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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
UnbalancedLoss | # 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 UnbalancedLoss(nn.Module):
NUM_LABELS = 2
def __init__(self):
super().__init__()
self.crit = nn.BCEWithLogitsLoss()
def forward(self, logits, label):
return self.crit(logits, label)
def get_inputs():
return [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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Kwonyoung-Ryu/DeepGlobalRegistration | UnbalancedLoss | false | 11,613 | [
"MIT"
] | 0 | 0045118d96182047f4c09c4c4fe2a1b2b527cc5f | https://github.com/Kwonyoung-Ryu/DeepGlobalRegistration/tree/0045118d96182047f4c09c4c4fe2a1b2b527cc5f | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
NUM_LABELS = 2
def __init__(self):
super().__init__()
self.crit = nn.BCEWithLogitsLoss()
def forward(self, logits, label):
return self.crit(logits, label)
def get_inputs():
return [torch.rand... |
Network | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
class Network(nn.Module):
def __init__(self):
super(Network, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Karansutradhar/Convolution-Neural-Network-Objection-Recognition-Dogs-Cats | Network | false | 11,614 | [
"MIT"
] | 0 | 85dfab2e8758a5cf49368938b03720f197a06b18 | https://github.com/Karansutradhar/Convolution-Neural-Network-Objection-Recognition-Dogs-Cats/tree/85dfab2e8758a5cf49368938b03720f197a06b18 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
... |
ConformerFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.utils.data
import torch.optim
class Swish(nn.Module):
"""
Swish activation function introduced in 'https://arxiv.org/abs/1710.05941'
"""
def forward(self, x):
return x * torch.sigmoid(x)
class ConformerFeedForward(nn.Module):
"""
feed-f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.optim
assert_size_stri... | JINHXu/NeMo | ConformerFeedForward | false | 11,615 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 | import torch
from torch import nn
import torch.utils.data
import torch.optim
class Swish(nn.Module):
"""
Swish activation function introduced in 'https://arxiv.org/abs/1710.05941'
"""
def forward(self, x):
return x * torch.sigmoid(x)
class Model(nn.Module):
"""
feed-forward module o... |
AngleSimpleLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Parameter
import torch.utils.data
class AngleSimpleLinear(nn.Module):
"""Computes cos of angles between input vectors and weights vectors"""
def __init__(self, in_features, out_features):
super(AngleSimpleLinear, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KhurramPirov/Twins-recognition | AngleSimpleLinear | false | 11,616 | [
"MIT"
] | 0 | f99ba1128afb3674a49db6a4b19afd5108c3fdf9 | https://github.com/KhurramPirov/Twins-recognition/tree/f99ba1128afb3674a49db6a4b19afd5108c3fdf9 | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Parameter
import torch.utils.data
class Model(nn.Module):
"""Computes cos of angles between input vectors and weights vectors"""
def __init__(self, in_features, out_features):
super().__init__()
self.in_fe... |
ScalarMix | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ScalarMix(nn.Module):
"""
Computes a parameterised scalar mixture of :math:`N` tensors, :math:`mixture = \\gamma * \\sum_{k}(s_k * tensor_k)`
where :math:`s = \\mathrm{softmax}(w)`, with :math:`w` and :math:`\\gamma` scalar parameters.
Args:
n_layers (... | 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... | KoichiYasuoka/SuPar | ScalarMix | false | 11,617 | [
"MIT"
] | 0 | 9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | https://github.com/KoichiYasuoka/SuPar/tree/9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Computes a parameterised scalar mixture of :math:`N` tensors, :math:`mixture = \\gamma * \\sum_{k}(s_k * tensor_k)`
where :math:`s = \\mathrm{softmax}(w)`, with :math:`w` and :math:`\\gamma` scalar parameters.
Args:
n_layers (int)... |
ConvGLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.utils.data
import torch.optim
def str2act(txt):
"""Translates text to neural network activation"""
return {'sigmoid': nn.Sigmoid(), 'relu': nn.ReLU(), 'none': nn.
Sequential(), 'lrelu': nn.LeakyReLU(0.2), 'selu': nn.SELU()}[txt.
lower()]
class 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 import nn
import torch.utils.data
import torch.optim
assert_size_stri... | JINHXu/NeMo | ConvGLU | false | 11,618 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 | import torch
from torch import nn
import torch.utils.data
import torch.optim
def str2act(txt):
"""Translates text to neural network activation"""
return {'sigmoid': nn.Sigmoid(), 'relu': nn.ReLU(), 'none': nn.
Sequential(), 'lrelu': nn.LeakyReLU(0.2), 'selu': nn.SELU()}[txt.
lower()]
class M... |
AdaptiveAvgMaxPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
def adaptive_avgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
import torchvision.transforms.functional as... | DifferentSC/pytorch-image-models | AdaptiveAvgMaxPool2d | false | 11,619 | [
"Apache-2.0"
] | 0 | ccfb5751abc70d80add4f197464190c4a2637c6c | https://github.com/DifferentSC/pytorch-image-models/tree/ccfb5751abc70d80add4f197464190c4a2637c6c | import torch
import torch.nn as nn
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
def adaptive_avgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_... |
DuelingQNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 DuelingQNetwork(nn.Module):
"""Dueling Q-network (https://arxiv.org/abs/1511.06581)"""
def __init__(self, state_size, action_size, hidsize1=128, hidsize2=128):
super(DuelingQNetwork, self).__init__()
self.fc1_val = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | KantiCodes/flatland-rl | DuelingQNetwork | false | 11,620 | [
"MIT"
] | 0 | fcc10e83d2548470ebaa5540b967db0940eb30dd | https://github.com/KantiCodes/flatland-rl/tree/fcc10e83d2548470ebaa5540b967db0940eb30dd | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
"""Dueling Q-network (https://arxiv.org/abs/1511.06581)"""
def __init__(self, state_size, action_size, hidsize1=128, hidsize2=128):
super().__init__()
self.fc1_val = nn.Linear(state_size, hidsize1)
... |
AsymmetricLossOptimized | # 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 AsymmetricLossOptimized(nn.Module):
""" Notice - optimized version, minimizes memory allocation and gpu uploading,
favors inplace operations
https://github.com/Alibaba-MIIL/ASL/blob/main/src/loss_functions/losses.py
"""
def __init__(self, gamma_neg=4, gamm... | 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... | LanXiangExcavator/python-classifier-2021 | AsymmetricLossOptimized | false | 11,621 | [
"BSD-2-Clause"
] | 0 | 851079e76db8e5070132d1120dba941967e1245b | https://github.com/LanXiangExcavator/python-classifier-2021/tree/851079e76db8e5070132d1120dba941967e1245b | import torch
import torch.nn as nn
class Model(nn.Module):
""" Notice - optimized version, minimizes memory allocation and gpu uploading,
favors inplace operations
https://github.com/Alibaba-MIIL/ASL/blob/main/src/loss_functions/losses.py
"""
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05... |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, input, target, logits=True):
if logits:
input = nn.Sigmoid()(input)
N = target.size(0)
smooth = 1
input_flat = input.view(N... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | LanXiangExcavator/python-classifier-2021 | DiceLoss | false | 11,622 | [
"BSD-2-Clause"
] | 0 | 851079e76db8e5070132d1120dba941967e1245b | https://github.com/LanXiangExcavator/python-classifier-2021/tree/851079e76db8e5070132d1120dba941967e1245b | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, target, logits=True):
if logits:
input = nn.Sigmoid()(input)
N = target.size(0)
smooth = 1
input_flat = input.view(N, -1)
tar... |
MultiLayerPerceptron | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.optim
class MultiLayerPerceptron(torch.nn.Module):
"""
A simple MLP that can either be used independently or put on top
of pretrained models (such as BERT) and act as a classifier.
Args:
hidden_size (int): the size of each layer
num_cla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JINHXu/NeMo | MultiLayerPerceptron | false | 11,623 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 | import torch
import torch.utils.data
import torch.optim
class Model(torch.nn.Module):
"""
A simple MLP that can either be used independently or put on top
of pretrained models (such as BERT) and act as a classifier.
