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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
CONV1d_FusionBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.parallel
import torch.optim
import torch
import torch.nn as nn
class CONV1d_FusionBlock(nn.Module):
def __init__(self, in_channels, n_segment, n_div):
super(CONV1d_FusionBlock, self).__init__()
self.n_div = n_div
self.fold = in_channels // n_div
self.n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.parallel
import torch.optim
import torch
import torch.nn as nn
a... | RongchangLi/DEN | CONV1d_FusionBlock | false | 17,884 | [
"MIT"
] | 4 | f8b744f96a3a68cf0784080ffd561a5279715727 | https://github.com/RongchangLi/DEN/tree/f8b744f96a3a68cf0784080ffd561a5279715727 | import torch
import torch.nn.parallel
import torch.optim
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_channels, n_segment, n_div):
super().__init__()
self.n_div = n_div
self.fold = in_channels // n_div
self.n_segment = n_segment
self.tem... |
channel_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
from torch import nn
class channel_attention(nn.Module):
def __init__(self, in_channels, feature_size):
super(channel_attention, self).__init__()
self.fc1 = nn.Linear(feature_size * feature_size, feature_size,
bias=False)
self.relu1 = nn.ReLU(inplace=True)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SCUT-AILab/AFA | channel_attention | false | 17,885 | [
"BSD-3-Clause"
] | 7 | acfb42236ce0114d63f22a821fc5954c8c149f45 | https://github.com/SCUT-AILab/AFA/tree/acfb42236ce0114d63f22a821fc5954c8c149f45 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, in_channels, feature_size):
super().__init__()
self.fc1 = nn.Linear(feature_size * feature_size, feature_size,
bias=False)
self.relu1 = nn.ReLU(inplace=True)
self.fc2 = nn.Linear(feature_size,... |
MLPSoftQNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 MLPSoftQNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size1=1400,
hidden_size2=1024, hidden_size3=256, init_w=0.003):
super(MLPSoftQNetwork, self).__init__()
self.linear1 = nn.Linear(num_inpu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | SAMMiCA/DL_based_E2E_Driving | MLPSoftQNetwork | false | 17,886 | [
"MIT"
] | 4 | 01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | https://github.com/SAMMiCA/DL_based_E2E_Driving/tree/01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size1=1400,
hidden_size2=1024, hidden_size3=256, init_w=0.003):
super().__init__()
self.linear1 = nn.Linear(num_inputs + num_actions, hidden_size1)... |
Fusion | # 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 Fusion(nn.Module):
""" Crazy multi-modal fusion: negative squared difference minus relu'd sum
"""
def __init__(self):
super().__init__()
def forward(self, x, y):
return -(x - y) ** 2 + torch.relu(x + y)
def get_inputs():
return [torch.ra... | 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... | Ruiver/CTCNet | Fusion | false | 17,887 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 | import torch
import torch.nn as nn
class Model(nn.Module):
""" Crazy multi-modal fusion: negative squared difference minus relu'd sum
"""
def __init__(self):
super().__init__()
def forward(self, x, y):
return -(x - y) ** 2 + torch.relu(x + y)
def get_inputs():
return [torch.ran... |
EncoderBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
class EncoderBlock(nn.Module):
"""
Encoder block class
"""
def __init__(self, in_channels, out_channels, k_size, pad_size):
super(EncoderBlock, self).__init__()
self.conv1 = nn.Conv3d(in_channels, out_channels, kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SVRTK/Segmentation_FetalMRI | EncoderBlock | false | 17,888 | [
"Apache-2.0"
] | 6 | 9344a2248cbe8e4cccbe05ca98214626dcf62805 | https://github.com/SVRTK/Segmentation_FetalMRI/tree/9344a2248cbe8e4cccbe05ca98214626dcf62805 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""
Encoder block class
"""
def __init__(self, in_channels, out_channels, k_size, pad_size):
super().__init__()
self.conv1 = nn.Conv3d(in_channels, out_channels, kernel_size=
k_size, ... |
pixel_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
from torch import nn
class pixel_attention(nn.Module):
def __init__(self, in_channels, feature_size):
super(pixel_attention, self).__init__()
self.fc1 = nn.Linear(feature_size * feature_size, feature_size,
bias=False)
self.relu1 = nn.ReLU(inplace=True)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SCUT-AILab/AFA | pixel_attention | false | 17,889 | [
"BSD-3-Clause"
] | 7 | acfb42236ce0114d63f22a821fc5954c8c149f45 | https://github.com/SCUT-AILab/AFA/tree/acfb42236ce0114d63f22a821fc5954c8c149f45 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, in_channels, feature_size):
super().__init__()
self.fc1 = nn.Linear(feature_size * feature_size, feature_size,
bias=False)
self.relu1 = nn.ReLU(inplace=True)
self.fc2 = nn.Linear(feature_size,... |
QREmbeddingBag | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class QREmbeddingBag(nn.Module):
"""Computes sums or means over two 'bags' of embeddings, one using the quotient
of the indices and the other using the remainder of the indices, witho... | 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 numpy as np
import torch.nn as nn
from torch.nn.parameter import Paramet... | STAR-Laboratory/Accelerating-RecSys-Training | QREmbeddingBag | false | 17,890 | [
"MIT"
] | 5 | e43cae6fd543813b352b01510e846febd67944ad | https://github.com/STAR-Laboratory/Accelerating-RecSys-Training/tree/e43cae6fd543813b352b01510e846febd67944ad | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class Model(nn.Module):
"""Computes sums or means over two 'bags' of embeddings, one using the quotient
of the indices and the other using the remainder of the indices, without
in... |
Classifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
self.activate = activate.low... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Ruiver/CTCNet | Classifier | false | 17,891 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 | import torch
import torch.nn as nn
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super().__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
self.activate = activate.lower() if act... |
MLPPolicyNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 torch.distributions import Normal
class MLPPolicyNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size1=1400,
hidden_size2=1024, hidden_size3=256, init_w=0.003, log_std_min=-20,
log_std_max=2):
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | SAMMiCA/DL_based_E2E_Driving | MLPPolicyNetwork | false | 17,892 | [
"MIT"
] | 4 | 01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | https://github.com/SAMMiCA/DL_based_E2E_Driving/tree/01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
class Model(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size1=1400,
hidden_size2=1024, hidden_size3=256, init_w=0.003, log_std_min=-20,
log_std_max=2):
super().__in... |
HardWeightedSum | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 HardWeightedSum(nn.Module):
def __init__(self, op_number=2, act=nn.ReLU, eps=0.0001):
super(HardWeightedSum, self).__init__()
shape = op_number, 1, 1, 1, 1
self.weights = nn.Parameter(torch.ones(shape), requires_grad=True)
self.act = act()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Senyaaa/detection-experiments | HardWeightedSum | false | 17,893 | [
"Apache-2.0"
] | 5 | 5e80dd458e886ca27db5420d25ade8f9d74ae5a8 | https://github.com/Senyaaa/detection-experiments/tree/5e80dd458e886ca27db5420d25ade8f9d74ae5a8 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, op_number=2, act=nn.ReLU, eps=0.0001):
super().__init__()
shape = op_number, 1, 1, 1, 1
self.weights = nn.Parameter(torch.ones(shape), requires_grad=True)
self.act = act()
self.eps = eps
def ... |
DecoderBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 functools import partial
import torch.nn.functional as F
from torch import nn
class DecoderBlock(nn.Module):
"""
Decoder block class
"""
def __init__(self, in_channels, middle_channels, out_channels, k_size,
pad_size):
super(DecoderBlock, self).__init__()
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SVRTK/Segmentation_FetalMRI | DecoderBlock | false | 17,894 | [
"Apache-2.0"
