repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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hawp | hawp-master/scripts/train.py | import torch
import random
import numpy as np
from parsing.config import cfg
from parsing.utils.comm import to_device
from parsing.dataset import build_train_dataset
from parsing.detector import WireframeDetector
from parsing.solver import make_lr_scheduler, make_optimizer
from parsing.utils.logger import setup_logger... | 6,098 | 31.614973 | 83 | py |
svrhm21_RNN_explain | svrhm21_RNN_explain-main/RNN_analyse_reprs_recurrence.py | # Script to perform decoding analyses on the trained layer activations and the recurrent flow
# Requires tensorflow 1.13, python 3.7, scikit-learn, and pytorch 1.6.0
############################# IMPORTING MODULES ##################################
import torch
import torch.nn as nn
import torch.nn.functional as F
im... | 23,313 | 54.509524 | 462 | py |
svrhm21_RNN_explain | svrhm21_RNN_explain-main/RNN_perturb.py | # Script to perform perturbation analyses on the trained RNN
# Requires tensorflow 1.13, python 3.7, scikit-learn, and pytorch 1.6.0
############################# IMPORTING MODULES ##################################
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import ... | 42,801 | 75.160142 | 590 | py |
svrhm21_RNN_explain | svrhm21_RNN_explain-main/RNN_gen.py | # Script to define the RNN and dataset and to train the RNN
# Requires tensorflow 1.13, python 3.7, scikit-learn, and pytorch 1.6.0
############################# IMPORTING MODULES ##################################
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import t... | 15,812 | 47.210366 | 462 | py |
MetaXLR | MetaXLR-main/mlt.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
try:
import apex
except ImportError:
pass
BERT_DIM = 768
BERT_LAYERS = 13 # (emb + 12 hidden from transformers)
IGNORED_INDEX = -100
'''
def trim_input(bert_ids, bert_mask, bert_labels=None):
max_length = (bert_mask !=0)... | 50,680 | 38.075559 | 132 | py |
MetaXLR | MetaXLR-main/data_utils.py | # this class wraps a torch.utils.data.DataLoader into an iterator for batch by batch fetching
import torch
class DataIterator(object):
def __init__(self, dataloader, nonstop=True):
assert isinstance(dataloader, torch.utils.data.DataLoader), 'Wrong loader type'
self.loader = dataloader
self.... | 674 | 29.681818 | 93 | py |
MetaXLR | MetaXLR-main/models.py | import numpy as np
import sys
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import BertTokenizer, BertForTokenClassification, BertPreTrainedModel, XLMRobertaTokenizer, XLMRobertaForTokenClassification
from transformers.modeling_bert import BertLayer, BertModel, BertEmbe... | 32,576 | 42.668901 | 190 | py |
MetaXLR | MetaXLR-main/mtrain.py | import argparse
import json
import random
import conllu
from glob import glob
import math
import pandas as pd
import numpy as np
from numpy.random import choice
from models import *
from mlt import *
from utils import *
from data_utils import DataIterator
from transformers import ( BertConfig,
... | 68,164 | 50.25188 | 698 | py |
glc | glc-master/SST/SST_experiments_pytorch.py | import numpy as np
import re
import collections
import pickle
import argparse
import torch
import torch.nn as nn
from torch.autograd import Variable as V
import torch.nn.functional as F
parser = argparse.ArgumentParser(description='sst label corruption experiments')
parser.add_argument('--method', default='ours', ty... | 19,072 | 38.00409 | 124 | py |
glc | glc-master/SST/SST_convex_combo.py | import numpy as np
import re
import collections
import pickle
import argparse
import torch
import torch.nn as nn
from torch.autograd import Variable as V
import torch.nn.functional as F
parser = argparse.ArgumentParser(description='sst label corruption experiments')
parser.add_argument('--method', default='combo', t... | 23,625 | 37.478827 | 134 | py |
glc | glc-master/SST/SST_gold_only.py | import numpy as np
import re
import collections
import pickle
import argparse
import torch
import torch.nn as nn
from torch.autograd import Variable as V
import torch.nn.functional as F
parser = argparse.ArgumentParser(description='sst label corruption experiments')
parser.add_argument('--method', default='gold_only... | 13,290 | 37.302594 | 114 | py |
glc | glc-master/MNIST/MNIST_gold_only.py | import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable as V
import torch.nn.functional as F
import pickle
from tensorflow.examples.tutorials.mnist import input_data
import argparse
mnist = input_data.read_data_sets(train_dir='mnist', one_hot=False)
parser = argparse.ArgumentParser(de... | 5,929 | 33.277457 | 113 | py |
glc | glc-master/MNIST/MNIST_experiments_pytorch.py | import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable as V
import torch.nn.functional as F
import pickle
from tensorflow.examples.tutorials.mnist import input_data
import argparse
mnist = input_data.read_data_sets(train_dir='mnist', one_hot=False)
parser = argparse.ArgumentParser(de... | 11,405 | 35.557692 | 124 | py |
glc | glc-master/MNIST/MNIST_convex_combo.py | import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable as V
import torch.nn.functional as F
import pickle
from tensorflow.examples.tutorials.mnist import input_data
import argparse
mnist = input_data.read_data_sets(train_dir='mnist', one_hot=False)
parser = argparse.ArgumentParser(de... | 15,832 | 35.231121 | 134 | py |
glc | glc-master/Twitter/Twitter_convex_combo.py | import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable as V
