10)
self.fc2 = nn.Linear(10, 10)
self.fc3 = nn.Linear(10, 3)
def forward(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | abhinavthomas/mlflow | IrisClassifier | false | 12,036 | [
"Apache-2.0"
] | 0 | 1942d788e98e565229615373b4fd6c0899b4026b | https://github.com/abhinavthomas/mlflow/tree/1942d788e98e565229615373b4fd6c0899b4026b | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 10)
self.fc2 = nn.Linear(10, 10)
self.fc3 = nn.Linear(10, 3)
def forward(self, x):
x = F.relu(se... |
MaskedLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class MaskedLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y, Y, mask):
diff = (y - Y) / 5.0
return torch.mean(torch.square(diff[mask]))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.ones(
... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | acycliq/cellpose | MaskedLoss | false | 12,037 | [
"BSD-3-Clause"
] | 0 | 6d7a3f692206bf791e3ea7bd9524ee6df628ed8a | https://github.com/acycliq/cellpose/tree/6d7a3f692206bf791e3ea7bd9524ee6df628ed8a | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y, Y, mask):
diff = (y - Y) / 5.0
return torch.mean(torch.square(diff[mask]))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.ones(
[4... |
DenseCrossEntropy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class DenseCrossEntropy(nn.Module):
def forward(self, x, target):
x = x.float()
target = target.float()
logprobs = torch.nn.functional.log_softmax(x, dim=-1)
loss = -logprobs * target
loss = loss.sum(-1)
return loss.mean()
def ge... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | aaron276h/kaggle-rcic-1st | DenseCrossEntropy | false | 12,038 | [
"MIT"
] | 0 | d35e97847df3c29f548e60bc936d3fec7a0a4c08 | https://github.com/aaron276h/kaggle-rcic-1st/tree/d35e97847df3c29f548e60bc936d3fec7a0a4c08 | import torch
from torch import nn
class Model(nn.Module):
def forward(self, x, target):
x = x.float()
target = target.float()
logprobs = torch.nn.functional.log_softmax(x, dim=-1)
loss = -logprobs * target
loss = loss.sum(-1)
return loss.mean()
def get_inputs():
... |
L1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class L1Loss(nn.Module):
"""L1Loss loss ."""
def __init__(self, use_target_weight=False, loss_weight=1.0):
super().__init__()
self.criterion = F.l1_loss
self.use_target_weight = use_target_weight
self.loss_weig... | 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
... | ZephyrII/mmpose_charger | L1Loss | false | 12,039 | [
"Apache-2.0"
] | 0 | ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | https://github.com/ZephyrII/mmpose_charger/tree/ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""L1Loss loss ."""
def __init__(self, use_target_weight=False, loss_weight=1.0):
super().__init__()
self.criterion = F.l1_loss
self.use_target_weight = use_target_weight
self.loss_weigh... |
ArcFaceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import math
import torch
from torch import nn
class DenseCrossEntropy(nn.Module):
def forward(self, x, target):
x = x.float()
target = target.float()
logprobs = torch.nn.functional.log_softmax(x, dim=-1)
loss = -logprobs * target
loss = loss.sum(-1)
return loss.mea... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math... | aaron276h/kaggle-rcic-1st | ArcFaceLoss | false | 12,040 | [
"MIT"
] | 0 | d35e97847df3c29f548e60bc936d3fec7a0a4c08 | https://github.com/aaron276h/kaggle-rcic-1st/tree/d35e97847df3c29f548e60bc936d3fec7a0a4c08 | import math
import torch
from torch import nn
class DenseCrossEntropy(nn.Module):
def forward(self, x, target):
x = x.float()
target = target.float()
logprobs = torch.nn.functional.log_softmax(x, dim=-1)
loss = -logprobs * target
loss = loss.sum(-1)
return loss.mea... |
SplAtConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Conv2d
from torch.nn import ReLU
from torch.nn.modules.utils import _pair
class DropBlock2D(object):
def __init__(self, *args, **kwargs):
raise NotImplementedError
class rSoftMax(nn.Modul... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | XuYongi/KiNet | SplAtConv2d | false | 12,041 | [
"MIT"
] | 0 | fab8865a09e3779baf0daf1db1bf59a9cfbde450 | https://github.com/XuYongi/KiNet/tree/fab8865a09e3779baf0daf1db1bf59a9cfbde450 | from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Conv2d
from torch.nn import ReLU
from torch.nn.modules.utils import _pair
class DropBlock2D(object):
def __init__(self, *args, **kwargs):
raise NotImplementedError
class rSoftMax(nn.Modul... |
Simplenet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.optim.lr_scheduler import *
import torch.nn.functional as F
import torch.optim
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.testing
class Simplenet(nn.Module):
def __init__(self):
super(Simplenet, self).__init__()
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.optim.lr_scheduler... | aam12/distiller | Simplenet | false | 12,042 | [
"Apache-2.0"
] | 0 | fd06fcba028d023e430cd37d1531bc2ac5202ea6 | https://github.com/aam12/distiller/tree/fd06fcba028d023e430cd37d1531bc2ac5202ea6 | import torch
from torch.optim.lr_scheduler import *
import torch.nn.functional as F
import torch.optim
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.testing
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2... |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
"""policy-value network module"""
def __init__(self, board_width, board_height):
super(Net, self).__init__()
self.board_width = board_width
self.board_height = board_height
self.conv1 = 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
from torch._inductor.runtime.... | ZiwenZhuang/AlphaZero_Gomoku | Net | false | 12,043 | [
"MIT"
] | 0 | 72db1c3eda1f6133da24c924da6032ea3569076e | https://github.com/ZiwenZhuang/AlphaZero_Gomoku/tree/72db1c3eda1f6133da24c924da6032ea3569076e | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""policy-value network module"""
def __init__(self, board_width, board_height):
super().__init__()
self.board_width = board_width
self.board_height = board_height
self.conv1 = nn.Conv2d... |
ResBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from torch.nn import functional as F
class ResBlock(nn.Module):
"""Residual block with bilinear upsampling/downsampling.
Args:
in_channels (int): Channel number of the input.
out_channels (int): Channel number of the output.
mode (str): Upsampling/do... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | abhishekm47/GFPGAN | ResBlock | false | 12,044 | [
"BSD-3-Clause"
] | 0 | 39d063749433b38d98c75740b052934ae8bc80f6 | https://github.com/abhishekm47/GFPGAN/tree/39d063749433b38d98c75740b052934ae8bc80f6 | import torch
import torch.nn as nn
from torch.nn import functional as F
class Model(nn.Module):
"""Residual block with bilinear upsampling/downsampling.
