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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RPMG | RPMG-main/ModelNet_Img/pylibs/pytorch_util/libtrain/__init__.py | from .init_torch import list_models, rm_models, copy_weights, cfg, init_weights_by_filling, count_parameters_all, \
count_parameters_trainable
try:
from hooks import Forward_Hook_Handlers, Backward_Hook_Handlers, fw_hook_percentile
except:
pass
| 258 | 31.375 | 115 | py |
RPMG | RPMG-main/ModelNet_Img/pylibs/pytorch_util/libtrain/init_torch.py | import os, sys
from basic.common import env, Open, add_path # rdict
import numpy as np
import math
import torch
import torch.nn as nn
import torchvision
import torch.utils.model_zoo as model_zoo
# `pip install easydict` if you don't have it
from easydict import EasyDict as edict
# Pixel mean values (BGR order) as a... | 18,165 | 43.415648 | 139 | py |
RPMG | RPMG-main/poselstm-pytorch/train.py | import time
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
from util.visualizer import Visualizer
opt = TrainOptions().parse()
## SEEDING
import torch
import numpy
import random
torch.manual_seed(opt.seed)
numpy.random.seed(opt.seed)
... | 2,288 | 33.681818 | 98 | py |
RPMG | RPMG-main/poselstm-pytorch/options/base_options.py | import argparse
import os
from util import util
import torch
class BaseOptions():
def __init__(self):
self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
self.initialized = False
def initialize(self):
self.parser.add_argument('--dataroot', requ... | 4,881 | 63.236842 | 228 | py |
RPMG | RPMG-main/poselstm-pytorch/models/base_model.py | import os
import torch
class BaseModel():
def name(self):
return 'BaseModel'
def initialize(self, opt):
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor
self.save_dir = o... | 1,774 | 28.098361 | 78 | py |
RPMG | RPMG-main/poselstm-pytorch/models/networks.py | import torch
import torch.nn as nn
from torch.nn import init
from torch.nn import functional as F
import functools
from torch.autograd import Variable
from torch.optim import lr_scheduler
import numpy as np
###############################################################################
# Functions
#####################... | 12,387 | 51.05042 | 157 | py |
RPMG | RPMG-main/poselstm-pytorch/models/poselstm_model.py | from tracemalloc import get_traced_memory
from builtins import NotImplementedError
import numpy as np
import torch
import torch.nn.functional as F
import os
from collections import OrderedDict
from torch.autograd import Variable
import util.util as util
from util.image_pool import ImagePool
from .base_model import Base... | 6,529 | 39.308642 | 106 | py |
RPMG | RPMG-main/poselstm-pytorch/util/image_pool.py | import random
import numpy as np
import torch
from torch.autograd import Variable
class ImagePool():
def __init__(self, pool_size):
self.pool_size = pool_size
if self.pool_size > 0:
self.num_imgs = 0
self.images = []
def query(self, images):
if self.pool_size =... | 1,116 | 30.914286 | 67 | py |
RPMG | RPMG-main/poselstm-pytorch/util/util.py | from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import inspect, re
import numpy as np
import os
import collections
# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
def tensor2im(image_tensor, imtype=np.uint8):
image_numpy =... | 2,265 | 29.621622 | 97 | py |
RPMG | RPMG-main/poselstm-pytorch/data/custom_dataset_data_loader.py | import torch.utils.data
from data.base_data_loader import BaseDataLoader
def CreateDataset(opt):
dataset = None
if opt.dataset_mode == 'unaligned_posenet':
from data.unaligned_posenet_dataset import UnalignedPoseNetDataset
dataset = UnalignedPoseNetDataset()
else:
raise ValueError(... | 1,324 | 27.191489 | 75 | py |
RPMG | RPMG-main/poselstm-pytorch/data/unaligned_posenet_dataset.py | import os.path
import torchvision.transforms as transforms
from data.base_dataset import BaseDataset, get_posenet_transform
from data.image_folder import make_dataset
from PIL import Image
import PIL
import random
import numpy
class UnalignedPoseNetDataset(BaseDataset):
def initialize(self, opt):
self.opt ... | 1,554 | 34.340909 | 113 | py |
RPMG | RPMG-main/poselstm-pytorch/data/base_dataset.py | import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
import numpy
import torch
class BaseDataset(data.Dataset):
def __init__(self):
super(BaseDataset, self).__init__()
def name(self):
return 'BaseDataset'
