def get_conferences():
files = util.listdir(CONFERENCE_FOLDER)
util.mkdir(CONFERENCE_CRALWED_FOLDER)
cnt = 0
conf = util.load_json('conf_name.json')
for file_name in files:
save_path = os.path.join(CONFERENCE_CRALWED_FOLDER, file_name)
if util.exists(save_path):
continue
data = util.load_json(os.path.join(CONFERENCE_FOLDER, file_name))
if data['short'] not in conf.keys():
continue
html = util.get_page(data['url'])
subs = get_subs(data['short'], html)
data['name'] = conf[data['short']]
data['sub'] = {}
for sub in subs:
if sub not in conf.keys():
continue
html = util.get_page('http://dblp.uni-trier.de/db/conf/' + sub)
data['sub'][sub] = {}
data['sub'][sub]['pub'] = get_publications(html)
data['sub'][sub]['name'] = conf[sub]
cnt += 1
print cnt, len(files), data['short']
util.save_json(save_path, data)
def getTeamSeasonHtml(self):
html = INVALID_STRING
if cmp(self._htmlWebSite, INVALID_STRING) == 0:
print "htmlWebSite has not been initialized yet"
else:
#If we have already synced the data we need to fetch the html from local instead of reloading the website again
pathNeed2Test = CURRENT_PATH + self._Season0 + self._Season1 + '/' + self._Team_id
print "Constructing path as ", pathNeed2Test
self._dataStoredPath = pathNeed2Test
util.mkdir(pathNeed2Test)
htmlFile = pathNeed2Test + '/' + self._GameType + '.html'
self._seasonDataHtmlFile = htmlFile
print "Check if html file exist or not ", htmlFile
if os.path.isfile(htmlFile):
print "html file exists, open the file, read it and return the string"
html = util.openFile(htmlFile)
#print html
else:
print "html file does not exist, now loading the webpage from network"
html = util.getHtmlFromUrl(self._htmlWebSite)
if cmp(html, INVALID_STRING)!=0:
util.saveFile(htmlFile,html)
return html
return html
def sub(self):
util.mkdir(self.log)
sub = SubWithProcId.SubWithProcId()
sub.setlog(self.log)
logger.debug("self.job = {}".format(self.job))
sub.setpath(os.path.abspath(self.job))
sub.sub()
def ajob(self, dst, name):
logger.info("Process " + dst)
logger.info('each job contain about {} {}'.format(self.size, 'G dsts'))
dsts = int(do('ls -1 -F ' + dst + r' | grep -v [/$] | wc -l'))
logger.debug("total dsts: {}".format(dsts))
size = int(int(do('du ' + dst).split()[0]) / 1024. / 1024. / self.size)
job = self.job + name
root = self.rawpth + name
util.mkdir(job)
util.mkdir(root)
j = subjobs.subjobs()
j.setbody(self.body)
jobnum = self._num
if jobnum < size:
jobnum = size + 1
if dsts < jobnum:
jobnum = dsts
j.setjobnum(jobnum)
j.setjobname("jobs_")
j.setname(self.rootnm)
j.setdstpath(dst)
j.setjobpath(job)
j.setProcesser(20)
j.drop(self._drop)
j.setrootpath(root)
j.jobs()
def addDimFaceData():
face_data_dir = '/home/SSD3/maheen-data/face_data/npy'
out_dir = '/home/SSD3/maheen-data/face_data/npy_dimAdd'
util.mkdir(out_dir)
face_data_file = os.path.join(face_data_dir, 'data_list.txt')
in_files = util.readLinesFromFile(face_data_file)
args = []
for idx, in_file in enumerate(in_files):
out_file = in_file.replace(face_data_dir, out_dir)
if os.path.exists(out_file):
continue
folder_curr = out_file[:out_file.rindex('/')]
util.mkdir(folder_curr)
args.append((idx, in_file, out_file))
print len(args)
# for arg in args:
# addDim(arg);
# break;
p = multiprocessing.Pool(multiprocessing.cpu_count())
p.map(addDim, args)
def saveFaceBBoxIm():
path_txt = '/home/laoreja/deep-landmark-master/dataset/train/trainImageList.txt'
path_pre = '/home/laoreja/deep-landmark-master/dataset/train'
path_im, bbox, anno_points = parseAnnoFile(path_txt, path_pre, face=True)
out_dir_im = '/home/SSD3/maheen-data/face_data/im'
out_dir_npy = '/home/SSD3/maheen-data/face_data/npy'
args = []
args_bbox_npy = []
for idx, path_im_curr, bbox_curr, key_pts in zip(range(len(path_im)),
path_im, bbox,
anno_points):
path_curr, file_name = os.path.split(path_im_curr)
path_curr = path_curr.split('/')
path_pre_curr = path_curr[-1]
out_dir_curr = os.path.join(out_dir_im, path_pre_curr)
out_dir_npy_curr = os.path.join(out_dir_npy, path_pre_curr)
util.mkdir(out_dir_curr)
util.mkdir(out_dir_npy_curr)
out_file = os.path.join(out_dir_curr, file_name)
args.append((path_im_curr, out_file, bbox_curr, idx))
out_file_npy = os.path.join(out_dir_npy_curr,
file_name[:file_name.rindex('.')] + '.npy')
args_bbox_npy.append((bbox_curr, key_pts, out_file_npy, idx))
# break;
print len(args_bbox_npy)
p = multiprocessing.Pool(multiprocessing.cpu_count())
# p.map(saveBBoxIm,args);
p.map(saveBBoxNpy, args_bbox_npy)
def get_conferences():
files = util.listdir(CONFERENCE_FOLDER)
util.mkdir(CONFERENCE_CRALWED_FOLDER)
cnt = 0
conf = util.load_json('conf_name.json')
for file_name in files:
save_path = os.path.join(CONFERENCE_CRALWED_FOLDER, file_name)
if util.exists(save_path):
continue
data = util.load_json(os.path.join(CONFERENCE_FOLDER, file_name))
if data['short'] not in conf.keys():
continue
html = util.get_page(data['url'])
subs = get_subs(data['short'], html)
data['name'] = conf[data['short']]
data['sub'] = {}
for sub in subs:
if sub not in conf.keys():
continue
html = util.get_page('http://dblp.uni-trier.de/db/conf/' + sub)
data['sub'][sub] = {}
data['sub'][sub]['pub'] = get_publications(html)
data['sub'][sub]['name'] = conf[sub]
cnt += 1
print cnt, len(files), data['short']
util.save_json(save_path, data)
def get_journals():
pos, cnt = 1, 0
util.mkdir(JOURNAL_FOLDER)
while True:
html = util.get_page(JOURNAL_URL + str(pos))
links = util.find_journals(html)
once_cnt = 0
for link in links:
if link[0] == '' or '?' in link[0]:
continue
data = {}
data['type'] = 'journal'
data['short'] = link[0]
data['name'] = link[1]
data['url'] = 'http://dblp.uni-trier.de/db/journals/' + data[
'short']
util.save_json(
os.path.join(JOURNAL_FOLDER, util.hex_hash(data['short'])),
data)
cnt += 1
once_cnt += 1
if once_cnt == 0:
break
pos += 100
print 'Journal', cnt
def script_saveToExploreBoxes():
# dir_mat_overlap='/disk3/maheen_data/headC_160_noFlow_bbox/mat_overlaps_1000';
dir_mat_overlap='/disk3/maheen_data/pedro_val/mat_overlap';
scratch_dir='/disk3/maheen_data/scratch_dir'
util.mkdir(scratch_dir);
out_file=os.path.join(scratch_dir,'them_neg_box.p');
overlap_files=util.getFilesInFolder(dir_mat_overlap,ext='.npz');
print len(overlap_files)
to_explore=[];
for idx_overlap_file,overlap_file in enumerate(overlap_files):
print idx_overlap_file
meta_data=np.load(overlap_file);
pred_boxes=meta_data['pred_boxes'];
# print pred_boxes.shape
min_boxes=np.min(pred_boxes,axis=1);
num_neg=np.sum(min_boxes<0);
if num_neg>0:
to_explore.append((overlap_file,pred_boxes));
print len(to_explore);
pickle.dump(to_explore,open(out_file,'wb'));
def create_videos(dirs):
util.mkdir(VIDEO_DIR)
for dir in dirs:
basename = os.path.basename(dir)
target = os.path.join(VIDEO_DIR, basename)
cmd = f"ffmpeg -f image2 -pattern_type glob -i '{dir}/*.png' -t 30 -c:v libx264 -profile:v high -crf 25 -pix_fmt yuv420p {target}.mp4"
subprocess.call(cmd, shell=True)
def save_one_img_of_batch(self, batch, dirpath, fname):
util.mkdir(dirpath)
imgpath = osp.join(dirpath, fname)
assert len(batch.shape) == 4
# img = util.saveTensorAsImg(batch[0], imgpath)
torchvision.utils.save_image(batch[0], imgpath)
def main():
parser = get_parser()
args = parser.parse_args()
# load model. model_options defined in models/__init__.py
model = sk_model_options[args.model](args.choice, args.freq_floor)
# load data
data_path = data_paths[args.dataset]
train_set, dev_set, test_set = get_datasets(model.batch_size,
data_path,
model.preprocess_inputs,
sk=True)
print 'training...'
