def memoize(fn, paths):
cwd = os.getcwd()
def change_to_target_dir():
for dir in paths[:-1]:
try:
os.mkdir(dir)
except OSError as e:
pass
os.chdir(dir)
change_to_target_dir()
filename = paths[-1]
if os.path.exists(filename):
data = hdf5.read(filename)
os.chdir(cwd)
return data
os.chdir(cwd)
data = fn()
change_to_target_dir()
tmp = '%s.pid.%d.tmp' % (filename, os.getpid())
hdf5.write(tmp, data)
os.rename(tmp, filename)
os.chdir(cwd)
return jsdict(data)
def process_and_merge_segments(target, data_type, out_dir, metadata, N_jobs):
filename_out = os.path.join(out_dir, '%s_%s.hdf5' % (target, data_type))
if os.path.exists(filename_out):
return 0
print 'Processing %s ...' % filename_out
filename_in_fmt = '%s_%s_segment_%%.4d' % (target, data_type)
filename_out_fmt = '%s/%s_%s_segment_%%d.hdf5' % (out_dir, target, data_type)
# process_data_sub_job(settings, filename_in_fmt, filename_out_fmt, 0, 1)
pool = Pool(N_jobs)
results = [pool.apply_async(process_data_sub_job, [settings, filename_in_fmt, filename_out_fmt, id, N_jobs])
for id in range(N_jobs)]
pool.close()
pool.join()
num_processed = np.sum([r.get() for r in results])
for i in xrange(num_processed):
data = hdf5.read(filename_out_fmt % i)
collect_metadata(data, metadata)
_, accum_meta = accumulate_data(settings, target, data_type, tag=None,
output_to_original_data_dir=True, quiet=True)
return accum_meta.num_segments
def process_and_merge_segments(target, data_type, out_dir, metadata, N_jobs):
filename_out = os.path.join(out_dir, '%s_%s.hdf5' % (target, data_type))
if os.path.exists(filename_out):
return 0
print('Processing %s ...' % filename_out)
filename_in_fmt = '%s_%s_segment_%%.4d' % (target, data_type)
filename_out_fmt = '%s/%s_%s_segment_%%d.hdf5' % (out_dir, target,
data_type)
# process_data_sub_job(settings, filename_in_fmt, filename_out_fmt, 0, 1)
pool = Pool(N_jobs)
results = [
pool.apply_async(
process_data_sub_job,
[settings, filename_in_fmt, filename_out_fmt, id, N_jobs])
for id in range(N_jobs)
]
pool.close()
pool.join()
num_processed = np.sum([r.get() for r in results])
for i in range(num_processed):
data = hdf5.read(filename_out_fmt % i)
collect_metadata(data, metadata)
_, accum_meta = accumulate_data(settings,
target,
data_type,
tag=None,
output_to_original_data_dir=True,
quiet=True)
return accum_meta.num_segments
# features in FeatureConcatPipeline.
def memoize(fn, paths):
cwd = os.getcwd()
def change_to_target_dir():
for dir in paths[:-1]:
try:
os.mkdir(dir)
except OSError, e:
pass
os.chdir(dir)
change_to_target_dir()
filename = paths[-1]
if os.path.exists(filename):
data = hdf5.read(filename)
os.chdir(cwd)
return data
os.chdir(cwd)
data = fn()
change_to_target_dir()
tmp = '%s.pid.%d.tmp' % (filename, os.getpid())
hdf5.write(tmp, data)
os.rename(tmp, filename)
os.chdir(cwd)
return jsdict(data)
# Fast process-if-not-yet-processed method for training data
# features in FeatureConcatPipeline.
def memoize(fn, paths):
cwd = os.getcwd()
def change_to_target_dir():
for dir in paths[:-1]:
try:
os.mkdir(dir)
except OSError, e:
pass
os.chdir(dir)
change_to_target_dir()
filename = paths[-1]
if os.path.exists(filename):
data = hdf5.read(filename)
os.chdir(cwd)
return data
os.chdir(cwd)
data = fn()
change_to_target_dir()
tmp = '%s.pid.%d.tmp' % (filename, os.getpid())
hdf5.write(tmp, data)
os.rename(tmp, filename)
os.chdir(cwd)
return jsdict(data)
# Fast process-if-not-yet-processed method for training data
