def main() -> None:
"""
Entrypoint into the command-line interface.
"""
parser = argparse.ArgumentParser(
description="Lists files in a directory."
)
parser.add_argument(
"path",
type=str,
help="The path to show files in."
)
# Parse and process commands
args = parser.parse_args()
if args.path:
path = args.path
files = list_files(path)
for f in files:
print(
"d" if f.isdir else "f",
f" {f.human_readable_bytes:<12}",
f.path
)
def read_games(tournament_dir):
game_files = list_files(tournament_dir, '.json')
games = []
for game_file in game_files:
with open(game_file, 'r') as fin:
games.append(json.load(fin))
return games
def read_data(data_path, max_games=None):
game_files = list(sorted(list_files(data_path, '.json')))
augmentations = list(Augmentation.iter_augmentations())
if max_games is not None:
game_files = list(game_files)[-max_games:]
print('Using game files from %s to %s' % (game_files[0], game_files[-1]))
x = []
y_policy = []
y_reward = []
for game_path in tqdm(game_files):
with open(game_path, 'r') as fin:
game_data = json.load(fin)
winner, starter, actions, policies = AlphaConnectSerializer.deserialize(game_data)
state = State.empty()
states = [state]
for action in actions:
state = state.take_action(action)
states.append(state)
states, final_state = states[:-1], states[-1]
n_samples = min(len(states), len(augmentations))
game_samples = sample(list(range(len(states))), n_samples)
for augmentation, i in zip(augmentations, game_samples):
augmentend_action_order = sorted(Action.iter_actions(), key=lambda a: a.augment(augmentation).to_int())
x.append(states[i].to_numpy(augmentation))
y_policy.append([policies[i].get(action, 0.0) for action in augmentend_action_order])
y_reward.append(winner_value(final_state.winner, states[i]))
return np.array(x), np.array(y_policy), np.array(y_reward)
def pageRank(self):
sourceDirectory =settings.PAGERANK_RESOURCE_DIRECTORY
destDirectory = PAGERANK_DESTINATION_DIRECTORY
docs = []
id2index = {}
# print('start read files')
# read files
for file in map(lambda x: os.path.join(sourceDirectory,x),list_files(sourceDirectory, '*.json')):
with open(file, 'r') as readFile:
doc = json.load(readFile)
id2index[doc['id']] = len(docs)
self.progress_bar.next()
docs.append(doc)
# print('start calc page rank')
# create links matrix
n = len(docs)
p = []
for doc in docs:
pp = [0] * n
for linkID in filter(lambda x: x in id2index.keys() , (set(doc['cited_in']) |set(doc['refrences'])) ):
pp[id2index[linkID]] = 1
p.append(pp)
# calculate page rank
pr = self.pageRankMathCalculation(p,PAGERANK_ALFA,PAGERANK_ERROR)
# print('start save files')
# save docs
os.makedirs(destDirectory, exist_ok=True)
for doc,pagerank in zip(docs,pr):
doc['pageRank'] = pagerank
file_name = '{}.json'.format(doc['id'])
with open(os.path.join(destDirectory , file_name), 'w') as outfile:
json.dump(doc, outfile)
def load_subspace_pairs(subspace_pairs):
"""Load the pairs used for gender subspace definition.
Returns:
Dict[str, List[Tuple[str, str]]]: Dictionary of gender word pairs. The
value is a list of (male, female) words; the key is the name of
that list.