train(model, train_set)
print 'done training.'
truth_file = os.path.join(data_path, 'truth.jsonl')
mkdir(os.path.join(CKPT, args.sess_name))
results_dir = os.path.join(CKPT, args.sess_name, 'results')
mkdir(results_dir)
print 'evaluating...'
evaluate(model, train_set, results_dir, 'train', truth_file)
evaluate(model, dev_set, results_dir, 'dev', truth_file)
evaluate(model, test_set, results_dir, 'test', truth_file)
print 'done evaluating.'
def get_base_parses(run_id, trials_per=800, reverse=False, dry_run=False):
print('run_id: %i' % run_id)
print('Loading model...')
type_model = TypeModel().eval()
if torch.cuda.is_available():
type_model = type_model.cuda()
score_fn = lambda parses: model_score_fn(type_model, parses)
print('Loading classification dataset...')
dataset = ClassificationDataset(osc_folder='./one-shot-classification')
run = dataset.runs[run_id]
if reverse:
imgs = run.test_imgs
else:
imgs = run.train_imgs
run_dir = './run%0.2i' % (run_id + 1)
save_dir = os.path.join(run_dir, 'base_parses')
if not dry_run:
mkdir(run_dir)
mkdir(save_dir)
print('Collecting top-K parses for each train image...')
nimg = len(imgs)
for i in range(nimg):
start_time = time.time()
parses, log_probs = get_topK_parses(imgs[i],
k=5,
score_fn=score_fn,
configs_per=1,
trials_per=trials_per)
total_time = time.time() - start_time
print('image %i/%i took %s' % (i + 1, nimg, time_string(total_time)))
if dry_run:
continue
save_img_results(save_dir, i, parses, log_probs, reverse)
def load(cls, conf, lang, bert=None):
mkdir(conf.cache_dir)
fasttext_emb = conf.fasttext_emb_file if conf.use_fasttext else None
fname = (
f"{conf.dataset}.{lang}." +
(f"max{conf.max_ninst}." if conf.max_ninst else "") +
(f"maxeval{conf.max_eval_ninst}." if conf.max_eval_ninst else "") +
(f"cv{conf.crossval_idx}." if conf.crossval_idx is not None else "") +
(f"bert{conf.bert_max_seq_len}." if bert is not None else "") +
(f"fasttext." if fasttext_emb is not None else "") +
f"vs{conf.vocab_size}.{conf.tag}." +
(f"{conf.tag_scheme}." if conf.tag_scheme else "") +
"pt"
)
cache_file = conf.cache_dir / fname
ds = None
try:
print("loading", cache_file)
ds = torch.load(cache_file)
print("loaded", cache_file)
ds.bpemb = BPEmb(
lang=conf.bpemb_lang,
vs=conf.vocab_size,
dim=conf.bpemb_dim,
add_pad_emb=True)
except FileNotFoundError:
pass
if ds is None:
print(f"Loading dataset {conf.dataset} {lang}")
ds = cls(conf, lang, bert=bert)
bpemb = ds.bpemb
ds.bpemb = None # cannot pickle SwigPyObject
torch.save(ds, cache_file)
ds.bpemb = bpemb
return ds
def script_reSaveMatOverlap():
dir_old='/disk3/maheen_data/pedro_val/mat_overlap';
dir_new='/disk3/maheen_data/pedro_val/mat_overlap_check';
util.mkdir(dir_new);
path_to_im='/disk2/ms_coco/val2014';
path_to_gt='/disk2/mayExperiments/validation_anno';
mat_overlap_files=util.getFilesInFolder(dir_old,ext='.npz');
im_names=util.getFileNames(mat_overlap_files,ext=False);
args=[];
for idx,(mat_overlap_file,im_name) in enumerate(zip(mat_overlap_files,im_names)):
gt_file=os.path.join(path_to_gt,im_name+'.npy');
im_file=os.path.join(path_to_im,im_name+'.jpg');
out_file=os.path.join(dir_new,im_name+'.npz');
# if os.path.exists(out_file):
# continue;
args.append((mat_overlap_file,gt_file,im_file,out_file,idx));
p = multiprocessing.Pool(32);
p.map(fixMatOverlap, args);
def build_test_res_dir(self):
assert self.opt[CHECKPOINT_PATH]
modelpath = pathlib.Path(self.opt[CHECKPOINT_PATH])
# when passing valid_ds, ignore ds.name and use valid_ds.name instead.
ds_type = VALID_DATA if self.opt[VALID_DATA][INPUT] else DATA
fname = modelpath.name + '@' + self.opt[ds_type][NAME]
dirpath = modelpath.parent / TEST_RESULT_DIRNAME
if (dirpath / fname).exists():
if len(os.listdir(dirpath / fname)) == 0:
# an existing but empty dir
pass
else:
input_str = input(
f'[ WARN ] Result directory "{fname}" exists. Press ENTER to overwrite or input suffix to create a new one:\n> New name: {fname}.')
if input_str == '':
console.log(f"[ WARN ] Overwrite result_dir: {fname}")
pass
else:
fname += '.' + input_str
# fname += '.new'
dirpath /= fname
util.mkdir(dirpath)
console.log('TEST - Result save path:')
console.log(str(dirpath))
global TEST_DIRPATH
TEST_DIRPATH = dirpath
return str(dirpath)
def codeProject(args,flag,data):
PARAM_KEY = 1
PARAM_PATH = 2
PARAM_FORMATTER = 3
ARGUMENTS = len(args)-1
# JSON mapping files and storage of this
if( keyExists("projects",args[1])):
if( "stdout" in args[2]):
project = json.loads(load("projects/"+args[PARAM_KEY])); # Uses key value storage
directory = args[PARAM_PATH] + "/" + args[PARAM_KEY]
mkdir(directory)
for x in project.keys(): # Reflect that with here
_file = json.loads(load("files/"+x));
out = '';
for y in _file:
block = str(load("blocks/"+ y))
if(ARGUMENTS == PARAM_FORMATTER): # Alter all the blocks in said fashion
block = format.block(block, args[PARAM_FORMATTER])
out += block
# Output the file with the correct file name
save(directory + "/" + project[x],out)
else:
error("Error: Project does not exist")
def moveFilesIntoFolders(in_dir,mat_file,out_dir,out_file_commands,pad_zeros_in=8,pad_zeros_out=4): arr=scipy.io.loadmat(mat_file)['ranges']; # videos=np.unique(arr); commands=[]; for shot_no in range(arr.shape[1]): print shot_no,arr.shape[1]; start_idx=arr[0,shot_no]; end_idx=arr[1,shot_no]; video_idx=arr[2,shot_no]; out_dir_video=os.path.join(out_dir,str(video_idx)); util.mkdir(out_dir_video); # print # raw_input(); shot_idx=np.where(shot_no==np.where(video_idx==arr[2,:])[0])[0][0]+1; out_dir_shot=os.path.join(out_dir_video,str(shot_idx)); util.mkdir(out_dir_shot); # print start_idx,end_idx for idx,frame_no in enumerate(range(start_idx,end_idx+1)): in_file=os.path.join(in_dir,padZeros(frame_no,pad_zeros_in)+'.jpg'); out_file=os.path.join(out_dir_shot,'frame'+padZeros(idx+1,pad_zeros_out)+'.jpg'); command='mv '+in_file+' '+out_file; commands.append(command); print len(commands); util.writeFile(out_file_commands,commands);
def __init__(self, opt, running_modes):
'''
logger_img_group_names: images group names in wandb logger. recommand: ['train', 'valid']
'''
super().__init__()
self.save_hyperparameters(opt)
console.log('Running initialization for BaseModel')
if IMG_DIRPATH in opt:
# in training mode.
# if in test mode, configLogging is not called.
if TRAIN in running_modes:
self.train_img_dirpath = osp.join(opt[IMG_DIRPATH], TRAIN)
util.mkdir(self.train_img_dirpath)
if VALID in running_modes and opt[VALID_DATA][INPUT]:
self.valid_img_dirpath = osp.join(opt[IMG_DIRPATH], VALID)
util.mkdir(self.valid_img_dirpath)
self.opt = opt
self.MODEL_WATCHED = False # for wandb watching model
self.global_valid_step = 0
assert isinstance(running_modes, Iterable)
self.logger_image_buffer = {k: [] for k in running_modes}
def initialize():
print '\nWelcome to the interactive Svndae configuration!'
options = get_configuration_defaults()
print "\nInitializing file system in '%s'" % options['path']
util.mkdir(options['path'])
util.mkdir(join(options['path'],options['dir']))
print "Generating configuration file '%s'" % options['conf']
SvndaeConfig.generate_config(options['path'],options['conf'],options['dir'],options['file'])
config = SvndaeConfig(options['path'],name=options['conf'])
print "Adding default administrative groups"
config.create_group(init.ADMIN_GROUP)
config.add_permission(init.ADMIN_GROUP,SvndaeConfig._WRITE,SvndaeConfig._ALL_REPOS)
config.add_permission(init.ADMIN_GROUP,SvndaeConfig._READ,SvndaeConfig._ALL_REPOS)
while True:
correct_in = raw_input('Would you like to add any repository ACLs? ([y]/n) ')
if correct_in == '' or correct_in == 'y':
get_repo_manage(config)
break
elif correct_in == 'n':
break
else:
print_confirmation_error()
while True:
correct_in = raw_input('Would you like to add any additional groups? ([y]/n) ')
if correct_in == '' or correct_in == 'y':
get_user_groups(config)
break
elif correct_in == 'n':
break
else:
print_confirmation_error()
print_final()
def rescaleImAndSaveMeta(img_paths,meta_dir,power_scale_range=(-2,1),step_size=0.5): img_names=util.getFileNames(img_paths); power_range=np.arange(power_scale_range[0],power_scale_range[1]+1,step_size); scales=[2**val for val in power_range]; scale_infos=[]; for idx,scale in enumerate(scales): out_dir_curr=os.path.join(meta_dir,str(idx)); util.mkdir(out_dir_curr); scale_infos.append((out_dir_curr,scale)); args=[]; idx=0; for idx_img,img_path in enumerate(img_paths): for out_dir_curr,scale in scale_infos: out_file=os.path.join(out_dir_curr,img_names[idx_img]); if os.path.exists(out_file): continue; args.append((img_path,out_file,scale,idx)); idx=idx+1; p=multiprocessing.Pool(multiprocessing.cpu_count()); p.map(rescaleImAndSave,args);
def script_makeNegImages():
in_dir='/disk2/aprilExperiments/testing_neg_fixed_test/'
out_dir=os.path.join(in_dir,'visualizing_negs');
util.mkdir(out_dir);
# return
im_paths=[os.path.join(in_dir,file_curr) for file_curr in os.listdir(in_dir) if file_curr.endswith('.png')];
for im_path in im_paths:
print im_path
bbox=np.load(im_path.replace('.png','_bbox.npy'));
crop_box=np.load(im_path.replace('.png','_crop.npy'));
# print im_path.replace('.png','_bbox.npy')
# print im_path.replace('.png','_crop.npy')
# break;
im = Image.open(im_path);
im=drawCropAndBBox(im,bbox,crop_box)
# write to stdout
im.save(os.path.join(out_dir,im_path[im_path.rindex('/')+1:]), "PNG");
# break;
visualize.writeHTMLForFolder(out_dir,ext='png',height=300,width=300);
def run(method_name):
processed_fnames = []
imgs = glob(args.input)
c.log(f'Method: {method_name}, Input Images:')
c.log(imgs)
model = method_map[method_name]()
dstdir = osp.join(args.output, method_name)
util.mkdir(dstdir)
for x in track(imgs):
img = cv2.imread(x)
res = model.process_one(np.array(img, dtype=np.uint8))
# save result img:
fname = osp.basename(x)
if fname not in processed_fnames:
processed_fnames.append(fname)
else:
new_fname = fname + '.dup.png'
processed_fnames.append(new_fname)
c.log(f'[*] {fname} already exists, rename to {new_fname}')
fname = new_fname
dst = osp.join(dstdir, fname)
# if not osp.exists(dstdir):
# os.makedirs(dstdir)
cv2.imwrite(dst, res)
def example():
"""
Comparison of my own neighbor classifier with sklearn.neighbors.RadiusNeighborsClassifier
"""
# measure classification performance of both classifiers for the full dataset
aprint('\nTest performance of classifiers for full dataset', fmt='bi')
odir = mkdir(os.path.join(_par_.odir, 'full_dataset'))
test_performance(odir=odir)
aprint('\nMy NeighborsClassifier should have slightly better performance'
'\nthan sklearn.neighbors.RadiusNeighborsClassifier.')
# classify the feature space using the full dataset and create figures
classify_feature_space(odir=odir)
# measure classification performance of selected data containing
# 50 samples of versicolor and the first 20 of virginica (asymmetric label frequency)
aprint('\nTest performance of classifiers for asymmetric data', fmt='bi')
aprint(
'(using no setosa, all 50 versicolor, and only 20 versicolor samples)')
odir = mkdir(os.path.join(_par_.odir, 'asymmetric'))
test_performance(_par_.xx[50:120], _par_.yy[50:120], odir=odir)
aprint(
'\nMy NeighborsClassifier should have significantly better performance'
'\nthan sklearn.neighbors.RadiusNeighborsClassifier.')
# classify the feature space using asymmetric data and create figures
classify_feature_space(_par_.xx[50:120], _par_.yy[50:120], odir=odir)
# compare runtime of classifiers
aprint('\nCompare execution time of classifiers', fmt='bi')
compare_runtime()
def save_new_parses(parses_j, log_probs_j, save_dir, K_per_img, test_id, reverse=False):
"""
i : train image index
k : parse index
"""
appendix_i = 'test' if reverse else 'train'
appendix_j = 'train' if reverse else 'test'
curr = 0
for train_id, K in K_per_img.items():
# get savedir paths
save_dir_i = os.path.join(save_dir, appendix_i+'_%0.2i' % train_id)
mkdir(save_dir_i)
save_dir_ij = os.path.join(save_dir_i, appendix_j+'_%0.2i' % test_id)
mkdir(save_dir_ij)
# get data subset
parses_ij = parses_j[curr : curr+K]
log_probs_ij = log_probs_j[curr : curr+K]
curr += K
# save log-probs
lp_file = os.path.join(save_dir_ij, 'log_probs.pt')
torch.save(log_probs_ij, lp_file)
# save parses
for k in range(K):
parse = parses_ij[k]
parse_file = os.path.join(save_dir_ij, 'parse_%i.pt' % k)
torch.save(parse.state_dict(), parse_file)
def makepkg(self, path):
"""Creates an Arch Linux package archive.
A package archive is generated in the location 'path', based on the data
from the object.
