Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
values |
|---|---|---|---|---|---|---|
null | ParlAI-main/website/postprocess_docs.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
NEEDLE3 = '— ParlAI documentation</title>'
REPLACEMENT3 = """
— ParlAI Documentation</title>
<li... | 1,132 | 38.068966 | 95 | py |
null | ParlAI-main/website/static/css/home-theme.css | /*! normalize.css v5.0.0 | MIT License | github.com/necolas/normalize.css */
/*
* Copyright (c) Facebook, Inc. and its affiliates.
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
html {
font-family: sans-serif;
line-height: 1.15... | 16,470 | 19.46087 | 130 | css |
null | ParlAI-main/website/static/css/parlai.css | /*
* Copyright (c) Facebook, Inc. and its affiliates.
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
.navbar-inverse .navbar-nav>.active>a, .navbar-inverse .navbar-nav>.active>a:focus, .navbar-inverse .navbar-nav>.active>a:hover {
b... | 18,972 | 19.205538 | 130 | css |
null | ParlAI-main/website/templates/about.html | <section class="about wrap" style="margin-bottom: -20px">
<hr style="margin-top: 0px">
<article class="markdown-body">
{{{CONTENT}}}
</article>
<hr>
</section>
| 184 | 22.125 | 57 | html |
null | ParlAI-main/website/templates/base.html | <!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags -->
... | 4,191 | 52.063291 | 213 | html |
null | ParlAI-main/website/templates/error.html | <section class="about wrap">
<hr style="margin-top: 0px">
<div class="container">
<div style="margin-top: 30px">
Page not found.
</div>
</div>
</section>
| 194 | 20.666667 | 38 | html |
null | ParlAI-main/website/templates/home.html | <div style="padding: 0 0; background-color: #000;">
<div style=" font-weight: bold; font-size: 20px; padding: 20px; max-width: 768px; margin: 0 auto; text-align: center; color: white;">
Support Ukraine 🇺🇦
<a href="https://opensource.fb.com/support-ukraine">
Help Provide Humanitarian Aid to... | 8,782 | 52.883436 | 460 | html |
null | ParlAI-main/website/templates/project.html | <section class="project wrap" style="margin-bottom: -20px">
<hr style="margin-top: 0px">
<article class="markdown-body">
{{{CONTENT}}}
</article>
<hr>
</section>
| 186 | 22.375 | 59 | html |
panopticapi | panopticapi-master/CONVERTERS.md | # Converters for COCO panoptic segmentation format
Different COCO formats are described [here](http://cocodataset.org/#format-data).
## From COCO detection format to COCO panoptic format
COCO detection format is used to store both COCO instance segmentation and COCO stuff annotations.
The script `converters/detectio... | 4,400 | 60.985915 | 490 | md |
panopticapi | panopticapi-master/README.md | # COCO 2018 Panoptic Segmentation Task API (Beta version)
This API is an experimental version of [COCO 2018 Panoptic Segmentation Task API](http://cocodataset.org/#panoptic-2018).
To install panopticapi, run:
```
pip install git+https://github.com/cocodataset/panopticapi.git
```
## Summary
**Evaluation script**
[pan... | 2,235 | 53.536585 | 345 | md |
panopticapi | panopticapi-master/setup.py | #-*- coding: utf-8 -*-
from setuptools import setup, Extension
setup(
name='panopticapi',
packages=['panopticapi'],
package_dir = {'panopticapi': 'panopticapi'},
install_requires=[
'numpy',
'Pillow',
],
version='0.1',
)
| 263 | 15.5 | 49 | py |
panopticapi | panopticapi-master/visualization.py | #!/usr/bin/env python2
'''
Visualization demo for panoptic COCO sample_data
The code shows an example of color generation for panoptic data (with
"generate_new_colors" set to True). For each segment distinct color is used in
a way that it close to the color of corresponding semantic class.
'''
from __future__ import a... | 2,478 | 28.164706 | 78 | py |
panopticapi | panopticapi-master/cityscapes_gt_converter/__init__.py | 0 | 0 | 0 | py | |
panopticapi | panopticapi-master/cityscapes_gt_converter/cityscapes_panoptic_converter.py | #!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os, sys
import json
import glob
import numpy as np
import PIL.Image as Image
from panopticapi.utils import IdGenerator, save_json
try:
#... | 4,146 | 33.848739 | 103 | py |
panopticapi | panopticapi-master/converters/2channels2panoptic_coco_format.py | #!/usr/bin/env python
'''
This script converts panoptic segmentation predictions stored in 2 channels
panoptic format to COCO panoptic format.