"""
filepairs = []
for direction_file in list_files(DIRECTIONS_PATH):
with open(direction_file) as fd:
filepairs.extend(
(os.path.basename(direction_file), tuple(line.strip().split()))
for line in fd)
if subspace_pairs == 'pair':
keyfunc = (lambda filepair: '-'.join(filepair[1]))
elif subspace_pairs == 'group':
keyfunc = (lambda filepair: filepair[0])
elif subspace_pairs == 'all':
keyfunc = (lambda filepair: 'all')
else:
raise ValueError(
'unrecognized subspace pair parameter: {}'.format(subspace_pairs))
return {
key: [filepair[1] for filepair in group]
for key, group in groupby(filepairs, key=keyfunc)
}
def __init__(self, path, x_sufix, page_size):
self.path = path
self.x_sufix = x_sufix
self.v_sufix = None
self.array_x_files = util.list_files(os.path.join(path, x_sufix),
ext='png')
self.pos = 0
self.page_size = page_size
def build_all_fasttext_models(model_type='skipgram'):
if model_type not in ['skipgram', 'cbow']:
raise ValueError('model_type must be "skipgram" or "cbow" but got "' +
str(model_type) + '"')
for corpus_file in list_files(CORPORA_PATH):
if not corpus_file.endswith('-swapped'):
create_pronoun_swapped_corpus(corpus_file, 'swap-pairs/pronouns')
for corpus_file in list_files(CORPORA_PATH):
model_stub = os.path.join(
MODELS_PATH,
os.path.basename(corpus_file) + '.' + model_type,
)
if not os.path.exists(model_stub + '.bin'):
subprocess.run(args=[
'fasttext', 'skipgram', '-input', corpus_file, '-output',
model_stub
])
def main():
if len(sys.argv) < 3 or len(sys.argv) > 4:
print "usage: indexer </path/to/files/for/indexing/> <indexer> [cutoff]"
exit()
use_cutoff_freq = False
if len(sys.argv) == 4 and sys.argv[3].lower() == "cutoff":
use_cutoff_freq = True
idxr = getattr(sys.modules[__name__], sys.argv[2])()
for inp_file in list_files(sys.argv[1]):
idxr.index_file(inp_file, use_cutoff_freq)
def bulk_add_documents_in_directory(self, folder, index_name, document_type, pattern='*.json'):
"""
add documents in directory that their name matches given pattern
:param folder: directory address
:param index_name: name of index (e.g. articles)
:param document_type: type of documents (e.g. paper)
:param pattern: file name pattern (wildcard). default: *.json
:return:
"""
return self.bulk_add_documents_file_list(
map(lambda name: self.base_folder+name, list_files(folder, pattern=pattern)),
index_name, document_type)
def clusterDocs(self):
api = TermVectorAPI(ELASTIC_URL)
#print('start read files')
for file in map(lambda x: os.path.join(CLUSTER_SOURCE_DIRECTORY,x),list_files(CLUSTER_SOURCE_DIRECTORY, '*.json')):
with open(file, 'r') as readFile:
doc = json.load(readFile)
self.docsJson[doc['id']]= doc
self.docVector[doc['id']] = Vector(api.get_term_vector(INDEX_NAME, DOCUMENT_TYPE, doc['id']))
#print('read all files successfully')
#print('start init centroid')
self.initCentroid(CLUSTER_NUM)
#print('end init centroid')
while True:
self.oldDocCluster = self.docCluster.copy()
self.docCluster = {}
for docID in self.docsJson.keys():
self.docCluster[docID] = self.nearestCentroid(docID)
self.updateCentroid()
self.progress_bar.next()
#print('one step clustring')
if (self.terminateCondition()):
self.progress_bar.finish()
break
#print('converge clustring')
print('K = ',CLUSTER_NUM,' J = ',self.J())
candids = self.findCandidateText(CLUSTER_CANDIDATE_TEXT_LEN)
#print('calc candid')
c = [[] for x in range(len(self.centroidList))]
for d in self.docCluster.keys():
c[self.docCluster[d]].append(d)
#print('start save result')
os.makedirs(CLUSTER_DESTINATION_DIRECTORY, exist_ok=True)
os.makedirs(CLUSTER_CANDIDATE_TEXT_DIRECTORY, exist_ok=True)
for i in range(len(self.centroidList)):
res = {}
res['id'] = i
res['name'] = candids[i]
res['pages'] = c[i]
fileName = i.__str__()+'.json'
print('Cluster {}: {}\tnumber of docs: {}', i, ' '.join(candids[i]), len(c[i]))
#print(res)
with open(os.path.join(CLUSTER_CANDIDATE_TEXT_DIRECTORY, fileName), 'w') as outfile:
json.dump(res, outfile)
for id in self.docsJson.keys():
self.docsJson[id]['cluster'] = self.docCluster[id]
file_name = '{}.json'.format(id)