def initialize(self, opt):
pass... | 2,999 | 34.294118 | 77 | py |
RPMG | RPMG-main/poselstm-pytorch/data/image_folder.py | ###############################################################################
# Code from
# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
# Modified the original code so that it also loads images from the current
# directory as well as the subdirectories
################################... | 1,954 | 27.333333 | 79 | py |
RPMG | RPMG-main/Pascal3D_Img/S3.3D_Rotation/dataset.py | import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as tfs
from PIL import Image
import scipy.io
import os
from enum import IntEnum
from pytorch3d import transforms as trans
# format
# data['record']: {
# 'filename': string, filename
# 'folder': string
# ... | 19,144 | 35.328273 | 152 | py |
RPMG | RPMG-main/Pascal3D_Img/S3.3D_Rotation/networks.py | import torch
from torch import nn
from torch import Tensor
from typing import Callable, Any, Optional, List
def get_network(config):
return MobileNetV2(config.num_classes)
def set_requires_grad(nets, requires_grad=False):
"""Set requies_grad=Fasle for all the networks to avoid unnecessary computations
P... | 8,009 | 35.244344 | 116 | py |
RPMG | RPMG-main/Pascal3D_Img/S3.3D_Rotation/trainval_workdir.py | """
@Author : Shuai Liao
"""
import torch
import torch.optim
import os, sys, time
from time import gmtime, strftime
import numpy as np
from math import pi
from easydict import EasyDict as edict
from collections import OrderedDict as odict
#
from basic.common import Open, env, add_path, RefObj as rdict, argv2dict, is... | 20,434 | 39.951904 | 146 | py |
RPMG | RPMG-main/Pascal3D_Img/S3.3D_Rotation/agent.py | from utils import TrainClock, KSchedule
import os
import torch
import torch.optim as optim
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
import sys
BASEPATH = os.path.dirname(__file__)
sys.path.append(os.path.join(BASEPATH, '..', '..', 'utils'))
import tools
import rpmg
from networks import ge... | 6,286 | 38.540881 | 106 | py |
RPMG | RPMG-main/Pascal3D_Img/S3.3D_Rotation/regQuatNet/regQuatNet.py | # coding: utf8
"""
@Author : Shuai Liao
"""
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.autograd import Variable
import torch
import torch.nn.functional as F
from basic.common import rdict
import numpy as np
from easydict import EasyDict as edict
from collections import OrderedDict as ... | 12,708 | 38.715625 | 143 | py |
RPMG | RPMG-main/Pascal3D_Img/S3.3D_Rotation/lib/helper.py | # coding: utf8
"""
@Author : Shuai Liao
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
from easydict import EasyDict as edict
from collections import OrderedDict as odict
from itertools import product
#
from collections import OrderedDict
class Cross_Entropy_Loss_Han... | 2,518 | 30.4875 | 118 | py |
RPMG | RPMG-main/Pascal3D_Img/S3.3D_Rotation/lib/datasets/Dataset_Base.py | """
@Author : Shuai Liao
"""
import os, sys
import numpy as np
from math import ceil, floor, pi
import torch
from torch.utils.data import Dataset, DataLoader
from collections import OrderedDict as odict
import cv2
path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
... | 8,723 | 38.654545 | 108 | py |
RPMG | RPMG-main/Pascal3D_Img/S3.3D_Rotation/lib/datasets/dataset_regQuatNet.py | """
@Author : Shuai Liao
"""
import numpy as np
from Dataset_Base import Dataset_Base, netcfg
import torch
from torch.utils.data import Dataset, DataLoader
import cv2
from basic.common import add_path, env, rdict, cv2_wait, cv2_putText
# ===============================================================
def pred2ang... | 1,914 | 27.58209 | 83 | py |
BVFSM | BVFSM-main/BVFSM.py | import matplotlib
import matplotlib.pyplot as plt
import torch
import hypergrad as hg
import numpy as np
from sklearn.model_selection import train_test_split
import torch.nn.functional as F
import copy
import time
import csv
import math
import os
import psutil as psutil
import argparse
import utils
import function
d... | 4,871 | 34.562044 | 123 | py |
BVFSM | BVFSM-main/utils.py |
import matplotlib
import matplotlib.pyplot as plt
import torch
import hypergrad as hg
import numpy as np
from sklearn.model_selection import train_test_split
import torch.nn.functional as F
import copy
import time
import csv
import math
import os
import psutil as psutil
import argparse
def np_to_list(arr):
this... | 952 | 17.326923 | 55 | py |
