python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
|---|---|---|
import os
import glob
import codecs
class Document(object):
''' '''
def __init__(self, doc_id, text, sentences=[], attributes={}):
self.doc_id = doc_id
self.text = text
self.sentences = sentences
self.attributes = attributes
def __repr__(self):
return "<Docu... | ddbiolib-master | ddbiolib/corpora/doc_parsers.py |
import itertools
class Corpus(object):
'''Simple iterator class for loading and parsing documents'''
def __init__(self, doc_parser, text_parser=None, attributes={}):
self.doc_parser = doc_parser
self.text_parser = text_parser
self.attributes = attributes
def __getitem__(sel... | ddbiolib-master | ddbiolib/corpora/base.py |
# -*- coding: utf-8 -*-
import bz2
import sys
import codecs
import itertools
from ddlite import *
from datasets import CdrCorpus
def build_hypenated_dict(labels,stopwords={}):
hypenated = {}
for label in labels:
pmid,sent_id,idxs,span,text = label
mention = [corpus[pmid]["sentences"][sent_id].... | ddbiolib-master | demos/relations/cdr/cdr_chemical_extraction.py |
'''
Created on Jun 17, 2016
@author: fries
'''
import bz2
import sys
import csv
import re
import os
import numpy as np
import itertools
import cPickle
import ddlite
from ddlite import SentenceParser,Entities
from ddlite import Union, DictionaryMatch, RegexNgramMatch
from utils import unescape_penn_treebank
from datase... | ddbiolib-master | demos/relations/cdr/cdr_disease_extraction.py |
'''
This demo requires access to the "BioCreative V Chemical-Disease Relation (CDR) Task Corpus"
(http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/) available after
signing up for an account at http://www.biocreative.org
This script extracts candidate relations from sentences. It assumes we
a... | ddbiolib-master | demos/relations/cdr/cdr_extraction.py |
import bz2
import sys
import csv
import codecs
import numpy as np
import itertools
import cPickle
from ddlite import SentenceParser,DictionaryMatch,Entities,Union
from utils import unescape_penn_treebank
from datasets import PubMedCentralCorpus
def load_stopwords():
dictfile = "dicts/stopwords.txt"
return [lin... | ddbiolib-master | demos/taggers/anatomy/anatomy_extraction.py |
# -*- coding: utf-8 -*-
import sys
import cPickle
from ddlite import *
from datasets import *
# ---------------------------------------------------------------------
#
# I. Load Candidates
#
# ---------------------------------------------------------------------
candidates = Entities("cache/pmc-ortho-candidates.pkl")... | ddbiolib-master | demos/taggers/chemicals/chemical_learning.py |
# -*- coding: utf-8 -*-
import sys
import codecs
import operator
import itertools
from ddlite import *
from datasets import *
import ontologies.umls
from ddlite.ddbiolib.utils import unescape_penn_treebank
from lexicons import RuleTokenizedDictionaryMatch
def rule_tokenizer(s):
s = re.sub("([,?!:;] )",r" \1",s)
... | ddbiolib-master | demos/taggers/chemicals/chemical_extraction.py |
from __future__ import print_function
import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
import itertools
import numpy as np
#from ddlite import *
from ddlite_candidates import Candidates
from ddlite_candidates import Ngrams,Ngram
from ddlite_matchers import DictionaryMatch,Union,Concat,RegexMatc... | ddbiolib-master | demos/taggers/diseases/disease_extraction.py |
import bz2
from ontologies.bioportal import load_bioportal_csv_dictionary
def load_disease_dictionary():
# UMLS SemGroup Disorders
#dictfile = "dicts/umls_disorders.bz2"
#dictfile = "dicts/umls_disorders_snomed_msh_mth.bz2"
dictfile = "dicts/umls_disorders_v2.bz2"
diseases = {line.strip().... | ddbiolib-master | demos/taggers/diseases/tools.py |
import bz2
from ontologies.umls import UmlsNoiseAwareDict
from ontologies.ctd import load_ctd_dictionary
from ontologies.bioportal import load_bioportal_csv_dictionary
#from tools import load_disease_dictionary,load_acronym_dictionary
def search(term,dictionary):
m = {}
for sty in dictionary:
for sab ... | ddbiolib-master | demos/taggers/diseases/dictionary_tests.py |
import bisect, re
from ddlite_matchers import *
from collections import defaultdict
