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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
OffsetNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 OffsetNet(nn.Module):
"""OffsetNet in Temporal interlace module.
The OffsetNet consists of one convolution layer and two fc layers
with a relu activation following with a sigmoid function. Following
the convolution layer, two fc layers and relu are applied to ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Viditagarwal7479/Video-Swin-Transformer | OffsetNet | false | 18,087 | [
"Apache-2.0"
] | 9 | 37910ef3141c7b2eef76544f9ec8bdf26ec94c7d | https://github.com/Viditagarwal7479/Video-Swin-Transformer/tree/37910ef3141c7b2eef76544f9ec8bdf26ec94c7d | import torch
import torch.nn as nn
class Model(nn.Module):
"""OffsetNet in Temporal interlace module.
The OffsetNet consists of one convolution layer and two fc layers
with a relu activation following with a sigmoid function. Following
the convolution layer, two fc layers and relu are applied to the ... |
BinaryLogisticRegressionLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > threshold).float()
num_positive... | 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
... | Viditagarwal7479/Video-Swin-Transformer | BinaryLogisticRegressionLoss | false | 18,088 | [
"Apache-2.0"
] | 9 | 37910ef3141c7b2eef76544f9ec8bdf26ec94c7d | https://github.com/Viditagarwal7479/Video-Swin-Transformer/tree/37910ef3141c7b2eef76544f9ec8bdf26ec94c7d | import torch
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > threshold).float()
num_positive... |
CharbonnierLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import functools
import torch
import torch.utils.data
from torch.utils import data as data
from torch.nn import functional as F
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
def reduce_loss(loss, reduction):
"""Reduce ... | 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 functools
import torc... | WoojunePark/BasicSR | CharbonnierLoss | false | 18,089 | [
"Apache-2.0"
] | 9 | e0910b022b924bb913045fc412a5470dc2242cf0 | https://github.com/WoojunePark/BasicSR/tree/e0910b022b924bb913045fc412a5470dc2242cf0 | import functools
import torch
import torch.utils.data
from torch.utils import data as data
from torch.nn import functional as F
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
def reduce_loss(loss, reduction):
"""Reduce ... |
Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Decoder(nn.Module):
def __init__(self, config):
super(Decoder, self).__init__()
self.linear = nn.Linear(config.hidden_size, 2)
def forward(self, x, encoder_output):
y = self.linear(encoder_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | XIAOYEJIAYOU/GSAN | Decoder | false | 18,090 | [
"MIT"
] | 6 | 8ca4fdf4c3d615af9cc10e1f9f22ceb7e27fe196 | https://github.com/XIAOYEJIAYOU/GSAN/tree/8ca4fdf4c3d615af9cc10e1f9f22ceb7e27fe196 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.linear = nn.Linear(config.hidden_size, 2)
def forward(self, x, encoder_output):
y = self.linear(encoder_output)
return ... |
MaxPool1d | # 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 MaxPool1d(nn.Module):
def __init__(self, win=2, stride=None, pad=0):
super().__init__()
self.pooling = nn.MaxPool1d(kernel_size=win, stride=stride, padding=pad
)
def forward(self, x):
"""
Args:
x: shape=(batch_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... | WiseDoge/Text-Classification-PyTorch | MaxPool1d | false | 18,091 | [
"MIT"
] | 6 | 9371eeed6bd7ecf1d529c8f2a6c997fcde67a559 | https://github.com/WiseDoge/Text-Classification-PyTorch/tree/9371eeed6bd7ecf1d529c8f2a6c997fcde67a559 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, win=2, stride=None, pad=0):
super().__init__()
self.pooling = nn.MaxPool1d(kernel_size=win, stride=stride, padding=pad
)
def forward(self, x):
"""
Args:
x: shape=(batch_size,... |
SelfExpression | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SelfExpression(nn.Module):
def __init__(self, n):
super(SelfExpression, self).__init__()
self.Coefficient = nn.Parameter(1e-08 * torch.ones(n, n, dtype=
torch.float32), requires_grad=True)
def forward(self, x):
y = torch.matmul(sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Xanadu12138/DSCN-superpixels | SelfExpression | false | 18,092 | [
"MIT"
] | 4 | babe16edde9c61699ef203effbfc9f03246765f3 | https://github.com/Xanadu12138/DSCN-superpixels/tree/babe16edde9c61699ef203effbfc9f03246765f3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n):
super().__init__()
self.Coefficient = nn.Parameter(1e-08 * torch.ones(n, n, dtype=
torch.float32), requires_grad=True)
def forward(self, x):
y = torch.matmul(self.Coefficient, x)
ret... |
ConvAE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv2dSamePad(nn.Module):
"""
Implement Tensorflow's 'SAME' padding mode in Conv2d.
When an odd number, say `m`, of pixels are need to pad, Tensorflow will pad one more column at right or one more
row at bottom. But P... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 math
import torch.nn a... | Xanadu12138/DSCN-superpixels | ConvAE | false | 18,093 | [
"MIT"
] | 4 | babe16edde9c61699ef203effbfc9f03246765f3 | https://github.com/Xanadu12138/DSCN-superpixels/tree/babe16edde9c61699ef203effbfc9f03246765f3 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv2dSamePad(nn.Module):
"""
Implement Tensorflow's 'SAME' padding mode in Conv2d.
When an odd number, say `m`, of pixels are need to pad, Tensorflow will pad one more column at right or one more
row at bottom. But P... |
FeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.cuda
class FeedForward(nn.Module):
def __init__(self, hidden_size, inner_size, dropout):
super(FeedForward, self).__init__()
self.linear_in = nn.Linear(hidden_size, inner_size, bias=False)
self.linear_out = nn.Linear(inner_size, hidden_size,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | XL2248/VHM | FeedForward | false | 18,094 | [
"MIT"
] | 8 | d6c21938f7cf095590b35e6ae7e0ef2b27d430f8 | https://github.com/XL2248/VHM/tree/d6c21938f7cf095590b35e6ae7e0ef2b27d430f8 | import torch
import torch.nn as nn
import torch.cuda
class Model(nn.Module):
def __init__(self, hidden_size, inner_size, dropout):
super().__init__()
self.linear_in = nn.Linear(hidden_size, inner_size, bias=False)
self.linear_out = nn.Linear(inner_size, hidden_size, bias=False)
se... |
CNNLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 CNNLayer(nn.Module):
"""Conv1d layer.
nn.Conv1d layer require the input shape is (batch_size, in_channels, length),
however, our input shape is (batch_size, length, in_channels), so we need to
transpose our input data into (B, C, L_in) and send it to conv layer... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | WiseDoge/Text-Classification-PyTorch | CNNLayer | false | 18,095 | [
"MIT"
] | 6 | 9371eeed6bd7ecf1d529c8f2a6c997fcde67a559 | https://github.com/WiseDoge/Text-Classification-PyTorch/tree/9371eeed6bd7ecf1d529c8f2a6c997fcde67a559 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Conv1d layer.
nn.Conv1d layer require the input shape is (batch_size, in_channels, length),
however, our input shape is (batch_size, length, in_channels), so we need to
transpose our input data into (B, C, L_in) and send it to conv layer, a... |
AttentiveStatsPooling | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 AttentiveStatsPooling(nn.Module):
"""
The attentive statistics pooling layer uses an attention
mechanism to give different weights to different frames and
generates not only weighted means but also weighted variances,
to form utterance-level features from 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.... | Wadaboa/titanet | AttentiveStatsPooling | false | 18,096 | [
"MIT"
] | 4 | b07e3074e79ea8c1129fb0adb8315e06bb4943ea | https://github.com/Wadaboa/titanet/tree/b07e3074e79ea8c1129fb0adb8315e06bb4943ea | import torch
import torch.nn as nn
class Model(nn.Module):
"""
The attentive statistics pooling layer uses an attention
mechanism to give different weights to different frames and
generates not only weighted means but also weighted variances,
to form utterance-level features from frame-level featu... |
AutoEncoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 AutoEncoder(nn.Module):
def __init__(self, channels):
"""
param:
channels: a list containing all channels in the network.
"""
super(AutoEncoder, self).__init__()
self.encoder = nn.Sequential()
for i in range(len(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Xanadu12138/DSCN-superpixels | AutoEncoder | false | 18,097 | [
"MIT"
] | 4 | babe16edde9c61699ef203effbfc9f03246765f3 | https://github.com/Xanadu12138/DSCN-superpixels/tree/babe16edde9c61699ef203effbfc9f03246765f3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, channels):
"""
param:
channels: a list containing all channels in the network.