BVFSM | BVFSM-main/BVFSM_constraint.py | #!/usr/bin/env python
# coding: utf-8
# In[1]:
# get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib
import matplotlib.pyplot as plt
import torch
import hypergrad as hg
import numpy as np
from sklearn.model_selection import train_test_split
import torch.nn.functional as F
import copy
import time
i... | 9,895 | 36.06367 | 200 | py |
BVFSM | BVFSM-main/demo.py | import matplotlib
import matplotlib.pyplot as plt
import torch
import hypergrad as hg
import numpy as np
from sklearn.model_selection import train_test_split
import torch.nn.functional as F
import copy
import time
import csv
import math
import os
import psutil as psutil
import argparse
import utils
import BVFSM
pars... | 1,684 | 26.177419 | 97 | py |
BVFSM | BVFSM-main/BVFSM_PBO.py |
import matplotlib
import matplotlib.pyplot as plt
import torch
import hypergrad as hg
import numpy as np
from sklearn.model_selection import train_test_split
import torch.nn.functional as F
import copy
import time
import csv
import math
import os
import psutil as psutil
def show_memory_info(hint):
pid = os.getp... | 8,740 | 36.515021 | 197 | py |
deep-speaker | deep-speaker-master/setup.py | import os
import platform
from setuptools import setup
tensorflow = 'tensorflow'
if platform.system() == 'Darwin' and platform.processor() == 'arm':
tensorflow = 'tensorflow-macos'
# https://github.com/grpc/grpc/issues/25082
os.environ['GRPC_PYTHON_BUILD_SYSTEM_OPENSSL'] = '1'
os.environ['GRPC_PYTHON_... | 853 | 20.897436 | 67 | py |
deep-speaker | deep-speaker-master/cli.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import logging
import os
import click
from deep_speaker.audio import Audio
from deep_speaker.batcher import KerasFormatConverter
from deep_speaker.constants import SAMPLE_RATE, NUM_FRAMES
from deep_speaker.test import test
from deep_speaker.train import start_training
fr... | 4,089 | 39.9 | 101 | py |
deep-speaker | deep-speaker-master/deep_speaker/batcher.py | import json
import logging
import os
from collections import deque, Counter
from random import choice
from time import time
import dill
import numpy as np
from tqdm import tqdm
from deep_speaker.audio import pad_mfcc, Audio
from deep_speaker.constants import NUM_FRAMES, NUM_FBANKS
from deep_speaker.conv_models import... | 23,984 | 46.401186 | 119 | py |
deep-speaker | deep-speaker-master/deep_speaker/triplet_loss.py | # pylint: disable=E0611,E0401
import tensorflow.keras.backend as K
# ALPHA = 0.2 # used in FaceNet https://arxiv.org/pdf/1503.03832.pdf
ALPHA = 0.1 # used in Deep Speaker.
def batch_cosine_similarity(x1, x2):
# https://en.wikipedia.org/wiki/Cosine_similarity
# 1 = equal direction ; -1 = opposite direction
... | 2,782 | 42.484375 | 90 | py |
deep-speaker | deep-speaker-master/deep_speaker/conv_models.py | import logging
import os
import numpy as np
import tensorflow as tf
# pylint: disable=E0611,E0401
import tensorflow.keras.backend as K
# pylint: disable=E0611,E0401
from tensorflow.keras import layers, regularizers
# pylint: disable=E0611,E0401
from tensorflow.keras.layers import (
BatchNormalization,
Conv2D,
... | 10,741 | 35.16835 | 119 | py |
deep-speaker | deep-speaker-master/deep_speaker/train.py | import logging
import os
# pylint: disable=E0611,E0401
from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
# pylint: disable=E0611,E0401
from tensorflow.keras.optimizers import SGD
from tqdm import tqdm
from deep_speaker.batcher import KerasFormatConverter, LazyTripletBatcher
from... | 5,455 | 47.714286 | 119 | py |
samurai | samurai-master/doc/source/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/master/config
# -- Path setup ------------------------------------------------------------... | 5,971 | 27.037559 | 79 | py |
LAMP | LAMP-main/loop_closure/src/loop_closure_batcher.py | from gnn.gnn_model import LoopClosureGNN
import torch
from pose_graph_msgs.msg import PoseGraph, LoopCandidateArray, LoopComputationStatus
import rospy
from copy import deepcopy
import traceback
import os
from subset_algorithms.heuristic import MaximallySeperatedNodes, MaximumCovarianceNodes, Random
from subset_algori... | 9,763 | 44.840376 | 196 | py |
LAMP | LAMP-main/loop_closure/src/subset_algorithms/gnn.py | from __future__ import division
from copy import deepcopy
import math
import time
from subset_algorithms.util import constuct_pytorch_geometric_graph, run_model_on_list_of_graphs, choices, fast_deepcopy