] | 6 | 9344a2248cbe8e4cccbe05ca98214626dcf62805 | https://github.com/SVRTK/Segmentation_FetalMRI/tree/9344a2248cbe8e4cccbe05ca98214626dcf62805 | import torch
from functools import partial
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""
Decoder block class
"""
def __init__(self, in_channels, middle_channels, out_channels, k_size,
pad_size):
super().__init__()
self.conv1 = nn.Conv3d(in_ch... |
SoftMaxWeightedSum | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SoftMaxWeightedSum(nn.Module):
def __init__(self, op_number=2):
super(SoftMaxWeightedSum, self).__init__()
shape = op_number, 1, 1, 1, 1
self.weights = nn.Parameter(torch.ones(shape), requires_grad=True)
def forward(self, x):
return tor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | Senyaaa/detection-experiments | SoftMaxWeightedSum | false | 17,895 | [
"Apache-2.0"
] | 5 | 5e80dd458e886ca27db5420d25ade8f9d74ae5a8 | https://github.com/Senyaaa/detection-experiments/tree/5e80dd458e886ca27db5420d25ade8f9d74ae5a8 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, op_number=2):
super().__init__()
shape = op_number, 1, 1, 1, 1
self.weights = nn.Parameter(torch.ones(shape), requires_grad=True)
def forward(self, x):
return torch.sum(torch.softmax(self.weights, di... |
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, n_features, n_modes, T):
super(Decoder, self).__init__()
self.n_modes = n_modes
self.T = T
self.linear1 = nn.Linear(n_features, 4096)
self.linear2 = nn.Linear(512, n_modes * T * 2)
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SambaranRepo/VectorNet_Waymo | Decoder | false | 17,896 | [
"MIT"
] | 4 | 454016a5020444e78943786c14e4e12a75ce052e | https://github.com/SambaranRepo/VectorNet_Waymo/tree/454016a5020444e78943786c14e4e12a75ce052e | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n_features, n_modes, T):
super().__init__()
self.n_modes = n_modes
self.T = T
self.linear1 = nn.Linear(n_features, 4096)
self.linear2 = nn.Linear(512, n_modes * T * 2)
self.linear3 = nn.L... |
resBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class resBlock(nn.Module):
def __init__(self, channelDepth, windowSize=3):
super(resBlock, self).__init__()
self.pad = nn.ReflectionPad2d(1)
self.IN_conv1 = nn.InstanceNorm2d(channelDepth)
self.conv1 = nn.Conv2d(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
from torch._inductor.runtime.... | SeokjaeLIM/DSSN_release-Pytorch | resBlock | false | 17,897 | [
"Apache-2.0"
] | 7 | fef1dac120d7b83367b4c69f239b089ab5f004d7 | https://github.com/SeokjaeLIM/DSSN_release-Pytorch/tree/fef1dac120d7b83367b4c69f239b089ab5f004d7 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, channelDepth, windowSize=3):
super().__init__()
self.pad = nn.ReflectionPad2d(1)
self.IN_conv1 = nn.InstanceNorm2d(channelDepth)
self.conv1 = nn.Conv2d(channelDepth, chann... |
WeightedFeatureFusion | # 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 torchvision.models.resnet import *
import torch.utils.data
class WeightedFeatureFusion(nn.Module):
def __init__(self, layers, weight=False):
super(WeightedFeatureFusion, self).__init__()
self.layers = layers
self.weight = weight
self.n = len... | 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
from torchvision.models.resnet import *
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_si... | PanJason/ML_Proj | WeightedFeatureFusion | false | 17,898 | [
"MIT"
] | 4 | 663be12e8eb6e30e3c902a4984ac0db33bfce605 | https://github.com/PanJason/ML_Proj/tree/663be12e8eb6e30e3c902a4984ac0db33bfce605 | import torch
import torch.nn as nn
from torchvision.models.resnet import *
import torch.utils.data
class Model(nn.Module):
def __init__(self, layers, weight=False):
super().__init__()
self.layers = layers
self.weight = weight
self.n = len(layers) + 1
if weight:
... |
ConformerFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.utils.data
import torch.optim
class Swish(nn.Module):
"""
Swish activation function introduced in 'https://arxiv.org/abs/1710.05941'
"""
def forward(self, x):
return x * torch.sigmoid(x)
class ConformerFeedForward(nn.Module):
"""
feed-f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.optim
assert_size_stri... | ShantanuNair/NeMo | ConformerFeedForward | false | 17,899 | [
"Apache-2.0"
] | 10 | d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b | https://github.com/ShantanuNair/NeMo/tree/d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b | import torch
from torch import nn
import torch.utils.data
import torch.optim
class Swish(nn.Module):
"""
Swish activation function introduced in 'https://arxiv.org/abs/1710.05941'
"""
def forward(self, x):
return x * torch.sigmoid(x)
class Model(nn.Module):
"""
feed-forward module o... |
FusionAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
class FusionAttention(nn.Module):
def __init__(self, dim):
super(FusionAttention, self).__init__()
self.attention_matrix = nn.Linear(dim, dim)
self.project_weight = nn.Linear(dim, 1)
def forward(self, inputs):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Seondong/Customs-Fraud-Detection | FusionAttention | false | 17,900 | [
"MIT"
] | 7 | eb9e4641a78cb32d73787de86dd72ebb09df1452 | https://github.com/Seondong/Customs-Fraud-Detection/tree/eb9e4641a78cb32d73787de86dd72ebb09df1452 | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, dim):
super().__init__()
self.attention_matrix = nn.Linear(dim, dim)
self.project_weight = nn.Linear(dim, 1)
def forward(self, inputs):
query_project = self.attention... |
MultiLayerPerceptron | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.optim
class MultiLayerPerceptron(torch.nn.Module):
"""
A simple MLP that can either be used independently or put on top
of pretrained models (such as BERT) and act as a classifier.
Args:
hidden_size (int): the size of each layer
num_cla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ShantanuNair/NeMo | MultiLayerPerceptron | false | 17,901 | [
"Apache-2.0"
] | 10 | d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b | https://github.com/ShantanuNair/NeMo/tree/d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b | import torch
import torch.utils.data
import torch.optim
class Model(torch.nn.Module):
"""
A simple MLP that can either be used independently or put on top
of pretrained models (such as BERT) and act as a classifier.
Args:
hidden_size (int): the size of each layer
num_classes (int): num... |
_Residual_Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class _Residual_Block(nn.Module):
def __init__(self):
super(_Residual_Block, self).__init__()
self.conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size
=3, stride=1, padding=1, bias=False)
self.in1 = nn.InstanceNorm2d(64, affine=Tru... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Shandilya21/Improved-Optimization-Tecniques-for-Super-Resoultion-in-Images | _Residual_Block | false | 17,902 | [
"MIT"
] | 10 | d903d99706f557d74e00d4395e7d316172a9f7ee | https://github.com/Shandilya21/Improved-Optimization-Tecniques-for-Super-Resoultion-in-Images/tree/d903d99706f557d74e00d4395e7d316172a9f7ee | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size
=3, stride=1, padding=1, bias=False)
self.in1 = nn.InstanceNorm2d(64, affine=True)
self.relu = nn.Leaky... |
DyIntraModalityUpdate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ruiver/CTCNet | DyIntraModalityUpdate | false | 17,903 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 | import torch
import torch.nn as nn
import torch.nn.functional as F
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super().__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
self.ac... |
ResnetDecoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ResnetDecoder(nn.Module):
"""
This class represents the tail of ResNet. It performs a global pooling and maps the output to the
correct class by using a fully connected layer.
"""
def __init__(self, in_features, n_classes):
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | SeffyVon/ECG_MICResNet | ResnetDecoder | false | 17,904 | [
"BSD-3-Clause"
] | 5 | 8c6a319b5822ddfb130738eb1d9cdc3c21b24209 | https://github.com/SeffyVon/ECG_MICResNet/tree/8c6a319b5822ddfb130738eb1d9cdc3c21b24209 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
This class represents the tail of ResNet. It performs a global pooling and maps the output to the
correct class by using a fully connected layer.