Args:
hidden_size (int): the size of each layer
num_classes (int): num... |
TransformerDecoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class TransformerDecoderLayer(nn.Module):
"""Decoder layer block. Follows an implementation in fairseq with args.decoder_normalize_before=True,
i.e. order of operation... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | IA3005/NLP_ens | TransformerDecoderLayer | false | 11,624 | [
"MIT"
] | 0 | 794ebbff46d5e6d5476f29b577b40bbb52991246 | https://github.com/IA3005/NLP_ens/tree/794ebbff46d5e6d5476f29b577b40bbb52991246 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class Model(nn.Module):
"""Decoder layer block. Follows an implementation in fairseq with args.decoder_normalize_before=True,
i.e. order of operations is different fro... |
FixedSubnetConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.multiprocessing
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class FixedSubnetConv(nn.Conv2d):
def __init__(self, *args, **kwargs):
super().__init__(*args... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.multiprocessing
import torch.nn as nn
import torch.nn.p... | Lasitha-93/CRTIDL_2021 | FixedSubnetConv | false | 11,625 | [
"Apache-2.0"
] | 0 | d6bc6fbe08161c3574511623230a7aa4895f65e1 | https://github.com/Lasitha-93/CRTIDL_2021/tree/d6bc6fbe08161c3574511623230a7aa4895f65e1 | import math
import torch
import torch.multiprocessing
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class Model(nn.Conv2d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs... |
AttentionBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.nn import functional as F
from torch import nn
import torch.utils.data
import torch.optim
def convert_pad_shape(pad_shape):
"""
Used to get arguments for F.pad
"""
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JINHXu/NeMo | AttentionBlock | false | 11,626 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 | import math
import torch
from torch.nn import functional as F
from torch import nn
import torch.utils.data
import torch.optim
def convert_pad_shape(pad_shape):
"""
Used to get arguments for F.pad
"""
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape
... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import LayerNorm
import torch.utils.data
import torch.optim
class LayerNorm(nn.Module):
def __init__(self, channels, eps=0.0001):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.utils.data
import torch.optim
assert_size_str... | JINHXu/NeMo | LayerNorm | false | 11,627 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 | import torch
from torch import nn
from torch.nn import LayerNorm
import torch.utils.data
import torch.optim
class Model(nn.Module):
def __init__(self, channels, eps=0.0001):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
... |
FusedDownsample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import functional as F
from math import sqrt
class FusedDownsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(out_channel, in_channel, kernel_size, kernel_size)
bi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | KUMartin77/AAA738_StyleGAN_pytorch | FusedDownsample | false | 11,628 | [
"BSD-2-Clause"
] | 0 | ed0689102c922d336f53e374e8be2ab532a84ccd | https://github.com/KUMartin77/AAA738_StyleGAN_pytorch/tree/ed0689102c922d336f53e374e8be2ab532a84ccd | import torch
from torch import nn
from torch.nn import functional as F
from math import sqrt
class Model(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(out_channel, in_channel, kernel_size, kernel_size)
bias = torch... |
Biaffine | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Biaffine(nn.Module):
"""
Biaffine layer for first-order scoring.
This function has a tensor of weights :math:`W` and bias terms if needed.
The score :math:`s(x, y)` of the vector pair :math:`(x, y)` is computed as :math:`x^T W y`,
in which :math:`x` and :m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | KoichiYasuoka/SuPar | Biaffine | false | 11,629 | [
"MIT"
] | 0 | 9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | https://github.com/KoichiYasuoka/SuPar/tree/9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Biaffine layer for first-order scoring.
This function has a tensor of weights :math:`W` and bias terms if needed.
The score :math:`s(x, y)` of the vector pair :math:`(x, y)` is computed as :math:`x^T W y`,
in which :math:`x` and :math... |
MulticlassDiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, input, target, logits=True):
if logits:
input = nn.Sigmoid()(input)
N = target.size(0)
smooth = 1
input_flat = input.view(N... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | LanXiangExcavator/python-classifier-2021 | MulticlassDiceLoss | false | 11,630 | [
"BSD-2-Clause"
] | 0 | 851079e76db8e5070132d1120dba941967e1245b | https://github.com/LanXiangExcavator/python-classifier-2021/tree/851079e76db8e5070132d1120dba941967e1245b | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, target, logits=True):
if logits:
input = nn.Sigmoid()(input)
N = target.size(0)
smooth = 1
input_flat = input.view(N, -1)
... |
Triaffine | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Triaffine(nn.Module):
"""
Triaffine layer for second-order scoring.
This function has a tensor of weights :math:`W` and bias terms if needed.
The score :math:`s(x, y, z)` of the vector triple :math:`(x, y, z)` is computed as :math:`x^T z^T W y`.
Usually, :... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | KoichiYasuoka/SuPar | Triaffine | false | 11,631 | [
"MIT"
] | 0 | 9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | https://github.com/KoichiYasuoka/SuPar/tree/9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Triaffine layer for second-order scoring.
This function has a tensor of weights :math:`W` and bias terms if needed.
The score :math:`s(x, y, z)` of the vector triple :math:`(x, y, z)` is computed as :math:`x^T z^T W y`.
Usually, :math... |
InvConvNear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch.nn import functional as F
from torch import nn
import torch.utils.data
import torch.optim
class InvConvNear(nn.Module):
def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
super().__init__()
assert n_split % 2 == 0
self.channels = channels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.optim
assert_size_stri... | JINHXu/NeMo | InvConvNear | false | 11,632 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 | import torch
from torch.nn import functional as F
from torch import nn
import torch.utils.data
import torch.optim
class Model(nn.Module):
def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
super().__init__()
assert n_split % 2 == 0
self.channels = channels
self.... |
TVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
import torch.utils.data
from torchvision.transforms import *
class TVLoss(nn.Module):
def __init__(self, tv_loss_weight=1):
super(TVLoss, self).__init__()
self.tv_loss_weight = tv_loss_weight
def forward(self, x):
batch_size = x.size()[0]
h_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 import nn
import torch.utils.data
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | HamsterBiz/iSeeBetter | TVLoss | false | 11,633 | [
"MIT"
] | 0 | a71cee61583bdedab1f3b368e2cb7dc5ad969aed | https://github.com/HamsterBiz/iSeeBetter/tree/a71cee61583bdedab1f3b368e2cb7dc5ad969aed | import torch
from torch import nn
import torch.utils.data
from torchvision.transforms import *
class Model(nn.Module):
def __init__(self, tv_loss_weight=1):
super().__init__()
self.tv_loss_weight = tv_loss_weight
def forward(self, x):
batch_size = x.size()[0]
h_x = x.size()[2... |
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
import torch.optim
import torch.nn as nn
import torch.utils.data
class SpaceToDepth(nn.Module):
def __init__(self, block_size):
super(SpaceToDepth, self).__init__()
self.block_size = block_size
self.block_size_sq = block_size * block_size
def forward(self, input):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.optim
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | LeikvollE/pytorch-superpoint | SpaceToDepth | false | 11,634 | [
"MIT"
] | 0 | 52144a760e0cc46259e57397a5a55f0585fe6d0b | https://github.com/LeikvollE/pytorch-superpoint/tree/52144a760e0cc46259e57397a5a55f0585fe6d0b | import torch
import torch.optim
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, block_size):
super().__init__()
self.block_size = block_size
self.block_size_sq = block_size * block_size
def forward(self, input):
output = input.permute(... |
GEGLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Lawliet19189/squad-1 | GEGLU | false | 11,635 | [
"MIT"
] | 0 | 75531054d74e20838d8acff81749f335973b9ae3 | https://github.com/Lawliet19189/squad-1/tree/75531054d74e20838d8acff81749f335973b9ae3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
... |
ScaleNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ScaleNorm(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1))
def forward(self, x):
n = torch.norm(x, dim=-1, keepdim=True).clamp(min=self.eps)
return x / n * s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Lawliet19189/squad-1 | ScaleNorm | false | 11,636 | [
"MIT"
] | 0 | 75531054d74e20838d8acff81749f335973b9ae3 | https://github.com/Lawliet19189/squad-1/tree/75531054d74e20838d8acff81749f335973b9ae3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1))
def forward(self, x):
n = torch.norm(x, dim=-1, keepdim=True).clamp(min=self.eps)
return x / n * self.... |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import nn
import torch.utils.data
import torch.optim
class MultiHeadAttention(nn.Module):
"""
Multi-head scaled dot-product attention layer.
Args:
hidden_size: size of the embeddings in the model, also known as d_model
num_attention_heads: number of hea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JINHXu/NeMo | MultiHeadAttention | false | 11,637 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 | import math
import torch
from torch import nn
import torch.utils.data
import torch.optim
class Model(nn.Module):
"""
Multi-head scaled dot-product attention layer.
Args:
hidden_size: size of the embeddings in the model, also known as d_model
num_attention_heads: number of heads in multi-h... |
Inception3 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=True, **kwargs)
def forward(self, x):
x ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Galaxies99/inception-cuda | Inception3 | false | 11,638 | [
"MIT"
] | 0 | ed8fdbe3caef415e60b52e671273be90e9423e44 | https://github.com/Galaxies99/inception-cuda/tree/ed8fdbe3caef415e60b52e671273be90e9423e44 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=True, **kwargs)
def forward(self, x):
x = self.conv(x)
... |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class SharedDropout(nn.Module):
"""
SharedDropout differs from the vanilla dropout strategy in that
the dropout mask is shared across one dimension.