import torch.nn.functional as F
import pickle
import argparse
from helper_functions_twitter import *
parser = argparse.ArgumentParser(description='Twitter label corruption experiments')
parser.add_argument('--method', defaul... | 17,589 | 35.569647 | 134 | py |
glc | glc-master/Twitter/Twitter_gold_only.py | import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable as V
import torch.nn.functional as F
import pickle
import argparse
from helper_functions_twitter import *
parser = argparse.ArgumentParser(description='Twitter label corruption experiments')
parser.add_argument('--method', defaul... | 7,050 | 34.079602 | 113 | py |
glc | glc-master/Twitter/Twitter_experiments_pytorch.py | import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable as V
import torch.nn.functional as F
import pickle
import argparse
from helper_functions_twitter import *
parser = argparse.ArgumentParser(description='Twitter label corruption experiments')
parser.add_argument('--method', defaul... | 12,745 | 36.269006 | 129 | py |
glc | glc-master/CIFAR/train_confusion.py | # -*- coding: utf-8 -*-
import argparse
import os
import time
import math
import json
import torch
from torch.autograd import Variable as V
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torchvision.datasets as dset
import torchvision.transforms as transforms
import wideresnet as wrn
impor... | 16,534 | 38.747596 | 126 | py |
glc | glc-master/CIFAR/train_ours.py | # -*- coding: utf-8 -*-
import argparse
import os
import time
import math
import json
import torch
from torch.autograd import Variable as V
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torchvision.datasets as dset
import torchvision.transforms as transforms
import wideresnet as wrn
impor... | 16,549 | 38.688249 | 126 | py |
glc | glc-master/CIFAR/train_forward_gold.py | # -*- coding: utf-8 -*-
import argparse
import os
import time
import math
import json
import torch
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torchvision.datasets as dset
import torchvision.transforms as transforms
import wideresnet as wrn
import numpy as np
from load_corrupted_data im... | 16,509 | 39.268293 | 141 | py |
glc | glc-master/CIFAR/train_gold_only.py | # -*- coding: utf-8 -*-
import argparse
import os
import time
import math
import json
import torch
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torchvision.datasets as dset
import torchvision.transforms as transforms
import wideresnet as wrn
import numpy as np
from load_corrupted_data im... | 17,700 | 39.691954 | 126 | py |
glc | glc-master/CIFAR/train_ideal.py | # -*- coding: utf-8 -*-
import argparse
import os
import time
import math
import json
import torch
from torch.autograd import Variable as V
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torchvision.datasets as dset
import torchvision.transforms as transforms
import wideresnet as wrn
impor... | 16,528 | 38.733173 | 126 | py |
glc | glc-master/CIFAR/train_ours_adjusted.py | # -*- coding: utf-8 -*-
import argparse
import os
import time
import math
import json
import torch
from torch.autograd import Variable as V
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torchvision.datasets as dset
import torchvision.transforms as transforms
import wideresnet as wrn
impor... | 17,771 | 38.145374 | 126 | py |
glc | glc-master/CIFAR/train_ours_calibrated.py | # -*- coding: utf-8 -*-
import argparse
import os
import time
import math
import json
import torch
from torch.autograd import Variable as V
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torchvision.datasets as dset
import torchvision.transforms as transforms
import wideresnet as wrn
impor... | 19,418 | 37.076471 | 126 | py |
glc | glc-master/CIFAR/train_forward.py | # -*- coding: utf-8 -*-
import argparse
import os
import time
import math
import json
import torch
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torchvision.datasets as dset
import torchvision.transforms as transforms
import wideresnet as wrn
import numpy as np
from load_corrupted_data im... | 15,082 | 39.986413 | 141 | py |
glc | glc-master/CIFAR/load_corrupted_data.py | from PIL import Image
import os
import os.path
import errno
import numpy as np
import sys
import pickle
import torch.utils.data as data
from torchvision.datasets.utils import download_url, check_integrity
import torch
import torch.nn.functional as F
from torch.autograd import Variable as V
import wideresnet as wrn
im... | 11,936 | 40.737762 | 139 | py |
glc | glc-master/CIFAR/train_convex_combo.py | # -*- coding: utf-8 -*-
import argparse
import os
import time
import math
import json
import torch
from torch.autograd import Variable as V
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torchvision.datasets as dset
import torchvision.transforms as transforms
import wideresnet as wrn
impor... | 27,037 | 39.235119 | 151 | py |
glc | glc-master/CIFAR/wideresnet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplace=True)
se... | 3,863 | 41 | 116 | py |
gccaps | gccaps-master/gccaps/gated_conv.py | from keras.layers import Activation
from keras.layers import BatchNormalization
from keras.layers import Dropout
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Multiply
def block(x, n_filters=64, pool_size=(2, 2), dropout_rate=0.2):
"""Apply two gated convolutions f... | 2,785 | 34.717949 | 79 | py |
gccaps | gccaps-master/gccaps/main.py | import argparse
import glob
import os
import pickle
import sys
import numpy as np
import config as cfg
import utils
def main():
"""Execute a task based on the given command-line arguments.