"""
def __init__(self, in_features, n_classes):
super().__init__()
self.... |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.init as init
class Net(nn.Module):
def __init__(self, upscale_factor):
super(Net, self).__init__()
self.upscale_factor = int(upscale_factor)
self.relu = nn.ReLU()
self.conv1 = nn.Conv2d(1, 64, kernel_size=5, padding=2)
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 ... | PiSchool/esa-superresolution-forecasting | Net | false | 17,905 | [
"MIT"
] | 4 | 3c01770dd64749d6b6c40e1068a96a3307c8c035 | https://github.com/PiSchool/esa-superresolution-forecasting/tree/3c01770dd64749d6b6c40e1068a96a3307c8c035 | import torch
import torch.nn as nn
import torch.nn.init as init
class Model(nn.Module):
def __init__(self, upscale_factor):
super().__init__()
self.upscale_factor = int(upscale_factor)
self.relu = nn.ReLU()
self.conv1 = nn.Conv2d(1, 64, kernel_size=5, padding=2)
self.conv2... |
OneSideInterModalityUpdate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ruiver/CTCNet | OneSideInterModalityUpdate | false | 17,906 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 | import torch
import torch.nn as nn
import torch.nn.functional as F
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super().__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
self.ac... |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, input, target):
N = target.size(0)
smooth = 1
input_flat = input.view(N, -1)
target_flat = target.view(N, -1)
intersection = 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | SeffyVon/ECG_MICResNet | DiceLoss | false | 17,907 | [
"BSD-3-Clause"
] | 5 | 8c6a319b5822ddfb130738eb1d9cdc3c21b24209 | https://github.com/SeffyVon/ECG_MICResNet/tree/8c6a319b5822ddfb130738eb1d9cdc3c21b24209 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, target):
N = target.size(0)
smooth = 1
input_flat = input.view(N, -1)
target_flat = target.view(N, -1)
intersection = input_flat * target... |
deepmind | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 deepmind(nn.Module):
def __init__(self):
super(deepmind, self).__init__()
self.conv1 = nn.Conv2d(4, 32, 8, stride=4)
self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
self.conv3 = nn.Conv2d(64, 32, 3, stride=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Rowing0914/TF2_RL | deepmind | false | 17,908 | [
"MIT"
] | 8 | c1b7f9b376cbecf01deb17f76f8e761035ed336a | https://github.com/Rowing0914/TF2_RL/tree/c1b7f9b376cbecf01deb17f76f8e761035ed336a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(4, 32, 8, stride=4)
self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
self.conv3 = nn.Conv2d(64, 32, 3, stride=1)
nn.init.orth... |
Bias | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 Bias(nn.Module):
def __init__(self):
super(Bias, self).__init__()
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, feat_img, feat_sound):
B, C, H, W = feat_sound.size()
feat_img = feat_img.view(B, 1, C)
z = torch.bmm(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | SheldonTsui/Minus-Plus-Network | Bias | false | 17,909 | [
"Apache-2.0"
] | 5 | 7aa281b17f637a9f168aaf250039e560027a3817 | https://github.com/SheldonTsui/Minus-Plus-Network/tree/7aa281b17f637a9f168aaf250039e560027a3817 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, feat_img, feat_sound):
B, C, H, W = feat_sound.size()
feat_img = feat_img.view(B, 1, C)
z = torch.bmm(feat_img,... |
projection_model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
class projection_model(torch.nn.Module):
def __init__(self, neo_hidden, clip_hidden=512):
super(projection_model, self).__init__()
self.fc1 = torch.nn.Linear(neo_hidden, neo_hidden // 2)
self.act = torch.nn.GELU()
self.fc2 = torch.nn.Linear(neo_hidden // 2, clip_hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | ShivanshuPurohit/GPT-Neo-visual-grounding | projection_model | false | 17,910 | [
"Apache-2.0"
] | 4 | 9c938257a688ef5ae8bc1b87b61d943aa158e880 | https://github.com/ShivanshuPurohit/GPT-Neo-visual-grounding/tree/9c938257a688ef5ae8bc1b87b61d943aa158e880 | import torch
class Model(torch.nn.Module):
def __init__(self, neo_hidden, clip_hidden=512):
super().__init__()
self.fc1 = torch.nn.Linear(neo_hidden, neo_hidden // 2)
self.act = torch.nn.GELU()
self.fc2 = torch.nn.Linear(neo_hidden // 2, clip_hidden)
def forward(self, input_t... |
DSCLoss | # 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 DSCLoss(nn.Module):
def __init__(self):
super(DSCLoss, self).__init__()
def forward(self, input, target):
N = target.size(0)
smooth = 1
input_flat = input.view(N, -1)
target_flat = target.view(N, -1)
input_flat * target... | 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... | SeffyVon/ECG_MICResNet | DSCLoss | false | 17,911 | [
"BSD-3-Clause"
] | 5 | 8c6a319b5822ddfb130738eb1d9cdc3c21b24209 | https://github.com/SeffyVon/ECG_MICResNet/tree/8c6a319b5822ddfb130738eb1d9cdc3c21b24209 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, target):
N = target.size(0)
smooth = 1
input_flat = input.view(N, -1)
target_flat = target.view(N, -1)
input_flat * target_flat
n... |
TwoMLPHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class TwoMLPHead(nn.Module):
"""
Standard heads for FPN-based models
Arguments:
in_channels (int): number of input channels
representation_size (int): size of the intermediate representation
"""
def __init__(self, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Sense-GVT/BigPretrain | TwoMLPHead | false | 17,912 | [
"Apache-2.0"
] | 8 | d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | https://github.com/Sense-GVT/BigPretrain/tree/d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Standard heads for FPN-based models
Arguments:
in_channels (int): number of input channels
representation_size (int): size of the intermediate representation
"""
def __init__(self, in_ch... |
InterModalityUpdate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ruiver/CTCNet | InterModalityUpdate | false | 17,913 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 | import torch
import torch.nn as nn
import torch.nn.functional as F
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super().__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
self.ac... |
C3D_mini | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 C3D_mini(nn.Module):
""" The C3D_mini network """
def __init__(self, num_classes=2, pretrained=False):
super(C3D_mini, self).__init__()
self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.pool1 = nn.MaxPool3d(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
import torch.nn as nn
assert_... | Ontheway361/C3D | C3D_mini | false | 17,914 | [
"MIT"
] | 7 | 7aa5364d8c0c6bddc17b1b8939b198fe66e282ca | https://github.com/Ontheway361/C3D/tree/7aa5364d8c0c6bddc17b1b8939b198fe66e282ca | import torch
import torch.nn as nn
class Model(nn.Module):
""" The C3D_mini network """
def __init__(self, num_classes=2, pretrained=False):
super().__init__()
self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.pool1 = nn.MaxPool3d(kernel_size=(1, 2, 2), stri... |
InnerProd | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 InnerProd(nn.Module):
def __init__(self, fc_dim):
super(InnerProd, self).__init__()
self.scale = nn.Parameter(torch.ones(fc_dim))
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, feat_img, feat_sound):
sound_size = feat_sound... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | SheldonTsui/Minus-Plus-Network | InnerProd | false | 17,915 | [
"Apache-2.0"
] | 5 | 7aa281b17f637a9f168aaf250039e560027a3817 | https://github.com/SheldonTsui/Minus-Plus-Network/tree/7aa281b17f637a9f168aaf250039e560027a3817 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, fc_dim):
super().__init__()
self.scale = nn.Parameter(torch.ones(fc_dim))
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, feat_img, feat_sound):
sound_size = feat_sound.size()
B, ... |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class Actor(nn.Module):
def __init__(self, nb_states, nb_actions, hidden1=400, hidden2=300,
init_w=0.003):
super(Actor, self).__init__()
self.fc1 = nn.Linear(nb_states, hidden1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Sharpiless/HAQ-for-Mobilenetv3-Quantization | Actor | false | 17,916 | [
"MIT"
] | 5 | 76b7d98471adb666ad140abd2518bce6f0de3cfa | https://github.com/Sharpiless/HAQ-for-Mobilenetv3-Quantization/tree/76b7d98471adb666ad140abd2518bce6f0de3cfa | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class Model(nn.Module):
def __init__(self, nb_states, nb_actions, hidden1=400, hidden2=300,
init_w=0.003):
super().__init__()
self.fc1 = nn.Linear(nb_states, hidden1)
self.fc2 = ... |
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 math
import torch
import torch.nn as nn
def activation(act_type='swish'):
if act_type == 'swish':
act = swish()
return act
else:
act = nn.ReLU(inplace=True)
return act
class swish(nn.Module):
def __init__(self):
super(swish, self).__init__()
def forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | Sense-GVT/BigPretrain | FeedForward | false | 17,917 | [
"Apache-2.0"
] | 8 | d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | https://github.com/Sense-GVT/BigPretrain/tree/d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | import math
import torch
import torch.nn as nn
def activation(act_type='swish'):
if act_type == 'swish':
act = swish()
return act
else:
act = nn.ReLU(inplace=True)
return act
class swish(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x)... |
SIMPA | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 typing import Optional
from typing import Tuple
import torch.nn as nn
from torch.nn.parameter import Parameter
from typing import Union
class SIMPA(nn.Module):
"""The signed mixed-path aggregation model.
Args:
hop (int): Number of hops to consider.
directed (bool, optional):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch.nn.parameter import Parameter
assert_size_strid... | SherylHYX/SSSNET_Signed_Clustering | SIMPA | false | 17,918 | [
"MIT"
] | 5 | 85736c18e86b396d64177d22b8c7f9859dfd794c | https://github.com/SherylHYX/SSSNET_Signed_Clustering/tree/85736c18e86b396d64177d22b8c7f9859dfd794c | import torch
from typing import Optional
from typing import Tuple
import torch.nn as nn
from torch.nn.parameter import Parameter
from typing import Union
class Model(nn.Module):
"""The signed mixed-path aggregation model.
Args:
hop (int): Number of hops to consider.