Args:
p (float):
The probability of an element to be zeroed. Default: 0.5.
batch_first (b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | KoichiYasuoka/SuPar | MLP | false | 11,639 | [
"MIT"
] | 0 | 9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | https://github.com/KoichiYasuoka/SuPar/tree/9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | import torch
import torch.nn as nn
class SharedDropout(nn.Module):
"""
SharedDropout differs from the vanilla dropout strategy in that
the dropout mask is shared across one dimension.
Args:
p (float):
The probability of an element to be zeroed. Default: 0.5.
batch_first (b... |
D_UpBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torchvision.transforms import *
assert_size_stride ... | HamsterBiz/iSeeBetter | D_UpBlock | false | 11,640 | [
"MIT"
] | 0 | a71cee61583bdedab1f3b368e2cb7dc5ad969aed | https://github.com/HamsterBiz/iSeeBetter/tree/a71cee61583bdedab1f3b368e2cb7dc5ad969aed | import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super().__init__()
self.conv = torch.nn.Conv2d(input_size,... |
LSTMClassCriterion | # 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 to_contiguous(tensor):
if tensor.is_contiguous():
return tensor
else:
return tensor.contiguous()
class LSTMClassCriterion(nn.Module):
def __init__(self):
super(LSTMClassCriterion, self).__init__()
def forward(self, pred, target, mask):... | 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... | LeoZDong/shape2prog | LSTMClassCriterion | false | 11,641 | [
"BSD-2-Clause"
] | 0 | 2185d1d4eb7a1c4c55e644c6af477fd8e8e70241 | https://github.com/LeoZDong/shape2prog/tree/2185d1d4eb7a1c4c55e644c6af477fd8e8e70241 | import torch
import torch.nn as nn
def to_contiguous(tensor):
if tensor.is_contiguous():
return tensor
else:
return tensor.contiguous()
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred, target, mask):
pred = pred.clone()
... |
LSTMRegressCriterion | # 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 LSTMRegressCriterion(nn.Module):
def __init__(self):
super(LSTMRegressCriterion, self).__init__()
def forward(self, pred, target, mask):
pred = pred.clone()
target = target.clone()
mask = mask.clone()
target = target[:, :pred.s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | LeoZDong/shape2prog | LSTMRegressCriterion | false | 11,642 | [
"BSD-2-Clause"
] | 0 | 2185d1d4eb7a1c4c55e644c6af477fd8e8e70241 | https://github.com/LeoZDong/shape2prog/tree/2185d1d4eb7a1c4c55e644c6af477fd8e8e70241 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred, target, mask):
pred = pred.clone()
target = target.clone()
mask = mask.clone()
target = target[:, :pred.size(1), :]
mask = mask[:, :pred.s... |
ViTStemPatchify | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn as nn
def patchify2d(w_in, w_out, k, *, bias=True):
"""Helper for building a patchify layer as used by ViT models."""
return nn.Conv2d(w_in, w_out, k, stride=k, padding=0, bias=bias)
def patchify2d_cx(cx, w_in, w_out, k, *, bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.utils.data
import torch.nn as nn
assert... | LicharYuan/pycls | ViTStemPatchify | false | 11,643 | [
"MIT"
] | 0 | 633529425f2c9ffadd892c1a0418b37891ee2d44 | https://github.com/LicharYuan/pycls/tree/633529425f2c9ffadd892c1a0418b37891ee2d44 | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn as nn
def patchify2d(w_in, w_out, k, *, bias=True):
"""Helper for building a patchify layer as used by ViT models."""
return nn.Conv2d(w_in, w_out, k, stride=k, padding=0, bias=bias)
def patchify2d_cx(cx, w_in, w_out, k, *, bia... |
RegressionModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 RegressionModel(nn.Module):
def __init__(self, num_features_in, num_anchors=9, feature_size=256):
super(RegressionModel, self).__init__()
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3,
padding=1)
self.act1 = nn.ReL... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Hyojin021/auto_labeling | RegressionModel | false | 11,644 | [
"Apache-2.0"
] | 0 | 1ccf0cd1c5adf34692751553a988aa0fcf4efefb | https://github.com/Hyojin021/auto_labeling/tree/1ccf0cd1c5adf34692751553a988aa0fcf4efefb | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_features_in, num_anchors=9, feature_size=256):
super().__init__()
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3,
padding=1)
self.act1 = nn.ReLU()
self.conv2 = nn.Con... |
NormedLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | LiuXiaoxuanPKU/mmdetection | NormedLinear | false | 11,645 | [
"Apache-2.0"
] | 0 | 05b46eccbe5c4953d5a406f545fe529ce4e146d3 | https://github.com/LiuXiaoxuanPKU/mmdetection/tree/05b46eccbe5c4953d5a406f545fe529ce4e146d3 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Linear):
"""Normalized Linear Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value of divi... |
MergeGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 MergeGate(nn.Module):
def __init__(self, hidden_size):
super(MergeGate, self).__init__()
self.hidden_size = hidden_size
self.WSh = nn.Linear(hidden_size, hidden_size)
self.WSc = nn.Linear(hidden_size, hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | LeiShenVictoria/Static-Dynamic-Attention-CNNRNN | MergeGate | false | 11,646 | [
"MIT"
] | 0 | e2823717d22c9e543428d471ff19113bbb59ebfe | https://github.com/LeiShenVictoria/Static-Dynamic-Attention-CNNRNN/tree/e2823717d22c9e543428d471ff19113bbb59ebfe | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.hidden_size = hidden_size
self.WSh = nn.Linear(hidden_size, hidden_size)
self.WSc = nn.Linear(hidden_size, hidden_size)
self.... |
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 as t
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, action_range):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, action_dim)
self.action_range ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LeonLester/Machin-title-in-progress- | Actor | false | 11,647 | [
"MIT"
] | 0 | 777479d47b520dcdc6b09c247591b5fe1d6cbe8c | https://github.com/LeonLester/Machin-title-in-progress-/tree/777479d47b520dcdc6b09c247591b5fe1d6cbe8c | import torch
import torch.nn as nn
import torch as t
class Model(nn.Module):
def __init__(self, state_dim, action_dim, action_range):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, action_dim)
self.action_range ... |
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 as t
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim + action_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, 1)
def forward(self, state, actio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | LeonLester/Machin-title-in-progress- | Critic | false | 11,648 | [
"MIT"
] | 0 | 777479d47b520dcdc6b09c247591b5fe1d6cbe8c | https://github.com/LeonLester/Machin-title-in-progress-/tree/777479d47b520dcdc6b09c247591b5fe1d6cbe8c | import torch
import torch.nn as nn
import torch as t
class Model(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim + action_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, 1)
def forward(self, state, action... |
NormedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class NormedConv2d(nn.Conv2d):
"""Normalized Conv2d Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value of divisor to
keep ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | LiuXiaoxuanPKU/mmdetection | NormedConv2d | false | 11,649 | [
"Apache-2.0"
] | 0 | 05b46eccbe5c4953d5a406f545fe529ce4e146d3 | https://github.com/LiuXiaoxuanPKU/mmdetection/tree/05b46eccbe5c4953d5a406f545fe529ce4e146d3 | import torch
from torch import nn
class Model(nn.Conv2d):
"""Normalized Conv2d Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value of divisor to
keep numeric... |
conv_head_pooling | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.autograd
class conv_head_pooling(nn.Module):
def __init__(self, in_feature, out_feature, stride, padding_mode='zeros'):
super(conv_head_pooling, self).__init__()
self.maxpool = nn.MaxPool2d(3, 2, 1)
self.avgpool = nn.AvgPool2d(3, 2, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | LeiZhang1998/TransReID | conv_head_pooling | false | 11,650 | [
"MIT"
] | 0 | 5a3f140633e3418c7cff2603ff2e814b9ab466ac | https://github.com/LeiZhang1998/TransReID/tree/5a3f140633e3418c7cff2603ff2e814b9ab466ac | import torch
import torch.nn as nn
import torch.autograd
class Model(nn.Module):
