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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ssm_ecg | ssm_ecg-main/code/dl_models/baseline_encoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
class Encoder(nn.Module):
def __init__(self, out_dim=64):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(16, 32, ... | 1,099 | 24 | 74 | py |
ssm_ecg | ssm_ecg-main/code/dl_models/s4original.py | #https://github.com/HazyResearch/state-spaces/blob/main/src/models/sequence/ss/standalone/s4.py
""" Standalone version of Structured (Sequence) State Space (S4) model. """
import logging
from functools import partial
import math
import numpy as np
from scipy import special as ss
import torch
import torch.nn as nn
imp... | 40,633 | 34.927498 | 218 | py |
ssm_ecg | ssm_ecg-main/code/dl_models/s4.py | # https://github.com/HazyResearch/state-spaces/blob/main/src/models/sequence/ss/standalone/s4.py
""" Standalone version of Structured (Sequence) State Space (S4) model. """
import logging
from functools import partial
import math
import numpy as np
from scipy import special as ss
import torch
import torch.nn as nn
im... | 41,085 | 33.759729 | 218 | py |
ssm_ecg | ssm_ecg-main/code/dl_models/ecg_resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# import torchvision.models as models
from .xresnet1d import xresnet1d50, xresnet1d101
from .basic_conv1d import bn_drop_lin
class ECGResNet(nn.Module):
def __init__(self, base_model, out_dim, widen=1.0, big_input=False, use_meta_information_in_he... | 2,151 | 35.474576 | 117 | py |
ssm_ecg | ssm_ecg-main/code/extensions/cauchy/cauchy.py | import torch
from einops import rearrange
from cauchy_mult import cauchy_mult_fwd, cauchy_mult_bwd, cauchy_mult_sym_fwd, cauchy_mult_sym_bwd
def cauchy_mult_torch(v: torch.Tensor, z: torch.Tensor, w: torch.Tensor,
symmetric=True) -> torch.Tensor:
"""
v: (B, N)
z: (L)
w: (B, N)
... | 3,877 | 33.625 | 117 | py |
ssm_ecg | ssm_ecg-main/code/extensions/cauchy/setup.py | from setuptools import setup, find_packages
import torch.cuda
from torch.utils.cpp_extension import CppExtension, CUDAExtension, BuildExtension
from torch.utils.cpp_extension import CUDA_HOME
ext_modules = []
if torch.cuda.is_available() and CUDA_HOME is not None:
extension = CUDAExtension(
'cauchy_mult', ... | 1,054 | 34.166667 | 81 | py |
ssm_ecg | ssm_ecg-main/code/extensions/cauchy/benchmark_cauchy_tune.py | import importlib
import json
import argparse
import torch
from benchmark.utils import benchmark_forward
def generate_data(batch_size, N, L, symmetric=True, device='cuda'):
if not symmetric:
v = torch.randn(batch_size, N, dtype=torch.complex64, device=device, requires_grad=True)
w = torch.randn(b... | 2,361 | 40.438596 | 96 | py |
ssm_ecg | ssm_ecg-main/code/extensions/cauchy/test_cauchy.py | import math
import torch
import pytest
from einops import rearrange
# from cauchy import cauchy_mult_torch, cauchy_mult_keops, cauchy_mult
def generate_data(batch_size, N, L, symmetric=True, device='cuda'):
if not symmetric:
v = torch.randn(batch_size, N, dtype=torch.complex64, device=device, requires_... | 5,451 | 53.52 | 122 | py |
ssm_ecg | ssm_ecg-main/code/extensions/cauchy/benchmark_cauchy.py | import math
from functools import partial
import torch
from einops import rearrange
from .cauchy import cauchy_mult_torch, cauchy_mult_keops, cauchy_mult
from benchmark.utils import benchmark_all, benchmark_combined, benchmark_forward, benchmark_backward
def generate_data(batch_size, N, L, symmetric=True, device='... | 1,670 | 37.860465 | 100 | py |
ssm_ecg | ssm_ecg-main/code/extensions/cauchy/tuning_setup.py | import os
from setuptools import setup
from pathlib import Path
import torch.cuda
from torch.utils.cpp_extension import CppExtension, CUDAExtension, BuildExtension
from torch.utils.cpp_extension import CUDA_HOME
extensions_dir = Path(os.getenv('TUNING_SOURCE_DIR')).absolute()
assert extensions_dir.exists()
source_fi... | 1,164 | 30.486486 | 81 | py |
ssm_ecg | ssm_ecg-main/code/extensions/cauchy/tuner.py | import os