directed (bool, optional):... |
SparseConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 torch import autograd
class Sparse(autograd.Function):
""""
Prune the unimprotant weight for the forwards phase,
but pass the gradient to dense weight using SR-STE in the backwards phase
"""
@staticmethod
def forward(ctx,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Sense-GVT/BigPretrain | SparseConv2d | false | 17,920 | [
"Apache-2.0"
] | 8 | d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | https://github.com/Sense-GVT/BigPretrain/tree/d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autograd
class Sparse(autograd.Function):
""""
Prune the unimprotant weight for the forwards phase,
but pass the gradient to dense weight using SR-STE in the backwards phase
"""
@staticmethod
def forward(ctx,... |
GCNConv_diag | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 sklearn.metrics.pairwise import *
from torch.optim.lr_scheduler import *
class GCNConv_diag(torch.nn.Module):
"""
A GCN convolution layer of diagonal matrix multiplication
"""
def __init__(self, input_size, device):
super(GCNConv_diag, self).__init__()
self.W = torch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from sklearn.metrics.pairwise import *
from torch.optim.lr_scheduler import *
as... | STK101/GRCN | GCNConv_diag | false | 17,921 | [
"MIT"
] | 4 | 7389000a13d5969bcc77dc4cf73a4107acc68403 | https://github.com/STK101/GRCN/tree/7389000a13d5969bcc77dc4cf73a4107acc68403 | import torch
from sklearn.metrics.pairwise import *
from torch.optim.lr_scheduler import *
class Model(torch.nn.Module):
"""
A GCN convolution layer of diagonal matrix multiplication
"""
def __init__(self, input_size, device):
super().__init__()
self.W = torch.nn.Parameter(torch.ones(... |
Balance_Theory | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from typing import Union
class Balance_Theory(nn.Module):
"""The signed graph clustering model with balance theory, restricted to 2 hops for fair compari... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SherylHYX/SSSNET_Signed_Clustering | Balance_Theory | false | 17,922 | [
"MIT"
] | 5 | 85736c18e86b396d64177d22b8c7f9859dfd794c | https://github.com/SherylHYX/SSSNET_Signed_Clustering/tree/85736c18e86b396d64177d22b8c7f9859dfd794c | import torch
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from typing import Union
class Model(nn.Module):
"""The signed graph clustering model with balance theory, restricted to 2 hops for fair comparison with ... |
LSN | # 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
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
import torch.nn.functional as F
class LSN(nn.Module):
""" Custom Linear layer that modifies standard ReLU layer"""
__constants__ = ['inplace']
inplace: 'bool'
def __init__(self, scale: 'int'=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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
assert_size_stride... | SindiLab/ACTIVA | LSN | false | 17,923 | [
"MIT"
] | 6 | 599f57478c5e13868d27879632c54964bf7b02ad | https://github.com/SindiLab/ACTIVA/tree/599f57478c5e13868d27879632c54964bf7b02ad | import torch
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
import torch.nn.functional as F
class Model(nn.Module):
""" Custom Linear layer that modifies standard ReLU layer"""
__constants__ = ['inplace']
inplace: 'bool'
def __init__(self, scale: 'int'... |
EncoderImagePrecomp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
from torch import nn
from collections import OrderedDict
import torch.nn.init
def l2norm(X):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()
X = torch.div(X, norm)
return X
class EncoderImagePrecomp(nn.Module):
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.triton_helpers import libdevice
import numpy as np
... | Shiyang-Yan/Discrete-continous-PG-for-Retrieval | EncoderImagePrecomp | false | 17,924 | [
"Apache-2.0"
] | 8 | 39fd7a81f732ae043c2ea20352a0c55b72834639 | https://github.com/Shiyang-Yan/Discrete-continous-PG-for-Retrieval/tree/39fd7a81f732ae043c2ea20352a0c55b72834639 | import torch
import numpy as np
from torch import nn
from collections import OrderedDict
import torch.nn.init
def l2norm(X):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()
X = torch.div(X, norm)
return X
class Model(nn.Module):
def __init__(self, img... |
SSSNET | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from typing import Union
class SIMPA(nn.Module):
"""The signed mixed-path aggregation model.
Args:
hop (int): Number of hops to consider.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SherylHYX/SSSNET_Signed_Clustering | SSSNET | false | 17,925 | [
"MIT"
] | 5 | 85736c18e86b396d64177d22b8c7f9859dfd794c | https://github.com/SherylHYX/SSSNET_Signed_Clustering/tree/85736c18e86b396d64177d22b8c7f9859dfd794c | import torch
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from typing import Union
class SIMPA(nn.Module):
"""The signed mixed-path aggregation model.
Args:
hop (int): Number of hops to consider.
... |
DenseNet_conv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
def xavier_init(module, gain=1, bias=0, distribution='normal'):
assert distribution in ['uniform', 'normal']
if distribution == 'uniform':
nn.init.xavier_uniform_(module.weight, gain=gain)
else:
nn.init.xavier_normal_(module.weight, gain=gain)
if hasa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Shiaoming/DensefromRGBS | DenseNet_conv | false | 17,926 | [
"MIT"
] | 7 | d69f5f60c5512da876b002a2007ec42d4a3fbb8e | https://github.com/Shiaoming/DensefromRGBS/tree/d69f5f60c5512da876b002a2007ec42d4a3fbb8e | import torch
import torch.nn as nn
def xavier_init(module, gain=1, bias=0, distribution='normal'):
assert distribution in ['uniform', 'normal']
if distribution == 'uniform':
nn.init.xavier_uniform_(module.weight, gain=gain)
else:
nn.init.xavier_normal_(module.weight, gain=gain)
if hasa... |
TripletLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torchvision.transforms import functional as F
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class TripletLoss(nn.Module):
"""
Triplet loss
Takes embeddings [N*dim_embed] of an anchor sample, a positive sample and a negative sample
"""
def __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
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards... | Sigma10010/nuclei_cells_det | TripletLoss | false | 17,927 | [
"MIT"
] | 4 | c074175fec8938472bb4cddabd83d1d0ea78f230 | https://github.com/Sigma10010/nuclei_cells_det/tree/c074175fec8938472bb4cddabd83d1d0ea78f230 | import torch
from torchvision.transforms import functional as F
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Triplet loss
Takes embeddings [N*dim_embed] of an anchor sample, a positive sample and a negative sample
"""
def __init__(self, ... |
CPULayerNorm | # 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 CPULayerNorm(nn.Module):
def __init__(self, features, eps=1e-06):
super().__init__()
self.features = features
self.eps = eps
def forward(self, x, gamma, beta):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Smerity/pytorch-qrnn | CPULayerNorm | false | 17,928 | [
"BSD-3-Clause"
] | 4 | 907c8ea53f689136fcc50996b6474de967745202 | https://github.com/Smerity/pytorch-qrnn/tree/907c8ea53f689136fcc50996b6474de967745202 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, features, eps=1e-06):
super().__init__()
self.features = features
self.eps = eps
def forward(self, x, gamma, beta):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
retu... |
MixerBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Sense-GVT/BigPretrain | MixerBlock | false | 17,929 | [
"Apache-2.0"
] | 8 | d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | https://github.com/Sense-GVT/BigPretrain/tree/d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | import torch
import torch.nn as nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is ... |
SelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SelfAttention(nn.Module):
def __init__(self, embed_dims, heads):
super(SelfAttention, self).__init__()
self.heads = heads
self.embed_dims = embed_dims
self.depth = embed_dims // heads
self.query = nn.Linear(self.depth, self.depth)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ShivamRajSharma/Transformer-Text-To-Spech | SelfAttention | false | 17,930 | [
"MIT"
] | 10 | 2e1cf84a791497e414fb72ae04d954fce934a32a | https://github.com/ShivamRajSharma/Transformer-Text-To-Spech/tree/2e1cf84a791497e414fb72ae04d954fce934a32a | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, embed_dims, heads):
super().__init__()
self.heads = heads
self.embed_dims = embed_dims
self.depth = embed_dims // heads
self.query = nn.Linear(self.depth, self.depth)
self.key = nn.Linear... |
ContrastiveLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torchvision.transforms import functional as F
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class ContrastiveLoss(nn.Module):
"""
Contrastive loss
Takes embeddings of two samples and a target label == 1 if samples are from the same class and label == 0 othe... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
from... | Sigma10010/nuclei_cells_det | ContrastiveLoss | false | 17,931 | [
"MIT"
] | 4 | c074175fec8938472bb4cddabd83d1d0ea78f230 | https://github.com/Sigma10010/nuclei_cells_det/tree/c074175fec8938472bb4cddabd83d1d0ea78f230 | import torch
from torchvision.transforms import functional as F
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Contrastive loss
Takes embeddings of two samples and a target label == 1 if samples are from the same class and label == 0 otherwise
... |
AttentionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import nn
from torch.nn import functional as F
import torch.nn.init
def Linear(in_features, out_features, dropout=0.0):
m = nn.Linear(in_features, out_features)
m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
m.bias.data.zero_()
return nn.u... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Shiyang-Yan/Discrete-continous-PG-for-Retrieval | AttentionLayer | false | 17,932 | [
"Apache-2.0"
] | 8 | 39fd7a81f732ae043c2ea20352a0c55b72834639 | https://github.com/Shiyang-Yan/Discrete-continous-PG-for-Retrieval/tree/39fd7a81f732ae043c2ea20352a0c55b72834639 | import math
import torch
from torch import nn
from torch.nn import functional as F
import torch.nn.init
def Linear(in_features, out_features, dropout=0.0):
m = nn.Linear(in_features, out_features)
m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
m.bias.data.zero_()
return nn.u... |
wide_basic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils
import torch.utils.data
def get_norm(n_filters, norm):
if norm is None:
return Identity()
elif norm == 'batch':
return nn.BatchNorm2d(n_filters, momentum=0.9)
elif norm == 'instance':
return nn.InstanceNorm2d(n_filters, affine=T... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.utils.data
assert_size_str... | Silent-Zebra/JEM | wide_basic | false | 17,933 | [
"Apache-2.0"
] | 6 | 33440aff8429d9a24a8ba858d0209f4b48be8e05 | https://github.com/Silent-Zebra/JEM/tree/33440aff8429d9a24a8ba858d0209f4b48be8e05 | import torch
import torch.nn as nn
import torch.utils
import torch.utils.data
def get_norm(n_filters, norm):
if norm is None:
return Identity()
elif norm == 'batch':
return nn.BatchNorm2d(n_filters, momentum=0.9)
elif norm == 'instance':
return nn.InstanceNorm2d(n_filters, affine=T... |
CPUReverseForgetMult | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class CPUReverseForgetMult(torch.nn.Module):
def __init__(self):
super(CPUReverseForgetMult, self).__init__()
def forward(self, f, x, hidden_init=None):
result = []
forgets = f.split(1, dim=0)[::-1]
inputs = (f * x).split(1, dim=0)[::-1]
prev_h = hidden_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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret... | Smerity/pytorch-qrnn | CPUReverseForgetMult | false | 17,934 | [
"BSD-3-Clause"
] | 4 | 907c8ea53f689136fcc50996b6474de967745202 | https://github.com/Smerity/pytorch-qrnn/tree/907c8ea53f689136fcc50996b6474de967745202 | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, f, x, hidden_init=None):
result = []
forgets = f.split(1, dim=0)[::-1]
inputs = (f * x).split(1, dim=0)[::-1]
prev_h = hidden_init
for i, h in enumerate(inputs)... |
Project3D | # 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 Project3D(nn.Module):
"""Layer which projects 3D points into a camera with intrinsics K and at position T
"""
def __init__(self, batch_size, height, width, eps=1e-07):
super(Project3D, self).__init__()
self.batch_size = batch_size
self.heig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Sid1057/sid1057.github.io | Project3D | false | 17,935 | [
"MIT"
] | 4 | 623d1731e308b42b6f86304dcfd671a061b414bf | https://github.com/Sid1057/sid1057.github.io/tree/623d1731e308b42b6f86304dcfd671a061b414bf | import torch
import torch.nn as nn
class Model(nn.Module):
"""Layer which projects 3D points into a camera with intrinsics K and at position T
"""
def __init__(self, batch_size, height, width, eps=1e-07):
super().__init__()
self.batch_size = batch_size
self.height = height
... |
ConvBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 Conv3x3(nn.Module):
"""Layer to pad and convolve input
"""
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | Sid1057/sid1057.github.io | ConvBlock | false | 17,936 | [
"MIT"
] | 4 | 623d1731e308b42b6f86304dcfd671a061b414bf | https://github.com/Sid1057/sid1057.github.io/tree/623d1731e308b42b6f86304dcfd671a061b414bf | import torch
import torch.nn as nn
class Conv3x3(nn.Module):
"""Layer to pad and convolve input
"""
def __init__(self, in_channels, out_channels, use_refl=True):
super().__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = nn.ZeroPad2d(... |
VirtualBatchNorm1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
import torch.utils
import torch.utils.data
from torch.nn.parameter import Parameter
from torch.nn.modules import Module
class VirtualBatchNorm1d(Module):
"""
Module for Virtual Batch Normalization.