2 channels format is described in the panoptic segmentation paper
(https://arxiv.org/pdf/1801.00868.pdf). Two labels are assigned to each pixel of
a segment:
- semantic class l... | 6,285 | 39.818182 | 108 | py |
panopticapi | panopticapi-master/converters/__init__.py | 0 | 0 | 0 | py | |
panopticapi | panopticapi-master/converters/detection2panoptic_coco_format.py | #!/usr/bin/env python
'''
This script converts detection COCO format to panoptic COCO format. More
information about the formats can be found here:
http://cocodataset.org/#format-data.
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import... | 6,231 | 40 | 101 | py |
panopticapi | panopticapi-master/converters/panoptic2detection_coco_format.py | #!/usr/bin/env python
'''
This script converts panoptic COCO format to detection COCO format. More
information about the formats can be found here:
http://cocodataset.org/#format-data. All segments will be stored in RLE format.
Additional option:
- using option '--things_only' the script can discard all stuff
segments... | 6,416 | 40.668831 | 106 | py |
panopticapi | panopticapi-master/converters/panoptic2semantic_segmentation.py | #!/usr/bin/env python
'''
This script converts data in panoptic COCO format to semantic segmentation. All
segments with the same semantic class in one image are combined together.
Additional option:
- using option '--things_others' the script combine all segments of thing
classes into one segment with semantic class '... | 9,338 | 42.640187 | 102 | py |
panopticapi | panopticapi-master/panopticapi/__init__.py | 0 | 0 | 0 | py | |
panopticapi | panopticapi-master/panopticapi/combine_semantic_and_instance_predictions.py | #!/usr/bin/env python
'''
The script uses a simple procedure to combine semantic segmentation and instance
segmentation predictions. The procedure is described in section 7 of the
panoptic segmentation paper https://arxiv.org/pdf/1801.00868.pdf.
On top of the procedure described in the paper. This script remove from
p... | 9,610 | 39.724576 | 111 | py |
panopticapi | panopticapi-master/panopticapi/evaluation.py | #!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os, sys
import numpy as np
import json
import time
from datetime import timedelta
from collections import defaultdict
import argparse
import m... | 10,692 | 40.285714 | 180 | py |
panopticapi | panopticapi-master/panopticapi/utils.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import functools
import traceback
import json
import numpy as np
# The decorator is used to prints an error trhown inside process
def get_traceback(f):
@functools.wr... | 3,301 | 32.02 | 83 | py |
Meta-GNN | Meta-GNN-master/README.md | ### Meta-GNN: On Few-shot Node Classification in Graph Meta-learning
#### Authors:
* [Fan Zhou]
* [Chengtai Cao](https://github.com/ChengtaiCao)
* [Kunpeng Zhang]
* [Goce Trajcevski]
* [Ting Zhong]
* [Ji Geng]
#### Desccription of Meta-GNN
source_code for Meta-GNN (implement of Meta-GNN): [Meta-GNN: On Few-shot Node... | 1,148 | 19.890909 | 164 | md |
Meta-GNN | Meta-GNN-master/gcn_fewshot/args.py | import torch
import argparse
def get_citation_args():
parser = argparse.ArgumentParser()
parser.add_argument('--train_shot', type=int, default=20, help='How many shot during meta-train')
parser.add_argument('--test_shot', type=int, default=1, help='How many shot during meta-test')
parser.add_argument(... | 1,881 | 66.214286 | 132 | py |
Meta-GNN | Meta-GNN-master/gcn_fewshot/citation.py | import torch
import torch.nn.functional as F
import torch.optim as optim
from itertools import combinations
import random
from args import get_citation_args
from utils import load_citation, set_seed
from models import get_model
from metrics import accuracy
def train_regression(model, train_features, train_labels, id... | 6,884 | 39.739645 | 150 | py |
Meta-GNN | Meta-GNN-master/gcn_fewshot/metrics.py | def accuracy(output, labels):
preds = output.max(1)[1].type_as(labels)
correct = preds.eq(labels).double()
correct = correct.sum()
return correct / len(labels) | 175 | 34.2 | 44 | py |
Meta-GNN | Meta-GNN-master/gcn_fewshot/models.py | import torch
import torch.nn as nn
from torch.nn import Module
import torch.nn.functional as F
from torch.nn.parameter import Parameter