import shutil
import subprocess
import sys
# import tempfile
# import importlib
import random
import string
import json
from functools import partial
from multiprocessing import Pipe, Pool, Process
from pathlib import Path
from tqdm import tqdm
import numpy as np
def read_file(filename):
""" return ... | 6,937 | 36.912568 | 108 | py |
DELIGHT | DELIGHT-main/delight/delight/delight.py | import os
import re
import subprocess
import numpy as np
import pandas as pd
import pkg_resources
import warnings
import xarray as xr
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from astropy.visualization import ZScaleInterval, ImageNormalize, MinMaxInterval, PercentileInterval, LogStretc... | 42,486 | 41.149802 | 328 | py |
NDLS | NDLS-main/src/utils.py | import numpy as np
import pickle as pkl
import networkx as nx
import scipy.sparse as sp
from scipy.sparse import csgraph
import sys
import time
import argparse
import torch
def aug_random_walk(adj):
adj = adj + sp.eye(adj.shape[0])
adj = sp.coo_matrix(adj)
row_sum = np.array(adj.sum(1))
d_inv = np.pow... | 13,486 | 32.549751 | 111 | py |
NDLS | NDLS-main/src/model.py | import scipy.sparse as sp
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.functional as F
from utils import *
class DNN(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(DNN, self).__init__()
self.fcn1 = nn.Linear(nfeat, nhid)
self.fcn2 ... | 626 | 28.857143 | 62 | py |
NDLS | NDLS-main/src/train.py | import numpy as np
import torch
import argparse
SEED = 123
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed_all(SEED)
import torch.nn.functional as F
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from utils import *
from model import DNN
import os
def aver(hop... | 5,918 | 35.091463 | 113 | py |
FGNN | FGNN-master/main.py | # -*- coding: utf-8 -*-
"""
Created on 4/4/2019
@author: RuihongQiu
"""
import os
import argparse
import logging
import torch
import time
from tqdm import tqdm
from dataset import MultiSessionsGraph
from torch_geometric.data import DataLoader
from model import GNNModel
from sort_pooling_model import SortPoolModel
fro... | 4,049 | 42.548387 | 120 | py |
FGNN | FGNN-master/weighted_gat.py | # -*- coding: utf-8 -*-
"""
Created on 5/5/2019
@author: RuihongQiu
"""
import torch
from torch.nn import Parameter
import torch.nn.functional as F
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import remove_self_loops, add_self_loops, softmax
from torch_geometric.nn.inits import glorot... | 4,925 | 37.186047 | 115 | py |
FGNN | FGNN-master/model.py | # -*- coding: utf-8 -*-
"""
Created on 4/4/2019
@author: RuihongQiu
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, GATConv, GatedGraphConv, SAGEConv
class Embedding2Score(nn.Module):
def __init__(self, hidden_size):
super(Embedd... | 3,390 | 40.864198 | 144 | py |
FGNN | FGNN-master/dataset.py | # -*- coding: utf-8 -*-
"""
Created on 31/3/2019
@author: RuihongQiu
"""
import pickle
import torch
import collections
from torch_geometric.data import InMemoryDataset, Data
class MultiSessionsGraph(InMemoryDataset):
"""Every session is a graph."""
def __init__(self, root, phrase, transform=None, pre_transfo... | 3,401 | 35.978261 | 112 | py |
FGNN | FGNN-master/train.py | # -*- coding: utf-8 -*-
"""
Created on 5/4/2019
@author: RuihongQiu
"""
import numpy as np
import logging
import torch.nn as nn
def forward(model, loader, device, writer, epoch, top_k=20, optimizer=None, train_flag=True):
if train_flag:
model.train()
else:
model.eval()
hit, mrr = [],... | 1,652 | 29.611111 | 104 | py |
FGNN | FGNN-master/gru_set2set.py | # -*- coding: utf-8 -*-
"""
Created on 3/5/2019
@author: RuihongQiu
"""
import torch
import torch.nn as nn
from torch_scatter import scatter_add
from torch_geometric.utils import softmax
class GRUSet2Set(torch.nn.Module):
r"""The global pooling operator based on iterative content-based attention
from the `"O... | 3,350 | 35.824176 | 119 | py |
SNAKE | SNAKE-main/setup.py | try:
from setuptools import setup
except ImportError:
from distutils.core import setup
from distutils.extension import Extension
from Cython.Build import cythonize
from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension
import numpy
# Get the numpy include directory.
numpy_include_di... | 2,309 | 23.83871 | 81 | py |
SNAKE | SNAKE-main/tools/test_net.py | import sys
import argparse
import torch
import ipdb
sys.path.append("./")
from core.utils.common import Config
from core.test import Evaluator
parser = argparse.ArgumentParser(description="3D Keypoint Detection Test and Visualization")
parser.add_argument("--test_model_root", default="", type=str)
parser.add_argument... | 1,170 | 26.232558 | 92 | py |
SNAKE | SNAKE-main/tools/show_kpts.py | import os
import sys
sys.path.append("./")
import numpy as np
from torch.utils.data import DataLoader
import argparse
import tqdm
from core.datasets.test_dataset import *
from core.utils.viz import *
from core.utils.common import Config
from tools.eval_iou import nms_usip
def main(kpts, test_data_config, save_path,... | 2,343 | 39.413793 | 118 | py |
SNAKE | SNAKE-main/tools/train_net.py | import sys
import argparse
sys.path.append("./")
from core.utils.common import Config
from core.solvers import solver_entry
import torch
import ipdb
parser = argparse.ArgumentParser(description="3D Keypoint Detection Training")
parser.add_argument("--load-path", default="", type=str)
parser.add_argument("--recover", ... | 1,123 | 24.545455 | 78 | py |
SNAKE | SNAKE-main/core/nets/snake_net.py | import torch
import torch.nn as nn
from torch import distributions as dist
from . import decoder
from .encoder import encoder_dict
import torch.nn.functional as F
import ipdb
# Decoder dictionary
decoder_dict = {
'simple_local': decoder.LocalDecoder,
'simple_local_crop': decoder.PatchLocalDecoder,
'simple... | 4,438 | 29.613793 | 92 | py |
SNAKE | SNAKE-main/core/nets/common.py | import torch
import numpy as np
import math
import ipdb
def compute_iou(occ1, occ2):
''' Computes the Intersection over Union (IoU) value for two sets of
occupancy values.
Args:
occ1 (tensor): first set of occupancy values
occ2 (tensor): second set of occupancy values
'''
occ1 = n... | 14,367 | 30.439825 | 109 | py |
SNAKE | SNAKE-main/core/nets/decoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .encoder.pointnet import ResnetBlockFC
from .common import normalize_coordinate, normalize_3d_coordinate, map2local
import ipdb
class LocalDecoder(nn.Module):
''' Decoder.
Instead of conditioning on global features, on plane/volume l... | 10,300 | 33.222591 | 153 | py |
SNAKE | SNAKE-main/core/nets/geometry.py | import numpy as np
import torch
from torch.nn import functional as F
import util
def compute_normal_map(x_img, y_img, z, intrinsics):
cam_coords = lift(x_img, y_img, z, intrinsics)
cam_coords = util.lin2img(cam_coords)
shift_left = cam_coords[:, :, 2:, :]
shift_right = cam_coords[:, :, :-2, :]
... | 4,939 | 30.265823 | 120 | py |
SNAKE | SNAKE-main/core/nets/encoder/pointnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_scatter import scatter_mean, scatter_max
from ..common import coordinate2index, normalize_coordinate, normalize_3d_coordinate, map2local
from ..encoder.unet import UNet
from ..encoder.unet3d import UNet3D
import ipdb
class ResnetBlockFC(nn... | 14,291 | 36.511811 | 134 | py |
SNAKE | SNAKE-main/core/nets/encoder/pointnetpp.py | '''
From the implementation of https://github.com/yanx27/Pointnet_Pointnet2_pytorch
'''
from time import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class PointNetSetAbstraction(nn.Module):
def __init__(self, npoint, radius, nsample, in_channel, mlp, group_all):
... | 10,383 | 34.2 | 139 | py |
SNAKE | SNAKE-main/core/nets/encoder/unet.py | '''
Codes are from:
https://github.com/jaxony/unet-pytorch/blob/master/model.py
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import numpy as np
def conv3x3(in_channels, out_channels, stride=1,
... | 8,696 | 32.579151 | 80 | py |
SNAKE | SNAKE-main/core/nets/encoder/unet3d.py | '''
Code from the 3D UNet implementation:
https://github.com/wolny/pytorch-3dunet/
'''
import importlib
import torch
import torch.nn as nn
from torch.nn import functional as F
from functools import partial
def number_of_features_per_level(init_channel_number, num_levels):
return [init_channel_number * 2 ** k for k... | 25,184 | 45.126374 | 174 | py |
SNAKE | SNAKE-main/core/nets/encoder/voxels.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_scatter import scatter_mean
from .unet import UNet
from .unet3d import UNet3D
from ..common import coordinate2index, normalize_coordinate, normalize_3d_coordinate