Args:
in_channels (int): Channel number of the input.
out_channels (int): Channel number of the output.
mode (str): Upsampling/downs... |
NormLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class NormLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y, Y, w, mask):
ny = torch.linalg.norm(y, dim=1, keepdim=False) / 5.0
nY = torch.linalg.norm(Y, dim=1, keepdim=False) / 5.0
diff = ny - nY
return torch.mean(torch.sq... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | acycliq/cellpose | NormLoss | false | 12,045 | [
"BSD-3-Clause"
] | 0 | 6d7a3f692206bf791e3ea7bd9524ee6df628ed8a | https://github.com/acycliq/cellpose/tree/6d7a3f692206bf791e3ea7bd9524ee6df628ed8a | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y, Y, w, mask):
ny = torch.linalg.norm(y, dim=1, keepdim=False) / 5.0
nY = torch.linalg.norm(Y, dim=1, keepdim=False) / 5.0
diff = ny - nY
return torch.mean(torch.squar... |
ArcCosDotLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class ArcCosDotLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y, w, mask):
eps = 1e-12
denom = torch.multiply(torch.linalg.norm(x, dim=1), torch.linalg.
norm(y, dim=1)) + eps
dot = x[:, 0, :, :] * y[:, 0, :, :] + 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | acycliq/cellpose | ArcCosDotLoss | false | 12,046 | [
"BSD-3-Clause"
] | 0 | 6d7a3f692206bf791e3ea7bd9524ee6df628ed8a | https://github.com/acycliq/cellpose/tree/6d7a3f692206bf791e3ea7bd9524ee6df628ed8a | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y, w, mask):
eps = 1e-12
denom = torch.multiply(torch.linalg.norm(x, dim=1), torch.linalg.
norm(y, dim=1)) + eps
dot = x[:, 0, :, :] * y[:, 0, :, :] + x[:, 1, :,... |
WeightedLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class WeightedLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y, Y, w):
diff = (y - Y) / 5.0
return torch.mean(torch.square(diff) * w)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), 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._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | acycliq/cellpose | WeightedLoss | false | 12,047 | [
"BSD-3-Clause"
] | 0 | 6d7a3f692206bf791e3ea7bd9524ee6df628ed8a | https://github.com/acycliq/cellpose/tree/6d7a3f692206bf791e3ea7bd9524ee6df628ed8a | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y, Y, w):
diff = (y - Y) / 5.0
return torch.mean(torch.square(diff) * w)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, ... |
MyLoss | # 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 MyLoss(nn.Module):
def __init__(self):
super(MyLoss, self).__init__()
None
self.reduce_var = True
pass
"""
weights has shape (n), multiply loss of point i with weights[i]
"""
def forward(self, outputs, y, weights, calculate_add... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | abrar-fahim/ann-benchmarks | MyLoss | false | 12,048 | [
"MIT"
] | 0 | e5493ddda333bf6a930415566d4f1c697b439aca | https://github.com/abrar-fahim/ann-benchmarks/tree/e5493ddda333bf6a930415566d4f1c697b439aca | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
None
self.reduce_var = True
pass
"""
weights has shape (n), multiply loss of point i with weights[i]
"""
def forward(self, outputs, y, weights, calculate_add=True):
... |
LocalizationNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
class LocalizationNet(nn.Module):
def __init__(self, num_bbox=2, num_digits=2):
super(LocalizationNet, self).__init__()
self.conv1 = nn.Conv2d(1, 64, 3, padding=1)
self.conv2 = nn.Conv2d(64, 64, 3, padding=1)
self.c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | YIFEI-MA/MultiDigitRecognition | LocalizationNet | false | 12,049 | [
"MIT"
] | 0 | f1f9567c31102ccdc7464a35b8a7c533b5d46734 | https://github.com/YIFEI-MA/MultiDigitRecognition/tree/f1f9567c31102ccdc7464a35b8a7c533b5d46734 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
def __init__(self, num_bbox=2, num_digits=2):
super().__init__()
self.conv1 = nn.Conv2d(1, 64, 3, padding=1)
self.conv2 = nn.Conv2d(64, 64, 3, padding=1)
self.conv3 = nn.Conv2d(64, 64, 3, pad... |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | adriaciurana/adriaciurana-udacity-project-2 | Critic | false | 12,050 | [
"MIT"
] | 0 | a0af7086df586b537cd10a880f1d354240ff31a5 | https://github.com/adriaciurana/adriaciurana-udacity-project-2/tree/a0af7086df586b537cd10a880f1d354240ff31a5 | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Model(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, f... |
GraphConvolution | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.init as init
class GraphConvolution(nn.Module):
def __init__(self, input_dim, output_dim, use_bias=True):
"""
图卷积: L*X*theta
:param input_dim: int 节点输入特征的维度
:param out_put_dim: int 输出特征维度
:param use_bias: boolean | opt... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.init as init
assert_size_stride = torch._C... | acproject/knowledge-graph-learning | GraphConvolution | false | 12,051 | [
"MIT"
] | 0 | fa62db6720f6da8e35de01b68acf82f1a367671f | https://github.com/acproject/knowledge-graph-learning/tree/fa62db6720f6da8e35de01b68acf82f1a367671f | import torch
import torch.nn as nn
import torch.nn.init as init
class Model(nn.Module):
def __init__(self, input_dim, output_dim, use_bias=True):
"""
图卷积: L*X*theta
:param input_dim: int 节点输入特征的维度
:param out_put_dim: int 输出特征维度
:param use_bias: boolean | optional 是否使用偏... |
eSEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | XDong18/AdelaiDet | eSEModule | false | 12,052 | [
"BSD-2-Clause"
] | 0 | 837cd1078923892fe6e84ac29fd0963f1b2c474f | https://github.com/XDong18/AdelaiDet/tree/837cd1078923892fe6e84ac29fd0963f1b2c474f | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super().__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
class Model(nn... |
SingleBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 DyIntraModalityUpdate(nn.Module):
"""
Dynamic Intra-modality Attention Flow
"""
def __init__(self, v_size, q_size, output_size, num_head, drop=0.0):
super(DyIntraModalityUpdate, 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
from torch._inductor.runtime.... | TranTony/DFAF-for-VQA.pytorch | SingleBlock | false | 12,053 | [
"MIT"
] | 0 | eba1a893e8e5d3d8bf85078611b0bcf4d56eea86 | https://github.com/TranTony/DFAF-for-VQA.pytorch/tree/eba1a893e8e5d3d8bf85078611b0bcf4d56eea86 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class DyIntraModalityUpdate(nn.Module):
"""
Dynamic Intra-modality Attention Flow
"""
def __init__(self, v_size, q_size, output_size, num_head, drop=0.0):
super().__init__()
self.v_size = v_size... |
PatchEmbed3D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
class PatchEmbed3D(nn.Module):
""" Video to Patch Embedding.
Args:
patch_size (int): Patch token size. Default: (2,4,4).
in_chans (int): Number of input video channels. Default: 3.
embed_dim (int): Number of linear pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | acewjh/Video-Swin-Transformer | PatchEmbed3D | false | 12,054 | [
"Apache-2.0"
] | 0 | bfbc8dde12e991455b34b921ca45a978b4dbfdbc | https://github.com/acewjh/Video-Swin-Transformer/tree/bfbc8dde12e991455b34b921ca45a978b4dbfdbc | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
""" Video to Patch Embedding.