"""
super().__init__()
self.encoder = nn.Sequential()
for i in range(len(channels) - 1):
... |
SAM_Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class SAM_Loss(nn.Module):
def __init__(self):
super(SAM_Loss, self).__init__()
def forward(self, output, label):
ratio = torch.sum((output + 1e-08).mul(label + 1e-08), dim=1
) / torch.sqrt(torch.sum((output + 1e-08).mul(output + 1e-08),
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | XiuhengWang/Sylvester_TSFN_MDC_HSI_superresolution | SAM_Loss | false | 18,098 | [
"MIT"
] | 5 | f70799c931d44d5d6cac635ef539a38bc573c7d9 | https://github.com/XiuhengWang/Sylvester_TSFN_MDC_HSI_superresolution/tree/f70799c931d44d5d6cac635ef539a38bc573c7d9 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, output, label):
ratio = torch.sum((output + 1e-08).mul(label + 1e-08), dim=1
) / torch.sqrt(torch.sum((output + 1e-08).mul(output + 1e-08),
dim=1) * tor... |
LinearRegression | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 LinearRegression(nn.Module):
def __init__(self, hidden_size):
super(LinearRegression, self).__init__()
self.linear1 = nn.Linear(hidden_size, 3)
def forward(self, x, mask):
y = self.linear1(x)
y = y * mask
return y.view(-1, 3)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | XIAOYEJIAYOU/GSAN | LinearRegression | false | 18,099 | [
"MIT"
] | 6 | 8ca4fdf4c3d615af9cc10e1f9f22ceb7e27fe196 | https://github.com/XIAOYEJIAYOU/GSAN/tree/8ca4fdf4c3d615af9cc10e1f9f22ceb7e27fe196 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.linear1 = nn.Linear(hidden_size, 3)
def forward(self, x, mask):
y = self.linear1(x)
y = y * mask
return y.view(-1, 3)
def get_inputs():
return [to... |
Q_Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Q_Critic(nn.Module):
def __init__(self, state_dim, action_dim, net_width):
super(Q_Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, net_width)
self.l2 = nn.Linear(net_width, net_width)
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
import ... | XinJingHao/RL | Q_Critic | false | 18,100 | [
"MIT"
] | 6 | eed54d6602b173e45ede722b0fcf82b5a203f14a | https://github.com/XinJingHao/RL/tree/eed54d6602b173e45ede722b0fcf82b5a203f14a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim, net_width):
super().__init__()
self.l1 = nn.Linear(state_dim + action_dim, net_width)
self.l2 = nn.Linear(net_width, net_width)
self.l3 = nn.Linear(... |
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
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, net_width, maxaction):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, net_width)
self.l2 = nn.Linear(net_width, net_width)
self.l3 = nn.Linear(net_width, action_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | XinJingHao/RL | Actor | false | 18,101 | [
"MIT"
] | 6 | eed54d6602b173e45ede722b0fcf82b5a203f14a | https://github.com/XinJingHao/RL/tree/eed54d6602b173e45ede722b0fcf82b5a203f14a | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, state_dim, action_dim, net_width, maxaction):
super().__init__()
self.l1 = nn.Linear(state_dim, net_width)
self.l2 = nn.Linear(net_width, net_width)
self.l3 = nn.Linear(net_width, action_dim)
sel... |
MaxMinGroup | # 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
def process_maxmin_groupsize(x, group_size, axis=-1):
size = list(x.size())
num_channels = size[axis]
if num_channels % group_size:
raise ValueError(
'number of features({}) is not a multiple of group_size({})'.
for... | 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
assert_size_stride = torch._C._dynamo.guard... | XinZhang525/fGAIL | MaxMinGroup | false | 18,102 | [
"MIT"
] | 4 | 682d70286685612558e072d9a1668779b8ae325b | https://github.com/XinZhang525/fGAIL/tree/682d70286685612558e072d9a1668779b8ae325b | import torch
import torch.nn as nn
import torch.utils.data
def process_maxmin_groupsize(x, group_size, axis=-1):
size = list(x.size())
num_channels = size[axis]
if num_channels % group_size:
raise ValueError(
'number of features({}) is not a multiple of group_size({})'.
for... |
MaskedL1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch
import torch.nn as nn
class MaskedL1Loss(nn.Module):
def __init__(self):
super(MaskedL1Loss, self).__init__()
self.criterion = nn.L1Loss()
def forward(self, input, target, mask):
mask = mask.expand(-1, input.size()[1], -1, -1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | WeisiX/ITAS3D | MaskedL1Loss | false | 18,103 | [
"MIT"
] | 4 | fc861e0cb2d4516905bfadab5e5e880c2b021832 | https://github.com/WeisiX/ITAS3D/tree/fc861e0cb2d4516905bfadab5e5e880c2b021832 | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.criterion = nn.L1Loss()
def forward(self, input, target, mask):
mask = mask.expand(-1, input.size()[1], -1, -1)
loss = self.criterion(in... |
squeeze | # 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 squeeze(nn.Module):
def __init__(self, block_size):
super(squeeze, self).__init__()
self.block_size = block_size
self.block_size_sq = block_size * block_size
def inverse(self, input):
output = input.permute(0, 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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | XinZhang525/fGAIL | squeeze | false | 18,104 | [
"MIT"
] | 4 | 682d70286685612558e072d9a1668779b8ae325b | https://github.com/XinZhang525/fGAIL/tree/682d70286685612558e072d9a1668779b8ae325b | import torch
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 inverse(self, input):
output = input.permute(0, 2, 3, 1)
... |
ModulatedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.autograd import Function
import math
import torch
import torch.utils.data
from torch.utils import data as data
from torch.nn import functional as F
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
def make_resample... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.autograd... | WoojunePark/BasicSR | ModulatedConv2d | false | 18,105 | [
"Apache-2.0"
] | 9 | e0910b022b924bb913045fc412a5470dc2242cf0 | https://github.com/WoojunePark/BasicSR/tree/e0910b022b924bb913045fc412a5470dc2242cf0 | from torch.autograd import Function
import math
import torch
import torch.utils.data
from torch.utils import data as data
from torch.nn import functional as F
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
def make_resample... |
Split | # 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 Split(nn.Module):
def __init__(self):
super(Split, self).__init__()
def forward(self, x):
n = int(x.size(1) / 2)
x1 = x[:, :n, :, :].contiguous()
x2 = x[:, n:, :, :].contiguous()
return x1, x2
def i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | XinZhang525/fGAIL | Split | false | 18,106 | [
"MIT"
] | 4 | 682d70286685612558e072d9a1668779b8ae325b | https://github.com/XinZhang525/fGAIL/tree/682d70286685612558e072d9a1668779b8ae325b | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
n = int(x.size(1) / 2)
x1 = x[:, :n, :, :].contiguous()
x2 = x[:, n:, :, :].contiguous()
return x1, x2
def inverse(self... |
MaskUpdate | # 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.multiprocessing
class MaskUpdate(nn.Module):
def __init__(self, alpha):
super(MaskUpdate, self).__init__()
self.func = nn.ReLU(True)
self.alpha = alpha
def forward(self, input_masks):
return torch.pow(self.func(input_masks), sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.multiprocessing
assert_size_stride = torch._C._dynamo.... | Xiefan-Guo/LBAM | MaskUpdate | false | 18,107 | [
"MIT"
] | 4 | 9795e2af4677a9f5e8e13b5d89fc6d50534c006a | https://github.com/Xiefan-Guo/LBAM/tree/9795e2af4677a9f5e8e13b5d89fc6d50534c006a | import torch
import torch.nn as nn
import torch.multiprocessing
class Model(nn.Module):
def __init__(self, alpha):
super().__init__()
self.func = nn.ReLU(True)
self.alpha = alpha
def forward(self, input_masks):
return torch.pow(self.func(input_masks), self.alpha)
def get_in... |
L1 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch
import torch.nn as nn
class L1(nn.Module):
def __init__(self):
super(L1, self).__init__()
def forward(self, output, target):
lossvalue = torch.abs(output - target).mean()
return lossvalue
def get_inputs():
return [torch.rand([4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | WeisiX/ITAS3D | L1 | false | 18,108 | [
"MIT"
] | 4 | fc861e0cb2d4516905bfadab5e5e880c2b021832 | https://github.com/WeisiX/ITAS3D/tree/fc861e0cb2d4516905bfadab5e5e880c2b021832 | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, output, target):
lossvalue = torch.abs(output - target).mean()
return lossvalue
def get_inputs():
return [torch.rand([4, 4, 4... |
GaussianActivation | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.multiprocessing
class GaussianActivation(nn.Module):
def __init__(self, a, mu, gamma_l, gamma_r):
super(GaussianActivation, self).__init__()
self.a = Parameter(torch.tensor(a, dtype=torch.float32))
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
... | Xiefan-Guo/LBAM | GaussianActivation | false | 18,109 | [
"MIT"
] | 4 | 9795e2af4677a9f5e8e13b5d89fc6d50534c006a | https://github.com/Xiefan-Guo/LBAM/tree/9795e2af4677a9f5e8e13b5d89fc6d50534c006a | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.multiprocessing
class Model(nn.Module):
def __init__(self, a, mu, gamma_l, gamma_r):
super().__init__()
self.a = Parameter(torch.tensor(a, dtype=torch.float32))
self.mu = Parameter(torch.tensor(mu, dt... |
BertIntermediate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class BertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