Implementation borrowed and modified from Rafael_Valle's code + hel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
import torch.utils
import torch.utils.data
from tor... | Silent-Zebra/JEM | VirtualBatchNorm1d | false | 17,937 | [
"Apache-2.0"
] | 6 | 33440aff8429d9a24a8ba858d0209f4b48be8e05 | https://github.com/Silent-Zebra/JEM/tree/33440aff8429d9a24a8ba858d0209f4b48be8e05 | from torch.nn import Module
import torch
import torch.utils
import torch.utils.data
from torch.nn.parameter import Parameter
from torch.nn.modules import Module
class Model(Module):
"""
Module for Virtual Batch Normalization.
Implementation borrowed and modified from Rafael_Valle's code + help of SimonW f... |
WeightNet_DW | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 WeightNet_DW(nn.Module):
""" Here we show a grouping manner when we apply
WeightNet to a depthwise convolution. The grouped
fc layer directly generates the convolutional kernel,
has fewer parameters while achieving comparable res... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Sense-GVT/BigPretrain | WeightNet_DW | false | 17,938 | [
"Apache-2.0"
] | 8 | d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | https://github.com/Sense-GVT/BigPretrain/tree/d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
""" Here we show a grouping manner when we apply
WeightNet to a depthwise convolution. The grouped
fc layer directly generates the convolutional kernel,
has fewer parameters while achieving comparable results.
... |
SSIM | # 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 SSIM(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
def __init__(self):
super(SSIM, self).__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
self.sig_x_pool = nn.AvgPool2d(... | 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
... | Sid1057/sid1057.github.io | SSIM | false | 17,939 | [
"MIT"
] | 4 | 623d1731e308b42b6f86304dcfd671a061b414bf | https://github.com/Sid1057/sid1057.github.io/tree/623d1731e308b42b6f86304dcfd671a061b414bf | import torch
import torch.nn as nn
class Model(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
def __init__(self):
super().__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
self.sig_x_pool = nn.AvgPool2d(3, 1)
... |
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... | from torch.nn import Module
import torch
import torch.autograd
import torch.nn as nn
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907.
"""
def __init__(self, state_dim, name='', out_state_dim=None):
su... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.autograd
import torch.nn as nn
from tor... | SowmyaAitha/Palmira | GraphConvolution | false | 17,940 | [
"MIT"
] | 6 | c3ae884e35b8b3703a5e4ba52d7b0bdae6da1bad | https://github.com/SowmyaAitha/Palmira/tree/c3ae884e35b8b3703a5e4ba52d7b0bdae6da1bad | from torch.nn import Module
import torch
import torch.autograd
import torch.nn as nn
from torch.nn.modules.module import Module
class Model(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907.
"""
def __init__(self, state_dim, name='', out_state_dim=None):
super().__ini... |
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):
super(Decoder, self).__init__()
self.pre11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(in_channels=512, out_channels=256,
kernel_size=3, stride=1)
self.relu11 = nn.ReLU(inpl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ShiZhuming/StyleTransfer | Decoder | false | 17,941 | [
"MIT"
] | 10 | cba2a3ceb733a2d129d52d4a3cac07c7651bd928 | https://github.com/ShiZhuming/StyleTransfer/tree/cba2a3ceb733a2d129d52d4a3cac07c7651bd928 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.pre11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(in_channels=512, out_channels=256,
kernel_size=3, stride=1)
self.relu11 = nn.ReLU(inplace=True)
... |
SH2Signal | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import numpy as np
import torch.nn as nn
from scipy import special as sci
def cart2sph(x, y, z):
"""
cart2sph(x, y, z) -> theta, phi, r
Computes the corresponding spherical coordinate of the given input parameters :attr:`x`, :attr:`y` and :attr:`x`.
Args:
x (Number): x position
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 numpy as np
import torch.nn as nn
from scipy import special as sci
assert... | SimonKoppers/DELIMIT | SH2Signal | false | 17,942 | [
"MIT"
] | 7 | d778a567bbec1beef2395ead60aa1e30086bb07c | https://github.com/SimonKoppers/DELIMIT/tree/d778a567bbec1beef2395ead60aa1e30086bb07c | import torch
import numpy as np
import torch.nn as nn
from scipy import special as sci
def cart2sph(x, y, z):
"""
cart2sph(x, y, z) -> theta, phi, r
Computes the corresponding spherical coordinate of the given input parameters :attr:`x`, :attr:`y` and :attr:`x`.
Args:
x (Number): x position
... |
GraphResConvolution | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
import torch.autograd
import torch.nn as nn
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907.
"""
def __init__(self, state_dim, name='', out_state_dim=None):
su... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | SowmyaAitha/Palmira | GraphResConvolution | false | 17,943 | [
"MIT"
] | 6 | c3ae884e35b8b3703a5e4ba52d7b0bdae6da1bad | https://github.com/SowmyaAitha/Palmira/tree/c3ae884e35b8b3703a5e4ba52d7b0bdae6da1bad | from torch.nn import Module
import torch
import torch.autograd
import torch.nn as nn
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907.
"""
def __init__(self, state_dim, name='', out_state_dim=None):
su... |
SharedDropoutMLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class SharedDropout(nn.Module):
"""
SharedDropout differs from the vanilla dropout strategy in that
the dropout mask is shared across one dimension.
Args:
p (float):
The probability of an element to be zeroed. Default: 0.5.
batch_first (b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Spico197/REx | SharedDropoutMLP | false | 17,944 | [
"MIT"
] | 4 | bb3cdb845765a63e9bd18070068af52a1b2db3f3 | https://github.com/Spico197/REx/tree/bb3cdb845765a63e9bd18070068af52a1b2db3f3 | import torch
import torch.nn as nn
class SharedDropout(nn.Module):
"""
SharedDropout differs from the vanilla dropout strategy in that
the dropout mask is shared across one dimension.
Args:
p (float):
The probability of an element to be zeroed. Default: 0.5.
batch_first (b... |
SubjObjSpan | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
from typing import Iterable
from typing import Optional
import torch.nn as nn
def find_closest_span_pairs(head: 'Iterable', tail: 'Iterable', backtrace:
'Optional[bool]'=True):
"""
Find all span pairs.
Args:
head: list of start position predictions, either 1 or... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 numpy as np
from typing import Iterable
from typing import Optional
impor... | Spico197/REx | SubjObjSpan | false | 17,945 | [
"MIT"
] | 4 | bb3cdb845765a63e9bd18070068af52a1b2db3f3 | https://github.com/Spico197/REx/tree/bb3cdb845765a63e9bd18070068af52a1b2db3f3 | import torch
import numpy as np
from typing import Iterable
from typing import Optional
import torch.nn as nn
def find_closest_span_pairs(head: 'Iterable', tail: 'Iterable', backtrace:
'Optional[bool]'=True):
"""
Find all span pairs.