def __init__(self, in_feature, out_feature, stride, padding_mode='zeros'):
super().__init__()
self.maxpool = nn.MaxPool2d(3, 2, 1)
self.avgpool = nn.AvgPool2d(3, 2, 1)
self.conv1 = nn.Conv2d(in_featur... |
CrossEn | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
import torch.nn.functional as F
import torch.cuda
class CrossEn(nn.Module):
def forward(self, sim_matrix):
logpt = F.log_softmax(sim_matrix, dim=-1)
logpt = torch.diag(logpt)
nce_loss = -logpt
sim_loss = nce_loss.mean()
return sim_loss
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 math as tl_math
from torch import nn
i... | LoveEachDay/towhee | CrossEn | false | 11,651 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 | import torch
from torch import nn
import torch.nn.functional as F
import torch.cuda
class Model(nn.Module):
def forward(self, sim_matrix):
logpt = F.log_softmax(sim_matrix, dim=-1)
logpt = torch.diag(logpt)
nce_loss = -logpt
sim_loss = nce_loss.mean()
return sim_loss
def... |
HardMish | # 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.cuda
def hard_mish(x, inplace: 'bool'=False):
if inplace:
return x.mul_(0.5 * (x + 2).clamp(min=0, max=2))
else:
return 0.5 * x * (x + 2).clamp(min=0, max=2)
class HardMish(nn.Module):
"""
Hard Mish
Experimental, based on notes by Mi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.cuda
assert_size_stride = torch._C._dynamo.guards.asser... | LoveEachDay/towhee | HardMish | false | 11,652 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 | import torch
from torch import nn
import torch.cuda
def hard_mish(x, inplace: 'bool'=False):
if inplace:
return x.mul_(0.5 * (x + 2).clamp(min=0, max=2))
else:
return 0.5 * x * (x + 2).clamp(min=0, max=2)
class Model(nn.Module):
"""
Hard Mish
Experimental, based on notes by Mish ... |
Conv2dSame | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 typing import List
from typing import Union
from torch import nn
import torch.nn.functional as F
from typing import Tuple
import torch.cuda
from typing import Optional
from torch.nn.common_types import _size_2_t
def get_same_padding(x: 'int', k: 'int', s: 'int', d: 'int') ->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
import math
from typing import List
from typing import Union
from torch import n... | LoveEachDay/towhee | Conv2dSame | false | 11,653 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 | import math
import torch
from typing import List
from typing import Union
from torch import nn
import torch.nn.functional as F
from typing import Tuple
import torch.cuda
from typing import Optional
from torch.nn.common_types import _size_2_t
def get_same_padding(x: 'int', k: 'int', s: 'int', d: 'int') ->int:
"""
... |
HardSwish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
import torch.nn.functional as F
import torch.cuda
def hard_swish(x: 'torch.Tensor', inplace: 'bool'=False) ->torch.Tensor:
inner = F.relu6(x + 3.0).div_(6.0)
return x.mul_(inner) if inplace else x.mul(inner)
class HardSwish(nn.Module):
"""
HardSwish activiation laye... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.nn.functional as F
import torch.cuda
assert_size_stride... | LoveEachDay/towhee | HardSwish | false | 11,654 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 | import torch
from torch import nn
import torch.nn.functional as F
import torch.cuda
def hard_swish(x: 'torch.Tensor', inplace: 'bool'=False) ->torch.Tensor:
inner = F.relu6(x + 3.0).div_(6.0)
return x.mul_(inner) if inplace else x.mul(inner)
class Model(nn.Module):
"""
HardSwish activiation layer.
... |
CMlp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.cuda
def conv_1x1x1(inp, oup, groups=1):
return nn.Conv3d(inp, oup, (1, 1, 1), (1, 1, 1), (0, 0, 0), groups=groups)
class CMlp(nn.Module):
"""
CMlp
"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GE... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | LoveEachDay/towhee | CMlp | false | 11,655 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 | import torch
from torch import nn
import torch.cuda
def conv_1x1x1(inp, oup, groups=1):
return nn.Conv3d(inp, oup, (1, 1, 1), (1, 1, 1), (0, 0, 0), groups=groups)
class Model(nn.Module):
"""
CMlp
"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.G... |
Upsampler | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.utils.data
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | HamsterBiz/iSeeBetter | Upsampler | false | 11,656 | [
"MIT"
] | 0 | a71cee61583bdedab1f3b368e2cb7dc5ad969aed | https://github.com/HamsterBiz/iSeeBetter/tree/a71cee61583bdedab1f3b368e2cb7dc5ad969aed | import math
import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super().__init__()
self.conv = torch.nn.Conv2d... |
NextSentencePrediction | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 NextSentencePrediction(nn.Module):
"""
2-class classification model : is_next, is_not_next
"""
def __init__(self, hidden):
"""
:param hidden: BERT model output size
"""
super().__init__()
self.linear = nn.Linear(hidden, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JacobTyo/Syntax-Encoding_EMNLP2018 | NextSentencePrediction | false | 11,657 | [
"MIT"
] | 0 | 5aed2fdd01dc7d0baebbd555c97a25fedbde0c39 | https://github.com/JacobTyo/Syntax-Encoding_EMNLP2018/tree/5aed2fdd01dc7d0baebbd555c97a25fedbde0c39 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
2-class classification model : is_next, is_not_next
"""
def __init__(self, hidden):
"""
:param hidden: BERT model output size
"""
super().__init__()
self.linear = nn.Linear(hidden, 2)
self.s... |
Attention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
"""
Compute 'Scaled Dot Product Attention
"""
def forward(self, query, key, value, mask=None, dropout=None):
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(query
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JacobTyo/Syntax-Encoding_EMNLP2018 | Attention | false | 11,658 | [
"MIT"
] | 0 | 5aed2fdd01dc7d0baebbd555c97a25fedbde0c39 | https://github.com/JacobTyo/Syntax-Encoding_EMNLP2018/tree/5aed2fdd01dc7d0baebbd555c97a25fedbde0c39 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Compute 'Scaled Dot Product Attention
"""
def forward(self, query, key, value, mask=None, dropout=None):
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(query
... |
ConvMlp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.cuda
class ConvMlp(nn.Module):
""" MLP using 1x1 convs that keeps spatial dims
"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.ReLU, norm_layer=None, drop=0.0):
super().__init__()
out_features... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | LoveEachDay/towhee | ConvMlp | false | 11,659 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 | import torch
from torch import nn
import torch.cuda
class Model(nn.Module):
""" MLP using 1x1 convs that keeps spatial dims
"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.ReLU, norm_layer=None, drop=0.0):
super().__init__()
out_features =... |
InnerProductDecoder | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class InnerProductDecoder(nn.Module):
def __init__(self, activation=torch.sigmoid, dropout=0.1):
super(InnerProductDecoder, self).__init__()
self.dropout = dropout
self.activation = activation
def forward(self, z):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | LymanSong/suwon_bus_stop_competition | InnerProductDecoder | false | 11,660 | [
"MIT"
] | 0 | 42297c8cfb0f109f28d8aeead097a57bb5d6be53 | https://github.com/LymanSong/suwon_bus_stop_competition/tree/42297c8cfb0f109f28d8aeead097a57bb5d6be53 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, activation=torch.sigmoid, dropout=0.1):
super().__init__()
self.dropout = dropout
self.activation = activation
def forward(self, z):
z = F.dropout(z, self.dropout)
... |
CrossAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.cuda
class MultiHeadAttention(nn.Module):
"""
Multi head attention for Perceiver https://arxiv.org/pdf/2103.03206.pdf.
Args:
num_q_channels (`int`):
Number of q channels.
num_kv_channels (`int`):
Number of k or v channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LoveEachDay/towhee | CrossAttention | false | 11,661 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 | import torch
from torch import nn
import torch.cuda
class MultiHeadAttention(nn.Module):
"""
Multi head attention for Perceiver https://arxiv.org/pdf/2103.03206.pdf.