import rospy
import numpy as np
from numpy.random import binomial
import random
from subset_algorithms.heuristic ... | 24,494 | 45.043233 | 296 | py |
LAMP | LAMP-main/loop_closure/src/subset_algorithms/util.py | import random
import torch
import numpy as np
from torch_geometric.data import Data, DataLoader
import rospy
import operator as op
from functools import reduce
from itertools import repeat as _repeat
from bisect import bisect as _bisect
import operator
import json
def constuct_pytorch_geometric_graph(nodes, edges, ed... | 2,853 | 33.804878 | 118 | py |
LAMP | LAMP-main/loop_closure/src/gnn/gnn_model.py | import torch
from torch.nn import Linear, ModuleList, BatchNorm1d
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, GraphConv, GATConv, SAGEConv, GMMConv, NNConv
from torch_geometric.nn import global_mean_pool, global_add_pool, GlobalAttention
from torch.nn import ModuleList, Embedding,Sequential,... | 9,031 | 43.27451 | 152 | py |
LAMP | LAMP-main/loop_closure/script/dataset.py | import itertools
import os
import numpy as np
import torch
from torch_geometric.data import InMemoryDataset, Data
from tqdm import tqdm
import random
class LoopClosureDataset(InMemoryDataset):
def __init__(self, root, transform=None, pre_transform=None, delete_bad_files=True):
super(LoopClosureDataset, s... | 14,544 | 44.311526 | 129 | py |
LAMP | LAMP-main/loop_closure/script/convert_rosbags_to_gnn_dataset.py | #!/usr/bin/env python
# This script converts bagfiles stored at fname to readable datasets for pytorch geometric in out_location
# Need pose_graph_opt.bag
import csv
import rosbag
import os
import yaml
import tqdm
fname = "/media/chris/hdd3/more_bags"
out_location = "/media/chris/hdd3/more_training_data"
def dum... | 5,335 | 36.577465 | 173 | py |
LAMP | LAMP-main/loop_closure/script/gnn_model.py | import torch
from torch.nn import Linear, ModuleList, BatchNorm1d
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, GraphConv, GATConv, SAGEConv, GMMConv
from torch_geometric.nn import global_mean_pool, global_add_pool, GlobalAttention
from torch.nn import ModuleList, Embedding,Sequential, ReLU
fr... | 9,517 | 44.54067 | 153 | py |
LAMP | LAMP-main/loop_closure/script/offline_refit.py | #!/usr/bin/env python
# This script fits the gnn model used in batching to new training data
from __future__ import division
import pickle
import os
import math
import torch
from torch_geometric.datasets import TUDataset
from torch_geometric.data import InMemoryDataset, Data, DataLoader
import numpy as np
import torch... | 5,887 | 36.987097 | 152 | py |
CMUDeepLens | CMUDeepLens-master/deeplens/base.py | # This file contains the base class for a Lasagne model
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_is_fitted
from random import shuffle
import time
import numpy as np
import cPickle as pickle
import theano
import theano.tensor as T
import lasagne
from keras.p... | 10,704 | 31.34139 | 106 | py |
xcit | xcit-main/main.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
The main training/evaluation loop
Modified from: https://github.com/facebookresearch/deit
"""
import argparse
import datetime
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
import json
import os
from pathlib impor... | 21,140 | 46.507865 | 119 | py |
xcit | xcit-main/losses.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Implements the knowledge distillation loss
Modified from: https://github.com/facebookresearch/deit
"""
import torch
from torch.nn import functional as F
import torch.nn as nn
class DistillationLoss(torch.nn.Module):
"""
This module wraps ... | 2,977 | 40.361111 | 114 | py |
xcit | xcit-main/engine.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Train and eval functions used in main.py
Modified from: https://github.com/facebookresearch/deit
"""
import math
import sys
from typing import Iterable, Optional
import torch
from timm.data import Mixup
from timm.utils import accuracy, ModelEma
... | 3,705 | 33.635514 | 98 | py |
xcit | xcit-main/hubconf.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
from xcit import *
dependencies = ["torch", "torchvision", "timm"]
| 136 | 21.833333 | 47 | py |
xcit | xcit-main/utils.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Misc functions, including distributed helpers.
Mostly copy-paste from torchvision references.