import math
class GraphConvolution(Module):
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, self).__init__()
self.in_features = ... | 2,014 | 30 | 83 | py |
Meta-GNN | Meta-GNN-master/gcn_fewshot/normalization.py | import numpy as np
import scipy.sparse as sp
def normalized_laplacian(adj):
adj = sp.coo_matrix(adj)
row_sum = np.array(adj.sum(1))
d_inv_sqrt = np.power(row_sum, -0.5).flatten()
d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
d_mat_inv_sqrt = sp.diags(d_inv_sqrt)
return (sp.eye(adj.shape[0]) - d_mat_inv_sqrt... | 3,025 | 28.666667 | 94 | py |
Meta-GNN | Meta-GNN-master/gcn_fewshot/utils.py | import numpy as np
import scipy.sparse as sp
import torch
import sys
import pickle as pkl
import networkx as nx
from normalization import fetch_normalization, row_normalize
from sklearn.metrics import f1_score
def accuracy(output, labels):
preds = output.max(1)[1].type_as(labels)
correct = preds.eq(labels).do... | 3,272 | 32.397959 | 85 | py |
Meta-GNN | Meta-GNN-master/graph_embedding_fewshot/deepwalk.py | import numpy as np
import networkx as nx
import os
from gensim.models import Word2Vec
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.externals import joblib
from itertools import combinations
import random
import torch
directed = True
p = 1.0
q = 1.0
num_walks = 1000
walk_length = 100
emb_si... | 10,016 | 33.6609 | 158 | py |
Meta-GNN | Meta-GNN-master/graph_embedding_fewshot/node2vec.py | import numpy as np
import networkx as nx
import os
from gensim.models import Word2Vec
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.externals import joblib
from itertools import combinations
import random
import torch
directed = True
p = 5.0
q = 1.0
num_walks = 1000
walk_length = 100
emb_si... | 10,016 | 33.6609 | 158 | py |
Meta-GNN | Meta-GNN-master/graphsage_fewshot/graphsage.py | import argparse
import random
import abc
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from dgl import DGLGraph
from dgl.data import register_data_args, load_data
import dgl.function as fn
from itertools import combinations
class Aggregator(nn.Module):
def __init__(self, g,... | 13,359 | 39 | 147 | py |
Meta-GNN | Meta-GNN-master/meta_gnn/learner.py | import torch
from torch import nn
from torch.nn import functional as F
class Learner(nn.Module):
def __init__(self, config):
super(Learner, self).__init__()
self.config = config
self.vars = nn.ParameterList()
self.vars_bn = nn.ParameterList()
for i, (name, param) in enumer... | 5,558 | 41.113636 | 128 | py |
Meta-GNN | Meta-GNN-master/meta_gnn/meta.py | import torch
import numpy as np
from torch import nn
from torch.nn import functional as F
from torch import optim
from learner import Learner
from utils import f1
class Meta(nn.Module):
def __init__(self, args, config):
super(Meta, self).__init__()
self.update_lr = args.update_lr
self.me... | 5,817 | 40.262411 | 110 | py |
Meta-GNN | Meta-GNN-master/meta_gnn/normalization.py | import numpy as np
import scipy.sparse as sp
def normalized_laplacian(adj):
adj = sp.coo_matrix(adj)
row_sum = np.array(adj.sum(1))
d_inv_sqrt = np.power(row_sum, -0.5).flatten()
d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
d_mat_inv_sqrt = sp.diags(d_inv_sqrt)
return (sp.eye(adj.shape[0]) - d_mat_inv_sqrt... | 3,025 | 28.666667 | 94 | py |
Meta-GNN | Meta-GNN-master/meta_gnn/sgc.py | import torch
import numpy as np
import argparse
from itertools import combinations
from utils import load_citation, sgc_precompute, set_seed
from meta import Meta
from sgc_data_generator import sgc_data_generator
def main(args):
step = args.step
set_seed(args.seed)
adj, features, labels = load_citation(... | 4,468 | 47.053763 | 160 | py |
Meta-GNN | Meta-GNN-master/meta_gnn/sgc_data_generator.py | import random
def sgc_data_generator(features, labels, node_num, select_array, task_num, n_way, k_spt, k_qry):
x_spt = []
y_spt = []
x_qry = []
y_qry = []
class1_idx = []
class2_idx = []
labels_local = labels.clone().detach()
select_class = random.sample(select_array, n_way)
for ... | 1,347 | 33.564103 | 96 | py |
Meta-GNN | Meta-GNN-master/meta_gnn/utils.py | import numpy as np
import scipy.sparse as sp
import torch
import sys
import pickle as pkl
import networkx as nx