with open(os.path.join(CLUSTER_DESTINATION_DIRECTORY , file_name), 'w') as outfile:
json.dump(self.docsJson[id], outfile)
def __init__(self, dir_path):
self._cache = {}
_clases = {}
# Open and load text file including the whole training data
for subfolder in list_dir(dir_path):
class_dir = os.path.join(dir_path, subfolder)
class_idx = len(_clases)
_clases[class_dir] = []
for filename in list_files(class_dir, '.JPEG'):
_clases[class_dir].append({
'path': os.path.join(class_dir, filename),
'class_idx': class_idx,
'name': os.path.splitext(filename)[0],
'rotation': 0
})
self.class_list = list(_clases.items())
def __init__(self, dir_path):
self._cache = {}
_clases = {}
for subfolder in list_dir(dir_path):
for character in list_dir(os.path.join(dir_path, subfolder)):
class_dir = os.path.join(dir_path, subfolder, character)
class_idx = len(_clases)
_clases[class_dir] = []
for filename in list_files(class_dir, '.png'):
_clases[class_dir].append({
'path': os.path.join(class_dir, filename),
'class_idx': class_idx,
'name': character + '_' + os.path.splitext(filename)[0],
'rotation': 0
})
self.class_list = list(_clases.items())
def process_request(directory, request):
if request == "get /":
return util.list_files(directory)
elif request.startswith("get /"):
filename = request.replace("get /", "")
return util.read_file(directory, filename)
elif request.startswith("post /"):
# obtain all request terms
terms = request.split()
# obtain filename
filename = terms[1].replace("/", "")
# obtain content
content = ""
content_terms = terms
content_terms.pop(0)
content_terms.pop(0)
for t in content_terms:
content = content + " " + t
return util.overwrite_file(directory, filename, content)
else:
return "sorry, '%s' command does not exist" % request
def list_players(model_dir):
players = [
(RandomPlayer, {}),
(GreedyPlayer, {}),
(MiniMaxPlayer, {
'depth': 1
}),
(MiniMaxPlayer, {
'depth': 2
}),
(MiniMaxPlayer, {
'depth': 3
}),
(MonteCarloPlayer, {
'budget': 400
}),
(MonteCarloPlayer, {
'budget': 800
}),
(MonteCarloPlayer, {
'budget': 1600
}),
(MonteCarloPlayer, {
'budget': 3200
}),
(MonteCarloPlayer, {
'budget': 6400
}),
]
model_files = list(sorted(list_files(model_dir, '.h5')))
for model_file in model_files:
if model_file.endswith('0.h5'):
players += [
(AlphaConnectPlayer, {
'model_path': model_file,
'search_budget': 1600
}),
]
return players
def train(model):
_train_epochs = train_conf['train_epochs']
_train_data = train_conf['train_data']
_batch_size = train_conf['batch_size']
for n in range(_train_epochs):
tf.logging.info('=' * 30 + ' START EPOCH {} '.format(n + 1) +
'=' * 30 + '\n')
train_data_list = list_files(_train_data) # dir to file list
for f in train_data_list:
t0 = time.time()
tf.logging.info('<EPOCH {}>: Start training {}'.format(n + 1, f))
model.train(
input_fn=lambda: input_fn(f, ModeKeys.TRAIN, _batch_size),
hooks=None,
steps=None,
max_steps=None,
saving_listeners=None)
tf.logging.info(
'<EPOCH {}>: Finish training {}, take {} mins'.format(
n + 1, f, elapse_time(t0)))
print('-' * 80)
def author_cluster_admin():
timer = Timer()
timer.start()
authors = list()
for file in list_files(AUTHOR_CLUSTER_SOURCE_DIRECTORY, '*.json'):
with open(os.path.join(AUTHOR_CLUSTER_SOURCE_DIRECTORY, file), 'r') as fp:
author_data = json.load(fp)
authors.append(Author(author_data))
from clustering.authors_cluster import Dendogram
clusters = Dendogram(authors)
clusters.cluster()
min_similarity = 0.375
cluster_list = list(map(
lambda cluster: list(map(lambda x: x.name, cluster)),
map(
lambda x: list(x.authors),
clusters.get_clusters(min_similarity)
)
))
cluster_dict = dict()
for cluster in cluster_list:
for author in cluster:
cluster_dict[author] = cluster
with open(AUTHOR_CLUSTER_FILE, 'w') as fp:
json.dump(cluster_dict, fp)
timer.end()
return render_template('indexing_result.html',
duration=timer.get_time_taken_pretty(),
elastic_response=json.dumps(cluster_list, indent=True),
success=True,
numdocs=len(cluster_list)
)
def gen_train_input(self, inputs, decode_fn):
#--------------------- train
logging.info('train_input: %s'%FLAGS.train_input)
print 'train_input: %s'%FLAGS.train_input