Args:
patch_size (int): Patch token size. Default: (2,4,4).
in_chans (int): Number of input video channels. Default: 3.
embed_dim (int): Number of linear projection... |
Vgg16 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
class Vgg16(nn.Module):
def __init__(self):
super(Vgg16, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
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... | YueZHOU0926/MUNIT_3D | Vgg16 | false | 12,055 | [
"MIT"
] | 0 | 5cb22b5f3cb127d5b2c4eea038254a7881bab372 | https://github.com/YueZHOU0926/MUNIT_3D/tree/5cb22b5f3cb127d5b2c4eea038254a7881bab372 | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.conv2_1 = ... |
SequenceBias | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from torch.nn.parameter import Parameter
class SequenceBias(nn.Module):
"""
Adds one bias element to the end of the sequence.
so if the input has a shape ``(L, N, E)``, where
``L`` 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 torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from torch.nn.parameter import Pa... | adriansarstedt/opacus | SequenceBias | false | 12,056 | [
"Apache-2.0"
] | 0 | a6c89e3d6a3a4e3e4b82bc8c68d53265a9a7cba1 | https://github.com/adriansarstedt/opacus/tree/a6c89e3d6a3a4e3e4b82bc8c68d53265a9a7cba1 | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from torch.nn.parameter import Parameter
class Model(nn.Module):
"""
Adds one bias element to the end of the sequence.
so if the input has a shape ``(L, N, E)``, where
``L`` is the s... |
SimpleCNN32Filter | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SimpleCNN32Filter(nn.Module):
"""
Defines a simple CNN arhcitecture with 1 layer
"""
def __init__(self, num_classes):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=10, stride=2)
self.fc1 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | adwaykanhere/df-dn-paper | SimpleCNN32Filter | false | 12,057 | [
"MIT"
] | 0 | 5df413e06ce33c6be5d005e6d1141de9fcd45cb4 | https://github.com/adwaykanhere/df-dn-paper/tree/5df413e06ce33c6be5d005e6d1141de9fcd45cb4 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Defines a simple CNN arhcitecture with 1 layer
"""
def __init__(self, num_classes):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=10, stride=2)
self.fc1 = nn.Linear(14... |
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... | adi1999/kornia | BinaryFocalLossWithLogits | false | 12,058 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | bb476a36e2725d687d1879b5a0d877c1ba860c25 | https://github.com/adi1999/kornia/tree/bb476a36e2725d687d1879b5a0d877c1ba860c25 | 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) = -... |
NeuralNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 NeuralNetwork(nn.Module):
def __init__(self, in_dim, out_dim, hidden_dim=None):
""" Simple two-layer neural network.
"""
super(NeuralNetwork, self).__init__()
if hidden_dim is None:
hidden_dim = in_dim * 2
self.l1 = nn.L... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | adewynter/Lightboard | NeuralNetwork | false | 12,059 | [
"Apache-2.0"
] | 0 | f02eae64f11a989030b52314aa66709477274eb3 | https://github.com/adewynter/Lightboard/tree/f02eae64f11a989030b52314aa66709477274eb3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_dim, out_dim, hidden_dim=None):
""" Simple two-layer neural network.
"""
super().__init__()
if hidden_dim is None:
hidden_dim = in_dim * 2
self.l1 = nn.Linear(in_dim, hidden_dim)
... |
bodypose_model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from collections import OrderedDict
import torch.nn as nn
def make_layers(block, no_relu_layers):
layers = []
for layer_name, v in block.items():
if 'pool' in layer_name:
layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], padding=v[2])
layers.append((layer_name, l... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 collections import Order... | Schwartz-Zha/My_Pose_Estimation | bodypose_model | false | 12,060 | [
"MIT"
] | 0 | 0ccaccf58498b2200842c155b735e1103c28c5ba | https://github.com/Schwartz-Zha/My_Pose_Estimation/tree/0ccaccf58498b2200842c155b735e1103c28c5ba | import torch
from collections import OrderedDict
import torch.nn as nn
def make_layers(block, no_relu_layers):
layers = []
for layer_name, v in block.items():
if 'pool' in layer_name:
layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], padding=v[2])
layers.append((layer_name, l... |
Scale | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.parallel
class Scale(nn.Module):
def __init__(self, init_value=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
return input * self.scale
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | XDong18/AdelaiDet | Scale | false | 12,061 | [
"BSD-2-Clause"
] | 0 | 837cd1078923892fe6e84ac29fd0963f1b2c474f | https://github.com/XDong18/AdelaiDet/tree/837cd1078923892fe6e84ac29fd0963f1b2c474f | import torch
from torch import nn
import torch.nn.parallel
class Model(nn.Module):
def __init__(self, init_value=1.0):
super().__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
return input * self.scale
def get_inputs():
return [tor... |
GCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Conv2D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, padding=
'same', stride=1, dilation=1, groups=1):
super(Conv2D, self).__init__()
assert type(kernel_size) in [int,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
as... | XDong18/AdelaiDet | GCN | false | 12,062 | [
"BSD-2-Clause"
] | 0 | 837cd1078923892fe6e84ac29fd0963f1b2c474f | https://github.com/XDong18/AdelaiDet/tree/837cd1078923892fe6e84ac29fd0963f1b2c474f | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Conv2D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, padding=
'same', stride=1, dilation=1, groups=1):
super().__init__()
assert type(kernel_size) in [int, tuple
... |
DPLSTMCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Tuple
class LSTMLinear(nn.Linear):
"""
This function is the same as a nn.Linear layer, except that in the backward pass
the grad_samples get accumulated (i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | adriansarstedt/opacus | DPLSTMCell | false | 12,063 | [
"Apache-2.0"
] | 0 | a6c89e3d6a3a4e3e4b82bc8c68d53265a9a7cba1 | https://github.com/adriansarstedt/opacus/tree/a6c89e3d6a3a4e3e4b82bc8c68d53265a9a7cba1 | import math
import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Tuple
class LSTMLinear(nn.Linear):
"""
This function is the same as a nn.Linear layer, except that in the backward pass
the grad_samples get accumulated (i... |
SimpleAtariNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 functional
class SimpleAtariNet(nn.Module):
def __init__(self):
super(SimpleAtariNet, self).__init__()
self.conv0 = nn.Conv2d(3, 16, 12, stride=(2, 8))
self.conv1 = nn.Conv2d(16, 32, 8, stride=4)
self.conv2 = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | aaronmckinstry706/pytorch-practice | SimpleAtariNet | false | 12,064 | [
"MIT"
] | 0 | d3fd28733ea6de6a2e522ec52ff3e748df21b85a | https://github.com/aaronmckinstry706/pytorch-practice/tree/d3fd28733ea6de6a2e522ec52ff3e748df21b85a | import torch