"""
self.path = os.path.join(path, self.filename())
curdir = os.getcwd()
tmpdir = tempfile.mkdtemp()
os.chdir(tmpdir)
# Generate package file system
for f in self.files:
util.mkfile(f, f)
self.size += os.lstat(self.parse_filename(f))[stat.ST_SIZE]
# .PKGINFO
data = ["pkgname = %s" % self.name]
data.append("pkgver = %s" % self.version)
data.append("pkgdesc = %s" % self.desc)
data.append("url = %s" % self.url)
data.append("builddate = %s" % self.builddate)
data.append("packager = %s" % self.packager)
data.append("size = %s" % self.size)
if self.arch:
data.append("arch = %s" % self.arch)
for i in self.license:
data.append("license = %s" % i)
for i in self.replaces:
data.append("replaces = %s" % i)
for i in self.groups:
data.append("group = %s" % i)
for i in self.depends:
data.append("depend = %s" % i)
for i in self.optdepends:
data.append("optdepend = %s" % i)
for i in self.conflicts:
data.append("conflict = %s" % i)
for i in self.provides:
data.append("provides = %s" % i)
for i in self.backup:
data.append("backup = %s" % i)
util.mkfile(".PKGINFO", "\n".join(data))
# .INSTALL
if any(self.install.values()):
util.mkfile(".INSTALL", self.installfile())
# safely create the dir
util.mkdir(os.path.dirname(self.path))
# Generate package archive
tar = tarfile.open(self.path, "w:gz")
for i in os.listdir("."):
tar.add(i)
tar.close()
os.chdir(curdir)
shutil.rmtree(tmpdir)
def scrape_sources(since, output_dir):
base_url = 'http://resource.ufocatch.com/atom/edinetx/'
namespace = '{http://www.w3.org/2005/Atom}'
page = 1
total_count = 0
util.mkdir(output_dir)
while True:
response_string = request(base_url, page)
ET_tree = ET.fromstring(response_string)
ET.register_namespace('', namespace[1:-1])
sources, done = get_sources(ET_tree, namespace, since)
total_count += len(sources)
if len(sources) > 0:
out_file_path = f'{output_dir}/source_{page}.json'
save(out_file_path, sources)
if done:
print(f'Done!')
break
print(f'processed {page}th page [total: {total_count} sources]')
page += 1
sleep(0.5)
def get_santiago_address(self):
if 'santiago' in cfg.users['admin'] and 'address' in cfg.users[
'admin']['santiago']:
return cfg.users['admin']['santiago']['address']
else:
admin = cfg.users['admin']
admin['santiago'] = {}
with open("/etc/tor/torrc", 'r') as INF:
rc = INF.read()
self.santiago_dir = os.path.join(cfg.file_root, "data", "santiago",
"tor")
self.tor_dir = os.path.join(self.santiago_dir, "general")
u.mkdir(self.santiago_dir)
os.system('chmod a+w %s' % self.santiago_dir)
hidden_service_config = "HiddenServiceDir %s\nHiddenServicePort 80 127.0.0.1:%d" % (
self.tor_dir, santiago_port)
if hidden_service_config in rc:
## get info from dir (but how? we need perms)
## just fake it for now
admin['santiago']['address'] = "b5wycujkfh2jxfdo.onion"
cfg.users['admin'] = admin
return cfg.users['admin']['santiago']['address']
print "Need to add these two lines to /etc/torrc:\n%s" % hidden_service_config
return ""
def getTeamSeasonHtml(self):
html = INVALID_STRING
if cmp(self._htmlWebSite, INVALID_STRING) == 0:
print "htmlWebSite has not been initialized yet"
else:
#If we have already synced the data we need to fetch the html from local instead of reloading the website again
pathNeed2Test = CURRENT_PATH + self._Season0 + self._Season1 + '/' + self._Team_id
print "Constructing path as ", pathNeed2Test
self._dataStoredPath = pathNeed2Test
util.mkdir(pathNeed2Test)
htmlFile = pathNeed2Test + '/' + self._GameType + '.html'
self._seasonDataHtmlFile = htmlFile
print "Check if html file exist or not ", htmlFile
if os.path.isfile(htmlFile):
print "html file exists, open the file, read it and return the string"
html = util.openFile(htmlFile)
#print html
else:
print "html file does not exist, now loading the webpage from network"
html = util.getHtmlFromUrl(self._htmlWebSite)
if cmp(html, INVALID_STRING) != 0:
util.saveFile(htmlFile, html)
return html
return html
def script_saveHumanOnlyNeg():
out_file='/disk2/aprilExperiments/positives_160_human.txt'
out_dir='/disk2/aprilExperiments/negatives_npy_onlyHuman';
util.mkdir(out_dir);
im_pre='COCO_train2014_'
lines=util.readLinesFromFile(out_file);
img_files=[line[:line.index(' ')] for line in lines];
img_names=util.getFileNames(img_files,ext=False);
img_name=img_names[0];
print img_name
img_name_split=img_name.split('_');
idx_all=[int(img_name.split('_')[-1]) for img_name in img_names];
print len(img_names),len(idx_all),idx_all[0];
cat_id=1;
path_to_anno='/disk2/ms_coco/annotations';
anno_file='instances_train2014.json';
anno=json.load(open(os.path.join(path_to_anno,anno_file),'rb'))['annotations'];
script_saveBboxFiles(anno,out_dir,im_pre,idx_all,cat_id)
def script_vizForHMDB():
out_dir='/disk2/mayExperiments/debug_finetuning/hmdb';
clusters_file='/home/maheenrashid/Downloads/debugging_jacob/optical_flow_prediction_test/examples/opticalflow/clusters.mat';
vid_list=os.path.join(out_dir,'video_list.txt');
out_dir_viz=os.path.join(out_dir,'im');
util.mkdir(out_dir_viz);
out_file_html=out_dir_viz+'.html';
path_to_hmdb='/disk2/marchExperiments/hmdb_try_2/hmdb'
dirs=util.readLinesFromFile(vid_list);
dirs=[os.path.join(path_to_hmdb,dir_curr) for dir_curr in dirs[2:]];
random.shuffle(dirs);
num_to_evaluate=100;
out_file_tif=os.path.join(out_dir,'tif_list.txt');
# recordContainingFiles(dirs,num_to_evaluate,out_file_flo,post_dir='images',ext='.flo');
tif_files=util.readLinesFromFile(out_file_tif);
tif_files=tif_files[:100];
img_files=[file_curr.replace('.tif','.jpg') for file_curr in tif_files];
flo_files=[file_curr.replace('.tif','.flo') for file_curr in tif_files];
clusters=po.readClustersFile(clusters_file);
script_writeFloVizHTML(out_file_html,out_dir_viz,flo_files,img_files,tif_files,clusters,True)
def organize_directory(self):
if (self.scenes):
# Iterate Over Scenes List
for curr, scene in enumerate(self.scenes, 1):
# Create Scene Folder
util.mkdir(
util.form_path([self.image_path,
SCENE.format(curr)]))
# Move Images To Scene Folder
for image in scene:
try:
# Generate Source and Destination Paths
src = util.absolute(image)
dst = util.normalize(
util.form_path([
util.dirname(image),
SCENE.format(curr),
util.basename(image)
]))
# Move Images To Scene Folder
util.move(src, dst)
except FileNotFoundError:
pass
# Update Prompt
print("Organized All Images ")
else:
util.perror("spectra: No scenes found to analyze")
def check(self, name):
data = self.getSource()
fnRef = self.fnRef(name)
fnOrig = self.fnOrig(name)
# save references only?
if self.save:
util.mkdir(self.config.REF_DIR)
util.write(fnRef, data)
else: # compare
assert os.path.exists(fnRef), "Cannot compare without reference file: %s" % fnRef
util.mkdir(self.outputDir)
# first save original file
util.write(fnOrig, data)
ref = self.cleanup(util.read(fnRef))
data = self.cleanup(data)
# htmldiff
result = htmldiff.htmldiff(ref, data, True)
util.write(self.fnHtmlDiff(name), result)
self.scenario.results[name] = self._eval_diff(result)
# difflib
linesRef = ref.splitlines()
linesData = data.splitlines()
result = difflib.HtmlDiff(wrapcolumn=80).make_file(linesRef, linesData, fnRef, fnOrig, context=True)
util.write(self.fnDiffLib(name), result)
def main():
args = get_arguments()
SEED = args.seed
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(SEED)
util.mkdir('runs')
test_acc_file = 'runs/test' + str(args.cosine) + str(args.bna) + str(
args.bnd) + '.txt'
train_acc_file = 'runs/train' + str(args.cosine) + str(args.bna) + str(
args.bnd) + '.txt'
open(test_acc_file, 'w')
open(train_acc_file, 'w')
print('Building model, loading data...\n')
if args.cuda:
torch.cuda.manual_seed(SEED)
model, optimizer, training_generator, test_generator = initialize(args)
best_pred_loss = 1000.0
print('\nCheckpoint folder:', args.save, '\n\nCosine:', args.cosine,
'\t\tBna:', args.bna, '\t\tBnd:', args.bnd, '\t\tContrastive:',
args.cont, '\n\nStart training...\n')
for epoch in range(1, args.nEpochs + 1):
train_metrics = train(args, model, training_generator, optimizer,
epoch)
test_metrics, confusion_matrix, ucsd_correct_total, sars_correct_total, ucsd_test_total, sars_test_total \
= validation(args, model, test_generator, epoch, mode='test')
best_pred_loss = util.save_model(model, optimizer, args, test_metrics,
epoch, best_pred_loss,
confusion_matrix)
print('COVID-CT Accuracy: {0:.2f}%\tSARS-Cov-2 Accuracy: {1:.2f}%\n'.