This function is the main entry-point of the program. It allows the
user to extract features, train a model, gener... | 13,757 | 34.367609 | 79 | py |
gccaps | gccaps-master/gccaps/capsnet.py | import numpy as np
from keras import backend as K
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Input
from keras.layers import Lambda
from keras.layers import Reshape
from keras.layers import TimeDistributed
from keras.layers import BatchNormalization
from keras.models import... | 3,817 | 33.089286 | 75 | py |
gccaps | gccaps-master/gccaps/training.py | import os
from sklearn import metrics
from keras.callbacks import Callback
from keras.callbacks import CSVLogger
from keras.callbacks import EarlyStopping
from keras.callbacks import LearningRateScheduler
from keras.callbacks import ModelCheckpoint
from keras.callbacks import TensorBoard
from keras.optimizers import ... | 6,619 | 32.948718 | 78 | py |
gccaps | gccaps-master/gccaps/capsules.py | """See Also: https://github.com/XifengGuo/CapsNet-Keras"""
import keras.backend as K
import keras.initializers as initializers
from keras.layers import Conv2D
from keras.layers import Layer
from keras.layers import Lambda
from keras.layers import Reshape
class CapsuleLayer(Layer):
"""A Keras layer implementing ... | 5,929 | 36.531646 | 79 | py |
gccaps | gccaps-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/stable/config
# -- Path setup ------------------------------------------------------------... | 4,903 | 29.08589 | 79 | py |
GradNCP | GradNCP-main/main.py | import torch
from torch.utils.data import DataLoader
from common.args import parse_args
from common.utils import InfiniteSampler, get_optimizer, load_model
from data.dataset import get_dataset
from models.model import get_model
from train.trainer import meta_trainer
from utils import Logger, set_random_seed
def main... | 2,721 | 32.195122 | 122 | py |
GradNCP | GradNCP-main/utils.py | import pickle
import random
import shutil
import sys
from datetime import datetime
import os
import time
from collections import OrderedDict, defaultdict, deque
import numpy as np
import torch
import torch.distributed as dist
from torch.utils.tensorboard import SummaryWriter
device = torch.device("cuda" if torch.cuda... | 11,299 | 29.376344 | 93 | py |
GradNCP | GradNCP-main/eval.py | import torch
from torch.utils.data import DataLoader
from common.args import parse_args
from common.utils import load_model
from data.dataset import get_dataset
from models.model import get_model
from utils import set_random_seed
def main():
""" argument define """
P = parse_args()
P.rank = 0
""" se... | 1,320 | 26.520833 | 74 | py |
GradNCP | GradNCP-main/common/utils.py | import os
import numpy as np
import torch
import torch.optim as optim
from utils import load_checkpoint
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def get_optimizer(P, model):
params = model.parameters()
optimizer = optim.Adam(params, lr=P.lr)
return optimizer
def is_resume... | 2,709 | 28.456522 | 95 | py |
GradNCP | GradNCP-main/models/wrapper.py | from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def exists(val):
return val is not None
class MetaWrapper(nn.Module):
def __init__(self, P, decoder):
... | 5,909 | 36.405063 | 105 | py |
GradNCP | GradNCP-main/models/model.py | import torch
from models.inr.metasiren import MetaSiren, MetaSirenPenultimate
from models.wrapper import MetaWrapper
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def get_inr(P):
if P.decoder == 'siren':
if P.sample_type in ['gradncp']:
model = MetaSirenPenultimate(P.... | 921 | 29.733333 | 112 | py |
GradNCP | GradNCP-main/models/inr/metasiren.py | import math
import torch
import torch.nn as nn
from models.metamodule import MetaModule, MetaSequential, MetaBatchLinear
class Sine(nn.Module):
def __init__(self, w0=30.):
super().__init__()
self.w0 = w0
def forward(self, x):
return torch.sin(self.w0*x)
class MetaSirenLayer(MetaMo... | 4,299 | 36.068966 | 91 | py |
GradNCP | GradNCP-main/models/metamodule/metamodule.py | import torch
import torch.nn as nn
import re
import warnings
from collections import OrderedDict
from einops import rearrange
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
-----... | 4,025 | 36.277778 | 99 | py |
GradNCP | GradNCP-main/evals/gradient_based/maml_full_evaluate.py | import torch
import torch.nn.functional as F
import lpips
from pytorch_msssim import ms_ssim, ssim
from train.gradient_based import inner_adapt, inner_adapt_test_scale
from utils import MetricLogger, psnr, get_meta_batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def check(P):
filena... | 3,863 | 37.64 | 112 | py |
GradNCP | GradNCP-main/evals/gradient_based/maml.py | import torch
