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import multiprocessing
import pickle
import time
import traceback
import cv2
import numpy as np
from core import mplib
from core.joblib import SubprocessGenerator, ThisThreadGenerator
from facelib import LandmarksProcessor
from samplelib import (SampleGeneratorBase, SampleLoader, SampleProcessor,
SampleType)
'''
arg
output_sample_types = [
[SampleProcessor.TypeFlags, size, (optional) {} opts ] ,
...
]
'''
class SampleGeneratorFaceDebug(SampleGeneratorBase):
def __init__ (self, samples_path, debug=False, batch_size=1,
random_ct_samples_path=None,
sample_process_options=SampleProcessor.Options(),
output_sample_types=[],
add_sample_idx=False,
generators_count=4,
rnd_seed=None,
**kwargs):
super().__init__(debug, batch_size)
self.sample_process_options = sample_process_options
self.output_sample_types = output_sample_types
self.add_sample_idx = add_sample_idx
if rnd_seed is None:
rnd_seed = np.random.randint(0x80000000)
if self.debug:
self.generators_count = 1
else:
self.generators_count = max(1, generators_count)
samples = SampleLoader.load (SampleType.FACE, samples_path)
self.samples_len = len(samples)
if self.samples_len == 0:
raise ValueError('No training data provided.')
if random_ct_samples_path is not None:
ct_samples = SampleLoader.load (SampleType.FACE, random_ct_samples_path)
else:
ct_samples = None
pickled_samples = pickle.dumps(samples, 4)
ct_pickled_samples = pickle.dumps(ct_samples, 4) if ct_samples is not None else None
if self.debug:
self.generators = [ThisThreadGenerator ( self.batch_func, (pickled_samples, ct_pickled_samples, rnd_seed) )]
else:
self.generators = [SubprocessGenerator ( self.batch_func, (pickled_samples, ct_pickled_samples, rnd_seed+i), start_now=False ) \
for i in range(self.generators_count) ]
SubprocessGenerator.start_in_parallel( self.generators )
self.generator_counter = -1
def __iter__(self):
return self
def __next__(self):
self.generator_counter += 1
generator = self.generators[self.generator_counter % len(self.generators) ]
return next(generator)
def batch_func(self, param ):
pickled_samples, ct_pickled_samples, rnd_seed = param
rnd_state = np.random.RandomState(rnd_seed)
samples = pickle.loads(pickled_samples)
idxs = [*range(len(samples))]
shuffle_idxs = []
if ct_pickled_samples is not None:
ct_samples = pickle.loads(ct_pickled_samples)
ct_idxs = [*range(len(ct_samples))]
ct_shuffle_idxs = []
else:
ct_samples = None
bs = self.batch_size
while True:
batches = None
for n_batch in range(bs):
if len(shuffle_idxs) == 0:
shuffle_idxs = idxs.copy()
rnd_state.shuffle(shuffle_idxs)
sample_idx = shuffle_idxs.pop()
sample = samples[sample_idx]
ct_sample = None
if ct_samples is not None:
if len(ct_shuffle_idxs) == 0:
ct_shuffle_idxs = ct_idxs.copy()
rnd_state.shuffle(ct_shuffle_idxs)
ct_sample_idx = ct_shuffle_idxs.pop()
ct_sample = ct_samples[ct_sample_idx]
try:
x, = SampleProcessor.process ([sample], self.sample_process_options, self.output_sample_types, self.debug, ct_sample=ct_sample, rnd_state=rnd_state)
except:
raise Exception ("Exception occured in sample %s. Error: %s" % (sample.filename, traceback.format_exc() ) )
if batches is None:
batches = [ [] for _ in range(len(x)) ]
if self.add_sample_idx:
batches += [ [] ]
i_sample_idx = len(batches)-1
for i in range(len(x)):
batches[i].append ( x[i] )
if self.add_sample_idx:
batches[i_sample_idx].append (sample_idx)
yield [ np.array(batch) for batch in batches]
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