class LocalVoxelEncoder(nn.Module):
''' 3D-convolutional encoder network ... | 5,607 | 35.415584 | 109 | py |
SNAKE | SNAKE-main/core/test/evaluate.py | import os
import ipdb
import tqdm
import shutil
import numpy as np
import open3d as o3d
from easydict import EasyDict as edict
from sklearn.metrics import pairwise_distances_argmin, pairwise_distances_argmin_min
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
import torch.nn.functional... | 18,793 | 42.105505 | 110 | py |
SNAKE | SNAKE-main/core/datasets/test_dataset.py | import numpy as np
import torch
import os
from core.utils.transform import *
from core.datasets.train_dataset import ModelNet40, make_3d_grid, naive_read_pcd
from core.datasets.smpl_model import SMPLModel, sample_vertex_from_mesh
class ModelNet40_Test(ModelNet40):
def __init__(self, config, mode):
super(... | 11,378 | 39.784946 | 121 | py |
SNAKE | SNAKE-main/core/datasets/augmentation.py | import numpy as np
import torch
def augment(pcd_data, grid_coords, config):
'''
rigid transformation
'''
if config['do_aug']:
# rotation
rotate_angle = config["rotate_angle"] / 180.0 * (np.pi)
# for outdoor scene
z_angle = np.random.uniform() * rotate_angle
... | 6,785 | 29.16 | 119 | py |
SNAKE | SNAKE-main/core/datasets/train_dataset.py | import numpy as np
import torch
from torch.utils.data import Dataset
import os
from core.datasets.augmentation import augment
from core.datasets.smpl_model import SMPLModel, sample_vertex_from_mesh
def naive_read_pcd(path):
lines = open(path, 'r').readlines()
idx = -1
for i, line in enumerate(lines):
... | 14,410 | 37.429333 | 103 | py |
SNAKE | SNAKE-main/core/solvers/solver.py | import os
import time
import random
import warnings
from collections import defaultdict
from tqdm import tqdm
import ipdb
from easydict import EasyDict as edict
from tensorboardX import SummaryWriter
import numpy as np
import torch
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torch.distribu... | 18,370 | 38.004246 | 100 | py |
SNAKE | SNAKE-main/core/utils/common.py | import os
import logging
import yaml
import numpy as np
import json
import torch
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, length):
self.length = length
self.reset()
def reset(self):
self.history = []
self.val =... | 5,620 | 30.227778 | 100 | py |
SNAKE | SNAKE-main/core/utils/match.py | import numpy as np
import torch
import open3d as o3d
from torch_scatter import scatter_max
from core.nets.common import normalize_3d_coordinate, coordinate2index
import ipdb
def nms(keypoints_np, sals_np, NMS_radius, total_num=2000):
'''
:param keypoints_np: Mx3
:param sigmas_np: M
:return: valid_ke... | 4,848 | 34.918519 | 110 | py |
SNAKE | SNAKE-main/core/losses/keypoint_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class CosimLoss(nn.Module):
"""
Try to make the saliency field repeatable from one point cloud to the other.
"""
def __init__(self, weight, x_res_grid, y_res_grid, z_res_grid,
cos_point_ratio=0.9, **kw):
... | 4,679 | 35.27907 | 94 | py |
SNAKE | SNAKE-main/core/losses/occupancy_loss.py | import torch
import torch.nn as nn
class Occupancy_loss (nn.Module):
def __init__(self, weight, **kw):
nn.Module.__init__(self)
self.name = 'occupancy_loss'
def forward_one(self, occ, occ_labels):
occ_loss = -1 * (occ_labels * torch.log(occ + 1e-5) +
(1 - occ_l... | 683 | 28.73913 | 76 | py |
SNAKE | SNAKE-main/core/losses/multi_losses.py | import torch.nn as nn
'''implemented by r2d2: https://github.com/naver/r2d2/blob/master/nets/losses.py'''
class MultiLoss(nn.Module):
""" Combines several loss functions for convenience."""
def __init__(self, loss_list):
super(MultiLoss, self).__init__()
self.losses = loss_list
se... | 1,123 | 25.139535 | 83 | py |
RSSC-transfer | RSSC-transfer-main/extract_features.py | #!/usr/bin/env python
# coding: utf-8
"""
Extract features using a pre-trained model.
Created on Thu Jul 4 23:37:36 2019
@author: vlado
"""
import os
import pickle
import random
import argparse
import numpy as np
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.applications.resnet5... | 4,146 | 34.144068 | 119 | py |
RSSC-transfer | RSSC-transfer-main/extract_features_multilabel.py | #!/usr/bin/env python
# coding: utf-8
"""
Extract features using a pre-trained model.
Created on Thu Jul 4 23:37:36 2019
@author: vlado
"""
import os
import pickle
import random
import argparse
import numpy as np
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.applications.resnet5... | 4,146 | 34.144068 | 119 | py |
RSSC-transfer | RSSC-transfer-main/train_multilabel.py | #!/usr/bin/env python
# coding: utf-8
"""
Convnet training/fine-tuning.