from train.gradient_based import inner_adapt
from utils import MetricLogger, psnr, get_meta_batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def check(P):
filename_with_today_date = True
return filename_with_today_date
def test_model(P, wrapper, loader, steps, logge... | 2,328 | 32.271429 | 88 | py |
GradNCP | GradNCP-main/evals/gradient_based/maml_scale.py | import torch
from train.gradient_based import inner_adapt, inner_adapt_test_scale
from utils import MetricLogger, psnr, get_meta_batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def check(P):
filename_with_today_date = True
return filename_with_today_date
def test_model(P, wrapp... | 4,273 | 40.495146 | 114 | py |
GradNCP | GradNCP-main/train/__init__.py | import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def setup(mode, P):
fname = f'{P.dataset}_{P.decoder}_{mode}_bs{P.batch_size}_inner{P.inner_steps}'
if mode in ['fomaml', 'maml']:
from train.gradient_based.maml import train_step
from train.gradient_based.ma... | 904 | 27.28125 | 89 | py |
GradNCP | GradNCP-main/train/trainer.py | import time
import torch
from common.utils import is_resume
from utils import MetricLogger, save_checkpoint, save_checkpoint_step
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def meta_trainer(P, train_func, test_func, model, optimizer, train_loader, test_loader, logger):
kwargs = {}
... | 2,065 | 35.245614 | 96 | py |
GradNCP | GradNCP-main/train/gradient_based/maml.py | import time
import torch
from train.gradient_based import inner_adapt
from utils import psnr, get_meta_batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def check(P):
filename_with_today_date = True
return filename_with_today_date
def train_step(P, steps, wrapper, optimizer, tas... | 2,608 | 33.786667 | 99 | py |
GradNCP | GradNCP-main/train/gradient_based/__init__.py | from collections import OrderedDict
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def get_grad_norm(grads, bs, detach=True):
grad_norm_list = []
for grad in grads:
if grad is None:
grad_norm = 0
else:
if detach:
gra... | 4,332 | 26.775641 | 88 | py |
GradNCP | GradNCP-main/train/gradient_based/maml_boot.py | import time
import torch
from train.gradient_based import inner_adapt
from utils import psnr, get_meta_batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def check(P):
filename_with_today_date = True
return filename_with_today_date
def param_consistency(P, params, params_bootstra... | 3,367 | 33.367347 | 99 | py |
GradNCP | GradNCP-main/data/dataset.py | import torchvision.transforms as T
from torchvision import datasets
from torch.utils.data import Dataset
DATA_PATH = '/data'
class ImgDataset(Dataset):
def __init__(self, data, sdf=False):
self.data = data
self.sdf = sdf
def __len__(self):
return len(self.data)
def __getitem__(s... | 1,413 | 22.566667 | 74 | py |
torch2trt | torch2trt-master/setup.py | import sys
import tensorrt
import torch
from setuptools import setup, find_packages
from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CppExtension
from packaging import version
def trt_inc_dir():
return "/usr/include/aarch64-linux-gnu"
def trt_lib_dir():
return "/usr/lib/aarch64-linux-gnu"... | 1,504 | 23.672131 | 81 | py |
torch2trt | torch2trt-master/build.py | import imp
import subprocess
import os
from string import Template
PLUGINS = [
'interpolate',
'group_norm',
]
BASE_FOLDER = 'torch2trt/converters'
NINJA_TEMPLATE = Template((
"rule link\n"
" command = g++ -shared -o $$out $$in -L$torch_dir/lib -L$cuda_dir/lib64 -L$trt_lib_dir -lc10 -lc10_cuda -ltorc... | 1,941 | 28.876923 | 185 | py |
torch2trt | torch2trt-master/examples/easyocr/generate_data.py | from argparse import ArgumentParser
import cv2
import torch
import glob
from easyocr import Reader
from torch2trt.dataset import FolderDataset
from torch2trt import torch2trt, TRTModule
import math
import os
parser = ArgumentParser()
parser.add_argument('--images', type=str, default='images')
parser.add_argument('--de... | 1,605 | 28.740741 | 77 | py |
torch2trt | torch2trt-master/examples/easyocr/optimize_recognizer.py | from argparse import ArgumentParser
from torch2trt.dataset import FolderDataset
from torch2trt import torch2trt, TRTModule
from easyocr import Reader
import tensorrt as trt
import torch
import time
from tempfile import mkdtemp
parser = ArgumentParser()
parser.add_argument('--detector_data', type=str, default='detecto... | 3,095 | 30.917526 | 141 | py |
torch2trt | torch2trt-master/examples/easyocr/run_end2end.py | from argparse import ArgumentParser
import cv2
import torch
import glob
from easyocr import Reader
from torch2trt.dataset import FolderDataset
from torch2trt import torch2trt, TRTModule
import math
import time