Args:
head: list of start position predictions, either 1 or... |
make_dense | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.model_zoo
class make_dense(nn.Module):
def __init__(self, channels_in, channels_out, kernel_size=3):
super(make_dense, self).__init__()
self.leaky_relu = nn.LeakyReLU(0.1, inplace=True)
self.conv = nn.Conv2d(channels_in, channels_out, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.model_zoo
assert_size_stride = torch._C... | SeleSchaefer/super_resolution | make_dense | false | 17,946 | [
"MIT"
] | 5 | bf28a959fb150ceeadbd9f0bcfc12f3025cf82f4 | https://github.com/SeleSchaefer/super_resolution/tree/bf28a959fb150ceeadbd9f0bcfc12f3025cf82f4 | import torch
import torch.nn as nn
import torch.utils.model_zoo
class Model(nn.Module):
def __init__(self, channels_in, channels_out, kernel_size=3):
super().__init__()
self.leaky_relu = nn.LeakyReLU(0.1, inplace=True)
self.conv = nn.Conv2d(channels_in, channels_out, kernel_size=
... |
CE_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
import torch.utils.model_zoo
class CE_loss(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.CrossEntropyLoss()
def forward(self, predict, target):
n, _c, h, w = target.data.shape
predict = predict.permute(0, 2, 3, 1).contigu... | 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
... | SeleSchaefer/super_resolution | CE_loss | false | 17,947 | [
"MIT"
] | 5 | bf28a959fb150ceeadbd9f0bcfc12f3025cf82f4 | https://github.com/SeleSchaefer/super_resolution/tree/bf28a959fb150ceeadbd9f0bcfc12f3025cf82f4 | import torch
import torch.nn as nn
import torch.utils.model_zoo
class Model(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.CrossEntropyLoss()
def forward(self, predict, target):
n, _c, h, w = target.data.shape
predict = predict.permute(0, 2, 3, 1).contiguou... |
LogSTFTMagnitude | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class LogSTFTMagnitude(nn.Module):
def __init__(self):
super().__init__()
def forward(self, predicts_mag, targets_mag):
log_predicts_mag = torch.log(predicts_mag)
log_targets_mag = torch.log(ta... | 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
... | SolomidHero/speech-regeneration-enhancer | LogSTFTMagnitude | false | 17,948 | [
"MIT"
] | 8 | eb43907ff085d68a707ff7bc3af14e93ff66fd65 | https://github.com/SolomidHero/speech-regeneration-enhancer/tree/eb43907ff085d68a707ff7bc3af14e93ff66fd65 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, predicts_mag, targets_mag):
log_predicts_mag = torch.log(predicts_mag)
log_targets_mag = torch.log(targets_mag)
... |
Smoother | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
from torch import Tensor
from typing import Optional
import torch.nn.functional as F
from torch.nn import Dropout
from torch.nn import LayerNorm
from torch.nn import Conv1d
from torch.nn import MultiheadAttention
class Smoother(Module):
"""Convolutional Transformer Encoder... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SolomidHero/FragmentVC-with-RAdam | Smoother | false | 17,949 | [
"MIT"
] | 6 | a0ee884155a4e8f47d8950a35258e58987f6289e | https://github.com/SolomidHero/FragmentVC-with-RAdam/tree/a0ee884155a4e8f47d8950a35258e58987f6289e | from torch.nn import Module
import torch
from torch import Tensor
from typing import Optional
import torch.nn.functional as F
from torch.nn import Dropout
from torch.nn import LayerNorm
from torch.nn import Conv1d
from torch.nn import MultiheadAttention
class Model(Module):
"""Convolutional Transformer Encoder La... |
Extractor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
from torch import Tensor
from typing import Optional
from typing import Tuple
import torch.nn.functional as F
from torch.nn import Dropout
from torch.nn import LayerNorm
from torch.nn import Conv1d
from torch.nn import MultiheadAttention
class Extractor(Module):
"""Convolu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SolomidHero/FragmentVC-with-RAdam | Extractor | false | 17,950 | [
"MIT"
] | 6 | a0ee884155a4e8f47d8950a35258e58987f6289e | https://github.com/SolomidHero/FragmentVC-with-RAdam/tree/a0ee884155a4e8f47d8950a35258e58987f6289e | from torch.nn import Module
import torch
from torch import Tensor
from typing import Optional
from typing import Tuple
import torch.nn.functional as F
from torch.nn import Dropout
from torch.nn import LayerNorm
from torch.nn import Conv1d
from torch.nn import MultiheadAttention
class Model(Module):
"""Convolution... |
State_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
from collections import OrderedDict
class State_Autoencoder(nn.Module):
def __init__(self, frame_stacks=1, channels=3):
super(State_Autoencoder, self).__init__()
self.encoder = nn.Sequential(OrderedDict([('encoder_conv1', nn.
Conv2d(channels * frame_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 co... | Squishy123/GDE_net | State_Autoencoder | false | 17,951 | [
"Apache-2.0"
] | 4 | 9094cbf58edbf0d62a2b2cd66743322597f66269 | https://github.com/Squishy123/GDE_net/tree/9094cbf58edbf0d62a2b2cd66743322597f66269 | import torch
import torch.nn as nn
from collections import OrderedDict
class Model(nn.Module):
def __init__(self, frame_stacks=1, channels=3):
super().__init__()
self.encoder = nn.Sequential(OrderedDict([('encoder_conv1', nn.
Conv2d(channels * frame_stacks, 16, kernel_size=3, stride=2... |
SmallMnistNoDropout | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class SmallMnistNoDropout(nn.Module):
def __init__(self):
super(SmallMnistNoDropout, self).__init__()
self.conv1 = nn.Conv2d(1, 10, 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.... | Rohan-Chaudhury/aimet | SmallMnistNoDropout | false | 17,952 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | import torch
import torch.nn as nn
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.relu1 = nn.ReLU()
... |
SmallMnist | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class SmallMnist(nn.Module):
def __init__(self):
super(SmallMnist, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Rohan-Chaudhury/aimet | SmallMnist | false | 17,953 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | import torch
import torch.nn as nn
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.relu1 = nn.ReLU()
... |
SingleBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ruiver/CTCNet | SingleBlock | false | 17,954 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 | import torch
import torch.nn as nn
import torch.nn.functional as F
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super().__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
self.ac... |
Subtract | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class Subtract(torch.nn.Module):
""" Subtract module for a functional subtract"""
def forward(self, x, y):
"""
Forward-pass routine for subtact op
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
assert_size_stride =... | Rohan-Chaudhury/aimet | Subtract | false | 17,955 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | import torch
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class Model(torch.nn.Module):
""" Subtract module for a functional subtract"""
def forward(self, x, y):
"""
Forward-pass routine for subtact op
"""
... |
SpectralConvergence | # 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 SpectralConvergence(nn.Module):
def __init__(self):
"""Initilize spectral convergence loss module."""
super().__init__()
def forward(self, predicts_mag, targets_mag):
"""Calculate norm of difference operator.
Args:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | SolomidHero/speech-regeneration-enhancer | SpectralConvergence | false | 17,956 | [
"MIT"
] | 8 | eb43907ff085d68a707ff7bc3af14e93ff66fd65 | https://github.com/SolomidHero/speech-regeneration-enhancer/tree/eb43907ff085d68a707ff7bc3af14e93ff66fd65 | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self):
"""Initilize spectral convergence loss module."""
super().__init__()
def forward(self, predicts_mag, targets_mag):
"""Calculate norm of difference operator.
Args:
predicts... |
GumbelSoftmaxLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
from torch.distributions import RelaxedOneHotCategorical
import torch.nn.parallel
import torch.utils.data
import torch.distributions
def gumbel_softmax_sample(logits: 'torch.Tensor', temperature: 'float'=1.0,
training: 'bool'=True, straight_through: 'bool'=False):
size = log... | 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
from torch.distributions import RelaxedOneHotCategorical
import torch.nn.parallel
import torch.utils.data
import torch... | Slowika/GameBias-EmeCom2020 | GumbelSoftmaxLayer | false | 17,957 | [
"MIT"
] | 5 | 5b94c47559f8202bca99c26fc1bcb078dd0509a6 | https://github.com/Slowika/GameBias-EmeCom2020/tree/5b94c47559f8202bca99c26fc1bcb078dd0509a6 | import torch
import torch.nn as nn
from torch.distributions import RelaxedOneHotCategorical
import torch.nn.parallel
import torch.utils.data
import torch.distributions
def gumbel_softmax_sample(logits: 'torch.Tensor', temperature: 'float'=1.0,
training: 'bool'=True, straight_through: 'bool'=False):
size = log... |
Hsigmoid | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Hsigmoid(nn.Module):
def __init__(self, add_stub=False):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
self.add_stub = add_stub
... | 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
from torch.quantization import QuantStub
from torch.quantization im... | T-head-Semi/tvm | Hsigmoid | false | 17,958 | [
"Apache-2.0"
] | 4 | c1b8e06685c92fb7cacbe989e147b0622aee4503 | https://github.com/T-head-Semi/tvm/tree/c1b8e06685c92fb7cacbe989e147b0622aee4503 | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Model(nn.Module):
def __init__(self, add_stub=False):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
self.add_stub = add_stub
... |
_TestNetStrided | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class _TestNetStrided(torch.nn.Module):
def __init__(self):
super(_TestNetStrided, self).__init__()
self.conv1 = torch.nn.Conv2d(1, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Rohan-Chaudhury/aimet | _TestNetStrided | false | 17,959 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | import torch
import torch.nn.functional as F
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Conv2d(1, 20, kernel_size=5)
self... |
Divide | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class Divide(torch.nn.Module):
""" Divide module for a functional divide"""
def forward(self, x, y):
"""
Forward-pass routine for divide op
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
assert_size_stride =... | Rohan-Chaudhury/aimet | Divide | false | 17,960 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | import torch
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class Model(torch.nn.Module):
""" Divide module for a functional divide"""
def forward(self, x, y):
"""
Forward-pass routine for divide op
"""
r... |
Hswish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Hswish(nn.Module):
def __init__(self, add_stub=False):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
self.add_stub = add_stub
... | 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
from torch.quantization import QuantStub
from torch.quantization im... | T-head-Semi/tvm | Hswish | false | 17,961 | [
"Apache-2.0"
] | 4 | c1b8e06685c92fb7cacbe989e147b0622aee4503 | https://github.com/T-head-Semi/tvm/tree/c1b8e06685c92fb7cacbe989e147b0622aee4503 | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Model(nn.Module):
def __init__(self, add_stub=False):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
self.add_stub = add_stub
... |
VirtualBatchNormNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
import torch.utils
import torch.utils.data
from torch.nn.parameter import Parameter
from torch.nn.modules import Module
class VirtualBatchNormNN(Module):
"""
Module for Virtual Batch Normalization.