self.intermediate_act_fn = nn.functional.gelu
def for... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | RyanWangZf/SurvTRACE | BertIntermediate | false | 18,110 | [
"MIT"
] | 8 | d55299a28629d233f49ad1feaea7ed00835f0dd0 | https://github.com/RyanWangZf/SurvTRACE/tree/d55299a28629d233f49ad1feaea7ed00835f0dd0 | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
self.intermediate_act_fn = nn.functional.gelu
def forward(self, ... |
L2 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch
import torch.nn as nn
class L2(nn.Module):
def __init__(self):
super(L2, self).__init__()
def forward(self, output, target):
lossvalue = torch.norm(output - target, p=2, dim=1).mean()
return lossvalue
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.triton_helpers import libdevice
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride =... | WeisiX/ITAS3D | L2 | false | 18,111 | [
"MIT"
] | 4 | fc861e0cb2d4516905bfadab5e5e880c2b021832 | https://github.com/WeisiX/ITAS3D/tree/fc861e0cb2d4516905bfadab5e5e880c2b021832 | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, output, target):
lossvalue = torch.norm(output - target, p=2, dim=1).mean()
return lossvalue
def get_inputs():
return [torch.... |
Inception_Temporal_Layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class CausalConv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1, groups=1, bias=True):
self.padding = (kernel_size - 1) * dilation
super(CausalConv1d, self).__init__(in_channels, out_channels,
ke... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | WoodSugar/GSTNet | Inception_Temporal_Layer | false | 18,112 | [
"MIT"
] | 8 | 3c21cfc8a873d61336f257030a28fdee12dcee2f | https://github.com/WoodSugar/GSTNet/tree/3c21cfc8a873d61336f257030a28fdee12dcee2f | import torch
import torch.nn as nn
class CausalConv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1, groups=1, bias=True):
self.padding = (kernel_size - 1) * dilation
super().__init__(in_channels, out_channels,
kernel_size=kernel_s... |
EqualLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.autograd import Function
import math
import torch
import torch.utils.data
from torch.utils import data as data
from torch.nn import functional as F
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
def fused_leaky_r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
import torch.utils.data
from tor... | WoojunePark/BasicSR | EqualLinear | false | 18,113 | [
"Apache-2.0"
] | 9 | e0910b022b924bb913045fc412a5470dc2242cf0 | https://github.com/WoojunePark/BasicSR/tree/e0910b022b924bb913045fc412a5470dc2242cf0 | from torch.autograd import Function
import math
import torch
import torch.utils.data
from torch.utils import data as data
from torch.nn import functional as F
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
def fused_leaky_r... |
RegLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
from itertools import product as product
from math import sqrt as sqrt
import torch.utils.data
def _reg_loss(regr, gt_regr, mask):
""" L1 regression loss
Arguments:
regr (batch x max_objects x dim)
gt_regr (batch x max_objects x dim)
mask (batch x max_objec... | 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
... | XiangLiK/cv_course | RegLoss | false | 18,114 | [
"MIT"
] | 8 | da7c2318fd4128bbdab96db26ddbb2524f37d0a0 | https://github.com/XiangLiK/cv_course/tree/da7c2318fd4128bbdab96db26ddbb2524f37d0a0 | import torch
import torch.nn as nn
from itertools import product as product
from math import sqrt as sqrt
import torch.utils.data
def _reg_loss(regr, gt_regr, mask):
""" L1 regression loss
Arguments:
regr (batch x max_objects x dim)
gt_regr (batch x max_objects x dim)
mask (batch x max_objec... |
RegWeightedL1Loss | # 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
from itertools import product as product
from math import sqrt as sqrt
import torch.utils.data
def _gather_feat(feat, ind, mask=None):
dim = feat.size(2)
ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
feat = feat.gather(1, in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | XiangLiK/cv_course | RegWeightedL1Loss | false | 18,115 | [
"MIT"
] | 8 | da7c2318fd4128bbdab96db26ddbb2524f37d0a0 | https://github.com/XiangLiK/cv_course/tree/da7c2318fd4128bbdab96db26ddbb2524f37d0a0 | import torch
import torch.nn as nn
import torch.nn.functional as F
from itertools import product as product
from math import sqrt as sqrt
import torch.utils.data
def _gather_feat(feat, ind, mask=None):
dim = feat.size(2)
ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
feat = feat.gather(1, in... |
MultiscaleL1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch
import torch.nn as nn
class MultiscaleL1Loss(nn.Module):
def __init__(self, scale=5):
super(MultiscaleL1Loss, self).__init__()
self.criterion = nn.L1Loss()
self.downsample = nn.AvgPool2d(2, stride=2, count_include_pad=False)
self.w... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | WeisiX/ITAS3D | MultiscaleL1Loss | false | 18,116 | [
"MIT"
] | 4 | fc861e0cb2d4516905bfadab5e5e880c2b021832 | https://github.com/WeisiX/ITAS3D/tree/fc861e0cb2d4516905bfadab5e5e880c2b021832 | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, scale=5):
super().__init__()
self.criterion = nn.L1Loss()
self.downsample = nn.AvgPool2d(2, stride=2, count_include_pad=False)
self.weights = [1, 0.5, 0.25, 0.125, 0.... |
ReverseAttentionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.multiprocessing
def weights_init(init_type='gaussian'):
def init_func(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasatt... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Xiefan-Guo/LBAM | ReverseAttentionLayer | false | 18,118 | [
"MIT"
] | 4 | 9795e2af4677a9f5e8e13b5d89fc6d50534c006a | https://github.com/Xiefan-Guo/LBAM/tree/9795e2af4677a9f5e8e13b5d89fc6d50534c006a | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.multiprocessing
def weights_init(init_type='gaussian'):
def init_func(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasatt... |
patch_extractor | # 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 patch_extractor(nn.Module):
"""
Module for creating custom patch extractor
"""
def __init__(self, patch_size, pad=False):
super(patch_extractor, self).__init__()
self.im2pat = nn.Unfold(kernel_size=patch_size)
self.pad = pad
sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Xmaster6y/wgenpatex | patch_extractor | false | 18,119 | [
"MIT"
] | 8 | 08079dc131cc2e9c74ee4f9e16cf9b58667f2b07 | https://github.com/Xmaster6y/wgenpatex/tree/08079dc131cc2e9c74ee4f9e16cf9b58667f2b07 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Module for creating custom patch extractor
"""
def __init__(self, patch_size, pad=False):
super().__init__()
self.im2pat = nn.Unfold(kernel_size=patch_size)
self.pad = pad
self.padsize = patch_size - 1
... |
gaussian_layer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import math
import torch
import torch.nn as nn
class gaussian_downsample(nn.Module):
"""
Downsampling module with Gaussian filtering
"""
def __init__(self, kernel_size, sigma, stride, pad=False):
super(gaussian_downsample, self).__init__()
self.gauss = nn.Conv2d(3, 3, kernel_size, str... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | Xmaster6y/wgenpatex | gaussian_layer | false | 18,120 | [
"MIT"
] | 8 | 08079dc131cc2e9c74ee4f9e16cf9b58667f2b07 | https://github.com/Xmaster6y/wgenpatex/tree/08079dc131cc2e9c74ee4f9e16cf9b58667f2b07 | import math
import torch
import torch.nn as nn
class gaussian_downsample(nn.Module):
"""
Downsampling module with Gaussian filtering
"""
def __init__(self, kernel_size, sigma, stride, pad=False):
super().__init__()
self.gauss = nn.Conv2d(3, 3, kernel_size, stride=stride, groups=3,
... |
ToRGB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.autograd import Function
import math
import torch
import torch.utils.data
from torch.utils import data as data
from torch.nn import functional as F
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
def make_resample... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
import torch.utils.data
from tor... | WoojunePark/BasicSR | ToRGB | false | 18,121 | [
"Apache-2.0"
] | 9 | e0910b022b924bb913045fc412a5470dc2242cf0 | https://github.com/WoojunePark/BasicSR/tree/e0910b022b924bb913045fc412a5470dc2242cf0 | from torch.autograd import Function
import math
import torch
import torch.utils.data
from torch.utils import data as data
from torch.nn import functional as F
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
def make_resample... |
PairwiseRankingLoss | # 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 PairwiseRankingLoss(nn.Module):
"""
Pairwise ranking loss
"""
def __init__(self, margin):
super(PairwiseRankingLoss, self).__init__()
self.margin = margin
def forward(self, anchor1, anchor2, img_sentc, sent_imgc):
cost_sent = torch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | YJiangcm/DCPCSE | PairwiseRankingLoss | false | 18,122 | [
"MIT"
] | 5 | 698255e2e66b402325ff611e098e01d2f322743e | https://github.com/YJiangcm/DCPCSE/tree/698255e2e66b402325ff611e098e01d2f322743e | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Pairwise ranking loss
"""
def __init__(self, margin):
super().__init__()
self.margin = margin
def forward(self, anchor1, anchor2, img_sentc, sent_imgc):
cost_sent = torch.clamp(self.margin - anchor1 + img_sent... |
Resv1Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from itertools import product as product