class NcbiDiseaseDictionaryMatch(NgramMatcher):
"""Selects according to ngram-matching against a dictionary i.e. list of words"""
def init(self):
# Load opts- this is from the kwargs dict
self.label = self.opts['la... | ddbiolib-master | demos/taggers/diseases/matchers.py |
import bz2
import sys
import csv
import re
import codecs
import numpy as np
import itertools
import cPickle
import ddlite
from ddlite import SentenceParser,Entities
from ddlite import Union, DictionaryMatch, RegexNgramMatch, CandidateExtractor
from utils import unescape_penn_treebank
from datasets import NcbiDiseaseCor... | ddbiolib-master | demos/taggers/diseases/old/candidate_generation.py |
import bz2
import sys
import csv
import re
import codecs
import numpy as np
import itertools
import cPickle
import ddlite
from ddlite import SentenceParser,Entities
from ddlite import Union, DictionaryMatch, RegexNgramMatch, CandidateExtractor
from utils import unescape_penn_treebank
from datasets import NcbiDiseaseCor... | ddbiolib-master | demos/taggers/diseases/old/disease_extraction_trees.py |
import bz2
import sys
import csv
import re
import os
import numpy as np
import itertools
import cPickle
import ddlite
from ddlite import SentenceParser,Entities
from ddlite import Union, DictionaryMatch, RegexNgramMatch
from utils import unescape_penn_treebank
from datasets import PubMedAbstractCorpus
def load_bioport... | ddbiolib-master | demos/taggers/diseases/old/disease_extraction_pmc.py |
import bz2
import sys
import csv
import re
import codecs
import numpy as np
import itertools
import cPickle
import ddlite
from ddlite import SentenceParser,Entities
from ddlite import Union, DictionaryMatch, RegexNgramMatch, CandidateExtractor
from utils import unescape_penn_treebank
from datasets import NcbiDiseaseCor... | ddbiolib-master | demos/taggers/diseases/old/disease_extraction.py |
import bz2
import sys
import cPickle
import numpy as np
import itertools
from ddlite import SentenceParser,DictionaryMatch,Entities,CandidateModel
from utils import unescape_penn_treebank
from datasets import NcbiDiseaseCorpus
from sklearn.metrics import precision_score,recall_score
def find_duplicates(candidates):
... | ddbiolib-master | demos/taggers/diseases/old/disease_learning.py |
import re
import os
import sys
import bz2
import csv
import codecs
import cPickle
import itertools
from operator import itemgetter
from itertools import groupby
sys.path.insert(1, "/users/fries/code/")
#import ddlite
from ddlite import *
rootdir = "/Users/fries/Code/HILDA-2016/candidates/"
#
# With training vocabula... | ddbiolib-master | demos/taggers/diseases/old/debug_dictionaries.py |
'''
Given a parse and aligned gold annotations, export
CoNLL format files
'''
import sys
import codecs
import numpy as np
import cPickle
from ddlite import SentenceParser
from datasets import NcbiDiseaseCorpus
INDIR = "/Users/fries/Desktop/dnorm/"
cache = "{}/cache3/".format(INDIR)
infile = "{}/disease_names/".forma... | ddbiolib-master | demos/taggers/diseases/old/export_conll.py |
from nltk.tree import Tree
import re
s = '''
(ROOT
(S
(S
(NP (PRP It))
(VP (VBD was)
(UCP
(ADJP (JJ rare))
(, ,)
(CONJP (IN as) (RB not))
(VP (VBN observed)
(PP (IN in)
(NP (NNS controls)))))))
(, ,)
(CC but)
(S (RB... | ddbiolib-master | demos/taggers/diseases/old/tree_parse.py |
'''
Created on Jun 17, 2016
@author: fries
'''
import bz2
import sys
import csv
import re
import os
import numpy as np
import itertools
import cPickle
import ddlite
from ddlite import SentenceParser,Entities
from ddlite import Union, DictionaryMatch, RegexNgramMatch
from utils import unescape_penn_treebank
from datase... | ddbiolib-master | demos/taggers/diseases/old/naive_entity_linking.py |
#!/usr/bin/env python
'''
<SAB>.<ACRONYMS>.<STY>.txt
snomedct.terms.enzyme.txt
snomedct.abbrv.enzyme.txt
'''
from __future__ import print_function
import os
import re
import sys
import codecs
import argparse
import ddbiolib.ontologies.umls as umls
def create_dictionaries(term_types,outdir,dict_type="",min_size=250... | ddbiolib-master | demos/umls/noise_aware_dict.py |
'''
UMLS Metathesaurus
Current Functionality
+ Simple Concept object (interface for definitions, term sets, etc.)