trainset = util.list_files(FLAGS.train_input)
logging.info('trainset:{} {}'.format(len(trainset), trainset[:2]))
print 'trainset:{} {}'.format(len(trainset), trainset[:2])
query, query_str, text, text_str = inputs(
trainset,
decode_fn=decode_fn,
batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs,
#seed=seed,
num_threads=FLAGS.num_threads,
batch_join=FLAGS.batch_join,
shuffle_files=FLAGS.shuffle_files,
fix_sequence=FLAGS.fix_sequence,
num_prefetch_batches=FLAGS.num_prefetch_batches,
min_after_dequeue=FLAGS.min_after_dequeue,
name=self.input_train_name)
return (query, query_str, text, text_str), trainset
def extract_meta(dirname, subdir, args):
source = args.get('source')
datasets = args.get('datasets')
# Make sure dirname don't end in '/', otherwise our poor basename is not going to work well
dirname = strip_dirname(dirname)
# Extract metadata from csv and create a record
# First check if okay by looking into processed.txt
processedFile = dirname + '/processed.txt'
processed = None
if util.is_non_zero_file(processedFile):
processed = util.read_properties(processedFile, log)
if not processed:
if not args.get('includeAll'):
return False # NOT ACCEPTABLE
else:
if not args.get('includeAll'):
return False # NOT ACCEPTABLE
# Process log
times = None
processLog = dirname + '/process.log'
if os.path.isfile(processLog):
times = timings.computeTimings(processLog)
# Take dirname and extract the final string as the id
id = os.path.basename(dirname)
if subdir is None:
subdir = id
meta1 = {
'fullId': source + '.' + id,
'id': id,
'source': source,
'datasets': datasets,
'path': subdir
}
if times is not None:
total = timings.getTotal(times)
meta1['totalProcessingSecs'] = total.get('secs')
meta1['totalProcessingTime'] = total.get('time')
# Extract create time from id matching: 2016-07-01_04-29-28
date_match = DATE_RE.search(id)
if date_match:
createdAt = date_match.group(0)
# Reformat to be in ISO8601 format
meta1['createdAt'] = createdAt[0:10] + 'T' + createdAt[
11:13] + ':' + createdAt[14:16] + ':' + createdAt[17:19]
# Look for dirname/id.txt and extract fields into our meta
# if there is txt file, read it and append to metadata record
metafile = dirname + '/' + id + '.txt'
if util.is_non_zero_file(metafile):
# Read in txt as dictionary
meta = util.read_properties(metafile, log)
else:
meta = {}
# go through files and find last time as updatedAt time for scan
# Take our basic info and merge it into meta (overwriting what maybe there)
if processed:
meta.update(processed)
meta.update(meta1)
# Check what files we have
meta['files'] = util.list_files(dirname)
lastModified = util.lastModified(meta['files'])
if lastModified:
meta['updatedAt'] = util.millisToIso(
lastModified.get('modifiedAtMillis'))
# Check what stage we are in
# NOTE: This requires meta to be filled in with id and files!!!
if args.get('stages'):
check_stages(args.get('stages'), meta, times)
# Check if we have a ply file and how big it is
filebase = id + '_vh_clean_2' if args.get('checkCleaned') else id
plyfile = dirname + '/' + filebase + '.ply'
plyfileSize = util.filesize(plyfile)
meta['hasCleaned'] = args.get('checkCleaned') and plyfileSize > 0
if plyfileSize > 0:
meta['fileType'] = 'ply'
meta['fileSize'] = plyfileSize
# Check if we have a png file
pngfile = dirname + '/' + filebase + '.png'
pngfileSize = util.filesize(pngfile)
meta['hasScreenshot'] = pngfileSize > 0
# Check if we have a thumbnail file
pngfile = dirname + '/' + filebase + '_thumb.png'
pngfileSize = util.filesize(pngfile)
meta['hasThumbnail'] = pngfileSize > 0
if source == 'nyuv2' and not meta.get('sceneType'):
idParts = meta.get('id').split('_')
meta['sceneType'] = '_'.join(idParts[0:len(idParts - 1)])
if meta.get('sceneLabel'):
# Derive sceneName from sceneLabel
sceneLabel = meta.get('sceneLabel')
match = SCENELABEL_RE.match(sceneLabel)
meta['sceneName'] = match.group(1) if match else sceneLabel
return meta
def test_list_files_in_order(directory_hierarchy):
files = list(list_files(directory_hierarchy))
assert list(sorted(files)) == files
def main() -> None:
path = "."