import os
parser = ArgumentParser()
parser.add_argument('--images', type=str, default='images')
parser.add_ar... | 2,555 | 26.782609 | 79 | py |
torch2trt | torch2trt-master/examples/easyocr/optimize_detector.py | from argparse import ArgumentParser
from torch2trt.dataset import FolderDataset, ListDataset
from torch2trt import torch2trt, TRTModule
from easyocr import Reader
import tensorrt as trt
import torch
import time
from tempfile import mkdtemp
parser = ArgumentParser()
parser.add_argument('--detector_data', type=str, defa... | 2,271 | 31 | 121 | py |
torch2trt | torch2trt-master/examples/contrib/quantization_aware_training/parser.py | import argparse
def parse_args():
"""
"""
parser = argparse.ArgumentParser(description='PyTorch QAT')
parser.add_argument('--tl','--transfer_learning',action='store_true',help='used to map weights correctly')
parser.add_argument('--iter',default=300, type=int, help='no of iterations')
parser.ad... | 2,300 | 70.90625 | 140 | py |
torch2trt | torch2trt-master/examples/contrib/quantization_aware_training/infer.py | import timeit
import torch
import torch.nn as nn
import numpy as np
import torchvision
import argparse
import os,sys
from datasets.cifar10 import Cifar10Loaders
from utils.utilities import calculate_accuracy, timeGraph,printStats
from models.resnet import resnet18,resnet34
from parser import parse_args
from torch2tr... | 2,897 | 34.341463 | 165 | py |
torch2trt | torch2trt-master/examples/contrib/quantization_aware_training/train.py | import torch
import torch.nn as nn
import numpy as np
import torchvision
import argparse
import os,sys
import torch.optim as optim
from datasets.cifar10 import Cifar10Loaders
from models.models import vanilla_cnn
from models.resnet import resnet18 , resnet34
from utils.utilities import calculate_accuracy , add_miss... | 6,565 | 35.276243 | 169 | py |
torch2trt | torch2trt-master/examples/contrib/quantization_aware_training/models/resnet.py | """
Resnet implementation from Pytorch
"""
import torch
import torch.nn as nn
from utils.utilities import qrelu,qconv2d
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_resnet101_2']
model_urls = ... | 14,399 | 40.618497 | 196 | py |
torch2trt | torch2trt-master/examples/contrib/quantization_aware_training/models/models.py | '''
Contains basic model definitions
'''
import torch
import torch.nn as nn
from utils.utilities import qrelu,qconv2d
class vanilla_cnn(nn.Module):
def __init__(self,qat_mode=False,infer=False):
super().__init__()
self.qat = qat_mode
self.layer1=qconv2d(3,32,padding=1,qat=qat_mode,infer=... | 1,035 | 27 | 71 | py |
torch2trt | torch2trt-master/examples/contrib/quantization_aware_training/datasets/cifar10.py | import torch
import torchvision
import torchvision.transforms as transforms
class Cifar10Loaders:
"""
Data loaders for cifar 10 dataset
"""
def __init__(self, data_dir='/tmp/cifar10', download=True, batch_size=128, pin_memory=True, num_workers=4):
self.data_dir = data_dir
self.download ... | 1,641 | 41.102564 | 162 | py |
torch2trt | torch2trt-master/examples/contrib/quantization_aware_training/utils/utilities.py | import torch
import torch.nn as nn
import numpy as np
import collections
from pytorch_quantization import tensor_quant
from torch2trt.contrib.qat.layers.quant_conv import QuantConvBN2d,QuantConv2d,IQuantConv2d, IQuantConvBN2d
from torch2trt.contrib.qat.layers.quant_activation import QuantReLU, IQuantReLU
import torchvi... | 9,171 | 32.845018 | 106 | py |
torch2trt | torch2trt-master/scripts/dump_converters.py | import argparse
import sys
import subprocess
import os
from importlib.machinery import SourceFileLoader
torch2trt = SourceFileLoader("torch2trt", "torch2trt/__init__.py").load_module() # to load relative to root
HEADER = """
# Converters
This table contains a list of supported PyTorch methods and their associated c... | 1,834 | 32.363636 | 109 | py |
torch2trt | torch2trt-master/scripts/profile_timm.py | import os
import timm
import torch
import time
import json
from torch2trt import torch2trt, TRTModule, trt
from dataclasses import dataclass, asdict
from argparse_dataclass import ArgumentParser
from typing import Literal
from enum import Enum
from contextlib import redirect_stderr, redirect_stdout
import io
class Sta... | 6,845 | 36.823204 | 109 | py |
torch2trt | torch2trt-master/torch2trt/flattener.py | import copy
import torch
def _default_condition(x):
return isinstance(x, torch.Tensor) and (x.dtype is torch.half or x.dtype is torch.float or x.dtype == torch.bool)
def _make_schema_from_value(value, condition=_default_condition, size=0):
if condition(value):
return size, size + 1
elif isinstan... | 3,178 | 33.182796 | 117 | py |