import torch.nn as nn
import torch.nn.functional as functional
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv0 = nn.Conv2d(3, 16, 12, stride=(2, 8))
self.conv1 = nn.Conv2d(16, 32, 8, stride=4)
self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
... |
Conv_ReLU_Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Conv_ReLU_Block(nn.Module):
def __init__(self):
super(Conv_ReLU_Block, self).__init__()
self.conv = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=
3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
def f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | advaza/pytorch-vdsr | Conv_ReLU_Block | false | 12,065 | [
"MIT"
] | 0 | 8011f7323de3c7756df3828612addfb122c2bfef | https://github.com/advaza/pytorch-vdsr/tree/8011f7323de3c7756df3828612addfb122c2bfef | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=
3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
return... |
Gate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Gate(nn.Module):
"""Gate Unit
g = sigmoid(Wx)
x = g * x
"""
def __init__(self, input_size):
super(Gate, self).__init__()
self.linear = nn.Linear(input_size, input_size, bias=False)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | albert-dot-ai/MnemonicReader | Gate | false | 12,066 | [
"BSD-3-Clause"
] | 0 | eb51eb679a58677a405953c0c579568377c0b0f8 | https://github.com/albert-dot-ai/MnemonicReader/tree/eb51eb679a58677a405953c0c579568377c0b0f8 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Gate Unit
g = sigmoid(Wx)
x = g * x
"""
def __init__(self, input_size):
super().__init__()
self.linear = nn.Linear(input_size, input_size, bias=False)
def forward(self, x):
"... |
ScoreLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torchvision.transforms import functional as F
from torch.nn import functional as F
import torch.nn as nn
class ScoreLayer(nn.Module):
def __init__(self, k):
super(ScoreLayer, self).__init__()
self.score = nn.Conv2d(k, 1, 1, 1)
def forward(self, x, x_size=None):
x = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | alejodosr/PoolNet | ScoreLayer | false | 12,067 | [
"MIT"
] | 0 | a6a19379933fe02c22f0eb0dd92038fe87cf0bd3 | https://github.com/alejodosr/PoolNet/tree/a6a19379933fe02c22f0eb0dd92038fe87cf0bd3 | import torch
from torchvision.transforms import functional as F
from torch.nn import functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, k):
super().__init__()
self.score = nn.Conv2d(k, 1, 1, 1)
def forward(self, x, x_size=None):
x = self.score(x)
... |
NaiveGroupNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import init
import torch.nn.parallel
class NaiveGroupNorm(Module):
"""NaiveGroupNorm implements Group Normalization with the high-level matrix operations in PyTorch.
It is a temporary solution to export GN by ONNX before 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 libdevice
from torch.nn import Module
from torch.nn import Parameter
from torch.nn import... | XDong18/AdelaiDet | NaiveGroupNorm | false | 12,068 | [
"BSD-2-Clause"
] | 0 | 837cd1078923892fe6e84ac29fd0963f1b2c474f | https://github.com/XDong18/AdelaiDet/tree/837cd1078923892fe6e84ac29fd0963f1b2c474f | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import init
import torch.nn.parallel
class Model(Module):
"""NaiveGroupNorm implements Group Normalization with the high-level matrix operations in PyTorch.
It is a temporary solution to export GN by ONNX before the official... |
SFU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SFU(nn.Module):
"""Semantic Fusion Unit
The ouput vector is expected to not only retrieve correlative information from fusion vectors,
but also retain partly unchange as the input vector
"""
def __init__(self, input_size, fu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | albert-dot-ai/MnemonicReader | SFU | false | 12,069 | [
"BSD-3-Clause"
] | 0 | eb51eb679a58677a405953c0c579568377c0b0f8 | https://github.com/albert-dot-ai/MnemonicReader/tree/eb51eb679a58677a405953c0c579568377c0b0f8 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Semantic Fusion Unit
The ouput vector is expected to not only retrieve correlative information from fusion vectors,
but also retain partly unchange as the input vector
"""
def __init__(self, input_size, ... |
GraphNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 GraphNet(nn.Module):
def __init__(self, input_size, n_classes, num_neurons=32):
super(GraphNet, self).__init__()
self.fc1 = nn.Linear(input_size, num_neurons)
self.fc2 = nn.Linear(num_neurons, num_neurons)
self.fc3 = nn.Linear(num_neurons, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | adam2392/dldo | GraphNet | false | 12,070 | [
"MIT"
] | 0 | fc57f8700eb048558ab205c2c77a064f1a7cc7f6 | https://github.com/adam2392/dldo/tree/fc57f8700eb048558ab205c2c77a064f1a7cc7f6 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_size, n_classes, num_neurons=32):
super().__init__()
self.fc1 = nn.Linear(input_size, num_neurons)
self.fc2 = nn.Linear(num_neurons, num_neurons)
self.fc3 = nn.Linear(num_neurons, n_classes)
... |
FCLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class FCLayer(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0.0,
use_activation=True):
super().__init__()
self.use_activation = use_activation
self.dropout = nn.Dropout(dropout_rate)
self.linear = nn.Linear(input_dim,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | alexandre-do/r-bert | FCLayer | false | 12,071 | [
"Apache-2.0"
] | 0 | 4e35bcbb0fe0602e708e18010e2394ebbfb074c4 | https://github.com/alexandre-do/r-bert/tree/4e35bcbb0fe0602e708e18010e2394ebbfb074c4 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0.0,
use_activation=True):
super().__init__()
self.use_activation = use_activation
self.dropout = nn.Dropout(dropout_rate)
self.linear = nn.Linear(input_dim, o... |
SimpleGCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import Parameter
import torch.nn
import torch.autograd
class SimpleGCN(nn.Module):
"""A simple graph convolution layer, similar to the one defined in
Kipf et al. https://arxiv.org/abs/1609.02907
.. note:... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
from torch.nn.parameter import Parameter
from ... | acivgin1/kaolin | SimpleGCN | false | 12,072 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 4c4e0098b2cd9a73709c81fea82de03abbd6cdd5 | https://github.com/acivgin1/kaolin/tree/4c4e0098b2cd9a73709c81fea82de03abbd6cdd5 | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import Parameter
import torch.nn
import torch.autograd
class Model(nn.Module):
"""A simple graph convolution layer, similar to the one defined in
Kipf et al. https://arxiv.org/abs/1609.02907
.. note::
... |
DepthwiseSeperableConv1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 DepthwiseSeperableConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size):
super(DepthwiseSeperableConv1d, self).__init__()
self.depthwise_conv1d = nn.Conv1d(in_channels, in_channels,