files = list_files(path)
for f in files:
print("d" if f.isdir else "f", f" {f.human_readable_bytes:<12}",
f.path)
def test_Given_validPath_When_listFiles_Then_listAllFiles(input, output):
assert set(list_files(input)) == set(output)
def new_model_path(model_dir):
model_files = list(list_files(model_dir, '.h5'))
model_iteration = len(model_files)
model_path = os.path.abspath(
os.path.join(model_dir, '%6.6d.h5' % model_iteration))
return model_iteration, model_path
def train_and_eval(model):
_train_epochs = train_conf['train_epochs']
_train_data = train_conf['train_data']
_eval_data = train_conf['eval_data']
_test_data = train_conf['test_data']
_batch_size = train_conf['batch_size']
_epochs_per_eval = train_conf['epochs_per_eval']
for n in range(_train_epochs):
tf.logging.info('=' * 30 + ' START EPOCH {} '.format(n + 1) +
'=' * 30 + '\n')
train_data_list = list_files(_train_data) # dir to file list
for f in train_data_list:
t0 = time.time()
tf.logging.info('<EPOCH {}>: Start training {}'.format(n + 1, f))
model.train(
input_fn=lambda: input_fn(f, ModeKeys.TRAIN, _batch_size),
hooks=None,
steps=None,
max_steps=None,
saving_listeners=None)
tf.logging.info(
'<EPOCH {}>: Finish training {}, take {} mins'.format(
n + 1, f, elapse_time(t0)))
print('-' * 80)
tf.logging.info('<EPOCH {}>: Start evaluating {}'.format(
n + 1, _eval_data))
t0 = time.time()
results = model.evaluate(
input_fn=lambda: input_fn(f, ModeKeys.EVAL, _batch_size),
steps=None, # Number of steps for which to evaluate model.
hooks=None,
checkpoint_path=None, # latest checkpoint in model_dir is used.
name=None)
tf.logging.info(
'<EPOCH {}>: Finish evaluation {}, take {} mins'.format(
n + 1, _eval_data, elapse_time(t0)))
print('-' * 80)
# Display evaluation metrics
for key in sorted(results):
print('{}: {}'.format(key, results[key]))
# every epochs_per_eval test the model (use larger test dataset)
if (n + 1) % _epochs_per_eval == 0:
tf.logging.info('<EPOCH {}>: Start testing {}'.format(
n + 1, _test_data))
results = model.evaluate(
input_fn=lambda: input_fn(f, ModeKeys.EVAL, _batch_size),
steps=None, # Number of steps for which to evaluate model.
hooks=None,
checkpoint_path=
None, # If None, the latest checkpoint in model_dir is used.
name=None)
tf.logging.info(
'<EPOCH {}>: Finish testing {}, take {} mins'.format(
n + 1, _test_data, elapse_time(t0)))
print('-' * 80)
# Display evaluation metrics
for key in sorted(results):
print('{}: {}'.format(key, results[key]))
def is_first_model(model_dir):
model_files = list(list_files(model_dir, '.h5'))
return len(model_files) == 0
# Load the OpenCV’s Caffe-based deep learning face detector model
print("[EXEC] Loading face detector model....")
detector = cv2.dnn.readNetFromCaffe(
"face_detection_model/deploy.prototxt",
"face_detection_model/res10_300x300_ssd_iter_140000.caffemodel")
# Load the embbeder model to extract a 128-D facial embedding vector
# It contains the OpenCV deep learning Torch embedding model.
print("[EXEC] Loading face recognizer model....")
embedder = cv2.dnn.readNetFromTorch("openface_nn4.small2.v1.t7")
print("[EXEC] Reading Image Paths.....")