Implementation borrowed and modified from Rafael_Valle's code + hel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
import torch.utils
import torch.utils.data
from tor... | Silent-Zebra/JEM | VirtualBatchNormNN | false | 17,962 | [
"Apache-2.0"
] | 6 | 33440aff8429d9a24a8ba858d0209f4b48be8e05 | https://github.com/Silent-Zebra/JEM/tree/33440aff8429d9a24a8ba858d0209f4b48be8e05 | from torch.nn import Module
import torch
import torch.utils
import torch.utils.data
from torch.nn.parameter import Parameter
from torch.nn.modules import Module
class Model(Module):
"""
Module for Virtual Batch Normalization.
Implementation borrowed and modified from Rafael_Valle's code + help of SimonW f... |
GEGLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn.functional as F
from torch import nn
class GEGLU(nn.Module):
def forward(self, x):
x, gates = x.chunk(2, dim=-1)
return x * F.gelu(gates)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | TabbenBenchmark/tabben | GEGLU | false | 17,963 | [
"MIT"
] | 5 | d74114afc4b6f67be488ab6bf8ad6fd316fdb888 | https://github.com/TabbenBenchmark/tabben/tree/d74114afc4b6f67be488ab6bf8ad6fd316fdb888 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
def forward(self, x):
x, gates = x.chunk(2, dim=-1)
return x * F.gelu(gates)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
Conv3x3 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 Conv3x3(nn.Module):
"""Layer to pad and convolve input
"""
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Sid1057/sid1057.github.io | Conv3x3 | false | 17,964 | [
"MIT"
] | 4 | 623d1731e308b42b6f86304dcfd671a061b414bf | https://github.com/Sid1057/sid1057.github.io/tree/623d1731e308b42b6f86304dcfd671a061b414bf | import torch
import torch.nn as nn
class Model(nn.Module):
"""Layer to pad and convolve input
"""
def __init__(self, in_channels, out_channels, use_refl=True):
super().__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = nn.ZeroPad2d(1)... |
ReinforcedReceiver | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
from torch.distributions import Bernoulli
import torch.distributions
class ReinforcedReceiver(nn.Module):
def __init__(self, n_bits, n_hidden):
super(ReinforcedReceiver, 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
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import to... | Slowika/GameBias-EmeCom2020 | ReinforcedReceiver | false | 17,965 | [
"MIT"
] | 5 | 5b94c47559f8202bca99c26fc1bcb078dd0509a6 | https://github.com/Slowika/GameBias-EmeCom2020/tree/5b94c47559f8202bca99c26fc1bcb078dd0509a6 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
from torch.distributions import Bernoulli
import torch.distributions
class Model(nn.Module):
def __init__(self, n_bits, n_hidden):
super().__init__()
self.emb_column = nn.Linear(n_b... |
VonmisesLossBiternion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class VonmisesLossBiternion(torch.nn.Module):
"""Von mises loss function for biternion inputs
see: Beyer et al.: Biternion Nets: Continuous Head Pose Regression from
Discrete Training Labels, GCPR 2015.
"""
def __init__(self, kappa):
super(VonmisesLossBiternion, 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 math as tl_math
assert_size_s... | TUI-NICR/multi-task-person-perception | VonmisesLossBiternion | false | 17,966 | [
"BSD-3-Clause"
] | 4 | 81666eb42be9522fd726448e82e8bbf04138ffa3 | https://github.com/TUI-NICR/multi-task-person-perception/tree/81666eb42be9522fd726448e82e8bbf04138ffa3 | import torch
class Model(torch.nn.Module):
"""Von mises loss function for biternion inputs
see: Beyer et al.: Biternion Nets: Continuous Head Pose Regression from
Discrete Training Labels, GCPR 2015.
"""
def __init__(self, kappa):
super().__init__()
self._kappa = kappa
... |
MulScalarNegative | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class MulScalarNegative(nn.Module):
def __init__(self):
super().__init__()
self.float_op = nn.quantized.FloatFunctional()
self.quant = QuantStub()
self.dequant = ... | 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
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
assert_size_stride = torch._C._dyn... | T-head-Semi/tvm | MulScalarNegative | false | 17,967 | [
"Apache-2.0"
] | 4 | c1b8e06685c92fb7cacbe989e147b0622aee4503 | https://github.com/T-head-Semi/tvm/tree/c1b8e06685c92fb7cacbe989e147b0622aee4503 | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Model(nn.Module):
def __init__(self):
super().__init__()
self.float_op = nn.quantized.FloatFunctional()
self.quant = QuantStub()
self.dequant = DeQuantStub(... |
InformedSender | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class InformedSender(nn.Module):
def __init__(self, game_size, feat_size, embedding_size, hidden_size,
vocab_size=100, temp=1.0):
super(InformedSender, se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Slowika/GameBias-EmeCom2020 | InformedSender | false | 17,968 | [
"MIT"
] | 5 | 5b94c47559f8202bca99c26fc1bcb078dd0509a6 | https://github.com/Slowika/GameBias-EmeCom2020/tree/5b94c47559f8202bca99c26fc1bcb078dd0509a6 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class Model(nn.Module):
def __init__(self, game_size, feat_size, embedding_size, hidden_size,
vocab_size=100, temp=1.0):
super().__init__()
self.g... |
UpsamplingBilinear | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class UpsamplingBilinear(nn.Module):
def __init__(self):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
def forward(self, x):
x = s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization im... | T-head-Semi/tvm | UpsamplingBilinear | false | 17,969 | [
"Apache-2.0"
] | 4 | c1b8e06685c92fb7cacbe989e147b0622aee4503 | https://github.com/T-head-Semi/tvm/tree/c1b8e06685c92fb7cacbe989e147b0622aee4503 | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Model(nn.Module):
def __init__(self):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
def forward(self, x):
x = self.quant(x)
... |
SmallMnistNoDropoutWithPassThrough | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class PassThroughOp(torch.nn.Module):
"""
This is a pass-through op, used for purpose of making an op a no-op
"""
def forward(self, inputx):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Rohan-Chaudhury/aimet | SmallMnistNoDropoutWithPassThrough | false | 17,970 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | import torch
import torch.nn as nn
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class PassThroughOp(torch.nn.Module):
"""
This is a pass-through op, used for purpose of making an op a no-op
"""
def forward(self, inputx):
... |
CovSepBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 M
def DepthwiseConv(in_channels, kernel_size, stride, padding):
return M.Conv2d(in_channels=in_channels, out_channels=in_channels,
kernel_size=kernel_size, stride=stride, padding=padding, groups=
in_channels, bias=False)
def PointwiseConv(in_channels, out_channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as M
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | SuperbTUM/RAW-image-denoising | CovSepBlock | false | 17,971 | [
"MIT"
] | 4 | 9f81be8da6a576f641022707d98b8c37f5c599ab | https://github.com/SuperbTUM/RAW-image-denoising/tree/9f81be8da6a576f641022707d98b8c37f5c599ab | import torch
import torch.nn as M
def DepthwiseConv(in_channels, kernel_size, stride, padding):
return M.Conv2d(in_channels=in_channels, out_channels=in_channels,
kernel_size=kernel_size, stride=stride, padding=padding, groups=
in_channels, bias=False)
def PointwiseConv(in_channels, out_channels... |
Upsample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as M
class Upsample(M.Module):
def __init__(self, in_channels, out_channels):
super(Upsample, self).__init__()
self.upsample = M.Upsample(scale_factor=2, mode='bilinear',
align_corners=True)
self.ordinaryConv = M.Conv2d(in_channels=in_channels, out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as M
assert_s... | SuperbTUM/RAW-image-denoising | Upsample | false | 17,972 | [
"MIT"
] | 4 | 9f81be8da6a576f641022707d98b8c37f5c599ab | https://github.com/SuperbTUM/RAW-image-denoising/tree/9f81be8da6a576f641022707d98b8c37f5c599ab | import torch
import torch.nn as M
class Model(M.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.upsample = M.Upsample(scale_factor=2, mode='bilinear',
align_corners=True)
self.ordinaryConv = M.Conv2d(in_channels=in_channels, out_channels=
... |
DownSample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as M
def DepthwiseConv(in_channels, kernel_size, stride, padding):
return M.Conv2d(in_channels=in_channels, out_channels=in_channels,
kernel_size=kernel_size, stride=stride, padding=padding, groups=
in_channels, bias=False)
def PointwiseConv(in_channels, out_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 M
assert_s... | SuperbTUM/RAW-image-denoising | DownSample | false | 17,973 | [
"MIT"