from math import sqrt as sqrt
import torch.utils.data
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from it... | XiangLiK/cv_course | Resv1Block | false | 18,124 | [
"MIT"
] | 8 | da7c2318fd4128bbdab96db26ddbb2524f37d0a0 | https://github.com/XiangLiK/cv_course/tree/da7c2318fd4128bbdab96db26ddbb2524f37d0a0 | import torch
import torch.nn as nn
from itertools import product as product
from math import sqrt as sqrt
import torch.utils.data
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False... |
Similarity | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class Similarity(nn.Module):
"""
Dot product or cosine similarity
"""
def __init__(self, temp):
super().__init__()
self.temp = temp
self.cos = nn.CosineSimilarity(dim=-1)
def forward(self, x, y):
return self.cos(x, y) / self.temp... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | YJiangcm/DCPCSE | Similarity | false | 18,125 | [
"MIT"
] | 5 | 698255e2e66b402325ff611e098e01d2f322743e | https://github.com/YJiangcm/DCPCSE/tree/698255e2e66b402325ff611e098e01d2f322743e | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Dot product or cosine similarity
"""
def __init__(self, temp):
super().__init__()
self.temp = temp
self.cos = nn.CosineSimilarity(dim=-1)
def forward(self, x, y):
return self.cos(x, y) / self.temp
de... |
ResidualBlockNoBN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
from torch.utils import data as data
from torch import nn as nn
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from torchvision.models import vgg as vgg
from torch import autograd as autograd
@torch.no_grad()
def default_init_weights(module_lis... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | WoojunePark/BasicSR | ResidualBlockNoBN | false | 18,127 | [
"Apache-2.0"
] | 9 | e0910b022b924bb913045fc412a5470dc2242cf0 | https://github.com/WoojunePark/BasicSR/tree/e0910b022b924bb913045fc412a5470dc2242cf0 | import torch
import torch.utils.data
from torch.utils import data as data
from torch import nn as nn
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from torchvision.models import vgg as vgg
from torch import autograd as autograd
@torch.no_grad()
def default_init_weights(module_lis... |
ActNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import Parameter
class ActNorm(nn.Module):
def __init__(self, num_channels, eps=1e-05):
super(ActNorm, self).__init__()
self.eps = eps
self.num_channels = num_channels
self._log_scale = Parameter(torch.Tensor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.utils.data
from torch.nn import Parame... | XinZhang525/fGAIL | ActNorm | false | 18,128 | [
"MIT"
] | 4 | 682d70286685612558e072d9a1668779b8ae325b | https://github.com/XinZhang525/fGAIL/tree/682d70286685612558e072d9a1668779b8ae325b | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import Parameter
class Model(nn.Module):
def __init__(self, num_channels, eps=1e-05):
super().__init__()
self.eps = eps
self.num_channels = num_channels
self._log_scale = Parameter(torch.Tensor(num_channels))... |
_MultipleInputNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 _MultipleInputNetwork(_nn.Module):
def __init__(self):
super(_MultipleInputNetwork, self).__init__()
self.conv = _nn.Conv2d(3, 16, 3)
def forward(self, inp1, inp2):
inp = inp1 * inp2
out = self.conv(inp)
return out
def get_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 as _nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Yifanfanfanfan/torchutils | _MultipleInputNetwork | false | 18,129 | [
"MIT"
] | 9 | 939331d28fcee97bfb0a4b2eaab8e799877fb0dc | https://github.com/Yifanfanfanfan/torchutils/tree/939331d28fcee97bfb0a4b2eaab8e799877fb0dc | import torch
import torch.nn as _nn
class Model(_nn.Module):
def __init__(self):
super().__init__()
self.conv = _nn.Conv2d(3, 16, 3)
def forward(self, inp1, inp2):
inp = inp1 * inp2
out = self.conv(inp)
return out
def get_inputs():
return [torch.rand([4, 3, 64, ... |
NextImgPrediction | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 NextImgPrediction(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)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | YanyuanQiao/HOP-VLN | NextImgPrediction | false | 18,130 | [
"MIT"
] | 8 | 4b26b2569afb3e7eb7d8c2ed814cd424e41cbade | https://github.com/YanyuanQiao/HOP-VLN/tree/4b26b2569afb3e7eb7d8c2ed814cd424e41cbade | 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... |
gaussian_downsample | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import math
import torch
import torch.nn as nn
class gaussian_downsample(nn.Module):
"""
Downsampling module with Gaussian filtering
"""
def __init__(self, kernel_size, sigma, stride, pad=False):
super(gaussian_downsample, self).__init__()
self.gauss = nn.Conv2d(3, 3, kernel_size, str... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | Xmaster6y/wgenpatex | gaussian_downsample | false | 18,131 | [
"MIT"
] | 8 | 08079dc131cc2e9c74ee4f9e16cf9b58667f2b07 | https://github.com/Xmaster6y/wgenpatex/tree/08079dc131cc2e9c74ee4f9e16cf9b58667f2b07 | import math
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Downsampling module with Gaussian filtering
"""
def __init__(self, kernel_size, sigma, stride, pad=False):
super().__init__()
self.gauss = nn.Conv2d(3, 3, kernel_size, stride=stride, groups=3,
bias=... |
BranchNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from itertools import product as product
from math import sqrt as sqrt
import torch.utils.data
def conv1x1(in_channels, out_channels):
"""1x1 convolution"""
return nn.Conv2d(in_channels, out_channels, 1, bias=True)
class BranchNet(nn.Module):
"""
The branch of Naiv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from it... | XiangLiK/cv_course | BranchNet | false | 18,132 | [
"MIT"
] | 8 | da7c2318fd4128bbdab96db26ddbb2524f37d0a0 | https://github.com/XiangLiK/cv_course/tree/da7c2318fd4128bbdab96db26ddbb2524f37d0a0 | import torch
import torch.nn as nn
from itertools import product as product
from math import sqrt as sqrt
import torch.utils.data
def conv1x1(in_channels, out_channels):
"""1x1 convolution"""
return nn.Conv2d(in_channels, out_channels, 1, bias=True)
class Model(nn.Module):
"""
The branch of NaiveNet... |
HighwayNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 HighwayNetwork(nn.Module):
def __init__(self, size):
super().__init__()
self.W1 = nn.Linear(size, size)
self.W2 = nn.Linear(size, size)
self.W1.bias.data.fill_(0.0)
def forward(self, x):
x1 = 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_... | YoghesWaran/tacotron | HighwayNetwork | false | 18,133 | [
"MIT"
] | 10 | 0b97486da7698229bad09e2072cfa3313ae7effe | https://github.com/YoghesWaran/tacotron/tree/0b97486da7698229bad09e2072cfa3313ae7effe | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, size):
super().__init__()
self.W1 = nn.Linear(size, size)
self.W2 = nn.Linear(size, size)
self.W1.bias.data.fill_(0.0)
def forward(self, x):
x1 = self.W1(x)
... |
ActNorm2D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import Parameter
class ActNorm2D(nn.Module):
def __init__(self, num_channels, eps=1e-05):
super(ActNorm2D, self).__init__()
self.eps = eps
self.num_channels = num_channels
self._log_scale = Parameter(torch.Te... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.utils.data
from torch.nn import Parame... | XinZhang525/fGAIL | ActNorm2D | false | 18,134 | [
"MIT"
] | 4 | 682d70286685612558e072d9a1668779b8ae325b | https://github.com/XinZhang525/fGAIL/tree/682d70286685612558e072d9a1668779b8ae325b | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import Parameter
class Model(nn.Module):
def __init__(self, num_channels, eps=1e-05):
super().__init__()
self.eps = eps
self.num_channels = num_channels
self._log_scale = Parameter(torch.Tensor(num_channels))... |
ParentChildClassifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ParentChildClassifier(nn.Module):
def __init__(self, parent_dim, child_short_dim, child_full_dim, hidden_dim
):
super(ParentChildClassifier, self).__init__()
if child_full_dim is not None:
self.hidden = nn.Linear(parent_dim + child_short... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | YilunZhou/wikihow-embedding | ParentChildClassifier | false | 18,135 | [
"MIT"
] | 8 | bfbcaf6aca854cd7e0dedfd5ecf77627138e8425 | https://github.com/YilunZhou/wikihow-embedding/tree/bfbcaf6aca854cd7e0dedfd5ecf77627138e8425 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, parent_dim, child_short_dim, child_full_dim, hidden_dim
):
super().__init__()
if child_full_dim is not None:
self.hidden = nn.Linear(parent_dim + child_short_dim +
child_full_dim, hidd... |
CenterLoss | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 CenterLoss(nn.Module):
def __init__(self, class_num, feature_num, alpha=0.5):
super(CenterLoss, self).__init__()
self.class_num = class_num
self.feature_num = feature_num
self.class_centers = nn.Parameter(torch.randn(self.class_num, self.
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | YangXuanyue/Neural-Unaligned-Phoneme-Sequence-Prediction | CenterLoss | false | 18,137 | [
"BSD-3-Clause"
] | 5 | 91ef1c95478367f5b421da125f07660cfc9bed98 | https://github.com/YangXuanyue/Neural-Unaligned-Phoneme-Sequence-Prediction/tree/91ef1c95478367f5b421da125f07660cfc9bed98 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, class_num, feature_num, alpha=0.5):
super().__init__()
self.class_num = class_num
self.feature_num = feature_num
self.class_centers = nn.Parameter(torch.randn(self.class_num, self.