Created on Thu Jul 4 23:37:36 2019
@author: vlado
"""
import os
import pickle
import random
import sklearn
import argparse
import numpy as np
import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras import Model
from... | 7,874 | 32.368644 | 119 | py |
RSSC-transfer | RSSC-transfer-main/classify_multilabel.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Multilabel classification based on extracted features
Created on Tue Aug 27 08:49:25 2019
@author: vlado
"""
import os
import random
import argparse
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.lay... | 1,702 | 22.985915 | 69 | py |
RSSC-transfer | RSSC-transfer-main/classify_softmax.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Classification using softmax on extracted features
Created on Tue Aug 27 08:49:25 2019
@author: vlado
"""
import os
import random
import argparse
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers... | 1,657 | 22.352113 | 69 | py |
RSSC-transfer | RSSC-transfer-main/train.py | #!/usr/bin/env python
# coding: utf-8
"""
Convnet training/fine-tuning.
Created on Thu Jul 4 23:37:36 2019
@author: vlado
"""
import os
import pickle
import random
import sklearn
import argparse
import numpy as np
import tensorflow as tf
from tensorflow.keras import Model
import tensorflow.keras.backend as K
from ... | 7,655 | 32.286957 | 119 | py |
RSSC-transfer | RSSC-transfer-main/ssl/fine_tune_multilabel.py | import os
import PIL
import time
import copy
import torch
import pickle
import random
import argparse
import torchvision
import numpy as np
import torch.nn as nn
import torch.optim as optim
import pytorch_warmup as warmup
import torch.backends.cudnn as cudnn
from torch.optim import lr_scheduler
from torch.utils.data im... | 8,072 | 33.063291 | 123 | py |
RSSC-transfer | RSSC-transfer-main/ssl/fine_tune.py | import os
import PIL
import time
import copy
import torch
import pickle
import random
import argparse
import torchvision
import numpy as np
import torch.nn as nn
import torch.optim as optim
import pytorch_warmup as warmup
import torch.backends.cudnn as cudnn
from torch.optim import lr_scheduler
from torch.utils.data im... | 7,662 | 32.17316 | 100 | py |
RSSC-transfer | RSSC-transfer-main/ssl/extract_features.py | import os
import PIL
import torch
import pickle
import random
import argparse
import torchvision
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.multiprocessing
import torch.backends.cudnn as cudnn
from torch.optim import lr_scheduler
from torch.utils.data import Dataset, DataLoader
fr... | 5,022 | 29.815951 | 105 | py |
RSSC-transfer | RSSC-transfer-main/ssl/extract_features_multilabel.py | import os
import PIL
import torch
import pickle
import random
import argparse
import torchvision
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.multiprocessing
import torch.backends.cudnn as cudnn
from torch.optim import lr_scheduler
from torch.utils.data import Dataset, DataLoader
fr... | 5,022 | 29.815951 | 105 | py |
Unsupervised-Classification | Unsupervised-Classification-master/simclr.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import argparse
import os
import torch
import numpy as np
from utils.config import create_config
from utils.common_config import get_criterion, get_model, get_train_dataset,... | 6,206 | 39.305195 | 102 | py |
Unsupervised-Classification | Unsupervised-Classification-master/scan.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import argparse
import os
import torch
from termcolor import colored
from utils.config import create_config
from utils.common_config import get_train_transformations, get_va... | 5,789 | 40.06383 | 108 | py |
Unsupervised-Classification | Unsupervised-Classification-master/moco.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import argparse
import os
import torch
import numpy as np
from utils.config import create_config
from utils.common_config import get_model, get_train_dataset,\
... | 4,514 | 36.008197 | 122 | py |
Unsupervised-Classification | Unsupervised-Classification-master/selflabel.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import argparse
import os
import torch
from utils.config import create_config
from utils.common_config import get_train_dataset, get_train_transformations,\
... | 4,688 | 36.214286 | 111 | py |
Unsupervised-Classification | Unsupervised-Classification-master/tutorial_nn.py | """
Authors: Wouter Van Gansbeke
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import argparse
import os
import numpy as np
import torch
from utils.config import create_config
from utils.common_config import get_model, get_train_dataset, \
... | 3,924 | 38.25 | 102 | py |
Unsupervised-Classification | Unsupervised-Classification-master/eval.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import argparse
import torch
import yaml
from termcolor import colored
from utils.common_config import get_val_dataset, get_val_transformations, get_val_dataloader,\