kernel_size, groups=in_channels, paddi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | allenye0119/pytorch-modules | DepthwiseSeperableConv1d | false | 12,074 | [
"MIT"
] | 0 | c7683ef63478becca3b79a7498840450da33f468 | https://github.com/allenye0119/pytorch-modules/tree/c7683ef63478becca3b79a7498840450da33f468 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size):
super().__init__()
self.depthwise_conv1d = nn.Conv1d(in_channels, in_channels,
kernel_size, groups=in_channels, padding=kernel_size // 2)
self.pointwise_conv1... |
LRN | # 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 LRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=True
):
super(LRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:
self.average = nn.AvgPool3d(kernel_size=(local... | 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_... | anas-awadalla/dissect | LRN | false | 12,075 | [
"MIT"
] | 0 | d74e9147731c6160274405a39ab1c98191929269 | https://github.com/anas-awadalla/dissect/tree/d74e9147731c6160274405a39ab1c98191929269 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=True
):
super().__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:
self.average = nn.AvgPool3d(kernel_size=(local_size, ... |
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 math
import torch
from torch import Tensor
import torch.nn as nn
from torch.nn import Parameter
from torch.autograd import Variable
class LayerNorm(nn.Module):
"""
Layer Normalization based on Ba & al.:
'Layer Normalization'
https://arxiv.org/pdf/1607.06450.pdf
"""
def __init__(self, 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 libdevice
import math
from torch import Tensor
import torch.nn as nn
from torch.nn import... | alex-kj-chin/universal-computation | LayerNorm | false | 12,076 | [
"MIT"
] | 0 | a41cc7d685a3e0c56c11bc346c25394464da2e06 | https://github.com/alex-kj-chin/universal-computation/tree/a41cc7d685a3e0c56c11bc346c25394464da2e06 | import math
import torch
from torch import Tensor
import torch.nn as nn
from torch.nn import Parameter
from torch.autograd import Variable
class Model(nn.Module):
"""
Layer Normalization based on Ba & al.:
'Layer Normalization'
https://arxiv.org/pdf/1607.06450.pdf
"""
def __init__(self, input... |
ResidualBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, norm
=None, bias=True):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(reflection_pad... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | alhsu713/fast_blind_video_consistency | ResidualBlock | false | 12,078 | [
"MIT"
] | 0 | 2037ec5f68a361b926c31b3a12c1cd04e2331797 | https://github.com/alhsu713/fast_blind_video_consistency/tree/2037ec5f68a361b926c31b3a12c1cd04e2331797 | import torch
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, norm
=None, bias=True):
super().__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(reflection_padding)
s... |
InnerProductModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
class InnerProductModel(torch.nn.Module):
@staticmethod
def is_valid_model_type(model_type):
raise NotImplementedError
@staticmethod
def get_model_from_type(model_type):
raise NotImplementedError
@property
def loss_criterion(self):
return torch.nn.MSELos... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret... | SheepiesLab/plato | InnerProductModel | false | 12,079 | [
"Apache-2.0"
] | 0 | 9f5bbfa4b6952d1b3af24be409982d303d54a169 | https://github.com/SheepiesLab/plato/tree/9f5bbfa4b6952d1b3af24be409982d303d54a169 | import torch
class Model(torch.nn.Module):
@staticmethod
def is_valid_model_type(model_type):
raise NotImplementedError
@staticmethod
def get_model_from_type(model_type):
raise NotImplementedError
@property
def loss_criterion(self):
return torch.nn.MSELoss()
def... |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, 1)
def... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | SheepiesLab/plato | Critic | false | 12,080 | [
"Apache-2.0"
] | 0 | 9f5bbfa4b6952d1b3af24be409982d303d54a169 | https://github.com/SheepiesLab/plato/tree/9f5bbfa4b6952d1b3af24be409982d303d54a169 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, 1)
def forward(self... |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.a1 = nn.Conv2d(19, 64, kernel_size=3, padding=1)
self.a2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.a3 = nn.Conv2d(128, 256, kerne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | afozk95/chess-dataset | Net | false | 12,081 | [
"MIT"
] | 0 | 08de7b251f67cb8553a5ee66f6fd76cefeb14bb4 | https://github.com/afozk95/chess-dataset/tree/08de7b251f67cb8553a5ee66f6fd76cefeb14bb4 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.a1 = nn.Conv2d(19, 64, kernel_size=3, padding=1)
self.a2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.a3 = nn.Conv2d(128, 256, kernel_size=... |
SimmatModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class SimmatModule(torch.nn.Module):
def __init__(self, padding=-1):
super().__init__()
self.padding = padding
self._hamming_index_loaded = None
self._hamming_index = None
def forward(self, query_embed, doc_embed, query_tok, doc_tok):
simmat = []
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | alpers/FlexNeuART | SimmatModule | false | 12,082 | [
"Apache-2.0"
] | 0 | 2ae263f46b6eb2f1435b9073dad629a2fef23ab9 | https://github.com/alpers/FlexNeuART/tree/2ae263f46b6eb2f1435b9073dad629a2fef23ab9 | import torch
class Model(torch.nn.Module):
def __init__(self, padding=-1):
super().__init__()
self.padding = padding
self._hamming_index_loaded = None
self._hamming_index = None
def forward(self, query_embed, doc_embed, query_tok, doc_tok):
simmat = []
for a_e... |
PACRRConvMax2dModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
class PACRRConvMax2dModule(torch.nn.Module):
def __init__(self, shape, n_filters, k, channels):
super().__init__()
self.shape = shape
if shape != 1:
self.pad = torch.nn.ConstantPad2d((0, shape - 1, 0, shape - 1), 0)
else:
self.pad = None
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | alpers/FlexNeuART | PACRRConvMax2dModule | false | 12,083 | [
"Apache-2.0"
] | 0 | 2ae263f46b6eb2f1435b9073dad629a2fef23ab9 | https://github.com/alpers/FlexNeuART/tree/2ae263f46b6eb2f1435b9073dad629a2fef23ab9 | import torch
class Model(torch.nn.Module):
def __init__(self, shape, n_filters, k, channels):
super().__init__()
self.shape = shape
if shape != 1:
self.pad = torch.nn.ConstantPad2d((0, shape - 1, 0, shape - 1), 0)
else:
self.pad = None
self.conv = t... |
AverageAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
Args:
d_model (int): the size of input for the first-layer of the FFN.
d_ff (int): the hidden layer size of th... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.cuda
import torch.distributed
assert_size_str... | Zer0-dev115/OpenNMT-py | AverageAttention | false | 12,084 | [
"MIT"
] | 0 | 028c76b34779223ee6b3eb224b99617552987100 | https://github.com/Zer0-dev115/OpenNMT-py/tree/028c76b34779223ee6b3eb224b99617552987100 | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
Args:
d_model (int): the size of input for the first-layer of the FFN.