feature_num... |
StepRankerLogistic3 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 StepRankerLogistic3(nn.Module):
"""a logistic ranker that includes a don't care token"""
def __init__(self, parent_dim, child_short_dim, child_full_dim, hidden_dim
):
super(StepRankerLogistic3, self).__init__()
if child_full_dim is not None:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | YilunZhou/wikihow-embedding | StepRankerLogistic3 | false | 18,138 | [
"MIT"
] | 8 | bfbcaf6aca854cd7e0dedfd5ecf77627138e8425 | https://github.com/YilunZhou/wikihow-embedding/tree/bfbcaf6aca854cd7e0dedfd5ecf77627138e8425 | import torch
from torch import nn
class Model(nn.Module):
"""a logistic ranker that includes a don't care token"""
def __init__(self, parent_dim, child_short_dim, child_full_dim, hidden_dim
):
super().__init__()
if child_full_dim is not None:
self.hidden = nn.Linear(parent... |
Normalizer | # 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 Normalizer(nn.Module):
def __init__(self, target_norm=1.0):
super().__init__()
self.target_norm = target_norm
def forward(self, input: 'torch.Tensor'):
return input * self.target_norm / input.norm(p=2, dim=1, keepdim=True)
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | YangXuanyue/Neural-Unaligned-Phoneme-Sequence-Prediction | Normalizer | false | 18,139 | [
"BSD-3-Clause"
] | 5 | 91ef1c95478367f5b421da125f07660cfc9bed98 | https://github.com/YangXuanyue/Neural-Unaligned-Phoneme-Sequence-Prediction/tree/91ef1c95478367f5b421da125f07660cfc9bed98 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, target_norm=1.0):
super().__init__()
self.target_norm = target_norm
def forward(self, input: 'torch.Tensor'):
return input * self.target_norm / input.norm(p=2, dim=1, keepdim=True)
def get_inputs():
re... |
StepRankerLogistic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 StepRankerLogistic(nn.Module):
"""a logistic ranker"""
def __init__(self, parent_dim, child_short_dim, child_full_dim, hidden_dim
):
super(StepRankerLogistic, self).__init__()
if child_full_dim is not None:
self.hidden = nn.Linear(pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | YilunZhou/wikihow-embedding | StepRankerLogistic | false | 18,140 | [
"MIT"
] | 8 | bfbcaf6aca854cd7e0dedfd5ecf77627138e8425 | https://github.com/YilunZhou/wikihow-embedding/tree/bfbcaf6aca854cd7e0dedfd5ecf77627138e8425 | import torch
from torch import nn
class Model(nn.Module):
"""a logistic ranker"""
def __init__(self, parent_dim, child_short_dim, child_full_dim, hidden_dim
):
super().__init__()
if child_full_dim is not None:
self.hidden = nn.Linear(parent_dim + child_short_dim +
... |
ChannelSELayer3D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ChannelSELayer3D(nn.Module):
"""
3D extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
"""
def __init__(self, num_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._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | YilinLiu97/AmygNet-Pytorch | ChannelSELayer3D | false | 18,141 | [
"MIT"
] | 3 | d5bb244fd930791345d38f09870a7ded633f4622 | https://github.com/YilinLiu97/AmygNet-Pytorch/tree/d5bb244fd930791345d38f09870a7ded633f4622 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
3D extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
"""
def __init__(self, num_channels, reduction... |
PreNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 PreNet(nn.Module):
def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
super().__init__()
self.fc1 = nn.Linear(in_dims, fc1_dims)
self.fc2 = nn.Linear(fc1_dims, fc2_dims)
self.p = dropout
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | YoghesWaran/tacotron | PreNet | false | 18,142 | [
"MIT"
] | 10 | 0b97486da7698229bad09e2072cfa3313ae7effe | https://github.com/YoghesWaran/tacotron/tree/0b97486da7698229bad09e2072cfa3313ae7effe | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
super().__init__()
self.fc1 = nn.Linear(in_dims, fc1_dims)
self.fc2 = nn.Linear(fc1_dims, fc2_dims)
self.p = dropout
... |
Scaler | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 Scaler(nn.Module):
def __init__(self, alpha=16.0):
super().__init__()
self.alpha = nn.Parameter(torch.tensor(alpha))
def forward(self, input):
return self.alpha * input
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | YangXuanyue/Neural-Unaligned-Phoneme-Sequence-Prediction | Scaler | false | 18,143 | [
"BSD-3-Clause"
] | 5 | 91ef1c95478367f5b421da125f07660cfc9bed98 | https://github.com/YangXuanyue/Neural-Unaligned-Phoneme-Sequence-Prediction/tree/91ef1c95478367f5b421da125f07660cfc9bed98 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, alpha=16.0):
super().__init__()
self.alpha = nn.Parameter(torch.tensor(alpha))
def forward(self, input):
return self.alpha * input
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inp... |
BertOutAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertOutAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | YanyuanQiao/HOP-VLN | BertOutAttention | false | 18,144 | [
"MIT"
] | 8 | 4b26b2569afb3e7eb7d8c2ed814cd424e41cbade | https://github.com/YanyuanQiao/HOP-VLN/tree/4b26b2569afb3e7eb7d8c2ed814cd424e41cbade | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hidden size... |
StepRankerMargin | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 StepRankerMargin(nn.Module):
def __init__(self, parent_dim, child_short_dim, child_full_dim, hidden_dim
):
super(StepRankerMargin, self).__init__()
if child_full_dim is not None:
self.hidden = nn.Linear(parent_dim + child_short_dim +
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | YilunZhou/wikihow-embedding | StepRankerMargin | false | 18,145 | [
"MIT"
] | 8 | bfbcaf6aca854cd7e0dedfd5ecf77627138e8425 | https://github.com/YilunZhou/wikihow-embedding/tree/bfbcaf6aca854cd7e0dedfd5ecf77627138e8425 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, parent_dim, child_short_dim, child_full_dim, hidden_dim
):
super().__init__()
if child_full_dim is not None:
self.hidden = nn.Linear(parent_dim + child_short_dim +
child_full_dim, hidd... |
ProjectExciteLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ProjectExciteLayer(nn.Module):
"""
Project & Excite Module, specifically designed for 3D inputs
*quote*
"""
def __init__(self, num_channels, reduction_ratio=2):
"""
:param num_channels: No of input ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | YilinLiu97/AmygNet-Pytorch | ProjectExciteLayer | false | 18,146 | [
"MIT"
] | 3 | d5bb244fd930791345d38f09870a7ded633f4622 | https://github.com/YilinLiu97/AmygNet-Pytorch/tree/d5bb244fd930791345d38f09870a7ded633f4622 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Project & Excite Module, specifically designed for 3D inputs
*quote*
"""
def __init__(self, num_channels, reduction_ratio=2):
"""
:param num_channels: No of input channels
... |
ChannelSpatialSELayer3D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ChannelSELayer3D(nn.Module):
"""
3D extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | YilinLiu97/AmygNet-Pytorch | ChannelSpatialSELayer3D | false | 18,147 | [
"MIT"
] | 3 | d5bb244fd930791345d38f09870a7ded633f4622 | https://github.com/YilinLiu97/AmygNet-Pytorch/tree/d5bb244fd930791345d38f09870a7ded633f4622 | import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelSELayer3D(nn.Module):
"""
3D extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
"""
... |
GELU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class GELU(nn.Module):
def forward(self, x):
return torch.sigmoid(1.702 * x) * 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | YuShen0118/SAAP_Auto-driving_Platform | GELU | false | 18,148 | [
"MIT"
] | 4 | 785f899fb3b3ad92075318f9fcb69b8e09597202 | https://github.com/YuShen0118/SAAP_Auto-driving_Platform/tree/785f899fb3b3ad92075318f9fcb69b8e09597202 | import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x):
return torch.sigmoid(1.702 * x) * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Encoder(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super().__init__()
padding = [((i - 1) // 2) for i in kernel_size]
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=
kernel_size, stride... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | YoshikiMas/YoshikiMas-speech-enhancement-with-pytorch-lightning | Encoder | false | 18,149 | [
"MIT"
] | 5 | 8fcb78cbf64cb61dd9d2dd9e1118a1aa1992dd65 | https://github.com/YoshikiMas/YoshikiMas-speech-enhancement-with-pytorch-lightning/tree/8fcb78cbf64cb61dd9d2dd9e1118a1aa1992dd65 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super().__init__()
padding = [((i - 1) // 2) for i in kernel_size]
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=
kernel_size, stride=s... |
SpatialSELayer3D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SpatialSELayer3D(nn.Module):
"""
3D extension of SE block -- squeezing spatially and exciting channel-wise described in:
*Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, MICCAI 201... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | YilinLiu97/AmygNet-Pytorch | SpatialSELayer3D | false | 18,150 | [
"MIT"
] | 3 | d5bb244fd930791345d38f09870a7ded633f4622 | https://github.com/YilinLiu97/AmygNet-Pytorch/tree/d5bb244fd930791345d38f09870a7ded633f4622 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
3D extension of SE block -- squeezing spatially and exciting channel-wise described in:
*Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, MICCAI 2018*
"""
... |
ReSentenceMatrixLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ReSentenceMatrixLayer(nn.Module):
def __init__(self, in_size, out_size=1):
super(ReSentenceMatrixLayer, self).__init__()
self.in_size = in_size
self.out_size = out_size
self.a_Asem = nn.Parameter(torch.tensor(0.0))
self.linear = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Yottaxx/T-LSTM | ReSentenceMatrixLayer | false | 18,151 | [
"MIT"
] | 9 | 92618d8c3ee2418b194a2e1592512548da955b77 | https://github.com/Yottaxx/T-LSTM/tree/92618d8c3ee2418b194a2e1592512548da955b77 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_size, out_size=1):
super().__init__()
self.in_size = in_size
self.out_size = out_size
self.a_Asem = nn.Parameter(torch.tensor(0.0))
self.linear = nn.Linear(in_size * 2, out_size)
def forw... |
ExgLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ExgLayer(nn.Module):
def __init__(self, x_size, h_size, g_size, out_size):
super(ExgLayer, self).__init__()
self.h_size = h_size
self.g_size = g_size
self.out_size = out_size
self.x_size = x_size
self.linear_x2 = nn.Linear(x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Yottaxx/T-LSTM | ExgLayer | false | 18,152 | [
"MIT"
] | 9 | 92618d8c3ee2418b194a2e1592512548da955b77 | https://github.com/Yottaxx/T-LSTM/tree/92618d8c3ee2418b194a2e1592512548da955b77 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, x_size, h_size, g_size, out_size):
super().__init__()
self.h_size = h_size
self.g_size = g_size
self.out_size = out_size
self.x_size = x_size
self.linear_x2 = nn.Linear(x_size, out_size)
... |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.distributions
class PositionwiseFeedForward(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | Yinghao-Li/GuiGen | PositionwiseFeedForward | false | 18,153 | [
"MIT"
] | 10 | 22ababcd8cacae0adcc4ee74b514b188dc5084f3 | https://github.com/Yinghao-Li/GuiGen/tree/22ababcd8cacae0adcc4ee74b514b188dc5084f3 | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.distributions
class Model(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hid)
self... |
Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 Decoder(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super().__init__()
padding = [((i - 1) // 2) for i in kernel_size]
self.tconv = nn.ConvTranspose2d(in_channels, out_channels,
kernel_size=kernel_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.... | YoshikiMas/YoshikiMas-speech-enhancement-with-pytorch-lightning | Decoder | false | 18,154 | [
"MIT"
] | 5 | 8fcb78cbf64cb61dd9d2dd9e1118a1aa1992dd65 | https://github.com/YoshikiMas/YoshikiMas-speech-enhancement-with-pytorch-lightning/tree/8fcb78cbf64cb61dd9d2dd9e1118a1aa1992dd65 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super().__init__()
padding = [((i - 1) // 2) for i in kernel_size]
self.tconv = nn.ConvTranspose2d(in_channels, out_channels,
kernel_size=kernel_size,... |
SentenceMatrixLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SentenceMatrixLayer(nn.Module):
def __init__(self, in_size, out_size=1, p_Asem=0.8):
super(SentenceMatrixLayer, self).__init__()
self.in_size = in_size
self.out_size = out_size
self.p_Asem = p_Asem
self.linear = nn.Linear(in_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Yottaxx/T-LSTM | SentenceMatrixLayer | false | 18,155 | [
"MIT"
] | 9 | 92618d8c3ee2418b194a2e1592512548da955b77 | https://github.com/Yottaxx/T-LSTM/tree/92618d8c3ee2418b194a2e1592512548da955b77 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_size, out_size=1, p_Asem=0.8):
super().__init__()
self.in_size = in_size
self.out_size = out_size
self.p_Asem = p_Asem
self.linear = nn.Linear(in_size * 2, out_size)
def forward(self, x, ... |
SVDBilinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
class SVDBilinear(nn.Module):
"""
my bilinear matmul but reducing parameter dimension using peusodu-SVD
"""
def __init__(self, num_basis, in1_features, in2_features, out_features):
supe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.nn.init as init
assert_size_strid... | Yindong-Zhang/myGAT | SVDBilinear | false | 18,156 | [
"MIT"
] | 6 | f69132f21785d3a6bf1ec014890adeb124c89e8d | https://github.com/Yindong-Zhang/myGAT/tree/f69132f21785d3a6bf1ec014890adeb124c89e8d | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
class Model(nn.Module):
"""
my bilinear matmul but reducing parameter dimension using peusodu-SVD
"""
def __init__(self, num_basis, in1_features, in2_features, out_features):
super().__... |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention --baseline version"""
def __init__(self, dropout=0.3):
super().__init__()
self.dropout = nn.Dropout(dropout)
def forward(self, q, k, v, mask=Non... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Yottaxx/T-LSTM | ScaledDotProductAttention | false | 18,157 | [
"MIT"
] | 9 | 92618d8c3ee2418b194a2e1592512548da955b77 | https://github.com/Yottaxx/T-LSTM/tree/92618d8c3ee2418b194a2e1592512548da955b77 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
""" Scaled Dot-Product Attention --baseline version"""
def __init__(self, dropout=0.3):
super().__init__()
self.dropout = nn.Dropout(dropout)
def forward(self, q, k, v, mask=None):
attn = t... |
GCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, dropout=0.3):
super(GraphConvolution, self).__init__()
self.in_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Yottaxx/T-LSTM | GCN | false | 18,158 | [
"MIT"
] | 9 | 92618d8c3ee2418b194a2e1592512548da955b77 | https://github.com/Yottaxx/T-LSTM/tree/92618d8c3ee2418b194a2e1592512548da955b77 | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, dropout=0.3):
super().__init__()
self.in_features = in_features
... |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class QNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_dim):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Yuibooo/BEAR | QNetwork | false | 18,159 | [
"MIT"
] | 4 | d8cf22e3bf0017db0702a6b8b8eb00f22e760991 | https://github.com/Yuibooo/BEAR/tree/d8cf22e3bf0017db0702a6b8b8eb00f22e760991 | import torch
import torch.nn as nn
import torch.nn.functional as F
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class Model(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_dim):
s... |
MeanStdExtractor | # 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 MeanStdExtractor(nn.Module):
def __init__(self):
super().__init__()
def forward(self, feature_maps_batch):
feature_maps_batch = feature_maps_batch.view(*feature_maps_batch.
shape[:2], -1)
feature_means_batch = feature_maps_batch.mea... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | YangXuanyue/Neural-Unaligned-Phoneme-Sequence-Prediction | MeanStdExtractor | false | 18,160 | [
"BSD-3-Clause"
] | 5 | 91ef1c95478367f5b421da125f07660cfc9bed98 | https://github.com/YangXuanyue/Neural-Unaligned-Phoneme-Sequence-Prediction/tree/91ef1c95478367f5b421da125f07660cfc9bed98 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, feature_maps_batch):
feature_maps_batch = feature_maps_batch.view(*feature_maps_batch.
shape[:2], -1)
feature_means_batch = feature_maps_batch.mean(dim=-1)
... |
GraphDiffusedAttentionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 GraphDiffusedAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha):
super(GraphDiffusedAttentionLayer, self).__init... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Yindong-Zhang/myGAT | GraphDiffusedAttentionLayer | false | 18,161 | [
"MIT"
] | 6 | f69132f21785d3a6bf1ec014890adeb124c89e8d | https://github.com/Yindong-Zhang/myGAT/tree/f69132f21785d3a6bf1ec014890adeb124c89e8d | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha):
super().__init__()
self.dropout = dropout
self.in_fea... |
TSAFusion | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
from torch.utils import data as data
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
class TSAFusion(nn.Module):
"""Temporal Spatial Attention (TSA) fusion module.
Temporal: Calc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | WoojunePark/BasicSR | TSAFusion | false | 18,162 | [
"Apache-2.0"
] | 9 | e0910b022b924bb913045fc412a5470dc2242cf0 | https://github.com/WoojunePark/BasicSR/tree/e0910b022b924bb913045fc412a5470dc2242cf0 | import torch
import torch.utils.data
from torch.utils import data as data
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
class Model(nn.Module):
"""Temporal Spatial Attention (TSA) fusion module.