from normalization import fetch_normalization, row_normalize
from sklearn.metrics import f1_score
def f1(output, labels):
output = output.max(1)[1]
output = output.cpu().detach().numpy()
labels ... | 3,234 | 31.35 | 85 | py |
Meta-GNN | Meta-GNN-master/sgc_fewshot/args.py | import torch
import argparse
def get_citation_args():
parser = argparse.ArgumentParser()
parser.add_argument('--train_shot', type=int, default=20, help='How many shot during meta-train')
parser.add_argument('--test_shot', type=int, default=1, help='How many shot during meta-test')
parser.add_argument(... | 1,679 | 63.615385 | 128 | py |
Meta-GNN | Meta-GNN-master/sgc_fewshot/citation.py | import torch
import torch.nn.functional as F
import torch.optim as optim
from itertools import combinations
import random
from args import get_citation_args
from utils import load_citation, sgc_precompute, set_seed
from models import get_model
from metrics import accuracy
def train_regression(model, train_features, ... | 6,616 | 39.347561 | 156 | py |
Meta-GNN | Meta-GNN-master/sgc_fewshot/metrics.py | def accuracy(output, labels):
preds = output.max(1)[1].type_as(labels)
correct = preds.eq(labels).double()
correct = correct.sum()
return correct / len(labels)
| 178 | 21.375 | 44 | py |
Meta-GNN | Meta-GNN-master/sgc_fewshot/models.py | import torch.nn as nn
class SGC(nn.Module):
def __init__(self, nfeat, nclass):
super(SGC, self).__init__()
self.W = nn.Linear(nfeat, nclass)
def forward(self, x):
return self.W(x)
def get_model(model_opt, nfeat, nclass, cuda=True):
if model_opt == 'SGC':
model = SGC(nfea... | 489 | 22.333333 | 83 | py |
Meta-GNN | Meta-GNN-master/sgc_fewshot/normalization.py | import numpy as np
import scipy.sparse as sp
def normalized_laplacian(adj):
adj = sp.coo_matrix(adj)
row_sum = np.array(adj.sum(1))
d_inv_sqrt = np.power(row_sum, -0.5).flatten()
d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
d_mat_inv_sqrt = sp.diags(d_inv_sqrt)
return (sp.eye(adj.shape[0]) - d_mat_inv_sqrt... | 3,025 | 28.666667 | 94 | py |
Meta-GNN | Meta-GNN-master/sgc_fewshot/utils.py | import numpy as np
import scipy.sparse as sp
import torch
import sys
import pickle as pkl
import networkx as nx
from normalization import fetch_normalization, row_normalize
from sklearn.metrics import f1_score
def accuracy(output, labels):
preds = output.max(1)[1].type_as(labels)
correct = preds.eq(labels).do... | 3,404 | 31.740385 | 85 | py |
null | Tamanduatei_Vulnerability-main/README.md | # Tamanduatei Vulnerability
This code calculates the point-wise vulnerability to the Tamanduatei basin. To execute this task we only need the <i>edgelist.dat</i> and <i>vulnerability.py</i>. The calculation will result in two results, one of them being the time it took in the <i>screen.out</i> file and the vulnerabili... | 2,977 | 61.041667 | 379 | md |
null | Tamanduatei_Vulnerability-main/vulnerability.py | import igraph as ig
import numpy as np
import time
import multiprocessing as mp
f2 = open("screen.out", 'w')
f = open("result.dat", "w")
# Calculating the efficiency of a given network
def eff_global(g, weights):
N = g.vcount()
eff = 0.0
sp = g.shortest_paths(weights=weights)
for l i... | 2,290 | 27.283951 | 108 | py |
null | FastDifferentiableMatSqrt-main/README.md | # Fast Differentiable Matrix Sqrt Root and Its Inverse
<div align=center><center><b>Geometric Interpretation of Matrix Square Root and Inverse Square Root</b></center></div>
<div align=center><img src="MatSqrt_Cover.jpg" width="60%"/></div>
This repository constains the official Pytorch implementation of ICLR 22 pape... | 2,749 | 40.666667 | 297 | md |
null | FastDifferentiableMatSqrt-main/torch_utils.py | from mpmath import *
import numpy as np
import torch
mp.dps = 32
one = mpf(1)
mp.pretty = True
def f(x):
return sqrt(one-x)
# Derive the taylor and pade' coefficients for MTP, MPA
a = taylor(f, 0, 10)
pade_p, pade_q = pade(a, 5, 5)
a = torch.from_numpy(np.array(a).astype(float))
pade_p = torch.from_numpy(np.arra... | 4,447 | 36.066667 | 123 | py |
null | FastDifferentiableMatSqrt-main/Decorrelated BN/README.md | ## Introduction
The directory contains the experiment of our method on Decorrelated Batch Normalization.
We use ResNet-18 and ResNet-50 models and validate on CIFAR10 and CIFAR100.