format(100. * ucsd_correct_total / ucsd_test_total,
100. * sars_correct_total / sars_test_total))
with open(test_acc_file, 'a+') as f:
f.write(
str(test_metrics.data['correct'] /
test_metrics.data['total']) + ' ' +
str(optimizer.param_groups[0]['lr']) + ' ' +
str(test_metrics.data['loss'] /
(test_metrics.data['total'] // args.batch_size + 1)) +
'\n')
with open(train_acc_file, 'a+') as f:
f.write(
str(train_metrics.data['correct'] /
train_metrics.data['total']) + ' ' +
str(optimizer.param_groups[0]['lr']) + ' ' +
str(train_metrics.data['loss'] /
(train_metrics.data['total'] // args.batch_size + 1)) +
'\n')
adjust_learning_rate(optimizer, epoch, args)
def generate(self):
pkg_entries = [(pkg, self.db_write(pkg)) for pkg in self.pkgs]
if self.dbdir:
for pkg, entry in pkg_entries:
path = os.path.join(self.dbdir, pkg.fullname())
util.mkdir(path)
for name, data in entry.items():
util.mkfile(path, name, data)
if self.dbfile:
tar = tarfile.open(self.dbfile, "w:gz")
for pkg, entry in pkg_entries:
# TODO: the addition of the directory is currently a
# requirement for successful reading of a DB by libalpm
info = tarfile.TarInfo(pkg.fullname())
info.type = tarfile.DIRTYPE
tar.addfile(info)
for name, data in entry.items():
filename = os.path.join(pkg.fullname(), name)
info = tarfile.TarInfo(filename)
info.size = len(data)
tar.addfile(info, StringIO(data))
tar.close()
# TODO: this is a bit unnecessary considering only one test uses it
serverpath = os.path.join(self.root, util.SYNCREPO, self.treename)
util.mkdir(serverpath)
shutil.copy(self.dbfile, serverpath)
def makepkg(self, path):
"""Creates an Arch Linux package archive.
A package archive is generated in the location 'path', based on the data
from the object.
"""
self.path = os.path.join(path, self.filename())
curdir = os.getcwd()
tmpdir = tempfile.mkdtemp()
os.chdir(tmpdir)
# Generate package file system
for f in self.files:
util.mkfile(f, f)
self.size += os.lstat(self.parse_filename(f))[stat.ST_SIZE]
# .PKGINFO
data = ["pkgname = %s" % self.name]
data.append("pkgver = %s" % self.version)
data.append("pkgdesc = %s" % self.desc)
data.append("url = %s" % self.url)
data.append("builddate = %s" % self.builddate)
data.append("packager = %s" % self.packager)
data.append("size = %s" % self.size)
if self.arch:
data.append("arch = %s" % self.arch)
for i in self.license:
data.append("license = %s" % i)
for i in self.replaces:
data.append("replaces = %s" % i)
for i in self.groups:
data.append("group = %s" % i)
for i in self.depends:
data.append("depend = %s" % i)
for i in self.optdepends:
data.append("optdepend = %s" % i)
for i in self.conflicts:
data.append("conflict = %s" % i)
for i in self.provides:
data.append("provides = %s" % i)
for i in self.backup:
data.append("backup = %s" % i)
util.mkfile(".PKGINFO", "\n".join(data))
# .INSTALL
if any(self.install.values()):
util.mkfile(".INSTALL", self.installfile())
# safely create the dir
util.mkdir(os.path.dirname(self.path))
# Generate package archive
tar = tarfile.open(self.path, "w:gz")
for i in os.listdir("."):
tar.add(i)
tar.close()
os.chdir(curdir)
shutil.rmtree(tmpdir)
def generate(self):
pkg_entries = [(pkg, self.db_write(pkg)) for pkg in self.pkgs]
if self.dbdir:
for pkg, entry in pkg_entries:
path = os.path.join(self.dbdir, pkg.fullname())
util.mkdir(path)
for name, data in entry.iteritems():
filename = os.path.join(path, name)
util.mkfile(filename, data)
if self.dbfile:
tar = tarfile.open(self.dbfile, "w:gz")
for pkg, entry in pkg_entries:
# TODO: the addition of the directory is currently a
# requirement for successful reading of a DB by libalpm
info = tarfile.TarInfo(pkg.fullname())
info.type = tarfile.DIRTYPE
tar.addfile(info)
for name, data in entry.iteritems():
filename = os.path.join(pkg.fullname(), name)
info = tarfile.TarInfo(filename)
info.size = len(data)
tar.addfile(info, StringIO(data))
tar.close()
# TODO: this is a bit unnecessary considering only one test uses it
serverpath = os.path.join(self.root, util.SYNCREPO, self.treename)
util.mkdir(serverpath)
shutil.copy(self.dbfile, serverpath)
def pack(self):
emb_root = self.target_root
if self.seed:
emb_root = emb_root.pjoin(self.target_root)
basedir = util.Path( os.path.dirname(self.tarpath) )
util.mkdir(basedir)
archive = tarfile.open(self.tarpath, 'w:bz2')
archive.add(emb_root,
arcname = '/',
recursive = True
)
archive.close()
curdir = os.path.realpath(os.curdir)
os.chdir(emb_root)
util.cmd('find ./ | cpio -H newc -o | gzip -c -9 > %s' % (self.cpiopath))
os.chdir(curdir)
if self.kernel:
r = util.Path('/')
if self.seed:
r = self.target_root
r = r.pjoin('/tmp/inhibitor/kernelbuild')
kernel_link = r.pjoin('/boot/kernel')
kernel_path = os.path.realpath( kernel_link )
if os.path.lexists(self.kernlinkpath):
os.unlink(self.kernlinkpath)
shutil.copy2(kernel_path, basedir.pjoin( os.path.basename(kernel_path) ))
os.symlink(os.path.basename(kernel_path), self.kernlinkpath)
def __init__(self,
data_path,
model_file,
s3_bucket,
epochs=50,
max_sample_records=500,
start_epoch=0,
restored_model=False,
restored_model_dir=None,
tf_timeline=False):
self.data_path = data_path
self.s3_bucket = format_s3_bucket(s3_bucket)
self.s3_data_dir = format_s3_data_dir(self.s3_bucket)
self.model_file = model_file
self.n_epochs = int(epochs)
self.max_sample_records = max_sample_records
self.tf_timeline = tf_timeline
# Always sync before training in case I ever train multiple models in parallel
sync_from_aws(s3_path=self.s3_data_dir, local_path=self.data_path)
if restored_model:
self.model_dir = restored_model_dir
else:
self.tfboard_basedir = os.path.join(self.data_path,
'tf_visual_data', 'runs')
self.model_dir = mkdir_tfboard_run_dir(self.tfboard_basedir)
self.results_file = os.path.join(self.model_dir, 'results.txt')
self.model_checkpoint_dir = os.path.join(self.model_dir, 'checkpoints')
self.saver = tf.train.Saver()
self.start_epoch = start_epoch
self.restored_model = restored_model
mkdir(self.model_checkpoint_dir)
def write_params(self):
"""
Write out the estimated parameters as csv.