... | 5,692 | 37.993151 | 112 | py |
Unsupervised-Classification | Unsupervised-Classification-master/models/resnet_stl.py | """
This code is based on the Torchvision repository, which was licensed under the BSD 3-Clause.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1, is_last=False):
super(BasicBlock, self).__... | 4,903 | 37.614173 | 104 | py |
Unsupervised-Classification | Unsupervised-Classification-master/models/resnet.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import torch.nn as nn
import torchvision.models as models
def resnet50():
backbone = models.__dict__['resnet50']()
backbone.fc = nn.Identity()
return {'backbone... | 346 | 25.692308 | 89 | py |
Unsupervised-Classification | Unsupervised-Classification-master/models/resnet_cifar.py | """
This code is based on the Torchvision repository, which was licensed under the BSD 3-Clause.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1, is_last=False):
super(BasicBlock, self).__... | 4,820 | 37.261905 | 104 | py |
Unsupervised-Classification | Unsupervised-Classification-master/models/models.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class ContrastiveModel(nn.Module):
def __init__(self, backbone, head='mlp', features_dim=128):
... | 2,228 | 34.380952 | 114 | py |
Unsupervised-Classification | Unsupervised-Classification-master/utils/common_config.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import os
import math
import numpy as np
import torch
import torchvision.transforms as transforms
from data.augment import Augment, Cutout
from utils.collate import collate_c... | 11,447 | 36.907285 | 112 | py |
Unsupervised-Classification | Unsupervised-Classification-master/utils/memory.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import numpy as np
import torch
class MemoryBank(object):
def __init__(self, n, dim, num_classes, temperature):
self.n = n
self.dim = dim
self.... | 3,213 | 35.11236 | 103 | py |
Unsupervised-Classification | Unsupervised-Classification-master/utils/utils.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import os
import torch
import numpy as np
import errno
def mkdir_if_missing(directory):
if not os.path.exists(directory):
try:
os.makedirs(directory)... | 3,018 | 29.494949 | 95 | py |
Unsupervised-Classification | Unsupervised-Classification-master/utils/train_utils.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import torch
import numpy as np
from utils.utils import AverageMeter, ProgressMeter
def simclr_train(train_loader, model, criterion, optimizer, epoch):
"""
Train a... | 4,681 | 35.294574 | 102 | py |
Unsupervised-Classification | Unsupervised-Classification-master/utils/evaluate_utils.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import numpy as np
import torch
import torch.nn.functional as F
from utils.common_config import get_feature_dimensions_backbone
from utils.utils import AverageMeter, confusio... | 6,864 | 35.71123 | 150 | py |
Unsupervised-Classification | Unsupervised-Classification-master/utils/collate.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import torch
import numpy as np
import collections
from torch._six import string_classes
""" Custom collate function """
def collate_custom(batch):
if isinstance(batch[... | 1,164 | 28.125 | 114 | py |
Unsupervised-Classification | Unsupervised-Classification-master/data/custom_dataset.py | """
Author: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import numpy as np
import torch
from torch.utils.data import Dataset
"""
AugmentedDataset
Returns an image together with an augmentation.
"""
class AugmentedDataset(... | 2,660 | 31.060241 | 89 | py |
Unsupervised-Classification | Unsupervised-Classification-master/data/augment.py | # List of augmentations based on randaugment
import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from torchvision.transforms.transforms import Compose
random_mirror = True
def ShearX(img, v):
if random_mirror and random.random() > 0.5:
v = -v
return... | 3,885 | 25.080537 | 75 | py |
Unsupervised-Classification | Unsupervised-Classification-master/data/cifar.py | """
This code is based on the Torchvision repository, which was licensed under the BSD 3-Clause.
"""
import os
import pickle
import sys
import numpy as np
import torch
from PIL import Image
from torch.utils.data import Dataset
from utils.mypath import MyPath
from torchvision.datasets.utils import check_integrity, downl... | 8,385 | 28.219512 | 418 | py |
Unsupervised-Classification | Unsupervised-Classification-master/data/imagenet.py | """
Author: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import os
import torch
import torchvision.datasets as datasets
import torch.utils.data as data
from PIL import Image
from utils.mypath import MyPath
from torchvision import tr... | 3,245 | 30.514563 | 118 | py |
Unsupervised-Classification | Unsupervised-Classification-master/data/stl.py | """
This code is based on the Torchvision repository, which was licensed under the BSD 3-Clause.