Args:
num_q_channels (`int`):
Number of q channels.
num_kv_channels (`int`):
Number of k or v channe... |
TVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
from torch.nn import functional as F
class TVLoss(nn.Module):
"""L2 total variation loss, as in Mahendran et al."""
def forward(self, input):
input = F.pad(input, (0, 1, 0, 1), 'replicate')
x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]
y_diff = ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | MED-YAHYAOUI/style-transfer-pytorch | TVLoss | false | 11,662 | [
"MIT"
] | 0 | 867a6a45d964c151d6b94f50153cf535385c9078 | https://github.com/MED-YAHYAOUI/style-transfer-pytorch/tree/867a6a45d964c151d6b94f50153cf535385c9078 | import torch
from torch import nn
from torch.nn import functional as F
class Model(nn.Module):
"""L2 total variation loss, as in Mahendran et al."""
def forward(self, input):
input = F.pad(input, (0, 1, 0, 1), 'replicate')
x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]
y_diff = i... |
AttentionPool2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.cuda
class AttentionPool2d(nn.Module):
"""
Attention
"""
def __init__(self, spacial_dim: 'int', embed_dim: 'int', num_heads:
'int', output_dim: 'int'=None):
super().__init__()
self.positional_embedding = nn.Parameter(torch.randn(s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LoveEachDay/towhee | AttentionPool2d | false | 11,663 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 | import torch
from torch import nn
import torch.cuda
class Model(nn.Module):
"""
Attention
"""
def __init__(self, spacial_dim: 'int', embed_dim: 'int', num_heads:
'int', output_dim: 'int'=None):
super().__init__()
self.positional_embedding = nn.Parameter(torch.randn(spacial_dim... |
ConvNetsModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ConvNetsModel(nn.Module):
def __init__(self, num_classes, cross_entropy_loss=False, kernel_size=3,
channel_size1=32, channel_size2=64, dropout=False):
super(ConvNetsModel, self).__init__()
self.cross_entropy_loss = 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._inductor.runtime.... | LidiaAlecci/ConvNet | ConvNetsModel | false | 11,664 | [
"MIT"
] | 0 | 23bc0919edfa346440588f79bc86d9c5f5fcc4d2 | https://github.com/LidiaAlecci/ConvNet/tree/23bc0919edfa346440588f79bc86d9c5f5fcc4d2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_classes, cross_entropy_loss=False, kernel_size=3,
channel_size1=32, channel_size2=64, dropout=False):
super().__init__()
self.cross_entropy_loss = cross_entropy_loss
s... |
ClassificationModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ClassificationModel(nn.Module):
def __init__(self, num_features_in, num_anchors=9, num_classes=80,
prior=0.01, feature_size=256):
super(ClassificationModel, self).__init__()
self.num_classes = num_classes
self.num_anchors = num_anchors
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Hyojin021/auto_labeling | ClassificationModel | false | 11,665 | [
"Apache-2.0"
] | 0 | 1ccf0cd1c5adf34692751553a988aa0fcf4efefb | https://github.com/Hyojin021/auto_labeling/tree/1ccf0cd1c5adf34692751553a988aa0fcf4efefb | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_features_in, num_anchors=9, num_classes=80,
prior=0.01, feature_size=256):
super().__init__()
self.num_classes = num_classes
self.num_anchors = num_anchors
self.conv1 = nn.Conv2d(num_features... |
TemporalDecay | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class TemporalDecay(nn.Module):
def __init__(self, input_size, rnn_hid_size):
super(TemporalDecay, self).__init__()
self.rnn_hid_size = rnn_hid_size
self.build(input_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._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LyapunovStability/BRITS | TemporalDecay | false | 11,666 | [
"MIT"
] | 0 | 92a889dd5946aae215d61b1854d9767c6f7fcf2c | https://github.com/LyapunovStability/BRITS/tree/92a889dd5946aae215d61b1854d9767c6f7fcf2c | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class Model(nn.Module):
def __init__(self, input_size, rnn_hid_size):
super().__init__()
self.rnn_hid_size = rnn_hid_size
self.build(input_size)
def build(self, inp... |
CSDN_Tem | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 CSDN_Tem(nn.Module):
def __init__(self, in_ch, out_ch):
super(CSDN_Tem, self).__init__()
self.depth_conv = nn.Conv2d(in_channels=in_ch, out_channels=in_ch,
kernel_size=3, padding=1, groups=in_ch)
self.point_conv = nn.Conv2d(in_channels=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Lundez/londogard-backend | CSDN_Tem | false | 11,667 | [
"MIT"
] | 0 | 90d9e405b832c2157e6fde00f58b9312cfc4ddbc | https://github.com/Lundez/londogard-backend/tree/90d9e405b832c2157e6fde00f58b9312cfc4ddbc | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.depth_conv = nn.Conv2d(in_channels=in_ch, out_channels=in_ch,
kernel_size=3, padding=1, groups=in_ch)
self.point_conv = nn.Conv2d(in_channels=in_ch, out_channe... |
MultinomialCELoss | # 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 MultinomialCELoss(nn.Module):
def __init__(self):
super(MultinomialCELoss, self).__init__()
def forward(self, x, y):
x = x + 1e-08
x = torch.log(x)
zlogz = y * x
loss = -zlogz.sum()
loss /= x.shape[0] * x.shape[2] * x.s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | MMujtabaRoohani/FlowerColorizer-PyTorch | MultinomialCELoss | false | 11,668 | [
"MIT"
] | 0 | 4c9c4c954a38babe1f10f816f8406eb4ab998842 | https://github.com/MMujtabaRoohani/FlowerColorizer-PyTorch/tree/4c9c4c954a38babe1f10f816f8406eb4ab998842 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
x = x + 1e-08
x = torch.log(x)
zlogz = y * x
loss = -zlogz.sum()
loss /= x.shape[0] * x.shape[2] * x.shape[3]
return loss
def g... |
DownBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torchvision.transforms import *
assert_size_stride ... | HamsterBiz/iSeeBetter | DownBlock | false | 11,669 | [
"MIT"
] | 0 | a71cee61583bdedab1f3b368e2cb7dc5ad969aed | https://github.com/HamsterBiz/iSeeBetter/tree/a71cee61583bdedab1f3b368e2cb7dc5ad969aed | import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super().__init__()
self.conv = torch.nn.Conv2d(input_size,... |
BoundSoftmaxImpl | # 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 BoundSoftmaxImpl(nn.Module):
def __init__(self, axis):
super().__init__()
self.axis = axis
def forward(self, x):
max_x = torch.max(x, dim=self.axis).values
assert self.axis == int(self.axis)
x = torch.exp(x - max_x.unsqueeze(se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Mahoumaru/auto_LiRPA | BoundSoftmaxImpl | false | 11,670 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, axis):
super().__init__()
self.axis = axis
def forward(self, x):
max_x = torch.max(x, dim=self.axis).values
assert self.axis == int(self.axis)
x = torch.exp(x - max_x.unsqueeze(self.axis))
... |
RegressionHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import abc
import torch
import torch.nn as nn
import torch.utils.data.dataset
class BaseHead(nn.Module, metaclass=abc.ABCMeta):
pass
class RegressionHead(BaseHead):
def __init__(self, hidden_size, hidden_dropout_prob):
"""From RobertaClassificationHead"""
super().__init__()
self.den... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 abc
import t... | HarshTrivedi/jiant-fork | RegressionHead | false | 11,671 | [
"MIT"
] | 0 | 6b0150a8d923b0fca33f244a25e1bf2c74ea5f30 | https://github.com/HarshTrivedi/jiant-fork/tree/6b0150a8d923b0fca33f244a25e1bf2c74ea5f30 | import abc
import torch
import torch.nn as nn
import torch.utils.data.dataset
class BaseHead(nn.Module, metaclass=abc.ABCMeta):
pass
class Model(BaseHead):
def __init__(self, hidden_size, hidden_dropout_prob):
"""From RobertaClassificationHead"""
super().__init__()
self.dense = nn.L... |
BertLayerNormNoVar | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 BertLayerNormNoVar(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
super(BertLayerNormNoVar, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsil... | 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... | Mahoumaru/auto_LiRPA | BertLayerNormNoVar | false | 11,672 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps
def forward(self, x):
... |
Transition | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 Transition(nn.Module):
def __init__(self, in_planes, out_planes):
super(Transition, self).__init__()
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=True)
def forward(self, x):
out = self.conv(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_... | Mahoumaru/auto_LiRPA | Transition | false | 11,673 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_planes, out_planes):
super().__init__()
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=True)
def forward(self, x):
out = self.conv(F.relu(x))
out... |
UpBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torchvision.transforms import *
assert_size_stride ... | HamsterBiz/iSeeBetter | UpBlock | false | 11,674 | [
"MIT"
] | 0 | a71cee61583bdedab1f3b368e2cb7dc5ad969aed | https://github.com/HamsterBiz/iSeeBetter/tree/a71cee61583bdedab1f3b368e2cb7dc5ad969aed | import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super().__init__()
self.conv = torch.nn.Conv2d(input_size,... |
mlp_2layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 mlp_2layer(nn.Module):
def __init__(self, in_ch, in_dim, width=1):
super(mlp_2layer, self).__init__()
self.fc1 = nn.Linear(in_ch * in_dim * in_dim, 256 * width)
self.fc2 = nn.Linear(256 * width, 10)
def forward(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Mahoumaru/auto_LiRPA | mlp_2layer | false | 11,675 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_ch, in_dim, width=1):
super().__init__()
self.fc1 = nn.Linear(in_ch * in_dim * in_dim, 256 * width)
self.fc2 = nn.Linear(256 * width, 10)
def forward(self, x):
x =... |
D_DownBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torchvision.transforms import *
assert_size_stride ... | HamsterBiz/iSeeBetter | D_DownBlock | false | 11,676 | [
"MIT"
] | 0 | a71cee61583bdedab1f3b368e2cb7dc5ad969aed | https://github.com/HamsterBiz/iSeeBetter/tree/a71cee61583bdedab1f3b368e2cb7dc5ad969aed | import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super().__init__()
self.conv = torch.nn.Conv2d(input_size,... |
RAEClassifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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
from typing import Callable
class ReactiveAutoencoder(nn.Module):
"""The RAE a.k.a. SRAE a.k.a. the autoencoder with the strict supervised sparsity matrix.