"""
import io
import os
import time
from collections import defaultdict, deque
import datetime
import torch
import torch.distributed as dist
class Smo... | 7,417 | 28.436508 | 94 | py |
xcit | xcit-main/xcit.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Implementation of Cross-Covariance Image Transformer (XCiT)
Based on timm and DeiT code bases
https://github.com/rwightman/pytorch-image-models/tree/master/timm
https://github.com/facebookresearch/deit/
"""
import math
import torch
import torch.nn... | 20,085 | 36.473881 | 100 | py |
xcit | xcit-main/datasets.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Modified from: https://github.com/facebookresearch/deit
"""
import os
import json
from torchvision import datasets, transforms
from torchvision.datasets.folder import ImageFolder, default_loader
from timm.data.constants import IMAGENET_DEFAULT_ME... | 5,145 | 36.838235 | 105 | py |
xcit | xcit-main/samplers.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Copied from: https://github.com/facebookresearch/deit
"""
import torch
import torch.distributed as dist
import math
class RASampler(torch.utils.data.Sampler):
"""Sampler that restricts data loading to a subset of the dataset for distributed,
... | 2,354 | 36.380952 | 103 | py |
xcit | xcit-main/semantic_segmentation/backbone/xcit.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Object detection and instance segmentation with XCiT backbone
Based on mmseg, timm and DeiT code bases
https://github.com/open-mmlab/mmsegmentation
https://github.com/rwightman/pytorch-image-models/tree/master/timm
https://github.com/facebookresear... | 16,958 | 36.854911 | 100 | py |
xcit | xcit-main/semantic_segmentation/tools/test.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Testing script modified from
https://github.com/open-mmlab/mmsegmentation
"""
import argparse
import os
import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist,... | 5,618 | 34.789809 | 79 | py |
xcit | xcit-main/semantic_segmentation/tools/train.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Training script modified from
https://github.com/open-mmlab/mmsegmentation
"""
import argparse
import copy
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv.runner import init_dist
from mmcv.utils import Config, DictA... | 5,885 | 33.22093 | 79 | py |
xcit | xcit-main/detection/backbone/xcit.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Object detection and instance segmentation with XCiT backbone
Based on mmdet, timm and DeiT code bases
https://github.com/open-mmlab/mmdetection
https://github.com/rwightman/pytorch-image-models/tree/master/timm
https://github.com/facebookresearch/... | 16,955 | 36.848214 | 100 | py |
xcit | xcit-main/detection/tools/test.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Testing script modified from
https://github.com/open-mmlab/mmdetection
"""
import argparse
import os
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataPa... | 8,846 | 37.633188 | 79 | py |
xcit | xcit-main/detection/tools/train.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Training script modified from
https://github.com/open-mmlab/mmdetection
"""
import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.runner impor... | 7,064 | 35.045918 | 79 | py |
psi_2022 | psi_2022-main/plotting.py | """
Use with saved model.
Produces the current sweep and latent space plots
"""
from src.models import DIVA
from src.data import MemMapDataset_O
from src.data._utils import get_dataloaders
from src.common.utils import load_model
from src.common.physics_approximations import *
import torch
import numpy as np
import... | 4,731 | 36.555556 | 146 | py |
psi_2022 | psi_2022-main/train.py | from src.models import DIVA
from src.data import MemMapDataset_O
from src.data._utils import initialize_dataloaders
from src.common.utils import save_model
from typing import List
import os
import timeit
import torch
import numpy as np
from tqdm import tqdm
""" Training Paramters """
EPOCHS: int = 100 # Around 50 ... | 4,108 | 35.362832 | 132 | py |
psi_2022 | psi_2022-main/src/common/utils.py | import torch
def save_model(model, hparams, dataset):
model_name = hparams['model_name']
save_dict = {'state_dict': model.state_dict(), 'hparams': hparams, 'dataset': dataset}
torch.save(save_dict, './' + model_name + '.pth')
def load_model(model_name):
save_dict = torch.load(f'./{model_name}')
r... | 393 | 42.777778 | 90 | py |
psi_2022 | psi_2022-main/src/common/physics_approximations.py | import torch
import numpy as np
boltzmann_constant = 1.380e-23
mu_0 = 1.256e-6
def find_tesep(profs):
ne, te = profs[:, 0], profs[:, 1]
teseps, neseps, rseps = np.empty(te.shape[0]), np.empty(te.shape[0]), np.empty(te.shape[0])
for k, (ne_slice, te_slice) in enumerate(zip(ne, te)):
l_idx, r_i... | 4,682 | 44.028846 | 149 | py |
psi_2022 | psi_2022-main/src/models/DIVA.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, List, Tuple
from .model_utils import PRIORREG, AUXREG, ENCODER, DECODER
from src.common.physics_approximations import *
MIN_STDDEV = 1e-15
class DIVA(nn.Module):
def __init__(self,
mach_latent_dim: in... | 11,083 | 53.871287 | 253 | py |
psi_2022 | psi_2022-main/src/models/model_utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List
import numpy as np
def get_conv_output_size(initial_input_size, number_blocks, max_pool=True):