+ Build dictionaries of UMLS semantic types
+ Build positive examples of UMLS relations
There are many other tools that do similar things, though none have a
distant supervision focus (and there is a bia... | ddbiolib-master | demos/umls/umls_demo.py |
'''
Simple demonstration of instantiating a concept graph and
computing some concept similarity measures
'''
from __future__ import print_function
import networkx as nx
from ddbiolib.ontologies.umls import Metathesaurus
from ddbiolib.ontologies.umls.config import DatabaseConfig
def pprint_path(path, ontology):
... | ddbiolib-master | demos/umls/kb_demo.py |
#!/usr/bin/env python
'''
Simple UMLS Metathesaurus Dictionary Builder
Build dictionaries of UMLS semantic types.
See umls/docs for list of UMLS Semantic Types
Example usage:
python create_dictionary.py -t "Disease or Syndrome" -s "SNOMEDCT_US" > outfile.txt
'''
from __future__ import print_function
import re
imp... | ddbiolib-master | demos/umls/create_dictionary.py |
# Copyright (c) Meta Platforms, Inc. and affiliates
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sys, os
import time
import re
mode="analyze"
m=3
pre_k=1
main_k=5
def run_infer(infer_out, k, model, quiet):
to... | data_driven_infer-main | bin/DDInfer.py |
# Copyright (c) Meta Platforms, Inc. and affiliates
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sys, os
import time
import re
def run(path, p, k, model):
total_time, total_alarms = 0, 0
try:
infe... | data_driven_infer-main | Table2/bin/eval_ml_infer.py |
# Copyright (c) Meta Platforms, Inc. and affiliates
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import sklearn
from sklearn.ensemble import GradientBoostingClassifier
import pickle
import itertools
im... | data_driven_infer-main | Table2/bin/collect.py |
# Copyright (c) Meta Platforms, Inc. and affiliates
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from multiprocessing import Process
import sklearn
from sklearn.ensemble import GradientBoostingClassifi... | data_driven_infer-main | Table2/bin/learn_classifier.py |
# Copyright (c) Meta Platforms, Inc. and affiliates
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sys, os
import infer
if len(sys.argv) < 6:
print("usage:")
print("python run_ml_infer.py bin/programs_test.... | data_driven_infer-main | Table2/bin/run_ml_infer.py |
# Copyright (c) Meta Platforms, Inc. and affiliates
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sys, os
import time
import re
import random
from multiprocessing import Process, Queue, Manager
def split_list(a, n... | data_driven_infer-main | Table2/bin/infer.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Modified from github.com/openai/CLIP
from collections import OrderedDict
import numpy as np
import timm
import torch
fr... | clip-rocket-main | models.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
A script to run multinode training with submitit.
"""
import argparse
import os
import uuid
from pathlib import Path
i... | clip-rocket-main | run_with_submitit.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from collections import defaultdict
import json
import os
import pickle
import zipfile
import numpy as np
from PIL import... | clip-rocket-main | datasets.py |
# Taken from https://github.com/rwightman/timm
""" Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`How to train your ViT? Data, Augmentation, and Regular... | clip-rocket-main | vit.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Modified from github.com/openai/CLIP
import gzip
import html
import os
from functools import lru_cache
import ftfy
impo... | clip-rocket-main | tokenizer.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import os
import random
import shutil
import torch
import torch.distributed as dist
import torch.autogr... | clip-rocket-main | utils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
from collections import OrderedDict, defaultdict
import json
import os
from sklearn import metrics
import ... | clip-rocket-main | eval_zeroshot.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
import utils
class C... | clip-rocket-main | losses.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
from collections import OrderedDict, defaultdict
import json
import math
import os
import sys
import time
... | clip-rocket-main | main.py |
import math
import numpy as np
def subsampled_dense_grid(d, D, gamma, deg=8):
"""Points and weights for the kernel feature map for the RBF kernel
exp(-gamma ||x - y||^2) using subsampled dense grid.