d_ff (int): the hidden layer size of th... |
BatchLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
-----
Objects inherited from `MetaModule` are fully compat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | aneesh-dandime/siren | BatchLinear | false | 12,085 | [
"MIT"
] | 0 | 7bc652e32d66c5792d24e8df2fffa565157679bd | https://github.com/aneesh-dandime/siren/tree/7bc652e32d66c5792d24e8df2fffa565157679bd | import torch
from torch import nn
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
-----
Objects inherited from `MetaModule` are fully compat... |
BertTextPooler | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertTextPooler(nn.Module):
def __init__(self, config):
super(BertTextPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.bi_hidden_size)
self.activation = nn.ReLU()
def forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | amitakamath/vilbert-multi-task | BertTextPooler | false | 12,086 | [
"MIT"
] | 0 | 5a11b8265fab3598fcdcd7f7c33453b914d8ff2c | https://github.com/amitakamath/vilbert-multi-task/tree/5a11b8265fab3598fcdcd7f7c33453b914d8ff2c | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.bi_hidden_size)
self.activation = nn.ReLU()
def forward(self, hidden_states):
... |
IOUloss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class IOUloss(nn.Module):
def __init__(self, reduction='none', loss_type='iou'):
super(IOUloss, self).__init__()
self.reduction = reduction
self.loss_type = loss_type
def forward(self, pred, target):
assert pred.shape[0] == target.shape[0]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ankandrew/YOLOX | IOUloss | false | 12,087 | [
"Apache-2.0"
] | 0 | 28da975944887d550f052ebadd8cbdd82d14aed6 | https://github.com/ankandrew/YOLOX/tree/28da975944887d550f052ebadd8cbdd82d14aed6 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, reduction='none', loss_type='iou'):
super().__init__()
self.reduction = reduction
self.loss_type = loss_type
def forward(self, pred, target):
assert pred.shape[0] == target.shape[0]
pred = p... |
Correct | # 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.utils.data.distributed
class Correct(nn.Module):
def forward(self, classifier, target):
return classifier.max(dim=1)[1] == target
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda ... | amitport/grace | Correct | false | 12,088 | [
"BSD-2-Clause"
] | 0 | b0e442057d2f36f09cd1817a4acb966c6b0b780f | https://github.com/amitport/grace/tree/b0e442057d2f36f09cd1817a4acb966c6b0b780f | import torch
from torch import nn
import torch.utils.data.distributed
class Model(nn.Module):
def forward(self, classifier, target):
return classifier.max(dim=1)[1] == target
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, action_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SheepiesLab/plato | Actor | false | 12,089 | [
"Apache-2.0"
] | 0 | 9f5bbfa4b6952d1b3af24be409982d303d54a169 | https://github.com/SheepiesLab/plato/tree/9f5bbfa4b6952d1b3af24be409982d303d54a169 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super().__init__()
self.l1 = nn.Linear(state_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, action_dim)
self.... |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class QNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_dim):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | SheepiesLab/plato | QNetwork | false | 12,090 | [
"Apache-2.0"
] | 0 | 9f5bbfa4b6952d1b3af24be409982d303d54a169 | https://github.com/SheepiesLab/plato/tree/9f5bbfa4b6952d1b3af24be409982d303d54a169 | import torch
import torch.nn as nn
import torch.nn.functional as F
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class Model(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_dim):
s... |
UniverseHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 UniverseHead(torch.nn.Module):
""" universe agent example
input: [None, 42, 42, 1]; output: [None, 288];
"""
def __init__(self, n):
super(UniverseHead, self).__init__()
self.conv1 = nn.Conv2d(n, 32, kernel_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | andy920262/pytorch-a2c-ppo-acktr | UniverseHead | false | 12,091 | [
"MIT"
] | 0 | 2e7e85219dfe737cb4036de3cf0c8b00706d640e | https://github.com/andy920262/pytorch-a2c-ppo-acktr/tree/2e7e85219dfe737cb4036de3cf0c8b00706d640e | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(torch.nn.Module):
""" universe agent example
input: [None, 42, 42, 1]; output: [None, 288];
"""
def __init__(self, n):
super().__init__()
self.conv1 = nn.Conv2d(n, 32, kernel_size=(3, 3), stride=(2, 2),... |
DQN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 DQN(nn.Module):
def __init__(self, state_dim, nb_actions, hidden1=50, hidden2=50):
super(DQN, self).__init__()
self.fc1 = nn.Linear(state_dim, hidden1)
self.fc2 = nn.Linear(hidden1, hidden2)
self.fc3 = nn.Linear(hidden2, nb_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
import torch.nn as nn
assert_... | alvinwan/explore | DQN | false | 12,092 | [
"MIT"
] | 0 | 358c076b8250f561394e32b1ee2de9bc5562dcdb | https://github.com/alvinwan/explore/tree/358c076b8250f561394e32b1ee2de9bc5562dcdb | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, state_dim, nb_actions, hidden1=50, hidden2=50):
super().__init__()
self.fc1 = nn.Linear(state_dim, hidden1)
self.fc2 = nn.Linear(hidden1, hidden2)
self.fc3 = nn.Linear(hidden2, nb_actions)
self.r... |
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
class Block(torch.nn.Module):
def __init__(self, in_channels, mid_channel, out_channels, batch_norm=False
):
super().__init__()
self.conv1 = torch.nn.Conv2d(in_channels=in_channels, out_channels=
mid_channel, kernel_size=3, padding=1)
self.conv2 = torch.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
assert_size_stride = torch._C... | amrane99/lung-segmentation | Block | false | 12,093 | [
"MIT"
] | 0 | ab29db75ac78918da5cbf66b830acaf36cf7b44a | https://github.com/amrane99/lung-segmentation/tree/ab29db75ac78918da5cbf66b830acaf36cf7b44a | import torch
class Model(torch.nn.Module):
def __init__(self, in_channels, mid_channel, out_channels, batch_norm=False
):
super().__init__()
self.conv1 = torch.nn.Conv2d(in_channels=in_channels, out_channels=
mid_channel, kernel_size=3, padding=1)
self.conv2 = torch.nn... |
ConvNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvNet(nn.Module):
""" convolutional neural network """
def __init__(self):
super(ConvNet, self).__init__()
nf = 8
self.conv1 = nn.Conv2d(1, nf * 1, 5, 1, 0)
self.conv2 = nn.Conv2d(nf * 1, nf * 2, 4, 2, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | animeshbchowdhury/robust-pnr-time | ConvNet | false | 12,094 | [
"BSD-3-Clause"
] | 0 | 301c5d973b8c024a85fdab915986ecf257e7698b | https://github.com/animeshbchowdhury/robust-pnr-time/tree/301c5d973b8c024a85fdab915986ecf257e7698b | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
""" convolutional neural network """
def __init__(self):
super().__init__()
nf = 8
self.conv1 = nn.Conv2d(1, nf * 1, 5, 1, 0)
self.conv2 = nn.Conv2d(nf * 1, nf * 2, 4, 2, 1)
self... |
NatureHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 NatureHead(torch.nn.Module):
""" DQN Nature 2015 paper
input: [None, 84, 84, 4]; output: [None, 3136] -> [None, 512];
"""
def __init__(self, n):
super(NatureHead, self).__init__()
self.conv1 = nn.Conv2d(n, 32... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | andy920262/pytorch-a2c-ppo-acktr | NatureHead | false | 12,095 | [