] | 4 | 9f81be8da6a576f641022707d98b8c37f5c599ab | https://github.com/SuperbTUM/RAW-image-denoising/tree/9f81be8da6a576f641022707d98b8c37f5c599ab | import torch
import torch.nn as M
def DepthwiseConv(in_channels, kernel_size, stride, padding):
return M.Conv2d(in_channels=in_channels, out_channels=in_channels,
kernel_size=kernel_size, stride=stride, padding=padding, groups=
in_channels, bias=False)
def PointwiseConv(in_channels, out_channels... |
Net_1 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
class Net_1(nn.Module):
def __init__(self):
super(Net_1, self).__init__()
self.conv1 = nn.Conv1d(1, 25, 9, padding=4)
self.conv2 = nn.Conv1d(25, 16, 7, padding=3)
self.conv3 = nn.Conv1d(16, 10, 7, padding=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
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | TakaraResearch/Signal-Detection-with-Wasserstein-Loss | Net_1 | false | 17,974 | [
"BSD-3-Clause"
] | 9 | f210bd0da7492a72bc204a5517e74ba515b5ad12 | https://github.com/TakaraResearch/Signal-Detection-with-Wasserstein-Loss/tree/f210bd0da7492a72bc204a5517e74ba515b5ad12 | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv1d(1, 25, 9, padding=4)
self.conv2 = nn.Conv1d(25, 16, 7, padding=3)
self.conv3 = nn.Conv1d(16, 10, 7, padding=3)
self.conv4... |
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.functional as F
import torch.autograd
import torch.nn as nn
class GraphConv(nn.Module):
def __init__(self, in_features, out_features, bias=False):
super(GraphConv, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.W ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SsGood/MMGL | GCN | false | 17,975 | [
"MIT"
] | 6 | ea769e46fffb42559e764e2912c5b1dc17c10af2 | https://github.com/SsGood/MMGL/tree/ea769e46fffb42559e764e2912c5b1dc17c10af2 | import torch
import torch.nn.functional as F
import torch.autograd
import torch.nn as nn
class GraphConv(nn.Module):
def __init__(self, in_features, out_features, bias=False):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.W = nn.Parameter(... |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedForward(nn.Module):
def __init__(self, individual_featured):
super(PositionwiseFeedForward, self).__init__()
self.w_1 = nn.Linear(individual_featured, 2 * individual_featured)
self.w_2 = nn.Linear(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
import torch.nn as nn
assert_... | Sunner4nwpu/RA-UWML-AU-Pytorch | PositionwiseFeedForward | false | 17,976 | [
"Apache-2.0"
] | 5 | 7d20b2f1ffa8a00595d1e75e0d1c15518a37a920 | https://github.com/Sunner4nwpu/RA-UWML-AU-Pytorch/tree/7d20b2f1ffa8a00595d1e75e0d1c15518a37a920 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, individual_featured):
super().__init__()
self.w_1 = nn.Linear(individual_featured, 2 * individual_featured)
self.w_2 = nn.Linear(2 * individual_featured, individual_featured)
... |
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.functional as F
import torch.autograd
import torch.nn as nn
class FeedForwardLayer(nn.Module):
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hid)
self.w_2 = nn.Linear(d_hid, d_in)
self.layer_norm = nn.Lay... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.autogr... | SsGood/MMGL | FeedForwardLayer | false | 17,977 | [
"MIT"
] | 6 | ea769e46fffb42559e764e2912c5b1dc17c10af2 | https://github.com/SsGood/MMGL/tree/ea769e46fffb42559e764e2912c5b1dc17c10af2 | import torch
import torch.nn.functional as F
import torch.autograd
import torch.nn as nn
class Model(nn.Module):
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hid)
self.w_2 = nn.Linear(d_hid, d_in)
self.layer_norm = nn.LayerNorm(d_in... |
Upsampling | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 M
class Upsampling(M.Module):
def __init__(self, in_channels, out_channels, kernel_size=2):
super().__init__()
self.upsample = M.ConvTranspose2d(in_channels, out_channels,
kernel_size=kernel_size, stride=2)
def forward(self, x):
return 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
import torch.nn as M
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | SuperbTUM/RAW-image-denoising | Upsampling | false | 17,978 | [
"MIT"
] | 4 | 9f81be8da6a576f641022707d98b8c37f5c599ab | https://github.com/SuperbTUM/RAW-image-denoising/tree/9f81be8da6a576f641022707d98b8c37f5c599ab | import torch
import torch.nn as M
class Model(M.Module):
def __init__(self, in_channels, out_channels, kernel_size=2):
super().__init__()
self.upsample = M.ConvTranspose2d(in_channels, out_channels,
kernel_size=kernel_size, stride=2)
def forward(self, x):
return self.upsa... |
Signal2SH | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import numpy as np
import torch.nn as nn
from scipy import special as sci
def cart2sph(x, y, z):
"""
cart2sph(x, y, z) -> theta, phi, r
Computes the corresponding spherical coordinate of the given input parameters :attr:`x`, :attr:`y` and :attr:`x`.
Args:
x (Number): x position
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 numpy as np
import torch.nn as nn
from scipy import special as sci
assert... | SimonKoppers/DELIMIT | Signal2SH | false | 17,979 | [
"MIT"
] | 7 | d778a567bbec1beef2395ead60aa1e30086bb07c | https://github.com/SimonKoppers/DELIMIT/tree/d778a567bbec1beef2395ead60aa1e30086bb07c | import torch
import numpy as np
import torch.nn as nn
from scipy import special as sci
def cart2sph(x, y, z):
"""
cart2sph(x, y, z) -> theta, phi, r
Computes the corresponding spherical coordinate of the given input parameters :attr:`x`, :attr:`y` and :attr:`x`.
Args:
x (Number): x position
... |
TransformerEncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class TransformerEncoderLayer(nn.Module):
def __init__(self, embed_dim, num_heads, hidden_size, dropout=0.0,
attention_dropout=0.0, activation_dropout=0.0):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Slowika/GameBias-EmeCom2020 | TransformerEncoderLayer | false | 17,980 | [
"MIT"
] | 5 | 5b94c47559f8202bca99c26fc1bcb078dd0509a6 | https://github.com/Slowika/GameBias-EmeCom2020/tree/5b94c47559f8202bca99c26fc1bcb078dd0509a6 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class Model(nn.Module):
def __init__(self, embed_dim, num_heads, hidden_size, dropout=0.0,
attention_dropout=0.0, activation_dropout=0.0):
super().__init_... |
PartialConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from itertools import product as product
import torch.nn as nn
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasattr(m, 'weight'):
if... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from itertools import product as product
import torch.nn as nn
asser... | TaroNakasendo/MaskEraser | PartialConv | false | 17,981 | [
"MIT"
] | 3 | 373af686194aff716f53785e40252beae7b26cff | https://github.com/TaroNakasendo/MaskEraser/tree/373af686194aff716f53785e40252beae7b26cff | import math
import torch
from itertools import product as product
import torch.nn as nn
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasattr(m, 'weight'):
if... |
NaiveGroupNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import init
import torch.nn.parallel
class NaiveGroupNorm(Module):
"""NaiveGroupNorm implements Group Normalization with the high-level matrix operations in PyTorch.
It is a temporary solution to export GN by ONNX before the... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
from torch.nn import Parameter
from torch.nn import... | Tanveer81/BoxVOS | NaiveGroupNorm | false | 17,982 | [
"BSD-2-Clause"
] | 4 | c30aa319f18f3fbee2a25e0ed25cb006a4598300 | https://github.com/Tanveer81/BoxVOS/tree/c30aa319f18f3fbee2a25e0ed25cb006a4598300 | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import init
import torch.nn.parallel
class Model(Module):
"""NaiveGroupNorm implements Group Normalization with the high-level matrix operations in PyTorch.
It is a temporary solution to export GN by ONNX before the official... |
eSEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | Tanveer81/BoxVOS | eSEModule | false | 17,983 | [
"BSD-2-Clause"
] | 4 | c30aa319f18f3fbee2a25e0ed25cb006a4598300 | https://github.com/Tanveer81/BoxVOS/tree/c30aa319f18f3fbee2a25e0ed25cb006a4598300 | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super().__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
class Model(nn... |
GCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Conv2D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, padding=
'same', stride=1, dilation=1, groups=1):
super(Conv2D, self).__init__()
assert type(kernel_size) in [int,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
as... | Tanveer81/BoxVOS | GCN | false | 17,984 | [
"BSD-2-Clause"
] | 4 | c30aa319f18f3fbee2a25e0ed25cb006a4598300 | https://github.com/Tanveer81/BoxVOS/tree/c30aa319f18f3fbee2a25e0ed25cb006a4598300 | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Conv2D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, padding=
'same', stride=1, dilation=1, groups=1):
super().__init__()
assert type(kernel_size) in [int, tuple
... |
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