Colums are: ['id', 'mode', 'p(k)', 'parameter', 'value', 'se']
"""
if self.id is None:
self.exception("can't write model, as filename not specified.")
if '.' not in self.id:
self.exception("model id is not a filename.")
# make sure the output folder exists
folder = '%s/fits/%s' % (util.data_path, self.model_type)
util.mkdir(folder)
with open('%s/%s' % (folder, self.id), 'w') as f:
writer = csv.writer(f)
# write each degree as a row
for i in range(len(self.u)):
row = [self.id,
self.model_type,
1,
i,
self.u[i],
self.se[i]]
writer.writerow(row)
if self.vvv:
self.message("wrote model to file")
def __init__(self, data_path, epochs=50, max_sample_records=500):
self.data_path = data_path
self.epochs = int(epochs)
self.max_sample_records = max_sample_records
tfboard_basedir = mkdir(data_path + '/tf_visual_data/runs/')
tfboard_run_dir = mkdir_tfboard_run_dir(tfboard_basedir)
model_checkpoint_path = mkdir(tfboard_run_dir + '/trained_model')
def auto_conf(self):
util.mkdir(self.out_root)
self.out_dir = os.path.join(self.out_root, self.name)
util.mkdir(self.out_dir)
self.model_path = os.path.join(self.out_dir, 'model')
self.log_path = os.path.join(self.out_dir, 'log')
self.test_out_path = os.path.join(self.out_dir, 'test_out')
def __write_permapage(self, posts):
"""Write blog posts to their permalink locations"""
perma_template = self.template_lookup.get_template("permapage.mako")
perma_template.output_encoding = "utf-8"
for post in posts:
if post.permalink:
path_parts = [self.output_dir]
path_parts.extend(urlparse.urlparse(
post.permalink)[2].lstrip("/").split("/"))
path = os.path.join(*path_parts)
logger.info("Writing permapage for post: "+path)
else:
#Permalinks MUST be specified. No permalink, no page.
logger.info("Post has no permalink: "+post.title)
continue
try:
util.mkdir(path)
except OSError:
pass
html = self.__template_render(
perma_template,
{ "post": post,
"posts": posts })
f = open(os.path.join(path,"index.html"), "w")
f.write(html)
f.close()
def __write_files(self):
"""Write all files for the blog to _site
Convert all templates to straight HTML
Copy other non-template files directly"""
#find mako templates in template_dir
for root, dirs, files in os.walk("."):
if root.startswith("./"):
root = root[2:]
for d in list(dirs):
#Exclude some dirs
d_path = util.path_join(root, d)
if util.should_ignore_path(d_path):
logger.debug("Ignoring directory: " + d_path)
dirs.remove(d)
try:
util.mkdir(util.path_join(self.output_dir, root))
except OSError: #pragma: no cover
pass
for t_fn in files:
t_fn_path = util.path_join(root, t_fn)
if util.should_ignore_path(t_fn_path):
#Ignore this file.
logger.debug("Ignoring file: " + t_fn_path)
continue
elif t_fn.endswith(".mako"):
logger.info("Processing mako file: " + t_fn_path)
#Process this template file
t_name = t_fn[:-5]
t_file = open(t_fn_path)
template = Template(t_file.read().decode("utf-8"),
output_encoding="utf-8",
lookup=self.template_lookup)
#Remember the original path for later when setting context
template.bf_meta = {"path": t_fn_path}
t_file.close()
path = util.path_join(self.output_dir, root, t_name)
html_file = open(path, "w")
html = self.template_render(template)
#Write to disk
html_file.write(html)
else:
#Copy this non-template file
f_path = util.path_join(root, t_fn)
logger.debug("Copying file: " + f_path)
out_path = util.path_join(self.output_dir, f_path)
if self.config.site.overwrite_warning and os.path.exists(
out_path):
logger.warn(
"Location is used more than once: {0}".format(
f_path))
if self.bf.config.site.use_hard_links:
# Try hardlinking first, and if that fails copy
try:
os.link(f_path, out_path)
except StandardError:
shutil.copyfile(f_path, out_path)
else:
shutil.copyfile(f_path, out_path)
def run(self):
environ = self.distribution.environment
if self.devel_support:
for tpl in self.devel_support:
if self.distribution.verbose:
print 'adding sysdevel support to ' + tpl[0]
target = os.path.abspath(os.path.join(self.build_lib,
*tpl[0].split('.')))
util.mkdir(target)
source_dir = os.path.abspath(os.path.join(
os.path.dirname(__file__), 'support'))
for mod in tpl[1]:
src_file = os.path.join(source_dir, mod + '.py.in')
if not os.path.exists(src_file):
src_file = src_file[:-3]
dst_file = os.path.join(target, mod + '.py')
util.configure_file(environ, src_file, dst_file)
if self.antlr_modules:
here = os.getcwd()
for grammar in self.antlr_modules:
if self.distribution.verbose:
print 'building antlr grammar "' + \
grammar.name + '" sources'
##TODO build in build_src, add to build_lib modules
target = os.path.abspath(os.path.join(self.build_lib,
grammar.directory))
util.mkdir(target)
source_dir = os.path.abspath(grammar.directory)
os.chdir(target)
reprocess = True
ref = os.path.join(target, grammar.name + '2Py.py')
if os.path.exists(ref):
reprocess = False
for src in grammar.sources:
src_path = os.path.join(source_dir, src)
if os.path.getmtime(ref) < os.path.getmtime(src_path):
reprocess = True
if reprocess:
for src in grammar.sources:
## ANTLR cannot parse from a separate directory
shutil.copy(os.path.join(source_dir, src), '.')
cmd_line = list(environ['ANTLR'])
cmd_line.append(src)
status = subprocess.call(cmd_line)
if status != 0:
raise Exception("Command '" + str(cmd_line) +
"' returned non-zero exit status "
+ str(status))
## Cleanup so that it's only Python modules
for f in glob.glob('*.g'):
os.unlink(f)
for f in glob.glob('*.tokens'):
os.unlink(f)
os.chdir(here)
_build_src.run(self)
def build(smbuildfile,
compiler,
plugins,
packages,
flags='',
output_dir='builds',
nosource=False):
"""Performs the entire build process."""
# setup directory structure, execute user-configurations
plugin_build_dir = os.path.join(output_dir, 'plugins')
util.mkdir(output_dir)
util.mkdir(plugin_build_dir)
# scan deps for what we need to do
packages_to_build = set()
for name, package in packages.items():
if smbuildfile == package.smbuildfile:
packages_to_build.add(name)
plugins_to_compile = set()
for name in packages_to_build:
for_this_package = base.find_plugin_deps(packages[name], packages)
for plugin_name in for_this_package:
plugins_to_compile.add(plugin_name)
if plugin_name not in plugins:
err = 'Package {} uses plugin {}, but it does not exist'.format(
name, plugin_name)
raise ValueError(err)
# also make sure plugin dependencies are met by the package
for dep in plugins[plugin_name].deps:
if dep not in for_this_package:
msg = 'Plugin {} depends on {}, but is not part of package {}'
msg = msg.format(plugin_name, dep, name)
util.warning(msg)
# also compile any plugins from this smbuildfile
for plugin_name in plugins:
if plugins[plugin_name].smbuildfile == smbuildfile:
plugins_to_compile.add(plugin_name)
# compile plugins
compiled_count = 0
for name in plugins_to_compile:
plugin = plugins[name]
if plugin.compile(compiler, plugin_build_dir, flags):
compiled_count += 1
# build packages
for name in packages_to_build:
package = packages[name]
print('Building package {}'.format(name))
package.create(output_dir, packages, plugins, nosource)
if len(plugins) == 0:
util.warning('No plugins were found in {}.'.format(smbuildfile))
elif compiled_count == 0:
print('All plugins up to date.')