"MIT"
] | 0 | 2e7e85219dfe737cb4036de3cf0c8b00706d640e | https://github.com/andy920262/pytorch-a2c-ppo-acktr/tree/2e7e85219dfe737cb4036de3cf0c8b00706d640e | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(torch.nn.Module):
""" DQN Nature 2015 paper
input: [None, 84, 84, 4]; output: [None, 3136] -> [None, 512];
"""
def __init__(self, n):
super().__init__()
self.conv1 = nn.Conv2d(n, 32, kernel_size=(8, 8),... |
SelfAttnPooler | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SelfAttnPooler(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.proj = nn.Linear(input_dim, 1)
def forward(self, encoder_out, padding_mask):
"""
encoder_out: T, B, C
padding_mask: T, B (True for padded positio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ankitapasad/slue-toolkit | SelfAttnPooler | false | 12,096 | [
"MIT"
] | 0 | db8155cf0fc803e21890cf4eee2ef87152aafbfc | https://github.com/ankitapasad/slue-toolkit/tree/db8155cf0fc803e21890cf4eee2ef87152aafbfc | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.proj = nn.Linear(input_dim, 1)
def forward(self, encoder_out, padding_mask):
"""
encoder_out: T, B, C
padding_mask: T, B (True for padded positions)
... |
DPSLTMAdapter | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Tuple
from typing import List
from typing import Optional
from typing import Dict
from typing import Union
from torch.nn.modules.module import _... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | adriansarstedt/opacus | DPSLTMAdapter | false | 12,097 | [
"Apache-2.0"
] | 0 | a6c89e3d6a3a4e3e4b82bc8c68d53265a9a7cba1 | https://github.com/adriansarstedt/opacus/tree/a6c89e3d6a3a4e3e4b82bc8c68d53265a9a7cba1 | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Tuple
from typing import List
from typing import Optional
from typing import Dict
from typing import Union
from torch.nn.modules.module import _... |
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.functional as F
class FCNet(nn.Module):
""" fully-connected neural network """
def __init__(self):
super(FCNet, self).__init__()
self.fc1 = nn.Linear(784, 400)
self.fc2 = nn.Linear(400, 200)
self.fc3 = nn.Linear(200, 100)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | animeshbchowdhury/robust-pnr-time | FCNet | false | 12,098 | [
"BSD-3-Clause"
] | 0 | 301c5d973b8c024a85fdab915986ecf257e7698b | https://github.com/animeshbchowdhury/robust-pnr-time/tree/301c5d973b8c024a85fdab915986ecf257e7698b | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
""" fully-connected neural network """
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(784, 400)
self.fc2 = nn.Linear(400, 200)
self.fc3 = nn.Linear(200, 100)
self.fc... |
StendLoss | # 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 itertools import chain as chain
import torch.utils.data
import torch.nn as nn
from torch.nn.modules.loss import _Loss
class StendLoss(_Loss):
def __init__(self, size_average=None, reduce=None, reduction='mean'):
super(StendLoss, self).__init__()
self.reduction = reduction
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from iterto... | anton-br/SlowFast | StendLoss | false | 12,099 | [
"Apache-2.0"
] | 0 | 6e8d68bc6f3191886a57f819db1c766c6ca32d21 | https://github.com/anton-br/SlowFast/tree/6e8d68bc6f3191886a57f819db1c766c6ca32d21 | import torch
from itertools import chain as chain
import torch.utils.data
import torch.nn as nn
from torch.nn.modules.loss import _Loss
class Model(_Loss):
def __init__(self, size_average=None, reduce=None, reduction='mean'):
super().__init__()
self.reduction = reduction
def forward(self, ou... |
ZeroLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.utils.data
class ZeroLayer(nn.Module):
def __init__(self, stride):
super(ZeroLayer, self).__init__()
self.stride = stride
def forward(self, x):
"""n, c, h, w = x.size()
h //= self.stride
w //= self.stride
device ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | airglow/nni | ZeroLayer | false | 12,100 | [
"MIT"
] | 0 | 751065b788f66a6b53446620293095b1fe1b1c65 | https://github.com/airglow/nni/tree/751065b788f66a6b53446620293095b1fe1b1c65 | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, stride):
super().__init__()
self.stride = stride
def forward(self, x):
"""n, c, h, w = x.size()
h //= self.stride
w //= self.stride
device = x.get_device() if... |
SpaceToDepth | # 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 torchvision import datasets as datasets
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
class SpaceToDepth(nn.Module):
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torchvision import datasets as datasets
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distr... | adam-dziedzic/ASL | SpaceToDepth | false | 12,101 | [
"MIT"
] | 0 | cc063f5e7eda1498544ad2c3b224985203b0774a | https://github.com/adam-dziedzic/ASL/tree/cc063f5e7eda1498544ad2c3b224985203b0774a | import torch
from torchvision import datasets as datasets
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
class Model(nn.Module):
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
def... |
HSwish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data.distributed
class HSwish(nn.Module):
""" Applies the Hard-Swish function element-wise.
`"Searching for MobileNetV3" <https://arxiv.org/pdf/1905.02244.pdf>`_
Examples:
>>> m = Mish()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.ut... | ardianumam/Vanilla-GAN | HSwish | false | 12,102 | [
"Apache-2.0"
] | 0 | 3fce9b60dca4609aad1d4e5eb834a2cc72cf07b3 | https://github.com/ardianumam/Vanilla-GAN/tree/3fce9b60dca4609aad1d4e5eb834a2cc72cf07b3 | import torch
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data.distributed
class Model(nn.Module):
""" Applies the Hard-Swish function element-wise.
`"Searching for MobileNetV3" <https://arxiv.org/pdf/1905.02244.pdf>`_
Examples:
>>> m = Mish()
... |
SpatialAttentionGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SpatialAttentionGate(nn.Module):
def __init__(self, channel, reduction=16):
super(SpatialAttentionGate, self).__init__()
self.fc1 = nn.Conv2d(channel, reduction, kernel_size=1, padding=0)
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
import ... | airglow/nni | SpatialAttentionGate | false | 12,103 | [
"MIT"
] | 0 | 751065b788f66a6b53446620293095b1fe1b1c65 | https://github.com/airglow/nni/tree/751065b788f66a6b53446620293095b1fe1b1c65 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Model(nn.Module):
def __init__(self, channel, reduction=16):
super().__init__()
self.fc1 = nn.Conv2d(channel, reduction, kernel_size=1, padding=0)
self.fc2 = nn.Conv2d(reduction, 1, kernel_siz... |
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
import torch.nn.parallel
import torch.utils.data.distributed
class HSigmoid(nn.Module):
""" Applies the Hard-Sigmoid function element-wise.
`"Searching for MobileNetV3" <https://arxiv.org/pdf/1905.02244.pdf>`_
Examples:
>>> m = Mish()... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.ut... | ardianumam/Vanilla-GAN | HSigmoid | false | 12,104 | [
"Apache-2.0"
] | 0 | 3fce9b60dca4609aad1d4e5eb834a2cc72cf07b3 | https://github.com/ardianumam/Vanilla-GAN/tree/3fce9b60dca4609aad1d4e5eb834a2cc72cf07b3 | import torch
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data.distributed
class Model(nn.Module):
""" Applies the Hard-Sigmoid function element-wise.