Temporal: Calculat... |
DepthwiseSeparableConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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
import torch
class DepthwiseSeparableConv(nn.Module):
def __init__(self, in_channels, output_channels, kernels_per_layer=1):
super(DepthwiseSeparableConv, self).__init__()
self.depthwise = nn.Conv2d(in_channels, 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
from torch import nn
import torch.nn.functional
import torch
assert_size_stride ... | YiminYang980510/A-TransUNet | DepthwiseSeparableConv | false | 18,163 | [
"MIT"
] | 10 | 600b9abef3460d9751d3a6b7b4e4586aec164aa7 | https://github.com/YiminYang980510/A-TransUNet/tree/600b9abef3460d9751d3a6b7b4e4586aec164aa7 | import torch
from torch import nn
import torch.nn.functional
import torch
class Model(nn.Module):
def __init__(self, in_channels, output_channels, kernels_per_layer=1):
super().__init__()
self.depthwise = nn.Conv2d(in_channels, in_channels *
kernels_per_layer, groups=in_channels, kern... |
sum_squared_error | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch.nn.modules.loss import _Loss
class sum_squared_error(_Loss):
"""
Definition: sum_squared_error = 1/2 * nn.MSELoss(reduction = 'sum')
The backward is defined as: input-target
"""
def __init__(self, size_average=None, reduce=None, reduction='sum'):
super(sum_squared_... | 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.nn.modules.... | ZerojumpLine/Denoise | sum_squared_error | false | 18,164 | [
"MIT"
] | 4 | 09182b07f451d85448ce3c7a53fc69144f91384e | https://github.com/ZerojumpLine/Denoise/tree/09182b07f451d85448ce3c7a53fc69144f91384e | import torch
from torch.nn.modules.loss import _Loss
class Model(_Loss):
"""
Definition: sum_squared_error = 1/2 * nn.MSELoss(reduction = 'sum')
The backward is defined as: input-target
"""
def __init__(self, size_average=None, reduce=None, reduction='sum'):
super().__init__(size_average,... |
GraphConvolution | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, dropout=0.3):
super(GraphConvolution, self).__init__()
self.in_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Yottaxx/T-LSTM | GraphConvolution | false | 18,165 | [
"MIT"
] | 9 | 92618d8c3ee2418b194a2e1592512548da955b77 | https://github.com/Yottaxx/T-LSTM/tree/92618d8c3ee2418b194a2e1592512548da955b77 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, dropout=0.3):
super().__init__()
self.in_features = in_features
self.o... |
L_1st | # 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 L_1st(nn.Module):
def __init__(self, alpha):
super(L_1st, self).__init__()
self.alpha = alpha
def forward(self, y_pred, y_true):
Y = y_pred
L = y_true
batch_size = Y.shape[0]
return 2 * self.alpha * torch.trace(torch.mm... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ZagHe568/graph_embedding | L_1st | false | 18,166 | [
"MIT"
] | 4 | 2a6f8214ce4b30b51eb9f1904b64fe782876f010 | https://github.com/ZagHe568/graph_embedding/tree/2a6f8214ce4b30b51eb9f1904b64fe782876f010 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, alpha):
super().__init__()
self.alpha = alpha
def forward(self, y_pred, y_true):
Y = y_pred
L = y_true
batch_size = Y.shape[0]
return 2 * self.alpha * torch.trace(torch.mm(torch.mm(Y... |
SimSiamLoss | # 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.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class SimSiamLoss(nn.Module):
def __init__(self, version='simplified'):
super().__init__()
self.ver = version
def asymmetric_loss(self, p, z):
if ... | 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
import ... | Yif-Yang/DSSL | SimSiamLoss | false | 18,167 | [
"MIT"
] | 8 | 79a000450cfe66836089ecd5e2467863cc702e1c | https://github.com/Yif-Yang/DSSL/tree/79a000450cfe66836089ecd5e2467863cc702e1c | import torch
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Model(nn.Module):
def __init__(self, version='simplified'):
super().__init__()
self.ver = version
def asymmetric_loss(self, p, z):
if self.v... |
feedforwardLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 feedforwardLayer(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.3):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hid)
self.w_2 = nn.Linear(d_hid, d_in)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Yottaxx/T-LSTM | feedforwardLayer | false | 18,168 | [
"MIT"
] | 9 | 92618d8c3ee2418b194a2e1592512548da955b77 | https://github.com/Yottaxx/T-LSTM/tree/92618d8c3ee2418b194a2e1592512548da955b77 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.3):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hid)
self.w_2 = nn.Linear(d_hid, d_in)
self.layer_no... |
L_2nd | # 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 L_2nd(nn.Module):
def __init__(self, beta):
super(L_2nd, self).__init__()
self.beta = beta
def forward(self, y_pred, y_true):
b = torch.ones_like(y_true)
b[y_true != 0] = self.beta
x = ((y_true - y_pred) * b) ** 2
t = 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ZagHe568/graph_embedding | L_2nd | false | 18,169 | [
"MIT"
] | 4 | 2a6f8214ce4b30b51eb9f1904b64fe782876f010 | https://github.com/ZagHe568/graph_embedding/tree/2a6f8214ce4b30b51eb9f1904b64fe782876f010 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, beta):
super().__init__()
self.beta = beta
def forward(self, y_pred, y_true):
b = torch.ones_like(y_true)
b[y_true != 0] = self.beta
x = ((y_true - y_pred) * b) ** 2
t = torch.sum(x,... |
net_nvidia_pytorch | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 LambdaLayer(nn.Module):
def __init__(self, lambd):
super(LambdaLayer, self).__init__()
self.lambd = lambd
def forward(self, x):
return self.lambd(x)
class net_nvidia_pytorch(nn.Module):
def __init__(self)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | YuShen0118/SAAP_Auto-driving_Platform | net_nvidia_pytorch | false | 18,170 | [
"MIT"
] | 4 | 785f899fb3b3ad92075318f9fcb69b8e09597202 | https://github.com/YuShen0118/SAAP_Auto-driving_Platform/tree/785f899fb3b3ad92075318f9fcb69b8e09597202 | import torch
import torch.nn as nn
import torch.nn.functional as F
class LambdaLayer(nn.Module):
def __init__(self, lambd):
super().__init__()
self.lambd = lambd
def forward(self, x):
return self.lambd(x)
class Model(nn.Module):
def __init__(self):
super().__init__()
... |
TverskyLoss | # 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 TverskyLoss(nn.Module):
"""DiceLoss implemented from 'Dice Loss for Data-imbalanced NLP Tasks'
Useful in dealing with unbalanced data
Add softmax automatically
"""
def __init__(self):
super(TverskyLoss, self).__init__()
self.m = nn.Sigmoid()... | 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... | ZhaoZhibin/Physionet2020model | TverskyLoss | false | 18,171 | [
"BSD-2-Clause",
"MIT"
] | 6 | ea7379bd1e4c145c84fd254faa0d5d1330cd2f6e | https://github.com/ZhaoZhibin/Physionet2020model/tree/ea7379bd1e4c145c84fd254faa0d5d1330cd2f6e | import torch
from torch import nn
class Model(nn.Module):
"""DiceLoss implemented from 'Dice Loss for Data-imbalanced NLP Tasks'
Useful in dealing with unbalanced data
Add softmax automatically
"""
def __init__(self):
super().__init__()
self.m = nn.Sigmoid()
self.gamma = 1... |
CAMBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 CAMBlock(nn.Module):
def __init__(self):
super(CAMBlock, self).__init__()
self.maxpool = nn.AdaptiveMaxPool1d(1)
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.conv = nn.Conv1d(2, 1, 7, padding=3)
self.sigmoid = nn.Sigmoid()
def f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | YuRui8879/CPSC2021_python | CAMBlock | false | 18,172 | [
"MIT"
] | 4 | bfa4c565ec3113528e73b064041082863cd228b4 | https://github.com/YuRui8879/CPSC2021_python/tree/bfa4c565ec3113528e73b064041082863cd228b4 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.maxpool = nn.AdaptiveMaxPool1d(1)
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.conv = nn.Conv1d(2, 1, 7, padding=3)
self.sigmoid = nn.Sigmoid()
def forward(self, 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
import torch.nn as nn
import torch.nn.functional as F
class MaxPoolStride1(nn.Module):
def __init__(self):
super(MaxPoolStride1, self).__init__()
def forward(self, x):
x = F.max_pool2d(F.pad(x, (0, 1, 0, 1), mode='replicate'), 2, stride=1)
return x
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Zhang-Jack/adversarial_yolo2 | MaxPoolStride1 | false | 18,173 | [
"MIT"
] | 8 | 91c2a4793047f656482cebf0309984db823e8030 | https://github.com/Zhang-Jack/adversarial_yolo2/tree/91c2a4793047f656482cebf0309984db823e8030 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x = F.max_pool2d(F.pad(x, (0, 1, 0, 1), mode='replicate'), 2, stride=1)
return x
def get_inputs():
return [torch.rand([4, 4, 4... |
GNN_Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | Zhen-Tan-dmml/GFCIL | GNN_Encoder | false | 18,174 | [
"MIT"
] | 7 | 9b78210418711a795280c588f55aef63f7df5b3b | https://github.com/Zhen-Tan-dmml/GFCIL/tree/9b78210418711a795280c588f55aef63f7df5b3b | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __in... |
ResidualDenseBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
from torch.utils import data as data
from torch import nn as nn
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from torchvision.models import vgg as vgg
from torch import autograd as autograd
@torch.no_grad()
def default_init_weights(module_lis... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch.utils import data as data
from torch import n... | WoojunePark/BasicSR | ResidualDenseBlock | false | 18,175 | [
"Apache-2.0"
] | 9 | e0910b022b924bb913045fc412a5470dc2242cf0 | https://github.com/WoojunePark/BasicSR/tree/e0910b022b924bb913045fc412a5470dc2242cf0 | import torch
import torch.utils.data
from torch.utils import data as data
from torch import nn as nn
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from torchvision.models import vgg as vgg
from torch import autograd as autograd
@torch.no_grad()
def default_init_weights(module_lis... |
TotalVariation | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class TotalVariation(nn.Module):
"""TotalVariation: calculates the total variation of a patch.
Module providing the functionality necessary to calculate the total vatiation (TV) of an adversarial patch.
"""
def __init__(self):
super(TotalVariation, self)._... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | Zhang-Jack/adversarial_yolo2 | TotalVariation | false | 18,176 | [
"MIT"
] | 8 | 91c2a4793047f656482cebf0309984db823e8030 | https://github.com/Zhang-Jack/adversarial_yolo2/tree/91c2a4793047f656482cebf0309984db823e8030 | import torch
import torch.nn as nn
class Model(nn.Module):
"""TotalVariation: calculates the total variation of a patch.
Module providing the functionality necessary to calculate the total vatiation (TV) of an adversarial patch.