# Discrete each image path into a list
imagePaths = list(list_files(rootPath="my_dataset"))
print(imagePaths)
knownEmbeddings = []
knownNames = []
total = 0
# Iterate over every single image
for (i, imagePath) in enumerate(imagePaths):
print("Processing image {} of {}".format(i + 1, len(imagePaths)))
# Extract name of the image
name = imagePath.split(os.path.sep)[-2]
image = cv2.imread(imagePath)
image = cv2.resize(image, dsize=(750, 600))
# Height and Width
def main():
md_files = list_files("www", is_markdown_file, recur=True)
for md_file in md_files:
html_file = md_file[:-2] + "html"
md_to_html(md_file, html_file)
def test_list_files():
ret = '\n'
ret += './=' + str(util.list_files('./dir2')) + '\n'
ret += './=' + str(util.list_files('./dir2', pattern=r'\d.+')) + '\n'
ret += './=' + str(util.list_files('./dir2', pattern=r'lang_.*')) + '\n'
return ret
def _get_files(self, folder):
if os.path.isdir(folder):
return sorted(list_files(folder, valid_exts=self.IMG_EXTS))
else:
raise (RuntimeError('No folder named "{}" found.'.format(folder)))
def inputs(files, decode_fn, batch_size=64,
num_epochs = None, num_threads=12,
shuffle_files=True, batch_join=True, shuffle_batch=True,
min_after_dequeue=None, seed=None, enqueue_many=False,
fix_random=False, no_random=False, fix_sequence=False,
allow_smaller_final_batch=False,
num_prefetch_batches=None,
dynamic_pad=False,
bucket_boundaries=None,
length_index=None,
length_fn=None,
name='input'):
"""Reads input data num_epochs times.
for sparse input here will do:
1. read serialized_example
2. shuffle serialized_examples
3. decdoe batch_serialized_examples
notice read_sparse.inputs and also be used for dense inputs,but if you
only need to decode part from serialized_example, then read.inputs will
be better, less to put to suffle
#--------decode example, can refer to libsvm-decode.py
# def decode(batch_serialized_examples):
# features = tf.parse_example(
# batch_serialized_examples,
# features={
# 'label' : tf.FixedLenFeature([], tf.int64),
# 'index' : tf.VarLenFeature(tf.int64),
# 'value' : tf.VarLenFeature(tf.float32),
# })
# label = features['label']
# index = features['index']
# value = features['value']
# return label, index, value
#string_input_reducer will shuffle files
#shuffle will read file by file and shuffle withn file(in shuffle queue)
#shuffle_batch_join will read multiple files and shuffle in shuffle queue(from many files)
To get fixed sequence
shuffle=False so by this way the sequence is as your data input unchange
or
shuffle=True
seed=1024 #set
batch_join=False by this way you have fixed random, so get same result
NOTICE, shuffle=True,seed=1024,batch_join=True will not get same result
shuffle=False,seed=1024,batch_join=True also, so batch_join seems seed only control inqueue random, can not get fixed result
for no random -> fixed result set shuffle=False wihch will force batch_join=False then use batch
for fixed random -> shuffle=True, seed set or fix_random=True
read-records.py show above ok, but train-evaluate.py show not, only shuffle=False can get fixed result.. @FIXME strange
for train-evaluate.py it looks you can set shuffle in string_input_producer True, but then must use batch,
batch_join and shuffle_batch join all not fixed even with seed set, may be due to trainset two inputs read ?
for read-records.py batch_join will be fixed, shuffle_batch_join not
defualt parmas will give max random...
Args:
decode: user defined decode
min_after_dequeue: set to >2w for production train, suggesed will be 0.4 * num_instances, but also NOTICE do not exceed mem
#--default parmas will make most randomness
shuffle_files: wehter shuffle file
shuffle_batch: batch or shuffle_batch
batch_join: wether to use multiple reader or use one reader mutlitple thread
fix_random: if True make at most random which can fix random result
allow_smaller_final_batch: set True usefull if you want verify on small dataset
"""
if isinstance(files, str):
files = util.list_files(files)
assert len(files) > 0
if not min_after_dequeue :
min_after_dequeue = 100000
if not num_epochs:
num_epochs = None
if fix_random:
if seed is None:
seed = 1024
shuffle_files = True
batch_join = False #check can be True ?