This module provides a framework for training an encoder to maximize information throug... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MHHukiewitz/SRAE_pytorch | RAEClassifier | false | 11,677 | [
"MIT"
] | 0 | 91f961f740c96cdb49739c9738ed330af59750d0 | https://github.com/MHHukiewitz/SRAE_pytorch/tree/91f961f740c96cdb49739c9738ed330af59750d0 | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable
class ReactiveAutoencoder(nn.Module):
"""The RAE a.k.a. SRAE a.k.a. the autoencoder with the strict supervised sparsity matrix.
This module provides a framework for training an encoder to maximize information throug... |
SuperPointNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.optim
import torch.utils.data
class SuperPointNet(torch.nn.Module):
""" Pytorch definition of SuperPoint Network. """
def __init__(self):
super(SuperPointNet, self).__init__()
self.relu = torch.nn.ReLU(inplace=True)
self.pool = torch.nn.MaxPool2d(kernel_size=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LeikvollE/pytorch-superpoint | SuperPointNet | false | 11,678 | [
"MIT"
] | 0 | 52144a760e0cc46259e57397a5a55f0585fe6d0b | https://github.com/LeikvollE/pytorch-superpoint/tree/52144a760e0cc46259e57397a5a55f0585fe6d0b | import torch
import torch.optim
import torch.utils.data
class Model(torch.nn.Module):
""" Pytorch definition of SuperPoint Network. """
def __init__(self):
super().__init__()
self.relu = torch.nn.ReLU(inplace=True)
self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2)
c1, c2... |
cnn_4layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class cnn_4layer(nn.Module):
def __init__(self, in_ch, in_dim, width=2, linear_size=256):
super(cnn_4layer, self).__init__()
self.conv1 = nn.Conv2d(in_ch, 4 * width, 4, stride=2, padding=1)
self.conv2 = nn.Conv2d(4 * width... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Mahoumaru/auto_LiRPA | cnn_4layer | false | 11,679 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_ch, in_dim, width=2, linear_size=256):
super().__init__()
self.conv1 = nn.Conv2d(in_ch, 4 * width, 4, stride=2, padding=1)
self.conv2 = nn.Conv2d(4 * width, 8 * width, 4, strid... |
mlp_3layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 mlp_3layer(nn.Module):
def __init__(self, in_ch, in_dim, width=1):
super(mlp_3layer, self).__init__()
self.fc1 = nn.Linear(in_ch * in_dim * in_dim, 256 * width)
self.fc2 = nn.Linear(256 * width, 128 * width)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Mahoumaru/auto_LiRPA | mlp_3layer | false | 11,680 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_ch, in_dim, width=1):
super().__init__()
self.fc1 = nn.Linear(in_ch * in_dim * in_dim, 256 * width)
self.fc2 = nn.Linear(256 * width, 128 * width)
self.fc3 = nn.Linear(... |
mlp_5layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 mlp_5layer(nn.Module):
def __init__(self, in_ch, in_dim, width=1):
super(mlp_5layer, self).__init__()
self.fc1 = nn.Linear(in_ch * in_dim * in_dim, 256 * width)
self.fc2 = nn.Linear(256 * width, 256 * width)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Mahoumaru/auto_LiRPA | mlp_5layer | false | 11,681 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_ch, in_dim, width=1):
super().__init__()
self.fc1 = nn.Linear(in_ch * in_dim * in_dim, 256 * width)
self.fc2 = nn.Linear(256 * width, 256 * width)
self.fc3 = nn.Linear(... |
cnn_7layer_alt | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class cnn_7layer_alt(nn.Module):
def __init__(self, in_ch, in_dim, width=2, linear_size=128):
super(cnn_7layer_alt, self).__init__()
self.conv1 = nn.Conv2d(in_ch, 4 * width, 3, stride=1, padding=1)
self.conv2 = nn.Conv2d(4... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Mahoumaru/auto_LiRPA | cnn_7layer_alt | false | 11,682 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_ch, in_dim, width=2, linear_size=128):
super().__init__()
self.conv1 = nn.Conv2d(in_ch, 4 * width, 3, stride=1, padding=1)
self.conv2 = nn.Conv2d(4 * width, 4 * width, 4, strid... |
ASPP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.cuda
class ASPP(nn.Module):
"""
Atrous spatial pyramid pooling used in object detection and segmentation.
"""
def __init__(self, in_channel=512, depth=256):
super().__init__()
self.mean = nn.AdaptiveAvgPool... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | LoveEachDay/towhee | ASPP | false | 11,683 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 | import torch
from torch import nn
import torch.nn.functional as F
import torch.cuda
class Model(nn.Module):
"""
Atrous spatial pyramid pooling used in object detection and segmentation.
"""
def __init__(self, in_channel=512, depth=256):
super().__init__()
self.mean = nn.AdaptiveAvgPoo... |
FCNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 FCNetwork(nn.Module):
def __init__(self, state_size, action_size, output_gate=None):
super(FCNetwork, self).__init__()
self.fc1 = nn.Linear(state_size, 256)
self.fc2 = nn.Linear(256, 256)
self.fc3 = nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | JoshVarty/Reacher | FCNetwork | false | 11,684 | [
"MIT"
] | 0 | cab41484aaaeeae177cc625c3697d7e7cd80c2ed | https://github.com/JoshVarty/Reacher/tree/cab41484aaaeeae177cc625c3697d7e7cd80c2ed | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_size, action_size, output_gate=None):
super().__init__()
self.fc1 = nn.Linear(state_size, 256)
self.fc2 = nn.Linear(256, 256)
self.fc3 = nn.Linear(256, action_size)
... |
Upsample_interpolate | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Upsample_interpolate(nn.Module):
def __init__(self, stride):
super(Upsample_interpolate, self).__init__()
self.stride = stride
def forward(self, x):
x_numpy = x.cpu().detach().numpy()
H = x_numpy.shape[2... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Mathiebhan/darknet_ros | Upsample_interpolate | false | 11,685 | [
"BSD-3-Clause"
] | 0 | 04a97b61b6b3b086da1a46331a747accd37d05f9 | https://github.com/Mathiebhan/darknet_ros/tree/04a97b61b6b3b086da1a46331a747accd37d05f9 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, stride):
super().__init__()
self.stride = stride
def forward(self, x):
x_numpy = x.cpu().detach().numpy()
H = x_numpy.shape[2]
W = x_numpy.shape[3]
H ... |
cnn_4layer_LeakyRelu | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class cnn_4layer_LeakyRelu(nn.Module):
def __init__(self, in_ch, in_dim, width=2, linear_size=256, alpha=0.1):
super(cnn_4layer_LeakyRelu, self).__init__()
self.conv1 = nn.Conv2d(in_ch, 4 * width, 4, stride=2, padding=1)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Mahoumaru/auto_LiRPA | cnn_4layer_LeakyRelu | false | 11,686 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_ch, in_dim, width=2, linear_size=256, alpha=0.1):
super().__init__()
self.conv1 = nn.Conv2d(in_ch, 4 * width, 4, stride=2, padding=1)
self.conv2 = nn.Conv2d(4 * width, 8 * widt... |
ReOrgLayer | # 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 ReOrgLayer(nn.Module):
def __init__(self, stride=2):
super(ReOrgLayer, self).__init__()
self.stride = stride
def forward(self, x):
assert x.data.dim() == 4
B, C, H, W = x.data.shape
hs = self.stride
ws = self.stride
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | MaoXianXin/pytorchx | ReOrgLayer | false | 11,687 | [
"MIT"
] | 0 | f46cc9692c3bd11ea9d5d54c20de3ac2f67dabcc | https://github.com/MaoXianXin/pytorchx/tree/f46cc9692c3bd11ea9d5d54c20de3ac2f67dabcc | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, stride=2):
super().__init__()
self.stride = stride
def forward(self, x):
assert x.data.dim() == 4
B, C, H, W = x.data.shape
hs = self.stride
ws = self.stride
assert H % hs == ... |
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, inputLayer):
super(Net, self).__init__()
self.fc1 = nn.Linear(inputLayer, 100)
self.fc2 = nn.Linear(100, 2)
def forward(self, x):
x = self.fc1(x)
x = F.tanh(x)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Marissa4/RPyCA | Net | false | 11,688 | [
"MIT"
] | 0 | e3c229361a4cd9ddd53accc5541b7c8b5f8939e0 | https://github.com/Marissa4/RPyCA/tree/e3c229361a4cd9ddd53accc5541b7c8b5f8939e0 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, inputLayer):
super().__init__()
self.fc1 = nn.Linear(inputLayer, 100)
self.fc2 = nn.Linear(100, 2)
def forward(self, x):
x = self.fc1(x)
x = F.tanh(x)
... |
MaxPoolStride1 | # 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
from torch.nn import functional as F
class MaxPoolStride1(nn.Module):
def __init__(self, kernel_size):
super(MaxPoolStride1, self).__init__()
self.kernel_size = kernel_size
self.pad = kernel_size - 1
def forward(self, x):
padded_x = F.pad(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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | MaoXianXin/pytorchx | MaxPoolStride1 | false | 11,689 | [
"MIT"
] | 0 | f46cc9692c3bd11ea9d5d54c20de3ac2f67dabcc | https://github.com/MaoXianXin/pytorchx/tree/f46cc9692c3bd11ea9d5d54c20de3ac2f67dabcc | import torch
from torch import nn
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, kernel_size):
super().__init__()
self.kernel_size = kernel_size
self.pad = kernel_size - 1
def forward(self, x):
padded_x = F.pad(x, (0, self.pad, 0, self.pad), m... |
Network | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self, input_size, nb_action):
super(Network, self).__init__()
self.input_size = input_size
self.nb_action = nb_action
self.fc1 = nn.Linear(input_size, 30)
self.fc2 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | MarcoPerdomo/Self-Automated-Driving_Car | Network | false | 11,690 | [
"MIT"
] | 0 | 943bf53a8b0dd26f8370b943d879e7dbaadb2201 | https://github.com/MarcoPerdomo/Self-Automated-Driving_Car/tree/943bf53a8b0dd26f8370b943d879e7dbaadb2201 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_size, nb_action):
super().__init__()
self.input_size = input_size
self.nb_action = nb_action
self.fc1 = nn.Linear(input_size, 30)
self.fc2 = nn.Linear(30, nb... |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
class QNetwork(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=20,
fc2_units=80):
"""Initialize parameters and build model.