Parameters:
d: input dimension
D: number of features
gamma: parameter of the RBF ke... | quadrature-features-master | dense_grid.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from absl import app
from absl import flags
import cv2
import os.path as osp
import sys
sys.path.insert(0,'third_party')
import pdb
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
from nnuti... | banmo-main | main.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from absl import flags, app
import sys
sys.path.insert(0,'third_party')
import numpy as np
import torch
import os
import glob
import pdb
import cv2
import trimesh
from scipy.spatial.transform import Rotation as R
import imageio
from utils.io impo... | banmo-main | extract.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import cv2
import glob
import numpy as np
import pdb
import os
import shutil
import detectron2
from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor
from detectron2.utils.visualizer import Visualizer, ColorMode
fro... | banmo-main | preprocess/mask.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
"""
python img2lines.py --seqname xx
"""
from absl import flags, app
import sys
sys.path.insert(0,'third_party')
sys.path.insert(0,'./')
import numpy as np
import torch
import os
import glob
import pdb
import cv2
import trimesh
from scipy.spatial... | banmo-main | preprocess/img2lines.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import configparser
import cv2
import glob
import pdb
import sys
seqname_pre=sys.argv[1]
ishuman=sys.argv[2] # 'y/n'
silroot='database/DAVIS/Annotations/Full-Resolution/'
config = configparser.ConfigParser()
config['data'] = {
'dframe': '1',
'in... | banmo-main | preprocess/write_config.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import cv2
import glob
import numpy as np
import pdb
import os
import shutil
import detectron2
from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor
from detectron2.utils.visualizer import Visualizer, ColorMode
from... | banmo-main | preprocess/compute_dp.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import os
import errno
from typing import Any, Dict, List, Tuple, Union
import cv2
import pdb
import configparser
import torch
import numpy as np
import imageio
import trimesh
import glob
import matplotlib.cm
import torch.nn.functional as F
from sc... | banmo-main | utils/io.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import pickle
import cv2
import numpy as np
import os
import torch
import torch.nn.functional as F
import pdb
import trimesh
from detectron2.config import get_cfg
from detectron2.modeling import build_model
from detectron2.checkpoint import Detect... | banmo-main | utils/cselib.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import numpy as np
def label_colormap():
"""
colormap for visualizing bones
"""
return np.asarray(
[[155, 122, 157],
[ 45, 245, 50],
[ 71, 25, 64],
[231, 176, 35],
[125, 249, 245],
[ 32, 75, 253],
[241, 31, 111],
[218, 71,... | banmo-main | utils/colors.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import os
import os.path as osp
import sys
sys.path.insert(0,'third_party')
import time
import pdb
impo... | banmo-main | nnutils/train_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
# adopted from nerf-pl
import numpy as np
import pdb
import torch
import torch.nn.functional as F
from pytorch3d import transforms
from nnutils.geom_utils import lbs, Kmatinv, mat2K, pinhole_cam, obj_to_cam,\
vec_to... | banmo-main | nnutils/rendering.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import numpy as np
import pdb
import torch
from torch import nn
import torch.nn.functional as F
import torchvision
from pytorch3d import transforms
import trimesh
from nnutils.geom_utils import fid_reindex
class Embedding(nn.Module):
def __in... | banmo-main | nnutils/nerf.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
from collections import defaultdict
import os
import os.path as osp
import pickle
import sys
s... | banmo-main | nnutils/banmo.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import cv2, pdb, os, sys, numpy as np, torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
curr_dir = os.path.abspath(os.getcwd())
sys.path.insert(0, curr_dir)
detbase = './third_party/detectron2/'
sys.path.insert(0, '%s... | banmo-main | nnutils/cse.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import torch
def image_grid(img, row, col):
"""
img: N,h,w,x
collage: 1,.., x
"""
bs,h,w,c=img.shape
device = img.device
collage = torch.zeros(h*row, w*col, c).to(device)
for i in range(row):