"""
def __init__(self):
super().__init__()
def forward(se... |
Reorg | # 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 Reorg(nn.Module):
def __init__(self, stride=2):
super(Reorg, self).__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert x.data.dim() == 4
B = x.data.size(0)
C = x.data.size(1)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Zhang-Jack/adversarial_yolo2 | Reorg | false | 18,177 | [
"MIT"
] | 8 | 91c2a4793047f656482cebf0309984db823e8030 | https://github.com/Zhang-Jack/adversarial_yolo2/tree/91c2a4793047f656482cebf0309984db823e8030 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, stride=2):
super().__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert x.data.dim() == 4
B = x.data.size(0)
C = x.data.size(1)
H = x.data.size(2... |
TimeDecayMSELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import Tensor
from torch import nn
class TimeDecayMSELoss(nn.Module):
def __init__(self, decay_factor=0.99):
super().__init__()
self.decay_factor = decay_factor
def forward(self, input: 'Tensor', target: 'Tensor') ->Tensor:
size = [input.size(0), -1]
i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Zinoex/hyperverlet | TimeDecayMSELoss | false | 18,178 | [
"MIT"
] | 7 | 431ef92fa2448ce69c357f01c0862353067bfa8a | https://github.com/Zinoex/hyperverlet/tree/431ef92fa2448ce69c357f01c0862353067bfa8a | import torch
from torch import Tensor
from torch import nn
class Model(nn.Module):
def __init__(self, decay_factor=0.99):
super().__init__()
self.decay_factor = decay_factor
def forward(self, input: 'Tensor', target: 'Tensor') ->Tensor:
size = [input.size(0), -1]
input = inpu... |
AvgConsensus | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class AvgConsensus(nn.Module):
def __init__(self, cfg):
super(AvgConsensus, self).__init__()
pass
def forward(self, input, dim=0):
assert isinstance(input, torch.Tensor)
output = input.mean(dim=d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ZJCV/X3D | AvgConsensus | false | 18,179 | [
"Apache-2.0"
] | 10 | 1635fe4ade5ac5e0bd8f272262cec73c7a12f0fb | https://github.com/ZJCV/X3D/tree/1635fe4ade5ac5e0bd8f272262cec73c7a12f0fb | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, cfg):
super().__init__()
pass
def forward(self, input, dim=0):
assert isinstance(input, torch.Tensor)
output = input.mean(dim=dim, keepdim=False)
... |
AEBatch | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch._utils
class AEBatch(nn.Module):
def __init__(self):
super(AEBatch, self).__init__()
def forward(self, estimated_density_map, gt_num):
return torch.abs(torch.sum(estimated_density_map, dim=(1, 2, 3)) -
gt_num)
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch._utils
assert_size_stride = torch._C._... | Zhaoyi-Yan/DCANet | AEBatch | false | 18,180 | [
"MIT"
] | 3 | 1d99481494f4ef3cfe5abf227fa49a51011364bf | https://github.com/Zhaoyi-Yan/DCANet/tree/1d99481494f4ef3cfe5abf227fa49a51011364bf | import torch
import torch.nn as nn
import torch._utils
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, estimated_density_map, gt_num):
return torch.abs(torch.sum(estimated_density_map, dim=(1, 2, 3)) -
gt_num)
def get_inputs():
return [torch... |
SEBatch | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch._utils
class SEBatch(nn.Module):
def __init__(self):
super(SEBatch, self).__init__()
def forward(self, estimated_density_map, gt_num):
return torch.pow(torch.sum(estimated_density_map, dim=(1, 2, 3)) -
gt_num, 2)
def get_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._utils
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyn... | Zhaoyi-Yan/DCANet | SEBatch | false | 18,181 | [
"MIT"
] | 3 | 1d99481494f4ef3cfe5abf227fa49a51011364bf | https://github.com/Zhaoyi-Yan/DCANet/tree/1d99481494f4ef3cfe5abf227fa49a51011364bf | import torch
import torch.nn as nn
import torch._utils
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, estimated_density_map, gt_num):
return torch.pow(torch.sum(estimated_density_map, dim=(1, 2, 3)) -
gt_num, 2)
def get_inputs():
return [to... |
SelfGating | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch
import torch.nn as nn
class SelfGating(nn.Module):
def __init__(self, input_dim):
super(SelfGating, self).__init__()
self.fc = nn.Linear(input_dim, input_dim)
def forward(self, input_tensor):
"""Feature gating as used in S3D-G"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = ... | ZhaofanQiu/Optimization-Planning-for-3D-ConvNets | SelfGating | false | 18,182 | [
"Apache-2.0"
] | 6 | d9f1b777811ca0d8f462798ca2efcea39b96fcc5 | https://github.com/ZhaofanQiu/Optimization-Planning-for-3D-ConvNets/tree/d9f1b777811ca0d8f462798ca2efcea39b96fcc5 | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.fc = nn.Linear(input_dim, input_dim)
def forward(self, input_tensor):
"""Feature gating as used in S3D-G"""
spatiotemporal_av... |
PatchApplier | # 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 PatchApplier(nn.Module):
"""PatchApplier: applies adversarial patches to images.
Module providing the functionality necessary to apply a patch to all detections in all images in the batch.
"""
def __init__(self):
super(PatchApplier, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Zhang-Jack/adversarial_yolo2 | PatchApplier | false | 18,183 | [
"MIT"
] | 8 | 91c2a4793047f656482cebf0309984db823e8030 | https://github.com/Zhang-Jack/adversarial_yolo2/tree/91c2a4793047f656482cebf0309984db823e8030 | import torch
import torch.nn as nn
class Model(nn.Module):
"""PatchApplier: applies adversarial patches to images.
Module providing the functionality necessary to apply a patch to all detections in all images in the batch.
"""
def __init__(self):
super().__init__()
def forward(self, im... |
BinaryReg | # 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 BinaryReg(nn.Module):
"""Regularization for encouraging the outputs to be binary.
"""
def __init__(self, alpha=1.0):
super().__init__()
self.alpha = alpha
def forward(self, input):
diff = input - 0.5
dif... | 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
... | aarushgupta/pytorch_connectomics | BinaryReg | false | 18,184 | [
"MIT"
] | 5 | eb90ada14dbd425a741f481761d1ed9ea633e67c | https://github.com/aarushgupta/pytorch_connectomics/tree/eb90ada14dbd425a741f481761d1ed9ea633e67c | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""Regularization for encouraging the outputs to be binary.
"""
def __init__(self, alpha=1.0):
super().__init__()
self.alpha = alpha
def forward(self, input):
diff = input - 0.5
diff = ... |
MeanNormLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import Tensor
from torch import nn
class MeanNormLoss(nn.Module):
def forward(self, input: 'Tensor', target: 'Tensor') ->Tensor:
size = [input.size(0), input.size(1), -1]
input = input.view(*size)
target = target.view(*size)
diff = target - input
lo... | 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... | Zinoex/hyperverlet | MeanNormLoss | false | 18,185 | [
"MIT"
] | 7 | 431ef92fa2448ce69c357f01c0862353067bfa8a | https://github.com/Zinoex/hyperverlet/tree/431ef92fa2448ce69c357f01c0862353067bfa8a | import torch
from torch import Tensor
from torch import nn
class Model(nn.Module):
def forward(self, input: 'Tensor', target: 'Tensor') ->Tensor:
size = [input.size(0), input.size(1), -1]
input = input.view(*size)
target = target.view(*size)
diff = target - input
loss = to... |
MSEScalarLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
from functools import reduce
class MSEScalarLoss(nn.Module):
def __init__(self):
super(MSEScalarLoss, self).__init__()
def forward(self, x, gt_map):
return torch.pow(x.sum() - gt_map.sum(), 2) / reduce(lambda a, b: a *
b, x.shape)
def get_inpu... | 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... | Zhaoyi-Yan/PFDNet | MSEScalarLoss | false | 18,186 | [
"MIT"
] | 4 | 86798fbc4fadc673e7912c08492ea3611bc20154 | https://github.com/Zhaoyi-Yan/PFDNet/tree/86798fbc4fadc673e7912c08492ea3611bc20154 | import torch
import torch.nn as nn
from functools import reduce
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, gt_map):
return torch.pow(x.sum() - gt_map.sum(), 2) / reduce(lambda a, b: a *
b, x.shape)
def get_inputs():
return [torch.ran... |
ResNetV2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 collections import OrderedDict
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
from collections.__init__ import OrderedDict
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BigFishMaster/tnt | ResNetV2 | false | 18,187 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
from collections.__init__ import OrderedDict
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation... |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, base_class_num, nway, dropout):
super(Attention, self).__init__()
self.fc1 = nn.Linear(base_class_num, base_class_num // 2)
self.fc2 = nn.Linear(base_class_num // 2, nway)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Zhen-Tan-dmml/GFCIL | Attention | false | 18,188 | [
"MIT"
] | 7 | 9b78210418711a795280c588f55aef63f7df5b3b | https://github.com/Zhen-Tan-dmml/GFCIL/tree/9b78210418711a795280c588f55aef63f7df5b3b | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, base_class_num, nway, dropout):
super().__init__()
self.fc1 = nn.Linear(base_class_num, base_class_num // 2)
self.fc2 = nn.Linear(base_class_num // 2, nway)
self.fc3 = nn.... |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, num_heads, model_dim, k_dim=None, v_dim=None,
out_dim=None, temperature=None, dropout=0, score_function=
'scaled_dot_product'):
super(Attention,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ZhengZixiang/OpenTC | Attention | false | 18,189 | [
"MIT"
] | 5 | 00306c4736d50f8f53c21c1dd0559144a8fcafa9 | https://github.com/ZhengZixiang/OpenTC/tree/00306c4736d50f8f53c21c1dd0559144a8fcafa9 | import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_heads, model_dim, k_dim=None, v_dim=None,
out_dim=None, temperature=None, dropout=0, score_function=
'scaled_dot_product'):
super().__init__()
... |
GlobalAvgPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class GlobalAvgPool2d(nn.Module):
def __init__(self):
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
N = x.data.size(0)
C = x.data.size(1)
H = x.data.size(2)
W = x.data.size(3)
x... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Zhang-Jack/adversarial_yolo2 | GlobalAvgPool2d | false | 18,190 | [
"MIT"
] | 8 | 91c2a4793047f656482cebf0309984db823e8030 | https://github.com/Zhang-Jack/adversarial_yolo2/tree/91c2a4793047f656482cebf0309984db823e8030 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
N = x.data.size(0)
C = x.data.size(1)
H = x.data.size(2)
W = x.data.size(3)
x = F.avg_pool2d(x, (H, W))
... |
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