Params
======
state_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Mavrepis/DeepLearning_FoodSafety | QNetwork | false | 11,691 | [
"MIT"
] | 0 | 4f70b575036b06cd0edd4fdf9fc9303728872fc1 | https://github.com/Mavrepis/DeepLearning_FoodSafety/tree/4f70b575036b06cd0edd4fdf9fc9303728872fc1 | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=20,
fc2_units=80):
"""Initialize parameters and build model.
Params
======
state_size ... |
DotProduct | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
class DotProduct(nn.Module):
def forward(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
"""
Inputs:
x - (N, F)
y - (N, F)
Output:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MicroTensor-ai/episodic-memory | DotProduct | false | 11,692 | [
"MIT"
] | 0 | 295a3752ab94c7a6f45355aa2c54bffbf84b574f | https://github.com/MicroTensor-ai/episodic-memory/tree/295a3752ab94c7a6f45355aa2c54bffbf84b574f | import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
class Model(nn.Module):
def forward(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
"""
Inputs:
x - (N, F)
y - (N, F)
Output:
out... |
SeparableBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 Linear
class SeparableBlock(Module):
def __init__(self, input_size, kernel_channels_in, kernel_channels_out,
kernel_size):
super(SeparableBlock, self).__init__()
self.input_size = input_size
self.kernel_size = 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.nn import Module
from torch.nn import Linear
assert_size_stride = tor... | Kiberchaika/hyperstyle | SeparableBlock | false | 11,693 | [
"MIT"
] | 0 | b67e5ca9c67dfdfa18f1d6cda6e8eff5da07db7b | https://github.com/Kiberchaika/hyperstyle/tree/b67e5ca9c67dfdfa18f1d6cda6e8eff5da07db7b | from torch.nn import Module
import torch
from torch.nn import Linear
class Model(Module):
def __init__(self, input_size, kernel_channels_in, kernel_channels_out,
kernel_size):
super().__init__()
self.input_size = input_size
self.kernel_size = kernel_size
self.kernel_channe... |
CmapPafHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.nn
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | J-C-Chang/human-pose-detect | CmapPafHeadAttention | false | 11,694 | [
"MIT"
] | 0 | 092e6ec53aa5058d644a30269abff606b74e3bf3 | https://github.com/J-C-Chang/human-pose-detect/tree/092e6ec53aa5058d644a30269abff606b74e3bf3 | import torch
import torch.utils.data
import torch.nn
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... |
HighLightLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
def mask_logits(inputs, mask, mask_value=-1e+30):
mask = mask.type(torch.float32)
return inputs + (1.0 - mask) * mask_value
class Conv1D(nn.Module):
def __init__(self, in_dim, out_dim, kernel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.parallel
import torch.nn as nn
import torch.utils.data
import to... | MicroTensor-ai/episodic-memory | HighLightLayer | false | 11,695 | [
"MIT"
] | 0 | 295a3752ab94c7a6f45355aa2c54bffbf84b574f | https://github.com/MicroTensor-ai/episodic-memory/tree/295a3752ab94c7a6f45355aa2c54bffbf84b574f | import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
def mask_logits(inputs, mask, mask_value=-1e+30):
mask = mask.type(torch.float32)
return inputs + (1.0 - mask) * mask_value
class Conv1D(nn.Module):
def __init__(self, in_dim, out_dim, kernel... |
enhance_net_nopool | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 CSDN_Tem(nn.Module):
def __init__(self, in_ch, out_ch):
super(CSDN_Tem, self).__init__()
self.depth_conv = nn.Conv2d(in_channels=in_ch, out_channels=in_ch,
kernel_size=3, padding=1, groups=in_ch)
self.poi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Lundez/londogard-backend | enhance_net_nopool | false | 11,696 | [
"MIT"
] | 0 | 90d9e405b832c2157e6fde00f58b9312cfc4ddbc | https://github.com/Lundez/londogard-backend/tree/90d9e405b832c2157e6fde00f58b9312cfc4ddbc | import torch
import torch.nn as nn
import torch.nn.functional as F
class CSDN_Tem(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.depth_conv = nn.Conv2d(in_channels=in_ch, out_channels=in_ch,
kernel_size=3, padding=1, groups=in_ch)
self.point_conv = nn.C... |
CQConcatenate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
def mask_logits(inputs, mask, mask_value=-1e+30):
mask = mask.type(torch.float32)
return inputs + (1.0 - mask) * mask_value
class Conv1D(nn.Module):
def __init__(self, in_dim, out_dim, kernel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MicroTensor-ai/episodic-memory | CQConcatenate | false | 11,697 | [
"MIT"
] | 0 | 295a3752ab94c7a6f45355aa2c54bffbf84b574f | https://github.com/MicroTensor-ai/episodic-memory/tree/295a3752ab94c7a6f45355aa2c54bffbf84b574f | import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
def mask_logits(inputs, mask, mask_value=-1e+30):
mask = mask.type(torch.float32)
return inputs + (1.0 - mask) * mask_value
class Conv1D(nn.Module):
def __init__(self, in_dim, out_dim, kernel... |
GEGLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn.functional as F
from torch import nn
class GEGLU(nn.Module):
def forward(self, x):
x, gates = x.chunk(2, dim=-1)
return F.gelu(gates) * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Mohan-Zhang-u/vit-pytorch | GEGLU | false | 11,698 | [
"MIT"
] | 0 | 76050c812474d7c10d67db4e811f537e26c3996a | https://github.com/Mohan-Zhang-u/vit-pytorch/tree/76050c812474d7c10d67db4e811f537e26c3996a | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
def forward(self, x):
x, gates = x.chunk(2, dim=-1)
return F.gelu(gates) * x
def get_inputs():
return [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 numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Mika412/deep-reinforcement-learning | Actor | false | 11,699 | [
"MIT"
] | 0 | 9b5ba901f760e50cd64d272939eff75881af5a9c | https://github.com/Mika412/deep-reinforcement-learning/tree/9b5ba901f760e50cd64d272939eff75881af5a9c | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Model(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... |
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... | Mika412/deep-reinforcement-learning | Critic | false | 11,700 | [
"MIT"
] | 0 | 9b5ba901f760e50cd64d272939eff75881af5a9c | https://github.com/Mika412/deep-reinforcement-learning/tree/9b5ba901f760e50cd64d272939eff75881af5a9c | 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... |
Conv1D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
class Conv1D(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size=1, stride=1, padding=0,
bias=True):
super(Conv1D, self).__init__()
self.conv1d = nn.Conv1d(in_channels=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
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import to... | MicroTensor-ai/episodic-memory | Conv1D | false | 11,701 | [
"MIT"
] | 0 | 295a3752ab94c7a6f45355aa2c54bffbf84b574f | https://github.com/MicroTensor-ai/episodic-memory/tree/295a3752ab94c7a6f45355aa2c54bffbf84b574f | import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
class Model(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size=1, stride=1, padding=0,
bias=True):
super().__init__()
self.conv1d = nn.Conv1d(in_channels=in_dim, out_ch... |
WeightedPool | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
def mask_logits(inputs, mask, mask_value=-1e+30):
mask = mask.type(torch.float32)
return inputs + (1.0 - mask) * mask_value
class WeightedPool(nn.Module):
def __init__(self, dim):
sup... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MicroTensor-ai/episodic-memory | WeightedPool | false | 11,702 | [