for j in r... | banmo-main | nnutils/vis_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import pdb
import time
import cv2
import numpy as np
import trimesh
from pytorch3d import transforms
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy.spatial.transform import Rotation as R
import sys
sys.path.insert(0... | banmo-main | nnutils/geom_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import pdb
import trimesh
import cv2
import numpy as np
import torch
from nnutils.geom_utils import rot_angle, mat2K, Kmatinv, obj_to_cam, \
pinhole_cam, lbs, gauss_mlp_skinning, evaluate_mlp
import torch.nn.functio... | banmo-main | nnutils/loss_utils.py |
"""
MIT License
Copyright (c) 2019 ThibaultGROUEIX
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 restriction, including without limitation the rights
to use, copy, modify, merge, publis... | banmo-main | third_party/fscore.py |
# MIT license
# Copyright (c) 2019 LI RUOTENG
#
# 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 restriction, including without limitation the rights
# to use, copy, modify, merge, p... | banmo-main | third_party/ext_utils/flowlib.py |
# MIT License
#
# Copyright (c) 2019 Carnegie Mellon University
# Copyright (c) 2021 Google LLC
#
# 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 restriction, including without limi... | banmo-main | third_party/ext_utils/util_flow.py |
from setuptools import setup, find_packages
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
CUDA_FLAGS = []
gencodes = [
'-gencode', 'arch=compute_52,code=sm_52',
'-gencode', 'arch=compute_60,code=sm_60',
'-gencode', 'arch=compute_61,code=sm_61',
... | banmo-main | third_party/softras/setup.py |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy
import soft_renderer as sr
class Renderer(nn.Module):
def __init__(self, image_size=256, background_color=[0,0,0], near=1, far=100,
anti_aliasing=True, fill_back=True, eps=1e-6,
camera... | banmo-main | third_party/softras/soft_renderer/renderer.py |
from . import functional
from .mesh import Mesh
from .renderer import Renderer, SoftRenderer
from .transform import Projection, LookAt, Look, Transform
from .lighting import AmbientLighting, DirectionalLighting, Lighting
from .rasterizer import SoftRasterizer
from .losses import LaplacianLoss, FlattenLoss
__version__... | banmo-main | third_party/softras/soft_renderer/__init__.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import soft_renderer.functional as srf
class Mesh(object):
'''
A simple class for creating and manipulating trimesh objects
'''
def __init__(self, vertices, faces, textures=None, texture_res=1, texture_type='surface... | banmo-main | third_party/softras/soft_renderer/mesh.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import soft_renderer.functional as srf
class AmbientLighting(nn.Module):
def __init__(self, light_intensity=0.5, light_color=(1,1,1)):
super(AmbientLighting, self).__init__()
self.light_intensity = light_intens... | banmo-main | third_party/softras/soft_renderer/lighting.py |
import math
import numpy as np
import torch
import torch.nn as nn
import soft_renderer.functional as srf
class Projection(nn.Module):
def __init__(self, P, dist_coeffs=None, orig_size=512):
super(Projection, self).__init__()
self.P = P
self.dist_coeffs = dist_coeffs
self.orig_siz... | banmo-main | third_party/softras/soft_renderer/transform.py |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import soft_renderer.functional as srf
class SoftRasterizer(nn.Module):
def __init__(self, image_size=256, background_color=[0, 0, 0], near=1, far=100,
anti_aliasing=False, fill_back=False, eps=1e-3,
... | banmo-main | third_party/softras/soft_renderer/rasterizer.py |
import torch
import torch.nn as nn
import numpy as np
class LaplacianLoss(nn.Module):
def __init__(self, vertex, faces, average=False):
super(LaplacianLoss, self).__init__()
self.nv = vertex.size(0)
self.nf = faces.size(0)
self.average = average
laplacian = np.zeros([self.n... | banmo-main | third_party/softras/soft_renderer/losses.py |
banmo-main | third_party/softras/soft_renderer/cuda/__init__.py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
import soft_renderer.cuda.voxelization as voxelization_cuda
def voxelize_sub1(faces, size, dim):
bs = faces.size(0)
nf = faces.size(1)
if dim == 0:
faces = faces[:, :, :, [2, 1, 0]].contiguous()... | banmo-main | third_party/softras/soft_renderer/functional/voxelization.py |
import numpy as np
import torch
import torch.nn.functional as F
def look_at(vertices, eye, at=[0, 0, 0], up=[0, 1, 0]):
"""
"Look at" transformation of vertices.