## Model Architecture
<div align=center><img src="DBN.png" width="60%"/></div>
| 265 | 25.6 | 89 | md |
null | FastDifferentiableMatSqrt-main/Decorrelated BN/ZCANorm.py | from torch.nn.parameter import Parameter
import torch
import torch.nn as nn
from torch_utils import *
eps=1e-10
class BatchNorm(nn.Module):
def __init__(self, num_features, groups=1, eps=1e-2, momentum=0.1, affine=True):
super(BatchNorm, self).__init__()
print('BatchNorm Group Num {}'.format(grou... | 6,668 | 40.166667 | 130 | py |
null | FastDifferentiableMatSqrt-main/Decorrelated BN/main_cifar10.py | '''Train CIFAR10 with PyTorch.'''
from __future__ import print_function
import os
os.environ['CUDA_LAUNCH_BLOCKING'] = "1"
import time
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argpa... | 5,988 | 31.906593 | 110 | py |
null | FastDifferentiableMatSqrt-main/Decorrelated BN/main_cifar100.py | '''Train CIFAR100 with PyTorch.'''
from __future__ import print_function
import os
os.environ['CUDA_LAUNCH_BLOCKING'] = "1"
import time
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as ... | 6,065 | 33.271186 | 111 | py |
null | FastDifferentiableMatSqrt-main/Decorrelated BN/run.sh | #!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python main_cifar100.py --norm='zcanormbatch' --batch_size=128
CUDA_VISIBLE_DEVICES=1 python main_cifar10.py --norm='zcanormbatch' --batch_size=128 | 191 | 47 | 85 | sh |
null | FastDifferentiableMatSqrt-main/Decorrelated BN/torch_utils.py | from mpmath import *
import numpy as np
import torch
mp.dps = 32
one = mpf(1)
mp.pretty = True
def f(x):
return sqrt(one-x)
# Derive the taylor and pade' coefficients for MTP, MPA
a = taylor(f, 0, 10)
pade_p, pade_q = pade(a, 5, 5)
a = torch.from_numpy(np.array(a).astype(float))
pade_p = torch.from_numpy(np.arra... | 4,410 | 36.067227 | 123 | py |
null | FastDifferentiableMatSqrt-main/Decorrelated BN/utils.py | '''Some helper functions for PyTorch, including:
- get_mean_and_std: calculate the mean and std value of dataset.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''
import os
import sys
import time
import math
import torch.nn as nn
import torch.nn.init as init
... | 3,446 | 26.576 | 96 | py |
null | FastDifferentiableMatSqrt-main/Decorrelated BN/models/__init__.py | from resnet import *
| 21 | 10 | 20 | py |
null | FastDifferentiableMatSqrt-main/Decorrelated BN/models/resnet.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch
__all__ = ['ResNet', 'resnet18', 'resnet18nips', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',... | 13,640 | 35.279255 | 106 | py |
null | FastDifferentiableMatSqrt-main/Neural Style Transfer/README.md | ## Introduction
This directory contains the codes for the experiment of WCT-based neural style transfer.
Our implementation is modified on the basis of the architecture used in [GD-WCT](https://github.com/WonwoongCho/GDWCT).
## Model Architecure
<div align=center><img src="StyleTransfer.png" width="70%"/></div>
| 319 | 28.090909 | 119 | md |
null | FastDifferentiableMatSqrt-main/Neural Style Transfer/data_loader.py | from torch.utils import data
from torchvision import transforms as T
from torchvision.datasets import ImageFolder
from PIL import Image
import torch
import os
import random
class UnalignedDataset(data.Dataset):
def __init__(self, dataset, image_dir, transform, mode):
self.transform = transform
sel... | 3,054 | 34.523256 | 144 | py |
null | FastDifferentiableMatSqrt-main/Neural Style Transfer/download.sh | FILE=$1
if [ $FILE == "celeba" ]; then
# CelebA images and attribute labels
URL=https://www.dropbox.com/s/d1kjpkqklf0uw77/celeba.zip?dl=0
ZIP_FILE=./datasets/celeba.zip
mkdir -p ./datasets/
wget -N $URL -O $ZIP_FILE
unzip $ZIP_FILE -d ./datasets/
rm $ZIP_FILE
elif [ $FILE == 'pretrained'... | 1,020 | 29.029412 | 97 | sh |
null | FastDifferentiableMatSqrt-main/Neural Style Transfer/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from utils.torch_utils import *
class Content_Encoder(nn.Module):
def __init__(self, conv_dim=64, repeat_num=4, norm='in', activation='relu'):
super(Content_Encoder, self).__init__()
layers = []
layers += ... | 13,473 | 37.497143 | 155 | py |
null | FastDifferentiableMatSqrt-main/Neural Style Transfer/run.py | import torch
import torch.nn as nn
import torch.optim as optim
from torch import cuda
from data_loader import *
from model import *
import time
import datetime
import os
from utils.util import *
from torch.backends import cudnn
from scipy.linalg import block_diag
import lpips
loss_fn = lpips.LPIPS(net='alex')
class R... | 15,403 | 39.324607 | 198 | py |
null | FastDifferentiableMatSqrt-main/Neural Style Transfer/configs/config.yaml | # GPU SETTINGS
GPU1: 0
GPU2: 1 # Not used if DATA_PARALLEL is False.