""" The conv blocks we use keep the same size but use max pooling, so the output of all convolution blocks will be of length... | 6,482 | 38.530488 | 178 | py |
psi_2022 | psi_2022-main/src/data/_utils.py | import numpy as np
import torch
def initialize_dataloaders(dataset, val_split: float = 0.3, test_split: float = 0.1, batch_size: int = 256, nw: int = 4):
pulse_idxs = np.arange(dataset.total_num_pulses)
make_vals = True
# Need to randomly shuffle the pulses, as they are sorted originally from 30000 -> ... | 3,007 | 47.516129 | 153 | py |
psi_2022 | psi_2022-main/src/data/memmap_dataset_observational.py | from torch.utils.data import Dataset
from mmap_ninja.ragged import RaggedMmap
import os
import numpy as np
from typing import Tuple
import torch
def compute_raggedmmap(data_dir: str, data_string: str, batch_size: int = 128) -> RaggedMmap:
"""Compute the ragged map
Parameters
----------
data_dir... | 6,190 | 43.221429 | 199 | py |
LLPFC | LLPFC-main/main.py | import json
import sys
import numpy as np # set the random seed for torchvision
import logging
import torch
from torch.utils.data.sampler import SubsetRandomSampler
from llpfc import llpfc
from kl import kl
from llpvat import llpvat
from llpgan import llpgan
from utils import set_optimizer, set_device, set_reproduci... | 3,444 | 35.648936 | 128 | py |
LLPFC | LLPFC-main/llpvat.py | import torch
import torch.nn as nn
from llpvatlib.train_fun import llpvat_train_by_bag
from llpfclib.train_fun import test_model
from llpvatlib.utils import VATLoss
def loss_f_test(x, y, device, epsilon=1e-8):
x = torch.clamp(x, epsilon, 1 - epsilon)
return nn.functional.nll_loss(torch.log(x), y, reduction='s... | 1,215 | 49.666667 | 120 | py |
LLPFC | LLPFC-main/llpfc.py | import torch
import torch.nn as nn
from torch.distributions.constraints import simplex
from torch.utils.data import SubsetRandomSampler
import numpy as np
from llpfclib.make_groups import make_groups_forward
from llpfclib.train_fun import train_model_forward_one_epoch, test_model, validate_model_forward
def loss_f(... | 4,654 | 48.521277 | 117 | py |
LLPFC | LLPFC-main/llpgan.py | import torch
import torch.nn as nn
from llpganlib.train_fun import llpgan_train_by_bag, test_llpgan
def loss_f_test(x, y, device, epsilon=1e-8):
x = torch.clamp(x, epsilon, 1 - epsilon)
return nn.functional.nll_loss(torch.log(x), y, reduction='sum')
def llpgan(kl_train_dataset,
dis,
ge... | 1,574 | 35.627907 | 120 | py |
LLPFC | LLPFC-main/utils.py | import argparse
import pickle
import random # set the random seed for torchvision
import numpy as np
import torch
from models.NIN import NIN
from models.WideRes import wide_resnet_d_w
from models.ResNet import resnet18
from models.vgg import vgg19_bn, vgg16_bn
from models.densenet import densenet121
from models.LLPGA... | 18,095 | 46.248042 | 127 | py |
LLPFC | LLPFC-main/kl.py | import torch
import torch.nn as nn
from kllib.train_fun import kl_train_by_bag, validate_model_kl
from llpfclib.train_fun import test_model
def loss_f_test(x, y, device, epsilon=1e-8):
x = torch.clamp(x, epsilon, 1 - epsilon)
return nn.functional.nll_loss(torch.log(x), y, reduction='sum')
def kl(model, opti... | 1,134 | 46.291667 | 115 | py |
LLPFC | LLPFC-main/make_data.py | import argparse
from llpfclib.make_bags import make_bags_dirichlet, InsufficientDataPoints, make_bags_uniform, truncate_data
import torchvision
import pickle
import os
import random # set random seed
import numpy as np # set random seed
class InvalidArguments(Exception):
pass
def get_args():
parser = argp... | 5,251 | 52.050505 | 149 | py |
LLPFC | LLPFC-main/llpfclib/utils.py | import torch
from PIL import Image
from torch.utils.data import Sampler
import numpy as np
def truncate_data_group(x, y, instance2group):
idx_list = []
for i in range(x.shape[0]):
if instance2group[i] != -1:
idx_list.append(i)
x_truncated = x[idx_list]
y_truncated = y[idx_list]
idx2new = {idx_list[i]: i for... | 2,296 | 34.890625 | 113 | py |
LLPFC | LLPFC-main/llpfclib/train_fun.py | import torch
import torch.nn as nn
def test_model(model, test_loader, criterion, device):
# test a model with fully label dataset
model.eval()
with torch.no_grad():
correct = 0
total = 0
total_loss = 0
for images, labels in test_loader:
images = images.to(device)
labels = labels.to(device)
outputs... | 2,465 | 34.73913 | 118 | py |
LLPFC | LLPFC-main/models/ResNet.py | # code in this file is modified from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilat... | 9,492 | 36.820717 | 111 | py |
LLPFC | LLPFC-main/models/LLPGAN_DIS.py | import torch.nn as nn
class LLPGAN_DIS(nn.Module):
# use the same discriminator as LLP-GAN paper
def __init__(self, num_class, image_size, in_channel=3, return_features=False):
super(LLPGAN_DIS, self).__init__()
self.conv_layers = nn.Sequential(
nn.Dropout(p=0.2, ),
nn.Conv2d(in_channel, 64, 3, padding=1,... | 1,421 | 29.913043 | 89 | py |
LLPFC | LLPFC-main/models/vgg.py | # code in this file is modified from https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
import torch
import torch.nn as nn
from typing import Union, List, Dict, Any, cast
class VGG(nn.Module):
def __init__(self,
features: nn.Module,
num_classes: int = 1000,
... | 3,722 | 37.78125 | 114 | py |
LLPFC | LLPFC-main/models/WideRes.py | # The code in this file is modified from https://github.com/kevinorjohn/LLP-VAT