def __write_files(self):
"""Write all files for the blog to _site
Convert all templates to straight HTML
Copy other non-template files directly"""
#find mako templates in template_dir
for root, dirs, files in os.walk("."):
if root.startswith("./"):
root = root[2:]
for d in list(dirs):
#Exclude some dirs
d_path = util.path_join(root,d)
if util.should_ignore_path(d_path):
logger.debug("Ignoring directory: " + d_path)
dirs.remove(d)
try:
util.mkdir(util.path_join(self.output_dir, root))
except OSError: #pragma: no cover
pass
for t_fn in files:
t_fn_path = util.path_join(root, t_fn)
if util.should_ignore_path(t_fn_path):
#Ignore this file.
logger.debug("Ignoring file: " + t_fn_path)
continue
elif t_fn.endswith(".mako"):
logger.info("Processing mako file: " + t_fn_path)
#Process this template file
t_name = t_fn[:-5]
t_file = open(t_fn_path)
template = Template(t_file.read().decode("utf-8"),
output_encoding="utf-8",
lookup=self.template_lookup)
t_file.close()
path = util.path_join(self.output_dir, root, t_name)
html_file = open(path, "w")
# Prepare the "path" variable for the template context.
page_path = util.path_join(root, t_name)
if page_path.startswith('./'):
page_path = page_path[2:]
page_path = '/' + page_path
context = dict(path=page_path, logger=template_logger)
#render the page
html = self.template_render(template, context)
#Write to disk
html_file.write(html)
else:
#Copy this non-template file
f_path = util.path_join(root, t_fn)
logger.debug("Copying file: " + f_path)
out_path = util.path_join(self.output_dir, f_path)
if self.bf.config.site.use_hard_links:
# Try hardlinking first, and if that fails copy
try:
os.link(f_path, out_path)
except StandardError:
shutil.copyfile(f_path, out_path)
else:
shutil.copyfile(f_path, out_path)
def make_profile_link(self):
# XXX: We also need to make the root profile link, Gentoo Bug 324179.
for d in (self.target_root, self.target_root.pjoin(self.portage_cr)):
targ = d.pjoin("/etc/make.profile")
util.mkdir(os.path.dirname(targ))
if os.path.lexists(targ):
os.unlink(targ)
os.symlink(self.env["PORTDIR"] + "/profiles/%s" % self.profile, targ)
def push(cmd_args):
opts, args = getopt.getopt(
cmd_args, "l:r:n:m",
["local-file=", "remote-path=", "remote-name=", "no-bak", "mod="])
log.info("opts %s args:%s" % (opts, args))
local_file, remote_path, remote_name = "", "", ""
bak_file, chmod = True, "777"
for op, value in opts:
if op == "-l" or op == "--local-file":
local_file = value
elif op == "-r" or op == "--remote-path":
remote_path = value
elif op == "-n" or op == "--remote-name":
remote_name = value
elif op == "-n" or op == "--mod":
chmod = value
elif op == "--no-bak":
bak_file = False
else:
log.error("unkown opt:%s value:%s" % (op, value))
return False
if len(opts) == 0:
local_file = args[0] if len(args) >= 1 else ""
remote_path = args[1] if len(args) >= 2 else ""
remote_name = args[2] if len(args) >= 3 else ""
if remote_path == "":
remote_path = "/data/local/tmp"
if os.path.isdir(local_file):
# push 目录
remote_file = remote_path + "/"
util.mkdir(remote_file)
elif os.path.isfile(os.path.join(os.getcwd(), local_file)):
# push 文件
# local_path = os.path.dirname(local_file)
local_fname = os.path.basename(local_file)
if remote_name == "":
remote_name = local_fname
remote_file = remote_path + "/" + remote_name
else:
log.error("local file:%s %s not exist" %
(local_file, os.path.join(os.getcwd(), local_file)))
return False
if bak_file:
shell_cmd = util.getshell('mv "%s" "%s.bak"' %
(remote_file, remote_file))
util.execute_cmd(shell_cmd)
log.info("local:%s remote:%s" % (local_file, remote_file))
shell_cmd = util.getcmd('push "%s" "%s"' % (local_file, remote_file))
ret, res_str = util.execute_cmd_with_stdout(shell_cmd)
if not ret:
return False
if chmod != "":
shell_cmd = util.getshell('chmod %s "%s"' % (chmod, remote_file))
if not util.execute_cmd(shell_cmd): return False
# shell_cmd = util.getshell('".%s"' % remote_file)
# return util.execute_cmd(shell_cmd)
return True
def main():
dir_clusters='/disk2/temp/youtube_clusters_check_nothresh';
out_dir=os.path.join(dir_clusters,'viz');
util.mkdir(out_dir);
script_seeMultipleClusters(dir_clusters,out_dir)
return
script_seeMultipleClusters();
return
dir_clusters='/disk3/maheen_data/debug_networks/clusters_youtube_multiple';
clusters_all=util.getFilesInFolder(dir_clusters,'.npy');
clusters_all=[file_curr for file_curr in clusters_all if 'harder' in file_curr];
clusters_all.append(os.path.join(dir_clusters,'clusters_original.npy'));
min_mags=[];
for file_curr in clusters_all:
clusters=np.load(file_curr);
mags=np.power(np.sum(np.power(clusters,2),axis=1),0.5);
min_mag=np.min(mags);
min_mags.append(min_mag);
print min_mags,np.max(min_mags);
thresh=1;
counts=[];
for file_curr in clusters_all:
clusters=np.load(file_curr);
print file_curr
mags=np.power(np.sum(np.power(clusters,2),axis=1),0.5);
count=np.sum(mags<=thresh);
print count
counts.append(count);
print np.mean(counts);
# return
dir_curr='/disk3/maheen_data/debug_networks/figuringClustering';
mag_file=os.path.join(dir_curr,'mags_all.npy');
mags=np.load(mag_file);
print len(mags),np.sum(mags<=thresh);
mags=mags[mags>thresh];
print len(mags);
out_file=os.path.join(dir_curr,'mag_hist_noZero.png');
visualize.hist(mags,out_file,bins=40,normed=True,xlabel='Value',ylabel='Frequency',title='',cumulative=False);
print out_file.replace('/disk3','vision3.cs.ucdavis.edu:1001');
print np.min(mags),np.max(mags),np.mean(mags),np.std(mags);
def main():
args = process_command_line_arguments()
util.mkdir(PATH['TIMBRADAS'])
xml = util.read_file(args.factura)
# Enviamos a timbrar con el PAC
timbra_xml(xml)
return
def __write_blog_categories(self, posts):
"""Write all the blog posts in categories"""
root = os.path.join(self.blog_dir,config.blog_category_dir)
chron_template = self.template_lookup.get_template("chronological.mako")
chron_template.output_encoding = "utf-8"
#Find all the categories:
categories = set()
for post in posts:
categories.update(post.categories)
for category in categories:
category_posts = [post for post in posts \
if category in post.categories]
#Write category RSS feed
self.__write_feed(category_posts,os.path.join(
config.blog_path,config.blog_category_dir,
category.url_name,"feed"),"rss.mako")
self.__write_feed(category_posts,os.path.join(
config.blog_path,config.blog_category_dir,
category.url_name,"feed","atom"),"rss.mako")
page_num = 1
while True:
path = os.path.join(root,category.url_name,
str(page_num),"index.html")
try:
util.mkdir(os.path.split(path)[0])
except OSError:
pass
f = open(path, "w")
page_posts = category_posts[:config.blog_posts_per_page]
category_posts = category_posts[config.blog_posts_per_page:]
#Forward and back links
if page_num > 1:
prev_link = util.blog_path_helper(
(config.blog_category_dir, category.url_name, str(page_num -1)))
else:
prev_link = None
if len(category_posts) > 0:
next_link = util.blog_path_helper(
(config.blog_category_dir, category.url_name, str(page_num + 1)))
else:
next_link = None
html = self.__template_render(
chron_template,
{ "posts": page_posts,
"prev_link": prev_link,
"next_link": next_link })
f.write(html)
f.close()
#Copy category/1 to category/index.html
if page_num == 1:
shutil.copyfile(path,os.path.join(
root,category.url_name,
"index.html"))
#Prepare next iteration
page_num += 1
if len(category_posts) == 0:
break
def make_profile_link(self):
if self.seed:
super(EmbeddedStage, self).make_profile_link()
else:
targ = self.portage_cr.pjoin('/etc/make.profile')
util.mkdir( os.path.dirname(targ) )
if os.path.lexists(targ):
os.unlink(targ)
os.symlink(self.env['PORTDIR'] + '/profiles/%s' % self.profile, targ)
def processDay(nodeName, day):
log(f'Processing day/node: {day}/{nodeName}')
dayPath = f"../data/s3/{nodeName}/{day}"
outputDir = os.path.join(DIR_PROCESSED, nodeName)
mkdir(outputDir)
createArchiveVideo(dayPath, outputDir, day)
videoPath = createVideo(outputDir, dayPath, nodeName, day)
createPreviewGif(videoPath, os.path.join(outputDir, f'{day}.gif'))
def __write_files(self):
"""Write all files for the blog to _site
Convert all templates to straight HTML
Copy other non-template files directly"""
#find mako templates in template_dir
for root, dirs, files in os.walk(".", followlinks=True):
if root.startswith("./"):
root = root[2:]
for d in list(dirs):
#Exclude some dirs
d_path = util.path_join(root,d)
if util.should_ignore_path(d_path):
logger.debug("Ignoring directory: " + d_path)
dirs.remove(d)
try:
util.mkdir(util.path_join(self.output_dir, root))
except OSError: #pragma: no cover
pass
for t_fn in files:
t_fn_path = util.path_join(root, t_fn)
if util.should_ignore_path(t_fn_path):
#Ignore this file.