torch2trt | torch2trt-master/torch2trt/module_test.py | import torch
import torchvision
class ModuleTest(object):
def __init__(self, module_fn, dtype, device, input_shapes, **torch2trt_kwargs):
self.module_fn = module_fn
self.dtype = dtype
self.device = device
self.input_shapes = input_shapes
self.torch2trt_kwargs = torch2trt_kw... | 908 | 24.25 | 93 | py |
torch2trt | torch2trt-master/torch2trt/test.py | from torch2trt import *
from .module_test import ModuleTest, MODULE_TESTS
import time
import argparse
import re
import runpy
import traceback
from termcolor import colored
import math
import tempfile
import numpy as np
def pSNR(model_op,trt_op):
#model_op = model_op.cpu().detach().numpy().flatten()
#trt_op = t... | 6,819 | 33.619289 | 243 | py |
torch2trt | torch2trt-master/torch2trt/dataset_test.py | import pytest
import torch
import torch.nn as nn
from torch2trt.dataset import (
TensorBatchDataset,
ListDataset,
FolderDataset
)
from tempfile import mkdtemp
def test_dataset_shapes():
dataset = ListDataset()
dataset.insert((torch.randn(1, 3, 32, 32), torch.randn(1, 4)))
dataset.insert((torc... | 3,805 | 26.185714 | 89 | py |
torch2trt | torch2trt-master/torch2trt/dataset.py | import os
import torch
import glob
from uuid import uuid1
from torch2trt.flattener import Flattener
__all__ = [
'DatasetRecorder',
'Dataset',
'ListDataset',
'TensorBatchDataset'
]
class DatasetRecorder(object):
def __init__(self, dataset, module):
self.dataset = dataset
self.mod... | 6,391 | 28.456221 | 123 | py |
torch2trt | torch2trt-master/torch2trt/dataset_calibrator_test.py | import pytest
import tensorrt as trt
import torch
import torch.nn as nn
from torch2trt.dataset import (
TensorBatchDataset,
ListDataset
)
from torch2trt import torch2trt
def test_dataset_calibrator_batch_dataset():
torch.manual_seed(0)
class TestModule(nn.Module):
def __init__(self):
... | 2,391 | 21.780952 | 89 | py |
torch2trt | torch2trt-master/torch2trt/flatten_module.py | import torch
import torch.nn as nn
from .flattener import Flattener
class Unflatten(nn.Module):
def __init__(self, module, input_flattener=None, output_flattener=None):
super().__init__()
self.module = module
self.input_flattener = input_flattener
self.output_flattener = output_fl... | 1,176 | 30.810811 | 76 | py |
torch2trt | torch2trt-master/torch2trt/dataset_calibrator.py | import torch
import tensorrt as trt
import os
from .flattener import Flattener
__all__ = [
'DEFAULT_CALIBRATION_ALGORITHM',
'DatasetCalibrator'
]
if trt.__version__ >= '5.1':
DEFAULT_CALIBRATION_ALGORITHM = trt.CalibrationAlgoType.ENTROPY_CALIBRATION_2
else:
DEFAULT_CALIBRATION_ALGORITHM = trt.Calibr... | 1,644 | 29.462963 | 106 | py |
torch2trt | torch2trt-master/torch2trt/__init__.py | from .torch2trt import *
from .converters import *
import tensorrt as trt
def load_plugins():
import torch2trt.torch_plugins
registry = trt.get_plugin_registry()
torch2trt_creators = [c for c in registry.plugin_creator_list if c.plugin_namespace == 'torch2trt']
for c in torch2trt_creators:
regi... | 400 | 24.0625 | 103 | py |
torch2trt | torch2trt-master/torch2trt/dynamic_shape_test.py | import pytest
import torch
import torch.nn as nn
import tensorrt as trt
from torch2trt import torch2trt
from torch2trt.dataset import ListDataset
def test_dynamic_shape_conv2d():
torch.manual_seed(0)
module = nn.Conv2d(3, 6, kernel_size=3, stride=1, padding=1).cuda().eval()
dataset = ListDataset()
... | 2,125 | 30.264706 | 86 | py |
torch2trt | torch2trt-master/torch2trt/flatten_module_test.py | import torch
import torch.nn as nn
from torch2trt import torch2trt
def test_flatten_nested_tuple_args():
class TestModule(nn.Module):
def forward(self, x, yz):
return torch.cat([x, yz[0], yz[1]], dim=-1)
module = TestModule().cuda().eval()
data = (
torch.randn(1, 3, 32, 32)... | 610 | 19.366667 | 62 | py |
torch2trt | torch2trt-master/torch2trt/flattener_test.py | import pytest
import torch
from torch2trt.flattener import Flattener
def test_flattener_from_value():
x = (torch.ones(3), torch.ones(3))
flattener = Flattener.from_value(x)
assert(isinstance(flattener.schema, tuple))
assert(flattener.schema[0] == 0)
assert(flattener.schema[1] == 1)
def test_f... | 3,441 | 20.647799 | 86 | py |
torch2trt | torch2trt-master/torch2trt/torch2trt.py | import torch
import tensorrt as trt
import copy
import numpy as np
import io
from collections import defaultdict
import importlib
from .dataset_calibrator import (
DatasetCalibrator,
DEFAULT_CALIBRATION_ALGORITHM,
)
from .dataset import (
Dataset,
TensorBatchDataset,
ListDataset
)
from .flattener... | 35,112 | 31.243343 | 158 | py |
torch2trt | torch2trt-master/torch2trt/tests/test_contiguous.py | import torch
from torch2trt import torch2trt