"""
from PIL import Image
from torchvision.datasets.utils import check_integrity, download_and_extract_archive, verify_str_arg
from torch.utils.data import Dataset
from utils.mypath import MyPath
import os
import numpy as n... | 7,455 | 38.449735 | 119 | py |
Unsupervised-Classification | Unsupervised-Classification-master/losses/losses.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
EPS=1e-8
class MaskedCrossEntropyLoss(nn.Module):
def __init__(self):
super(MaskedCrossEntrop... | 5,485 | 32.048193 | 100 | py |
fairlex | fairlex-main/utils.py | import sys
import os
import csv
import argparse
import random
from pathlib import Path
import numpy as np
import torch
import pandas as pd
try:
import wandb
except Exception as e:
pass
def update_average(prev_avg, prev_counts, curr_avg, curr_counts):
denom = prev_counts + curr_counts
if isinstance(cur... | 6,451 | 29.870813 | 104 | py |
fairlex | fairlex-main/scheduler.py | from transformers import get_linear_schedule_with_warmup
from torch.optim.lr_scheduler import ReduceLROnPlateau, StepLR
def initialize_scheduler(config, optimizer, n_train_steps):
# construct schedulers
if config.scheduler is None:
return None
elif config.scheduler=='linear_schedule_with_warmup':
... | 1,422 | 33.707317 | 105 | py |
fairlex | fairlex-main/train.py | from tqdm import tqdm
import torch
from utils import save_model, save_pred, get_pred_prefix, get_model_prefix
from configs.supported import process_outputs_functions
def run_epoch(algorithm, dataset, general_logger, epoch, config, train):
if dataset['verbose']:
general_logger.write(f"\n{dataset['name']}:\... | 7,872 | 40.436842 | 127 | py |
fairlex | fairlex-main/run_expt.py | import os
import argparse
import torch
from collections import defaultdict
from wilds.common.data_loaders import get_train_loader, get_eval_loader
from wilds.common.grouper import CombinatorialGrouper
from utils import set_seed, Logger, BatchLogger, log_config, ParseKwargs, load, initialize_wandb, log_group_data, par... | 13,161 | 41.185897 | 255 | py |
fairlex | fairlex-main/optimizer.py | from torch.optim import SGD, Adam
from transformers import AdamW
def initialize_optimizer(config, model):
# initialize optimizers
if config.optimizer=='SGD':
params = filter(lambda p: p.requires_grad, model.parameters())
optimizer = SGD(
params,
lr=config.lr,
... | 1,395 | 34.794872 | 141 | py |
fairlex | fairlex-main/transforms.py | import re
import nltk
from transformers import AutoTokenizer
from sklearn.feature_extraction.text import TfidfVectorizer
import torch
import os
from data import DATA_DIR
from nltk.corpus import stopwords
def initialize_transform(transform_name, config):
if transform_name is None:
return None
if trans... | 4,160 | 34.564103 | 114 | py |
fairlex | fairlex-main/language_modelling/decrease_vocab.py | import copy
import json
import os
import re
import shutil
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM, AutoConfig
from torch.nn import Parameter
import string
SAVE_DIR = os.path.join('my-xlm-roberta-base')
# CLEAN FOLDER
if os.path.exists(SAVE_DIR):
shutil.rmtree(SAVE_DIR)
os.mkdi... | 4,516 | 46.052083 | 156 | py |
fairlex | fairlex-main/language_modelling/run_mlm.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 24,081 | 42.54792 | 119 | py |
fairlex | fairlex-main/models/initializer.py | import torch.nn as nn
from models.bert.bert import HierBERTClassifier, HierBERTFeaturizer
def initialize_model(config, d_out, is_featurizer=False):
"""
Initializes models according to the config
Args:
- config (dictionary): config dictionary
- d_out (int): the dimensionality of... | 1,733 | 41.292683 | 127 | py |
fairlex | fairlex-main/models/bert/bert.py | from transformers import AutoModel
import numpy as np
import torch
def sinusoidal_init(num_embeddings: int, embedding_dim: int):
# keep dim 0 for padding token position encoding zero vector
position_enc = np.array([
[pos / np.power(10000, 2 * i / embedding_dim) for i in range(embedding_dim)]
i... | 7,547 | 51.416667 | 115 | py |
fairlex | fairlex-main/algorithms/minMax.py | import torch
from algorithms.single_model_algorithm import SingleModelAlgorithm
from models.initializer import initialize_model
class MinMax(SingleModelAlgorithm):
"""
MinMax optimization.
"""
def __init__(self, config, d_out, grouper, loss, metric, n_train_steps):
# check config
asser... | 1,338 | 30.880952 | 82 | py |
fairlex | fairlex-main/algorithms/REx.py | import torch
from algorithms.single_model_algorithm import SingleModelAlgorithm
from models.initializer import initialize_model
class REx(SingleModelAlgorithm):
"""
V-REx optimization.
Original paper:
@article{krueger-ood-rex,
title = {Out-of-Distribution Generalization via Risk... | 2,376 | 36.140625 | 95 | py |
fairlex | fairlex-main/algorithms/deepCORAL.py | import torch
from models.initializer import initialize_model
from algorithms.single_model_algorithm import SingleModelAlgorithm
from wilds.common.utils import split_into_groups
class DeepCORAL(SingleModelAlgorithm):
"""
Deep CORAL.