DATA_PARALLEL: False
N_GROUP: 1 # The number of groups
# DATA OPTIONS
INPUT_DIM: 3
DATA_PATH: /home/ysong/
DATASET: vangogh2photo # The name of a dataset located in DATA_PATH,
RESIZE: 216
CROP_SIZE: 216
# TRAINING SETTINGS
NUM_ITERS: 200001 # Artwor... | 1,678 | 26.52459 | 189 | yaml |
null | FastDifferentiableMatSqrt-main/Neural Style Transfer/utils/torch_utils.py | from mpmath import *
import numpy as np
import torch
mp.dps = 32
one = mpf(1)
mp.pretty = True
def f(x):
return sqrt(one-x)
# Derive the taylor and pade' coefficients for MTP, MPA
a = taylor(f, 0, 10)
pade_p, pade_q = pade(a, 5, 5)
a = torch.from_numpy(np.array(a).astype(float))
pade_p = torch.from_numpy(np.arra... | 4,410 | 36.067227 | 123 | py |
null | FastDifferentiableMatSqrt-main/Neural Style Transfer/utils/util.py | import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import torch.nn.functional as F
import yaml
from torch.optim import lr_scheduler
import torch.nn.init as init
import random
from torchvision.utils import save_image, make_grid
... | 2,700 | 34.539474 | 110 | py |
null | FastDifferentiableMatSqrt-main/So-ViT/README.md | ## Introduction
This directory is the code of Second-order Vision Transformer (So-ViT).
Our implementation is modified based on the official implementation in [So-ViT](https://github.com/jiangtaoxie/SoT.)
## Model Architecture
<div align=center><img src="So_ViT.png" width="60%"/></div>
| 292 | 28.3 | 116 | md |
null | FastDifferentiableMatSqrt-main/So-ViT/So_ViT_10.sh | model=So_vit_10
./distributed_train.sh 8 \
/imagenetpytorch \
--model $model \
-b 64 \
--lr 1e-3 \
--weight-decay .03 \
--img-size 224 \
--amp | 143 | 15 | 26 | sh |
null | FastDifferentiableMatSqrt-main/So-ViT/So_ViT_14.sh | model=So_vit_14
./distributed_train.sh 8 \
/imagenetpytorch \
--model $model \
-b 64 \
--lr 1e-3 \
--weight-decay .05 \
--img-size 224 \
--amp | 143 | 15 | 26 | sh |
null | FastDifferentiableMatSqrt-main/So-ViT/So_ViT_19.sh | model=So_vit_19
./distributed_train.sh 8 \
/data/datasets/ImageNet/images \
--model $model \
-b 64 \
--lr 1e-3 \
--weight-decay .05 \
--img-size 224 \
--amp | 157 | 16.555556 | 32 | sh |
null | FastDifferentiableMatSqrt-main/So-ViT/So_ViT_7.sh | model=So_vit_7
./distributed_train.sh 8 \
/imagenetpytorch \
--model $model \
-b 64 \
--lr 1e-3 \
--weight-decay .03 \
--img-size 224 \
--amp \
--resume | 152 | 14.3 | 26 | sh |
null | FastDifferentiableMatSqrt-main/So-ViT/distributed_train.sh | #!/bin/bash
NUM_PROC=$1
shift
python3 -m torch.distributed.launch --nproc_per_node=$NUM_PROC main.py "$@"
| 107 | 17 | 75 | sh |
null | FastDifferentiableMatSqrt-main/So-ViT/main.py | """
This script is modified from pytorch-image-models by Ross Wightman (https://github.com/rwightman/pytorch-image-models/)
It was started from an early version of the PyTorch ImageNet example
(https://github.com/pytorch/examples/tree/master/imagenet)
"""
import argparse
import time
import yaml
import os
import logging... | 40,629 | 46.134571 | 189 | py |
null | FastDifferentiableMatSqrt-main/So-ViT/src/__init__.py | from .so_vit import *
| 22 | 10.5 | 21 | py |
null | FastDifferentiableMatSqrt-main/So-ViT/src/so_vit.py | import torch
import torch.nn as nn
from timm.models.helpers import load_pretrained
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_
from timm.models.senet import SEModule
from timm.models.inception_resnet_v2 import BasicConv2d
from timm.models.densenet import DenseBlock
from... | 6,641 | 36.314607 | 142 | py |
null | FastDifferentiableMatSqrt-main/So-ViT/src/model/classifier.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
import numpy as np
from ..normalization.MPN import MPN
from ..normalization.svPN import svPN
from ..normalization.PadeSqt import PadeSqt
from ..normalization.SVDS import SVDS
from ..representation.covariance import Co... | 2,421 | 41.491228 | 106 | py |
null | FastDifferentiableMatSqrt-main/So-ViT/src/model/visualTokens.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
import numpy as np
from timm.models.densenet import DenseBlock