logger.debug("Ignoring file: " + t_fn_path)
continue
elif t_fn.endswith(".mako"):
logger.info("Processing mako file: " + t_fn_path)
#Process this template file
t_name = t_fn[:-5]
t_file = open(t_fn_path)
template = Template(t_file.read().decode("utf-8"),
output_encoding="utf-8",
lookup=self.template_lookup)
#Remember the original path for later when setting context
template.bf_meta = {"path":t_fn_path}
t_file.close()
path = util.path_join(self.output_dir, root, t_name)
html_file = open(path, "w")
html = self.template_render(template)
#Write to disk
html_file.write(html)
else:
#Copy this non-template file
f_path = util.path_join(root, t_fn)
logger.debug("Copying file: " + f_path)
out_path = util.path_join(self.output_dir, f_path)
if self.config.site.overwrite_warning and os.path.exists(out_path):
logger.warn("Location is used more than once: {0}".format(f_path))
if self.bf.config.site.use_hard_links:
# Try hardlinking first, and if that fails copy
try:
os.link(f_path, out_path)
except StandardError:
shutil.copyfile(f_path, out_path)
else:
shutil.copyfile(f_path, out_path)
def run (self):
old_data.run(self)
util.mkdir(self.data_install_dir)
if (not hasattr(self.distribution, 'using_py2exe') or \
not self.distribution.using_py2exe) and self.data_dirs:
for tpl in self.data_dirs:
target = os.path.join(self.data_install_dir, tpl[0])
for d in tpl[1]:
util.copy_tree(d, target, excludes=['.svn*', 'CVS*',
'Makefile*'])
def script_writeTrainFile(): dir_val='/disk2/ms_coco/train2014'; out_dir='/disk2/mayExperiments/train_data'; util.mkdir(out_dir); imgs=util.getEndingFiles(dir_val,'.jpg'); imgs=[os.path.join(dir_val,file_curr) for file_curr in imgs]; imgs.sort(); out_file=os.path.join(out_dir,'train.txt'); util.writeFile(out_file,imgs)
def __write_pygments_css(self):
css_dir = os.path.join(self.output_dir, self.config.site_css_dir.lstrip("/"))
try:
util.mkdir(css_dir)
except OSError:
pass
if config.syntax_highlight_enabled:
f = open(os.path.join(css_dir,"pygments.css"),"w")
f.write(config.html_formatter.get_style_defs(".highlight"))
f.close()
def script_writeCommandsForExperiment():
# out_dir='/disk3/maheen_data/debug_networks/noFixCopyByLayer';
# model_file='/home/maheenrashid/Downloads/debugging_jacob/optical_flow_prediction/examples/opticalflow/final.caffemodel';
out_dir='/disk3/maheen_data/debug_networks/noFixCopyByLayerAlexNet';
model_file='/home/maheenrashid/Downloads/debugging_jacob/optical_flow_prediction/models/bvlc_alexnet/bvlc_alexnet.caffemodel';
util.mkdir(out_dir);
train_txt_orig_path='/disk3/maheen_data/debug_networks/noFix/train.txt';
template_deploy_file='deploy_debug_noFix.prototxt';
template_solver_file='solver_debug.prototxt';
train_file=os.path.join(out_dir,'train.txt');
shutil.copyfile(train_txt_orig_path,train_file);
base_lr=0.0001;
snapshot=100;
layers=['conv1','conv2','conv3','conv4','conv5','fc6','fc7'];
command_pre=os.path.join(out_dir,'debug_');
commands=[];
for idx in range(len(layers)):
# if idx==0:
# fix_layers=layers[0];
# layer_str=str(fix_layers);
# model_file_curr=None;
# else:
fix_layers=layers[:idx+1];
layer_str='_'.join(fix_layers);
model_file_curr=model_file
# print fix_layers
if idx<len(layers)/2:
gpu=0;
else:
gpu=1;
snapshot_prefix=os.path.join(out_dir,'opt_noFix_'+layer_str+'_');
out_deploy_file=os.path.join(out_dir,'deploy_'+layer_str+'.prototxt');
out_solver_file=os.path.join(out_dir,'solver_'+layer_str+'.prototxt');
log_file=os.path.join(out_dir,'log_'+layer_str+'.log');
replaceSolverFile(out_solver_file,template_solver_file,out_deploy_file,base_lr,snapshot,snapshot_prefix,gpu);
replaceDeployFile(out_deploy_file,template_deploy_file,train_file,fix_layers);
command=printTrainingCommand(out_solver_file,log_file,model_file_curr);
commands.append(command);
command_file_1=command_pre+'0.sh';
util.writeFile(command_file_1,commands[:len(commands)/2]);
command_file_2=command_pre+'1.sh';
util.writeFile(command_file_2,commands[len(commands)/2:]);
def display_learning_curves(score_folders, picture_folder):
""""Draw pictures of learning curves"""
# Allow one folder to be submitted as input instead of list
if not isinstance(score_folders, list):
score_folders = [score_folders]
# Make picture folder if necessary
util.mkdir(picture_folder)
# Determine names of datasets
dataset_names = []
for score_folder in score_folders:
new_names = [name for name in os.listdir(score_folder) if os.path.isdir(os.path.join(score_folder, name))]
dataset_names = sorted(list(set(dataset_names + new_names)))
print('Datasets:')
for dataset_name in dataset_names:
print(' - %s' % dataset_name)
print('')
# Determine algorithm names
algo_names = []
for score_folder in score_folders:
for dataset_name in dataset_names:
if os.path.isdir(os.path.join(score_folder, dataset_name)):
new_names = [name for name in os.listdir(os.path.join(score_folder, dataset_name))
if os.path.isdir(os.path.join(score_folder, dataset_name, name))]
algo_names = sorted(list(set(algo_names + new_names)))
print('Algorithms:')
for algo_name in algo_names:
print(' - %s' % algo_name)
print('')
# For each dataset plot learning curve of every algorithm across score folders
for dataset_name in dataset_names:
print(dataset_name)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title(dataset_name)
ax.set_xlabel('Time (seconds)')
# ax.set_xscale('log')
ax.set_ylabel('Score')
n_algos = 0
for (algo_index, algo_name) in enumerate(algo_names):
print(algo_name)
for score_folder in score_folders:
time_score_file = os.path.join(score_folder, dataset_name, algo_name, 'learning_curve.csv')
if os.path.isfile(time_score_file):
time_scores = np.loadtxt(time_score_file, delimiter=',', skiprows=1)
if time_scores.size > 0:
ax.plot(time_scores[:, 0], time_scores[:, 1],
color=util.colorbrew(algo_index),
linestyle='dashed', marker='o',
label=algo_name)
n_algos += 1
if n_algos > 0:
leg = ax.legend(loc='best')
leg.get_frame().set_alpha(0.5)
fig.savefig(os.path.join(picture_folder, '%s.pdf' % dataset_name))
plt.show()