This algorithm was originally proposed as an unsupervised domain adaptatio... | 4,416 | 35.504132 | 124 | py |
fairlex | fairlex-main/algorithms/adversarialRemoval.py | import torch
from algorithms.single_model_algorithm import SingleModelAlgorithm
from models.initializer import initialize_model
from scheduler import initialize_scheduler
from optimizer import initialize_optimizer
from torch.nn.utils import clip_grad_norm_
from pytorch_revgrad import RevGrad
from configs.supported impo... | 5,422 | 37.190141 | 117 | py |
fairlex | fairlex-main/algorithms/algorithm.py | import torch
import torch.nn as nn
class Algorithm(nn.Module):
def __init__(self, device):
super().__init__()
self.device = device
self.out_device = 'cpu'
self._has_log = False
self.reset_log()
def update(self, batch):
"""
Process the batch, update the l... | 3,392 | 29.294643 | 101 | py |
fairlex | fairlex-main/algorithms/IRM.py | import torch
from models.initializer import initialize_model
from algorithms.single_model_algorithm import SingleModelAlgorithm
from wilds.common.utils import split_into_groups
import torch.autograd as autograd
from wilds.common.metrics.metric import ElementwiseMetric, MultiTaskMetric
from optimizer import initialize_o... | 4,125 | 38.295238 | 100 | py |
fairlex | fairlex-main/algorithms/group_algorithm.py | import torch, time
import numpy as np
from algorithms.algorithm import Algorithm
from utils import update_average
from scheduler import step_scheduler
from wilds.common.utils import get_counts
class GroupAlgorithm(Algorithm):
"""
Parent class for algorithms with group-wise logging.
Also handles schedulers.... | 10,077 | 40.991667 | 152 | py |
fairlex | fairlex-main/algorithms/groupDRO.py | import torch
from algorithms.single_model_algorithm import SingleModelAlgorithm
from models.initializer import initialize_model
class GroupDRO(SingleModelAlgorithm):
"""
Group distributionally robust optimization.
Original paper:
@inproceedings{sagawa2019distributionally,
title={Distrib... | 4,240 | 38.635514 | 142 | py |
fairlex | fairlex-main/algorithms/single_model_algorithm.py | import torch
from algorithms.group_algorithm import GroupAlgorithm
from scheduler import initialize_scheduler
from optimizer import initialize_optimizer
from torch.nn.utils import clip_grad_norm_
class SingleModelAlgorithm(GroupAlgorithm):
"""
An abstract class for algorithm that has one underlying model.
... | 5,545 | 34.101266 | 87 | py |
fairlex | fairlex-main/interpretability/trainer.py | from diff_mask.model import MetaModel
import os
import torch
from data import MODELS_DIR
DATASET = 'ecthr'
ATTRIBUTE = 'age'
model_path = os.path.join(MODELS_DIR, 'ecthr-mini-longformer')
checkpoint_path = f'/home/iliasc/fairlex-wilds/final_logs/{DATASET}/ERM/{ATTRIBUTE}/seed_1/{DATASET}_seed:1_epoch:best_model.pth'
... | 438 | 32.769231 | 129 | py |
fairlex | fairlex-main/interpretability/diff_mask/distributions.py | import torch
class BinaryConcrete(torch.distributions.relaxed_bernoulli.RelaxedBernoulli):
def __init__(self, temperature, logits):
super().__init__(temperature=temperature, logits=logits)
self.device = self.temperature.device
def cdf(self, value):
return torch.sigmoid(
(t... | 1,843 | 30.793103 | 88 | py |
fairlex | fairlex-main/interpretability/diff_mask/model.py | import torch
import numpy as np
from models.bert.bert import LongformerClassifier
from gates import DiffMaskGateInput
from getter_setter import bert_getter, bert_setter, f1_score
class MetaModel(torch.nn.Module):
def __init__(self, model_path):
super().__init__()
self.model = LongformerClassifier.... | 7,050 | 29.790393 | 90 | py |
fairlex | fairlex-main/interpretability/diff_mask/gates.py | import numpy as np
import torch
from .distributions import RectifiedStreched, BinaryConcrete
class MLPGate(torch.nn.Module):
def __init__(self, input_size, hidden_size, bias=True):
super().__init__()
self.f = torch.nn.Sequential(
torch.nn.utils.weight_norm(torch.nn.Linear(input_size, h... | 8,419 | 30.893939 | 88 | py |
fairlex | fairlex-main/interpretability/diff_mask/getter_setter.py | import torch
from transformers import BertForSequenceClassification
def bert_getter(model, inputs_dict, forward_fn=None):
hidden_states_ = []
def get_hook(i):
def hook(module, inputs, outputs=None):
if i == 0:
hidden_states_.append(outputs)
elif 1 <= i <= len(... | 5,764 | 28.116162 | 88 | py |
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