#to get fix_random
#shuffle_batch = True and num_threads = 1 ok
#shuffle_batch = False and num_threads >= 1 ok
#from models/iamge-text-sim/read_records shuffle_batch = True will be quick, even single thread
#and strange num_threas = 1 will be quicker then 12
shuffle_batch = True
num_threads = 1
#shuffle_batch = False
if fix_sequence:
no_random = True
allow_smaller_final_batch = True
num_threads = 1
if no_random:
shuffle_files = False
batch_join = False
shuffle_batch = False
if dynamic_pad:
#use tf.batch
shuffle_batch = False
#shuffle=True
#batch_join = True #setting to False can get fixed result
#seed = 1024
with tf.name_scope(name):
filename_queue = tf.train.string_input_producer(
files,
num_epochs=num_epochs,
shuffle=shuffle_files,
seed=seed)
# min_after_dequeue defines how big a buffer we will randomly sample
# from -- bigger means better shuffling but slower start up and more
# memory used.
# capacity must be larger than min_after_dequeue and the amount larger
# determines the maximum we will prefetch. Recommendation:
# min_after_dequeue + (num_threads + a small safety margin) * batch_size
#@TODO cifa10 always use num_prefetch_batches = 3, 3 * batch_size, check which is better
if not num_prefetch_batches:
num_prefetch_batches = num_threads + 3
capacity = min_after_dequeue + num_prefetch_batches * batch_size
#@TODO diff between tf.batch_join and tf.batch, batch_join below means shuffle_batch_join.. TODO
if batch_join:
batch_list = [_read(filename_queue) for _ in xrange(num_threads)]
#print batch_list
if shuffle_batch:
batch_serialized_examples = tf.train.shuffle_batch_join(
batch_list,
batch_size=batch_size,
capacity=capacity,
min_after_dequeue=min_after_dequeue,
seed=seed,
enqueue_many=enqueue_many,
allow_smaller_final_batch=allow_smaller_final_batch,
name='shuffle_batch_join_queue')
else:
batch_serialized_examples = tf.train.batch_join(
batch_list,
batch_size=batch_size,
capacity=capacity,
enqueue_many=enqueue_many,
allow_smaller_final_batch=allow_smaller_final_batch,
dynamic_pad=dynamic_pad,
name='batch_join_queue')
else:
serialized_example = list(_read(filename_queue))
#@FIXME... for bug now can not be more random if want fix random see D:\mine\tensorflow-exp\models\image-text-sim\train-evaluate-fixrandom.py
if shuffle_batch:
batch_serialized_examples = tf.train.shuffle_batch(
serialized_example,
batch_size=batch_size,
num_threads=num_threads,
capacity=capacity,
min_after_dequeue=min_after_dequeue,
seed=seed,
enqueue_many=enqueue_many,
allow_smaller_final_batch=allow_smaller_final_batch,
name='shuffle_batch_queue')
else:
batch_serialized_examples = tf.train.batch(
serialized_example,
batch_size=batch_size,
#@TODO to make really fxied result use num_threads=1, may be shuffle_batch will be fix random?
num_threads=num_threads,
capacity=capacity,
enqueue_many=enqueue_many,
allow_smaller_final_batch=allow_smaller_final_batch,
dynamic_pad=dynamic_pad,
name='batch_queue')
#print name
#print decode_fn
#print decode_fn(batch_serialized_examples)
return decode_fn(batch_serialized_examples) if decode_fn is not None else batch_serialized_examples
def latest_model_path(model_dir):
model_files = list(sorted(list_files(model_dir, '.h5')))
model_iteration = len(model_files)
model_path = os.path.abspath(os.path.join(model_dir, model_files[-1]))
return model_iteration - 1, model_path
def main():
md_files = list_files("www", is_markdown_file, recur=True)
for md_file in md_files:
html_file = md_file[:-2] + "html"
md_to_html(md_file, html_file)
def list_posts():
posts = [Post.constructFromPath(pf) for pf in list_files(conf.POST_PATH)]
posts = posts[::-1]
return json.dumps([p.postStringMap() for p in posts])