# MIT License
#
# Copyright (c) 2020 Kuen-Han Tsai
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without re... | 5,794 | 35.21875 | 118 | py |
LLPFC | LLPFC-main/models/densenet.py | # code in this file is modified from https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py
import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
from torch import Tensor
from typing import Any, List, Tu... | 9,286 | 39.911894 | 113 | py |
LLPFC | LLPFC-main/models/NIN.py | # code in this file is modified from https://github.com/yangqiongyongyu/Network-In-Network-Pytorch/blob/master/models/nin.py
# hyperparameters selected based on https://worksheets.codalab.org/worksheets/0x7b8f6fbc6b5c49c18ac7ca94aafaa1a7
import torch.nn as nn
import math
class NIN(nn.Module):
def __init__(self, num_... | 1,708 | 30.648148 | 124 | py |
LLPFC | LLPFC-main/models/LLPGAN_GEN.py | import torch.nn as nn
class LLPGAN_GEN_MNIST(nn.Module):
def __init__(self, noise_size=100, out_h=28, out_w=28):
self.out_h, self.out_w = out_h, out_w
super(LLPGAN_GEN_MNIST, self).__init__()
self.model = nn.Sequential(
nn.Linear(noise_size, 500),
nn.ReLU(),
... | 1,640 | 29.962264 | 79 | py |
LLPFC | LLPFC-main/llpvatlib/utils.py | # code in this file is modified from https://github.com/kevinorjohn/LLP-VAT/blob/a111d6785e8b0b79761c4d68c5b96288048594d6/llp_vat/
import contextlib
import torch
import torch.nn as nn
import torch.nn.functional as F
@contextlib.contextmanager
def _disable_tracking_bn_stats(model):
def switch_attr(m):
if h... | 1,989 | 30.587302 | 130 | py |
LLPFC | LLPFC-main/llpvatlib/train_fun.py | import torch
from kllib.train_fun import compute_kl_loss_on_bagbatch
import numpy as np
def sigmoid_rampup(current, rampup_length):
# modified from https://github.com/kevinorjohn/LLP-VAT/blob/a111d6785e8b0b79761c4d68c5b96288048594d6/llp_vat/
"""Exponential rampup from https://arxiv.org/abs/1610.02242"""
i... | 2,296 | 44.94 | 119 | py |
LLPFC | LLPFC-main/kllib/utils.py | from PIL import Image
import torch
import numpy as np
class KL_DATASET_BASE(torch.utils.data.Dataset):
def __init__(self, data, bag2indices, bag2prop, transform):
self.data = data
self.bag2indices = bag2indices
self.bag2prop = bag2prop
self.transform = transform
def __len__(se... | 2,417 | 36.2 | 114 | py |
LLPFC | LLPFC-main/kllib/train_fun.py | import torch
import torch.nn as nn
def compute_kl_loss_on_bagbatch(model, images, props, device, epsilon=1e-8):
# Move tensors to the configured device
images = images.to(device)
props = props.to(device)
# Forward pass
batch_size, bag_size, channel, height, width = images.shape
images = images... | 1,840 | 38.170213 | 107 | py |
LLPFC | LLPFC-main/llpganlib/train_fun.py | import numpy as np
import torch
import torch.nn as nn
from torch.nn.functional import mse_loss
from torch.autograd import Variable
def test_llpgan(model, test_loader, criterion, device):
# test a model with fully label dataset
model.eval()
with torch.no_grad():
correct = 0
total = 0
... | 4,536 | 40.245455 | 115 | py |
DehazeFormer | DehazeFormer-main/test.py | import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_msssim import ssim
from torch.utils.data import DataLoader
from collections import OrderedDict
from utils import AverageMeter, write_img, chw_to_hwc
from datasets.loader import PairLoader
from models import *
pa... | 3,451 | 30.962963 | 100 | py |
DehazeFormer | DehazeFormer-main/train.py | import os
import argparse
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import autocast, GradScaler
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from tqdm import tqdm
from utils import AverageMeter
from datasets.loader import PairLo... | 4,879 | 31.972973 | 121 | py |
DehazeFormer | DehazeFormer-main/models/dehazeformer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
from torch.nn.init import _calculate_fan_in_and_fan_out
from timm.models.layers import to_2tuple, trunc_normal_
class RLN(nn.Module):
r"""Revised LayerNorm"""
def __init__(self, dim, eps=1e-5, detach_grad=False):
sup... | 17,111 | 29.502674 | 108 | py |
DehazeFormer | DehazeFormer-main/datasets/loader.py | import os
import random
import numpy as np
import cv2
from torch.utils.data import Dataset
from utils import hwc_to_chw, read_img
def augment(imgs=[], size=256, edge_decay=0., only_h_flip=False):
H, W, _ = imgs[0].shape
Hc, Wc = [size, size]
# simple re-weight for the edge
if random.random() < Hc / H * edge_dec... | 2,768 | 25.122642 | 109 | py |
DehazeFormer | DehazeFormer-main/utils/data_parallel.py | from torch.nn.parallel import DataParallel
import torch
from torch.nn.parallel._functions import Scatter
from torch.nn.parallel.parallel_apply import parallel_apply
def scatter(inputs, target_gpus, chunk_sizes, dim=0):
r"""
Slices tensors into approximately equal chunks and
distributes them across given GP... | 4,069 | 38.514563 | 84 | py |
vector-quantize-pytorch | vector-quantize-pytorch-master/setup.py | from setuptools import setup, find_packages
setup(
name = 'vector_quantize_pytorch',
packages = find_packages(),
version = '1.6.30',
license='MIT',
description = 'Vector Quantization - Pytorch',
long_description_content_type = 'text/markdown',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com'... | 810 | 25.16129 | 65 | py |
vector-quantize-pytorch | vector-quantize-pytorch-master/examples/autoencoder.py | # FashionMnist VQ experiment with various settings.