"""
if (vertices.ndimension() != 3):
raise ValueError('vertices Tensor should have 3 dimensions')
device = vertices.device
... | banmo-main | third_party/softras/soft_renderer/functional/look_at.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def directional_lighting(light, normals, light_intensity=0.5, light_color=(1,1,1),
light_direction=(0,1,0)):
# normals: [nb, :, 3]
device = light.device
if isinstance(light_color, tuple) or is... | banmo-main | third_party/softras/soft_renderer/functional/directional_lighting.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def ambient_lighting(light, light_intensity=0.5, light_color=(1,1,1)):
device = light.device
if isinstance(light_color, tuple) or isinstance(light_color, list):
light_color = torch.tensor(light_color, dtype=torch.fl... | banmo-main | third_party/softras/soft_renderer/functional/ambient_lighting.py |
import math
import torch
def perspective(vertices, angle=30.):
'''
Compute perspective distortion from a given angle
'''
if (vertices.ndimension() != 3):
raise ValueError('vertices Tensor should have 3 dimensions')
device = vertices.device
angle = torch.tensor(angle / 180 * math.pi, dt... | banmo-main | third_party/softras/soft_renderer/functional/perspective.py |
import os
import torch
import numpy as np
from skimage.io import imread
import soft_renderer.cuda.load_textures as load_textures_cuda
def load_mtl(filename_mtl):
'''
load color (Kd) and filename of textures from *.mtl
'''
texture_filenames = {}
colors = {}
material_name = ''
with open(fil... | banmo-main | third_party/softras/soft_renderer/functional/load_obj.py |
import torch
def face_vertices(vertices, faces):
"""
:param vertices: [batch size, number of vertices, 3]
:param faces: [batch size, number of faces, 3]
:return: [batch size, number of faces, 3, 3]
"""
assert (vertices.ndimension() == 3)
assert (faces.ndimension() == 3)
assert (vertice... | banmo-main | third_party/softras/soft_renderer/functional/face_vertices.py |
import numpy as np
import torch
import torch.nn.functional as F
def look(vertices, eye, direction=[0, 1, 0], up=None):
"""
"Look" transformation of vertices.
"""
if (vertices.ndimension() != 3):
raise ValueError('vertices Tensor should have 3 dimensions')
device = vertices.device
if ... | banmo-main | third_party/softras/soft_renderer/functional/look.py |
from .get_points_from_angles import get_points_from_angles
from .ambient_lighting import ambient_lighting
from .directional_lighting import directional_lighting
from .load_obj import load_obj
from .look import look
from .look_at import look_at
from .perspective import perspective
from .orthogonal import orthogonal
from... | banmo-main | third_party/softras/soft_renderer/functional/__init__.py |
import os
import torch
from skimage.io import imsave
import soft_renderer.cuda.create_texture_image as create_texture_image_cuda
def create_texture_image(textures, texture_res=16):
num_faces = textures.shape[0]
tile_width = int((num_faces - 1.) ** 0.5) + 1
tile_height = int((num_faces - 1.) / tile_width... | banmo-main | third_party/softras/soft_renderer/functional/save_obj.py |
import math
import torch
def get_points_from_angles(distance, elevation, azimuth, degrees=True):
if isinstance(distance, float) or isinstance(distance, int):
if degrees:
elevation = math.radians(elevation)
azimuth = math.radians(azimuth)
return (
distance * math... | banmo-main | third_party/softras/soft_renderer/functional/get_points_from_angles.py |
import torch
def orthogonal(vertices, scale):
'''
Compute orthogonal projection from a given angle
To find equivalent scale to perspective projection
set scale = focal_pixel / object_depth -- to 0~H/W pixel range
= 1 / ( object_depth * tan(half_fov_angle) ) -- to -1~1 pixel range
''... | banmo-main | third_party/softras/soft_renderer/functional/orthogonal.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
import numpy as np
import soft_renderer.cuda.soft_rasterize as soft_rasterize_cuda
class SoftRasterizeFunction(Function):
@staticmethod
def forward(ctx, face_vertices, textures, image_size=256,
... | banmo-main | third_party/softras/soft_renderer/functional/soft_rasterize.py |
import torch
def projection(vertices, P, dist_coeffs, orig_size):
'''
Calculate projective transformation of vertices given a projection matrix
P: 3x4 projection matrix
dist_coeffs: vector of distortion coefficients
orig_size: original size of image captured by the camera
'''
vertices = to... | banmo-main | third_party/softras/soft_renderer/functional/projection.py |
import torch
import torch.nn.functional as F
def vertex_normals(vertices, faces):
"""
:param vertices: [batch size, number of vertices, 3]
:param faces: [batch size, number of faces, 3]
:return: [batch size, number of vertices, 3]
"""
assert (vertices.ndimension() == 3)
assert (faces.ndimen... | banmo-main | third_party/softras/soft_renderer/functional/vertex_normals.py |
from torch import nn
from torch.autograd import Function