from timm.models.densenet import DenseTransition
class Bottleneck(nn.Module):
"""
Bottleneck block from ResNet
"""
def __init__(self, inp... | 11,898 | 38.92953 | 145 | py |
null | FastDifferentiableMatSqrt-main/So-ViT/src/model/vitBlock.py | import torch
import torch.nn as nn
import numpy as np
from timm.models.layers import DropPath
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_feat... | 3,050 | 36.666667 | 118 | py |
null | FastDifferentiableMatSqrt-main/So-ViT/src/normalization/MPN.py | '''
@file: MPNCOV.py
@author: Jiangtao Xie
@author: Peihua Li
Please cite the paper below if you use the code:
Peihua Li, Jiangtao Xie, Qilong Wang and Zilin Gao. Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization. IEEE Int. Conf. on Computer Vision and Pattern ... | 8,981 | 37.384615 | 246 | py |
null | FastDifferentiableMatSqrt-main/So-ViT/src/normalization/PadeSqt.py | import torch
import torch.nn as nn
from torch.autograd import Function
from mpmath import *
import numpy as np
mp.dps = 32
one = mpf(1)
mp.pretty = True
def f(x):
return sqrt(one-x)
a = taylor(f, 0, 10)
pade_p, pade_q = pade(a, 5, 5)
a = torch.from_numpy(np.array(a).astype(float)).half()
pade_p = torch.from_numpy(... | 5,478 | 31.229412 | 148 | py |
null | FastDifferentiableMatSqrt-main/So-ViT/src/normalization/svPN.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .MPN import Triuvec
from torch.autograd import Function
import numpy as np
class svPN(nn.Module):
def __init__(self,
alpha=0.5,
iterNum=1,
svNum=1,
init_weight='learn',
... | 3,517 | 34.18 | 86 | py |
null | FastDifferentiableMatSqrt-main/So-ViT/src/representation/covariance.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
import numpy as np
class Covariance(nn.Module):
def __init__(self,
cov_type='norm',
remove_mean=True,
dimension_reduction=None,
input_dim=2048,
... | 3,290 | 32.581633 | 94 | py |
null | FastDifferentiableMatSqrt-main/TACP/CosineAnnealingLR.py | import math
from torch.optim.lr_scheduler import _LRScheduler
from bisect import bisect_right
class WarmupCosineLR(_LRScheduler):
def __init__(self, optimizer, milestones, min_ratio=0., cycle_decay=1., warmup_iters=1000, warmup_factor=1./10, last_epoch=-1):
if not list(milestones) == sorted(milestones):
... | 1,556 | 46.181818 | 131 | py |
null | FastDifferentiableMatSqrt-main/TACP/README.md | ## Introduction
This directory constains the implementation of our model for video action recognition.
The codes are based on the official implementation in [TACP](https://github.com/ZilinGao/Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition).
## Model Arhictecture
<div align=center><img src="T... | 350 | 30.909091 | 159 | md |
null | FastDifferentiableMatSqrt-main/TACP/functions.py | import os
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import scipy.io as sio
import torch
import torch.nn as nn
import numpy as np
from src.torchviz import make_dot, make_dot_from_trace
class stats:
def __init__(self, path, start_epoch):
if start_epoch is not 0:
... | 3,874 | 34.87963 | 83 | py |
null | FastDifferentiableMatSqrt-main/TACP/main.py | '''
Implementation of 'Temporal-attentive Covariance Pooling Networks for Action Recognition'
Authors: Zilin Gao, Qilong Wang, Bingbing Zhang, Qinghua Hu and Peihua Li.
'''
import pdb
import os
import time
import shutil
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
from torch.nn.util... | 18,211 | 37.831557 | 116 | py |
null | FastDifferentiableMatSqrt-main/TACP/opts.py | '''
Implementation of 'Temporal-attentive Covariance Pooling Networks for Action Recognition'
Authors: Zilin Gao, Qilong Wang, Bingbing Zhang, Qinghua Hu and Peihua Li.
'''
import argparse
parser = argparse.ArgumentParser(description="PyTorch implementation of Temporal-attentive Covariance Pooling Networks")
parser.ad... | 6,125 | 63.484211 | 120 | py |
null | FastDifferentiableMatSqrt-main/TACP/test_models.py | '''
Implementation of 'Temporal-attentive Covariance Pooling Networks for Action Recognition'
Authors: Zilin Gao, Qilong Wang, Bingbing Zhang, Qinghua Hu and Peihua Li.