# From https://github.com/minyoungg/vqtorch/blob/main/examples/autoencoder.py
from tqdm.auto import trange
import torch
import torch.nn as nn
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from vector_quantize_pytorch import Ve... | 3,076 | 31.052083 | 83 | py |
vector-quantize-pytorch | vector-quantize-pytorch-master/vector_quantize_pytorch/vector_quantize_pytorch.py | from functools import partial
import torch
from torch import nn, einsum
import torch.nn.functional as F
import torch.distributed as distributed
from torch.optim import Optimizer
from torch.cuda.amp import autocast
from einops import rearrange, repeat, reduce, pack, unpack
from typing import Callable
def exists(val)... | 35,993 | 33.911736 | 195 | py |
vector-quantize-pytorch | vector-quantize-pytorch-master/vector_quantize_pytorch/random_projection_quantizer.py | import torch
from torch import nn, einsum
import torch.nn.functional as F
from vector_quantize_pytorch.vector_quantize_pytorch import VectorQuantize
from einops import rearrange, repeat, pack, unpack
def exists(val):
return val is not None
class RandomProjectionQuantizer(nn.Module):
""" https://arxiv.org/abs... | 1,723 | 24.731343 | 107 | py |
vector-quantize-pytorch | vector-quantize-pytorch-master/vector_quantize_pytorch/residual_vq.py | from math import ceil
from functools import partial
from itertools import zip_longest
from random import randrange
import torch
from torch import nn
import torch.nn.functional as F
from vector_quantize_pytorch.vector_quantize_pytorch import VectorQuantize
from einops import rearrange, repeat, pack, unpack
# helper f... | 8,989 | 31.930403 | 188 | py |
vector-quantize-pytorch | vector-quantize-pytorch-master/vector_quantize_pytorch/__init__.py | from vector_quantize_pytorch.vector_quantize_pytorch import VectorQuantize
from vector_quantize_pytorch.residual_vq import ResidualVQ, GroupedResidualVQ
from vector_quantize_pytorch.random_projection_quantizer import RandomProjectionQuantizer | 242 | 80 | 89 | py |
smt | smt-master/doc/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# SMT documentation build configuration file, created by
# sphinx-quickstart on Sun Aug 6 19:36:14 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autoge... | 5,197 | 28.873563 | 79 | py |
LongTailCXR | LongTailCXR-main/src/main.py | import os
import shutil
import argparse
import numpy as np
import pandas as pd
import torch
import torchvision
from sklearn.utils import class_weight
from datasets import *
from utils import *
from losses import *
def main(args):
# Set model/output directory name
MODEL_NAME = args.dataset
MODEL_NAME += ... | 10,072 | 52.86631 | 311 | py |
LongTailCXR | LongTailCXR-main/src/losses.py | import numpy as np
import torch
import torch.nn.functional as F
def get_loss(args, weights, train_dataset):
if args.loss == 'ce':
loss_fxn = torch.nn.CrossEntropyLoss(weight=weights, reduction='mean')
elif args.loss == 'focal':
loss_fxn = torch.hub.load('adeelh/pytorch-multi-class-focal-loss', ... | 1,722 | 34.163265 | 147 | py |
LongTailCXR | LongTailCXR-main/src/utils.py | import os
import random
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import tqdm
from copy import deepcopy
from mlxtend.plotting import plot_confusion_matrix
from sklearn.metrics import roc_auc_score, balanced_accuracy_score, classification_report, confusion_matrix, matthews_co... | 15,395 | 38.88601 | 186 | py |
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