import torch
import importlib
import os
chamfer_found = importlib.find_loader("chamfer_3D") is not None
if not chamfer_found:
## Cool trick from https://github.com/chrdiller
print("Jitting Chamfer 3D")
from torch.utils.cpp_extension import load
c... | banmo-main | third_party/chamfer3D/dist_chamfer_3D.py |
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name='chamfer_3D',
ext_modules=[
CUDAExtension('chamfer_3D', [
"/".join(__file__.split('/')[:-1] + ['chamfer_cuda.cpp']),
"/".join(__file__.split('/')[:-1] + ['chamfer3D.cu'])... | banmo-main | third_party/chamfer3D/setup.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import glob
import os
import shutil
from os import path
from setuptools import find_packages, setup
from typing import List
import torch
from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
from torch.utils.hipify import h... | banmo-main | third_party/detectron2_old/setup.py |
# Copyright (c) Facebook, Inc. and its affiliates.
import atexit
import bisect
import multiprocessing as mp
from collections import deque
import cv2
import torch
from detectron2.data import MetadataCatalog
from detectron2.engine.defaults import DefaultPredictor
from detectron2.utils.video_visualizer import VideoVisual... | banmo-main | third_party/detectron2_old/demo/predictor.py |
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import glob
import multiprocessing as mp
import numpy as np
import os
import tempfile
import time
import warnings
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.... | banmo-main | third_party/detectron2_old/demo/demo.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import pickle as pkl
import sys
import torch
"""
Usage:
# download one of the ResNet{18,34,50,101,152} models from torchvision:
wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O r50.pth
# run the conversion
./convert-torc... | banmo-main | third_party/detectron2_old/tools/convert-torchvision-to-d2.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
"""
A script to benchmark builtin models.
Note: this script has an extra dependency of psutil.
"""
import itertools
import logging
import psutil
import torch
import tqdm
from fvcore.common.timer import Timer
from torch.nn.parallel import Distrib... | banmo-main | third_party/detectron2_old/tools/benchmark.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import os
from itertools import chain
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_train_loader
from detectron2.data import detection_uti... | banmo-main | third_party/detectron2_old/tools/visualize_data.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
"""
Detectron2 training script with a plain training loop.
This script reads a given config file and runs the training or evaluation.
It is an entry point that is able to train standard models in detectron2.
In order to let one script support tr... | banmo-main | third_party/detectron2_old/tools/plain_train_net.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import json
import numpy as np
import os
from collections import defaultdict
import cv2
import tqdm
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.structures import Boxes, BoxMode, Instances
from dete... | banmo-main | third_party/detectron2_old/tools/visualize_json_results.py |
# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import numpy as np
from collections import Counter
import tqdm
from fvcore.nn import flop_count_table # can also try flop_count_str
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
f... | banmo-main | third_party/detectron2_old/tools/analyze_model.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
"""
Training script using the new "LazyConfig" python config files.
This scripts reads a given python config file and runs the training or evaluation.
It can be used to train any models or dataset as long as they can be
instantiated by the recurs... | banmo-main | third_party/detectron2_old/tools/lazyconfig_train_net.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
"""
A main training script.
This scripts reads a given config file and runs the training or evaluation.
It is an entry point that is made to train standard models in detectron2.
In order to let one script support training of many models,
this sc... | banmo-main | third_party/detectron2_old/tools/train_net.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import os
from typing import Dict, List, Tuple
import torch
from torch import Tensor, nn
import detectron2.data.transforms as T
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from det... | banmo-main | third_party/detectron2_old/tools/deploy/export_model.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
"""
TridentNet Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
import os
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.engine... | banmo-main | third_party/detectron2_old/projects/TridentNet/train_net.py |
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