def test_contiguous():
torch.manual_seed(0)
net = torch.nn.Conv2d(3, 10, kernel_size=3)
net.eval().cuda()
test_tensor = torch.randn((1, 25, 25, 3)).cuda().permute((0, 3, 1, 2))
with torch.no_grad():
test_out = net(test_tensor)
with torc... | 551 | 22 | 80 | py |
torch2trt | torch2trt-master/torch2trt/tests/test_flatten_dynamic.py | import pytest
from torch2trt import torch2trt, trt
import torch
class FlattenModule(torch.nn.Module):
def __init__(self, start_dim, end_dim):
super().__init__()
self.start_dim = start_dim
self.end_dim = end_dim
def forward(self, x):
return torch.flatten(x, self.start_dim, self... | 1,781 | 26.84375 | 87 | py |
torch2trt | torch2trt-master/torch2trt/tests/test_tensor_shape_div_batch.py | import pytest
import torch
from torch2trt import torch2trt, trt
def test_div_constant_batch():
class DivConstantBatch(torch.nn.Module):
def __init__(self):
super(DivConstantBatch, self).__init__()
self.register_buffer('y', torch.ones((1, 3, 10, 10)))
def forward(self, ... | 648 | 23.961538 | 73 | py |
torch2trt | torch2trt-master/torch2trt/tests/test_tensor_ne.py | import pytest
import torch
from torch2trt import torch2trt, trt
def test_tensor_ne():
class NotEqual(torch.nn.Module):
def __init__(self):
super(NotEqual, self).__init__()
def forward(self, x, y):
return x != y
module = NotEqual().cuda().eval()
x = torch.rand... | 562 | 21.52 | 72 | py |
torch2trt | torch2trt-master/torch2trt/tests/test_interpolate_dynamic.py | import pytest
import torch
import torch.nn.functional as F
from torch2trt import (
torch2trt,
trt
)
def test_interpolate_dynamic_size():
class TestModule(torch.nn.Module):
def forward(self, x):
size = x.size()
return F.interpolate(x, size=(size[2]*2, size[3]*3))
modul... | 1,622 | 30.211538 | 156 | py |
torch2trt | torch2trt-master/torch2trt/tests/test_tensor_shape.py | import pytest
import torch
import torch.nn.functional as F
from torch2trt import (
torch2trt,
trt,
SizeWrapper,
tensorrt_converter
)
def test_tensor_shape_view_trivial():
class TestModule(torch.nn.Module):
def forward(self, x):
size = x.size()
return x.view(size)
... | 4,515 | 27.948718 | 87 | py |
torch2trt | torch2trt-master/torch2trt/tests/test_legacy_max_batch_size.py | import torch.nn as nn
import torch
from torch2trt import torch2trt
def test_legacy_max_batch_size():
model = nn.Conv2d(3, 6, kernel_size=1).cuda().eval()
data = torch.randn(1, 3, 32, 32).cuda()
model_trt = torch2trt(model, [data], max_batch_size=4)
data = torch.randn(1, 3, 32, 32).cuda()
out ... | 1,195 | 22.45098 | 74 | py |
torch2trt | torch2trt-master/torch2trt/tests/timm/test_maxvit.py | import pytest
from torch2trt import torch2trt, trt
from timm.models.maxxvit import (
maxvit_tiny_224,
maxvit_tiny_224,
maxvit_rmlp_pico_rw_256,
maxvit_rmlp_small_rw_224
)
import torch
def _cross_validate_module(model, shape=(224, 224)):
data = torch.randn(1, 3, *shape).cuda()
model_trt = torch... | 842 | 24.545455 | 80 | py |
torch2trt | torch2trt-master/torch2trt/tests/torchvision/classification.py | import torch
import torchvision
from torch2trt.module_test import add_module_test
@add_module_test(torch.float16, torch.device('cuda'), [(1, 3, 224, 224)], fp16_mode=True)
def alexnet():
return torchvision.models.alexnet(pretrained=False)
@add_module_test(torch.float16, torch.device('cuda'), [(1, 3, 224, 22... | 4,998 | 32.777027 | 89 | py |
torch2trt | torch2trt-master/torch2trt/tests/torchvision/segmentation.py | import torch
import torchvision
from torch2trt.module_test import add_module_test
class ModelWrapper(torch.nn.Module):
def __init__(self, model):
super(ModelWrapper, self).__init__()
self.model = model
def forward(self, x):
return self.model(x)['out']
@add_module_test(torch.float... | 1,239 | 30.794872 | 91 | py |
torch2trt | torch2trt-master/torch2trt/tests/torchvision/save_load.py | from torch2trt import *
import torchvision
import torch
from .segmentation import deeplabv3_resnet50
if __name__ == '__main__':
model = deeplabv3_resnet50().cuda().eval().half()
data = torch.randn((1, 3, 224, 224)).cuda().half()
print('Running torch2trt...')
model_trt = torch2trt(model, [data], f... | 755 | 30.5 | 82 | py |
torch2trt | torch2trt-master/torch2trt/contrib/qat/layers/_utils.py | import torch
import copy
import inspect
from absl import logging
from torch import nn
from pytorch_quantization.nn import TensorQuantizer as TQ
from pytorch_quantization.tensor_quant import QuantDescriptor, QUANT_DESC_8BIT_PER_TENSOR
'''
Currently Nvidia quantization library quantizes the input of the conv layer as... | 4,740 | 33.107914 | 122 | py |
torch2trt | torch2trt-master/torch2trt/contrib/qat/layers/quant_conv.py | """
Original source code taken from nvidia quantization library.