"MIT"
] | 0 | 295a3752ab94c7a6f45355aa2c54bffbf84b574f | https://github.com/MicroTensor-ai/episodic-memory/tree/295a3752ab94c7a6f45355aa2c54bffbf84b574f | import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
def mask_logits(inputs, mask, mask_value=-1e+30):
mask = mask.type(torch.float32)
return inputs + (1.0 - mask) * mask_value
class Model(nn.Module):
def __init__(self, dim):
super().__... |
ELUPlus | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
import torch.nn
class ELUPlus(nn.Module):
def __init__(self):
super().__init__()
self.elu = nn.ELU()
def forward(self, x):
return self.elu(x) + 1.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.nn
assert_size_stride = torch._C._dynamo.guar... | MilesCranmer/nflows | ELUPlus | false | 11,703 | [
"MIT"
] | 0 | 6e2a267ad0f4ddc84e1db5592ce3c3e4551a7555 | https://github.com/MilesCranmer/nflows/tree/6e2a267ad0f4ddc84e1db5592ce3c3e4551a7555 | import torch
from torch import nn
import torch.nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.elu = nn.ELU()
def forward(self, x):
return self.elu(x) + 1.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
FrameMaxPool | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
class FrameMaxPool(nn.Module):
def __init__(self, input_size, hidden_size, stride):
super(FrameMaxPool, self).__init__()
self.vis_conv = nn.Conv1d(input_size, hidden_size, 1, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn.parallel
impo... | MicroTensor-ai/episodic-memory | FrameMaxPool | false | 11,704 | [
"MIT"
] | 0 | 295a3752ab94c7a6f45355aa2c54bffbf84b574f | https://github.com/MicroTensor-ai/episodic-memory/tree/295a3752ab94c7a6f45355aa2c54bffbf84b574f | import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
class Model(nn.Module):
def __init__(self, input_size, hidden_size, stride):
super().__init__()
self.vis_conv = nn.Conv1d(input_size, hidden_size, 1, 1)
self.max_pool = nn.MaxPo... |
NN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 NN(nn.Module):
def __init__(self, input_size):
super().__init__()
self.fc1 = nn.Linear(input_size, 50)
self.fc2 = nn.Linear(50, 40)
self.fc3 = nn.Linear(40, 20)
self.fc4 = nn.Linear(20, 9)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Meydand2001/Machine-Learning-project | NN | false | 11,705 | [
"MIT"
] | 0 | dc73bc3820024939ba66a1a3e2ae130d6bf35f9a | https://github.com/Meydand2001/Machine-Learning-project/tree/dc73bc3820024939ba66a1a3e2ae130d6bf35f9a | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_size):
super().__init__()
self.fc1 = nn.Linear(input_size, 50)
self.fc2 = nn.Linear(50, 40)
self.fc3 = nn.Linear(40, 20)
self.fc4 = nn.Linear(20, 9)
... |
L2Norm | # 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 L2Norm(nn.Module):
def forward(self, x, eps=1e-06):
norm = x.norm(dim=1, keepdim=True).clamp(min=eps)
return x / norm
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | Mohan-Zhang-u/vit-pytorch | L2Norm | false | 11,706 | [
"MIT"
] | 0 | 76050c812474d7c10d67db4e811f537e26c3996a | https://github.com/Mohan-Zhang-u/vit-pytorch/tree/76050c812474d7c10d67db4e811f537e26c3996a | import torch
from torch import nn
class Model(nn.Module):
def forward(self, x, eps=1e-06):
norm = x.norm(dim=1, keepdim=True).clamp(min=eps)
return x / norm
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
Downsample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class Downsample(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.conv = nn.Conv2d(dim_in, dim_out, 3, stride=2, padding=1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Mohan-Zhang-u/vit-pytorch | Downsample | false | 11,707 | [
"MIT"
] | 0 | 76050c812474d7c10d67db4e811f537e26c3996a | https://github.com/Mohan-Zhang-u/vit-pytorch/tree/76050c812474d7c10d67db4e811f537e26c3996a | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.conv = nn.Conv2d(dim_in, dim_out, 3, stride=2, padding=1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def g... |
CQAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
def mask_logits(inputs, mask, mask_value=-1e+30):
mask = mask.type(torch.float32)
return inputs + (1.0 - mask) * mask_value
class Conv1D(nn.Module):
def __init__(self, in_dim, out_dim, kernel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MicroTensor-ai/episodic-memory | CQAttention | false | 11,708 | [
"MIT"
] | 0 | 295a3752ab94c7a6f45355aa2c54bffbf84b574f | https://github.com/MicroTensor-ai/episodic-memory/tree/295a3752ab94c7a6f45355aa2c54bffbf84b574f | import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
def mask_logits(inputs, mask, mask_value=-1e+30):
mask = mask.type(torch.float32)
return inputs + (1.0 - mask) * mask_value
class Conv1D(nn.Module):
def __init__(self, in_dim, out_dim, kernel... |
MultiHeadAttentionBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
def mask_logits(inputs, mask, mask_value=-1e+30):
mask = mask.type(torch.float32)
return inputs + (1.0 - mask) * mask_value
class Conv1D(nn.Module):
def __init__(self, in_dim, out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MicroTensor-ai/episodic-memory | MultiHeadAttentionBlock | false | 11,709 | [
"MIT"
] | 0 | 295a3752ab94c7a6f45355aa2c54bffbf84b574f | https://github.com/MicroTensor-ai/episodic-memory/tree/295a3752ab94c7a6f45355aa2c54bffbf84b574f | import math
import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
def mask_logits(inputs, mask, mask_value=-1e+30):
mask = mask.type(torch.float32)
return inputs + (1.0 - mask) * mask_value
class Conv1D(nn.Module):
def __init__(self, in_dim, out... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class LayerNorm(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
std = torch.var(x... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Mohan-Zhang-u/vit-pytorch | LayerNorm | false | 11,710 | [
"MIT"
] | 0 | 76050c812474d7c10d67db4e811f537e26c3996a | https://github.com/Mohan-Zhang-u/vit-pytorch/tree/76050c812474d7c10d67db4e811f537e26c3996a | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
std = torch.var(x, di... |
PEG | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(x, **kwargs) + x
class PEG(nn.Module):
def __init__(self, dim, kernel_size=3):
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
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Mohan-Zhang-u/vit-pytorch | PEG | false | 11,711 | [
"MIT"
] | 0 | 76050c812474d7c10d67db4e811f537e26c3996a | https://github.com/Mohan-Zhang-u/vit-pytorch/tree/76050c812474d7c10d67db4e811f537e26c3996a | import torch
from torch import nn
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(x, **kwargs) + x
class Model(nn.Module):
def __init__(self, dim, kernel_size=3):
super().__init__()
... |
ClassHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.cuda
class ClassHead(nn.Module):
"""
ClassHead
RetinaFace head for classification branch.
Args:
inchannels (`int`):
number of input channels.
num_anchors (`int`):
number of anchors.
"""
def __init__(self,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.cuda
assert_size_stride = torch._C._dynamo.gua... | LoveEachDay/towhee | ClassHead | false | 11,712 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 | import torch
from torch import nn
import torch.cuda
class Model(nn.Module):
"""
ClassHead
RetinaFace head for classification branch.
Args:
inchannels (`int`):
number of input channels.
num_anchors (`int`):
number of anchors.
"""
def __init__(self, inc... |
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