'''
# Notice that this file has been modified to support ensemble testing
import argparse
import time
import torch.nn.parallel
import torch.optim
fr... | 13,551 | 37.174648 | 125 | py |
null | FastDifferentiableMatSqrt-main/TACP/ops/Identity.py | import torch
class Identity(torch.nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x | 155 | 18.5 | 40 | py |
null | FastDifferentiableMatSqrt-main/TACP/ops/TRNmodule.py | # the relation consensus module by Bolei
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import numpy as np
import pdb
class RelationModule(torch.nn.Module):
# this is the naive implementation of the n-frame relation module, as num... | 7,696 | 43.75 | 149 | py |
null | FastDifferentiableMatSqrt-main/TACP/ops/__init__.py | from ops.basic_ops import * | 27 | 27 | 27 | py |
null | FastDifferentiableMatSqrt-main/TACP/ops/basic_ops.py | import torch
class Identity(torch.nn.Module):
def forward(self, input):
return input
class SegmentConsensus(torch.nn.Module):
def __init__(self, consensus_type, dim=1):
super(SegmentConsensus, self).__init__()
self.consensus_type = consensus_type
self.dim = dim
self.... | 1,015 | 25.736842 | 87 | py |
null | FastDifferentiableMatSqrt-main/TACP/ops/dataset.py | import torch.utils.data as data
from PIL import Image
import os
import numpy as np
from numpy.random import randint
import pandas as pd
class VideoRecord(object):
def __init__(self, row):
self._data = row
@property
def path(self):
return self._data[0]
@property
def num_frames(sel... | 10,941 | 48.067265 | 121 | py |
null | FastDifferentiableMatSqrt-main/TACP/ops/dataset_config.py | '''
Implementation of 'Temporal-attentive Covariance Pooling Networks for Action Recognition'
Authors: Zilin Gao, Qilong Wang, Bingbing Zhang, Qinghua Hu and Peihua Li.
'''
import os
ROOT_DATASET = './'
def return_ucf101(modality):
filename_categories = 'UCF101/labels/classInd.txt'
if modality == 'RGB':
... | 6,547 | 42.946309 | 113 | py |
null | FastDifferentiableMatSqrt-main/TACP/ops/keys_mapping.py |
'''
Implementation of 'Temporal-attentive Covariance Pooling Networks for Action Recognition'
Authors: Zilin Gao, Qilong Wang, Bingbing Zhang, Qinghua Hu and Peihua Li.
This file is modified from non-local code.
'''
def adaptive_mapping_old_style_keys(checkpoint, k_new, args):
if 'resnet152' in args.arch:
... | 5,991 | 34.455621 | 102 | py |
null | FastDifferentiableMatSqrt-main/TACP/ops/models.py | from torch import nn
import pdb
from ops.basic_ops import ConsensusModule
from ops.transforms import *
from torch.nn.init import normal_, constant_
from ops.TCP.TCP_module import TCP
# from ops.torchvision import res_224 as rsn #gzl 01.18
class TSN(nn.Module):
def __init__(self, num_class, num_segments, modali... | 19,933 | 40.966316 | 132 | py |
null | FastDifferentiableMatSqrt-main/TACP/ops/temporal_shift.py | # Code for "TSM: Temporal Shift Module for Efficient Video Understanding"
# arXiv:1811.08383
# Ji Lin*, Chuang Gan, Song Han
# {jilin, songhan}@mit.edu, ganchuang@csail.mit.edu
import torch
import torch.nn as nn
import torch.nn.functional as F
class TemporalShift(nn.Module):
def __init__(self, net, n_segment=3, ... | 7,405 | 35.303922 | 101 | py |
null | FastDifferentiableMatSqrt-main/TACP/ops/transforms.py | import torchvision
import random
from PIL import Image, ImageOps
import numpy as np
import numbers
import math
import torch
class GroupRandomCrop(object):
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = siz... | 12,718 | 33.375676 | 116 | py |
null | FastDifferentiableMatSqrt-main/TACP/ops/utils.py | import numpy as np
def softmax(scores):
es = np.exp(scores - scores.max(axis=-1)[..., None])
return es / es.sum(axis=-1)[..., None]
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0... | 984 | 22.452381 | 64 | py |
null | FastDifferentiableMatSqrt-main/TACP/ops/MPNCOV/MPNCOV.py | '''
@file: MPNCOV.py
@author: Jiangtao Xie
@author: Peihua Li
Please cite the paper below if you use the code:
Peihua Li, Jiangtao Xie, Qilong Wang and Zilin Gao. Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization. IEEE Int. Conf. on Computer Vision and Pattern ... | 10,756 | 38.116364 | 246 | py |