Changes made to correctly map quantized pytorch layers to TensorRT layers at INT8
Original source: tools/pytorch_quantization/pytorch_quantization/nn/modules/quant_conv.py under
https://github.com/NVIDIA/TensorRT.git
"""
import torch
import torch.nn a... | 9,912 | 38.494024 | 161 | py |
torch2trt | torch2trt-master/torch2trt/contrib/qat/layers/quant_activation.py | import torch
from . import _utils
from pytorch_quantization import tensor_quant
from pytorch_quantization.nn.modules import _utils as utils
class QuantReLU(torch.nn.ReLU,utils.QuantInputMixin):
"""
Quantized ReLu. However, output of relu needs to be quantized for it to correclty map to a TRT layer
"""
... | 1,438 | 34.097561 | 109 | py |
torch2trt | torch2trt-master/torch2trt/contrib/qat/converters/QuantRelu.py | from torch2trt.torch2trt import *
import tensorrt as trt
@tensorrt_converter('torch2trt.contrib.qat.layers.quant_activation.IQuantReLU.forward',enabled=trt_version() >= '7.0')
def convert_QuantReLU(ctx):
module = ctx.method_args[0]
input = ctx.method_args[1]
input_trt = add_missing_trt_tensors(ctx.network,... | 771 | 34.090909 | 118 | py |
torch2trt | torch2trt-master/torch2trt/contrib/qat/converters/QuantConv.py | from torch2trt.torch2trt import *
from torch2trt.module_test import add_module_test
import tensorrt as trt
@tensorrt_converter('torch2trt.contrib.qat.layers.quant_conv.IQuantConv2d.forward', enabled=trt_version() >= '7.0')
def convert_QuantConv(ctx):
module = ctx.method_args[0]
input = ctx.method_args[1]
... | 3,568 | 33.990196 | 116 | py |
torch2trt | torch2trt-master/torch2trt/contrib/qat/converters/QuantConvBN.py | from torch2trt.torch2trt import *
from torch2trt.module_test import add_module_test
import tensorrt as trt
@tensorrt_converter('torch2trt.contrib.qat.layers.quant_conv.IQuantConvBN2d.forward', enabled=trt_version() >= '7.0')
def convert_QuantConv(ctx):
module = ctx.method_args[0]
input = ctx.method_args[1]
... | 3,594 | 34.245098 | 118 | py |
torch2trt | torch2trt-master/torch2trt/converters/einsum.py | import torch.nn as nn
from torch2trt.torch2trt import *
from torch2trt.module_test import add_module_test
@tensorrt_converter('torch.einsum')
def convert_einsum(ctx):
einsum_eq = ctx.method_args[0]
input_tensors = ctx.method_args[1:]
output = ctx.method_return
# parts = einsum_eq.split('->')
... | 1,147 | 23.956522 | 95 | py |
torch2trt | torch2trt-master/torch2trt/converters/unsqueeze.py | import tensorrt as trt
import numpy as np
import torch
from torch2trt.torch2trt import tensorrt_converter, get_arg, torch_dim_resolve_negative, add_missing_trt_tensors, torch_dim_to_trt_axes
from torch2trt.module_test import add_module_test
@tensorrt_converter('torch.Tensor.unsqueeze')
@tensorrt_converter('torch.unsq... | 1,725 | 27.766667 | 135 | py |
torch2trt | torch2trt-master/torch2trt/converters/squeeze.py | import tensorrt as trt
import numpy as np
import torch
from torch2trt.torch2trt import tensorrt_converter, get_arg, torch_dim_resolve_negative, add_missing_trt_tensors, torch_dim_to_trt_axes
from torch2trt.module_test import add_module_test
@tensorrt_converter('torch.Tensor.squeeze')
@tensorrt_converter('torch.squeez... | 1,912 | 29.854839 | 135 | py |
torch2trt | torch2trt-master/torch2trt/converters/batch_norm.py | from torch2trt.torch2trt import *
from torch2trt.module_test import add_module_test
@tensorrt_converter('torch.nn.functional.batch_norm', enabled=trt_version() >= '7.0')
def convert_batch_norm_trt7(ctx):
input = get_arg(ctx, 'input', pos=0, default=None)
running_mean = get_arg(ctx, 'running_mean', pos=1, def... | 1,915 | 42.545455 | 125 | py |
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