`"Searching for MobileNetV3" <https://arxiv.org/pdf/1905.02244.pdf>`_
Examples:
>>> m = Mish()
... |
_AddNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 _TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torc... | amadejkocbek/darts | _AddNorm | false | 12,105 | [
"Apache-2.0"
] | 0 | 074be2a76eee11258da066878c564badf40834e9 | https://github.com/amadejkocbek/darts/tree/074be2a76eee11258da066878c564badf40834e9 | import torch
import torch.nn as nn
import torch.nn.functional as F
class _TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch... |
_ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class _ScaledDotProductAttention(nn.Module):
def __init__(self, dropout: 'float'=None, scale: 'bool'=True):
super(_ScaledDotProductAttention, self).__init__()
if dropout is not None:
self.dropout = nn.Dropout(p=dropout)
else:
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | amadejkocbek/darts | _ScaledDotProductAttention | false | 12,106 | [
"Apache-2.0"
] | 0 | 074be2a76eee11258da066878c564badf40834e9 | https://github.com/amadejkocbek/darts/tree/074be2a76eee11258da066878c564badf40834e9 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, dropout: 'float'=None, scale: 'bool'=True):
super().__init__()
if dropout is not None:
self.dropout = nn.Dropout(p=dropout)
else:
self.dropout = dropout
self.softmax = nn.Softmax(... |
_ResampleNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 _TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torc... | amadejkocbek/darts | _ResampleNorm | false | 12,107 | [
"Apache-2.0"
] | 0 | 074be2a76eee11258da066878c564badf40834e9 | https://github.com/amadejkocbek/darts/tree/074be2a76eee11258da066878c564badf40834e9 | import torch
import torch.nn as nn
import torch.nn.functional as F
class _TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch... |
_GateAddNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 _TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | amadejkocbek/darts | _GateAddNorm | false | 12,108 | [
"Apache-2.0"
] | 0 | 074be2a76eee11258da066878c564badf40834e9 | https://github.com/amadejkocbek/darts/tree/074be2a76eee11258da066878c564badf40834e9 | import torch
import torch.nn as nn
import torch.nn.functional as F
class _TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch... |
_GatedLinearUnit | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 _GatedLinearUnit(nn.Module):
"""Gated Linear Unit"""
def __init__(self, input_size: 'int', hidden_size: 'int'=None, dropout:
'float'=None):
super().__init__()
if dropout is not None:
self.dropout = 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | amadejkocbek/darts | _GatedLinearUnit | false | 12,109 | [
"Apache-2.0"
] | 0 | 074be2a76eee11258da066878c564badf40834e9 | https://github.com/amadejkocbek/darts/tree/074be2a76eee11258da066878c564badf40834e9 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Gated Linear Unit"""
def __init__(self, input_size: 'int', hidden_size: 'int'=None, dropout:
'float'=None):
super().__init__()
if dropout is not None:
self.dropout = nn.Dropout(dr... |
TReLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 TReLU(nn.Module):
def __init__(self):
super(TReLU, self).__init__()
self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
self.alpha.data.fill_(0)
def forward(self, x):
x = F.relu(x - self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | archiroid003/ICCV2019-LearningToPaint | TReLU | false | 12,110 | [
"MIT"
] | 0 | 4b5fc263e4843c159a61e5956956b3f7812693f8 | https://github.com/archiroid003/ICCV2019-LearningToPaint/tree/4b5fc263e4843c159a61e5956956b3f7812693f8 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
self.alpha.data.fill_(0)
def forward(self, x):
x = F.relu(x - self.alpha) + se... |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class SharedDropout(torch.nn.Module):
def __init__(self, p):
super(SharedDropout, self).__in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | albertkx/GeDi | MLP | false | 12,111 | [
"BSD-3-Clause"
] | 0 | 27532eb6ac5dd42d817d25a905401504e916f9fb | https://github.com/albertkx/GeDi/tree/27532eb6ac5dd42d817d25a905401504e916f9fb | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class SharedDropout(torch.nn.Module):
def __init__(self, p):
super().__init__()
self... |
_GatedResidualNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 _TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | amadejkocbek/darts | _GatedResidualNetwork | false | 12,112 | [
"Apache-2.0"
] | 0 | 074be2a76eee11258da066878c564badf40834e9 | https://github.com/amadejkocbek/darts/tree/074be2a76eee11258da066878c564badf40834e9 | import torch
import torch.nn as nn
import torch.nn.functional as F
class _TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch... |
SEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torchvision import datasets as datasets
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
class FastAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super(FastAvgPool2d, self).__init__()
self.flatten = flatten
def ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torchvision import datas... | adam-dziedzic/ASL | SEModule | false | 12,113 | [
"MIT"
] | 0 | cc063f5e7eda1498544ad2c3b224985203b0774a | https://github.com/adam-dziedzic/ASL/tree/cc063f5e7eda1498544ad2c3b224985203b0774a | import torch
from torchvision import datasets as datasets
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
class FastAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super().__init__()
self.flatten = flatten
def forward(self, x):
... |
PreActBlockNoBN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 PreActBlockNoBN(nn.Module):
"""Pre-activation version of the BasicBlock."""
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(PreActBlockNoBN, self).__init__()
self.conv1 = nn.Conv2d(in_planes, pla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | arhik/LoCo | PreActBlockNoBN | false | 12,114 | [
"MIT"
] | 0 | de3792a8c5650ee1efa0682ad494a3b1b1be3dd0 | https://github.com/arhik/LoCo/tree/de3792a8c5650ee1efa0682ad494a3b1b1be3dd0 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Pre-activation version of the BasicBlock."""
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super().__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=
... |
up | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 up(nn.Module):
def __init__(self, in_ch, out_ch):
super(up, self).__init__()
self.up_scale = nn.ConvTranspose2d(in_ch, out_ch, 2, stride=2)
def forward(self, x1, x2):
x2 = self.up_scale(x2)
diffY = x1.siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | aribryan/segmentation_revisit | up | false | 12,115 | [
"MIT"
] | 0 | a37747cfa7bfa7bfd4c0c01983421f632cd719ba | https://github.com/aribryan/segmentation_revisit/tree/a37747cfa7bfa7bfd4c0c01983421f632cd719ba | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.up_scale = nn.ConvTranspose2d(in_ch, out_ch, 2, stride=2)
def forward(self, x1, x2):
x2 = self.up_scale(x2)
diffY = x1.size()[2... |
ResnetBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
import torch.utils.data.distributed
def actvn(x):
out = F.leaky_relu(x, 0.2)
return out
class ResnetBlock(nn.Module):
def __init__(self, fin, fout, fhidden=None, is_bias=True):
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 torch import nn
from torch.nn import functional as F
import torch.utils.dat... | arnabgho/GAN_stability | ResnetBlock | false | 12,116 | [
"MIT"
] | 0 | 5037d1d856be58818d1c825cd415e0d90d90aff2 | https://github.com/arnabgho/GAN_stability/tree/5037d1d856be58818d1c825cd415e0d90d90aff2 | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
import torch.utils.data.distributed
def actvn(x):
out = F.leaky_relu(x, 0.2)
return out
class Model(nn.Module):
def __init__(self, fin, fout, fhidden=None, is_bias=True):
super().__init__()
se... |
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