def __init__(self,
samples_path,
debug,
batch_size,
sort_by_yaw=False,
sort_by_yaw_target_samples_path=None,
with_close_to_self=False,
sample_process_options=SampleProcessor.Options(),
output_sample_types=[],
add_sample_idx=False,
add_pitch=False,
add_yaw=False,
generators_count=2,
**kwargs):
super().__init__(samples_path, debug, batch_size)
self.sample_process_options = sample_process_options
self.output_sample_types = output_sample_types
self.add_sample_idx = add_sample_idx
self.add_pitch = add_pitch
self.add_yaw = add_yaw
if sort_by_yaw_target_samples_path is not None:
self.sample_type = SampleType.FACE_YAW_SORTED_AS_TARGET
elif sort_by_yaw:
self.sample_type = SampleType.FACE_YAW_SORTED
elif with_close_to_self:
self.sample_type = SampleType.FACE_WITH_CLOSE_TO_SELF
else:
self.sample_type = SampleType.FACE
self.samples = SampleLoader.load(self.sample_type, self.samples_path,
sort_by_yaw_target_samples_path)
if self.debug:
self.generators_count = 1
self.generators = [
iter_utils.ThisThreadGenerator(self.batch_func, 0)
]
else:
self.generators_count = min(generators_count, len(self.samples))
self.generators = [
iter_utils.SubprocessGenerator(self.batch_func, i)
for i in range(self.generators_count)
]
self.generators_sq = [
multiprocessing.Queue() for _ in range(self.generators_count)
]
self.generator_counter = -1
def __init__(self,
samples_path,
debug,
batch_size,
sort_by_yaw=False,
sort_by_yaw_target_samples_path=None,
sample_process_options=SampleProcessor.Options(),
output_sample_types=[],
**kwargs):
super().__init__(samples_path, debug, batch_size)
self.sample_process_options = sample_process_options
self.output_sample_types = output_sample_types
if sort_by_yaw_target_samples_path is not None:
self.sample_type = SampleType.FACE_YAW_SORTED_AS_TARGET
elif sort_by_yaw:
self.sample_type = SampleType.FACE_YAW_SORTED
else:
self.sample_type = SampleType.FACE
self.samples = SampleLoader.load(self.sample_type, self.samples_path,
sort_by_yaw_target_samples_path)
if self.debug:
self.generator_samples = [self.samples]
self.generators = [
iter_utils.ThisThreadGenerator(self.batch_func, 0)
]
else:
if len(self.samples) > 1:
self.generator_samples = [
self.samples[0::2], self.samples[1::2]
]
self.generators = [
iter_utils.SubprocessGenerator(self.batch_func, 0),
iter_utils.SubprocessGenerator(self.batch_func, 1)
]
else:
self.generator_samples = [self.samples]
self.generators = [
iter_utils.SubprocessGenerator(self.batch_func, 0)
]
self.generator_counter = -1
def batch_func(self, generator_id):
samples = self.generator_samples[generator_id]
samples_len = len(samples)
if samples_len == 0:
raise ValueError('No training data provided.')
if samples_len - self.temporal_image_count < 0:
raise ValueError('Not enough samples to fit temporal line.')
shuffle_idxs = []
samples_sub_len = samples_len - self.temporal_image_count + 1
while True:
batches = None
for n_batch in range(self.batch_size):
if len(shuffle_idxs) == 0:
shuffle_idxs = random.sample(range(samples_sub_len),
samples_sub_len)
idx = shuffle_idxs.pop()
temporal_samples = []
for i in range(self.temporal_image_count):
sample = samples[idx + i]
try:
temporal_samples += SampleProcessor.process(
sample, self.sample_process_options,
self.output_sample_types, self.debug)
except:
raise Exception(
"Exception occured in sample %s. Error: %s" %
(sample.filename, traceback.format_exc()))
if batches is None:
batches = [[] for _ in range(len(temporal_samples))]
for i in range(len(temporal_samples)):
batches[i].append(temporal_samples[i])
yield [np.array(batch) for batch in batches]
def __init__(self,
samples_path,
debug,
batch_size,
temporal_image_count,
sample_process_options=SampleProcessor.Options(),
output_sample_types=[],
**kwargs):
super().__init__(samples_path, debug, batch_size)
self.temporal_image_count = temporal_image_count
self.sample_process_options = sample_process_options
self.output_sample_types = output_sample_types
self.samples = SampleLoader.load(SampleType.IMAGE, self.samples_path)
self.generator_samples = [self.samples]
self.generators = [iter_utils.ThisThreadGenerator ( self.batch_func, 0 )] if self.debug else \
[iter_utils.SubprocessGenerator ( self.batch_func, 0 )]
self.generator_counter = -1
def batch_func(self, generator_id):
gen_sq = self.generators_sq[generator_id]
samples = self.samples
samples_len = len(samples)
samples_idxs = [ *range(samples_len) ] [generator_id::self.generators_count]
repeat_samples_idxs = []
if len(samples_idxs) == 0:
raise ValueError('No training data provided.')
if self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
if all ( [ samples[idx] == None for idx in samples_idxs] ):
raise ValueError('Not enough training data. Gather more faces!')
if self.sample_type == SampleType.FACE or self.sample_type == SampleType.FACE_WITH_CLOSE_TO_SELF:
shuffle_idxs = []
elif self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
shuffle_idxs = []
shuffle_idxs_2D = [[]]*samples_len
while True:
while not gen_sq.empty():
idxs = gen_sq.get()
for idx in idxs:
if idx in samples_idxs:
repeat_samples_idxs.append(idx)
batches = None
for n_batch in range(self.batch_size):
while True:
sample = None
if len(repeat_samples_idxs) > 0:
idx = repeat_samples_idxs.pop()
if self.sample_type == SampleType.FACE or self.sample_type == SampleType.FACE_WITH_CLOSE_TO_SELF:
sample = samples[idx]
elif self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
sample = samples[(idx >> 16) & 0xFFFF][idx & 0xFFFF]
else:
if self.sample_type == SampleType.FACE or self.sample_type == SampleType.FACE_WITH_CLOSE_TO_SELF:
if len(shuffle_idxs) == 0:
shuffle_idxs = samples_idxs.copy()
np.random.shuffle(shuffle_idxs)
idx = shuffle_idxs.pop()
sample = samples[ idx ]
elif self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
if len(shuffle_idxs) == 0:
shuffle_idxs = samples_idxs.copy()
np.random.shuffle(shuffle_idxs)
idx = shuffle_idxs.pop()
if samples[idx] != None:
if len(shuffle_idxs_2D[idx]) == 0:
shuffle_idxs_2D[idx] = random.sample( range(len(samples[idx])), len(samples[idx]) )
idx2 = shuffle_idxs_2D[idx].pop()
sample = samples[idx][idx2]
idx = (idx << 16) | (idx2 & 0xFFFF)
if sample is not None:
try:
x = SampleProcessor.process (sample, self.sample_process_options, self.output_sample_types, self.debug)
except:
raise Exception ("Exception occured in sample %s. Error: %s" % (sample.filename, traceback.format_exc() ) )
if type(x) != tuple and type(x) != list:
raise Exception('SampleProcessor.process returns NOT tuple/list')
if batches is None:
batches = [ [] for _ in range(len(x)) ]
if self.add_sample_idx:
batches += [ [] ]
for i in range(len(x)):
batches[i].append ( x[i] )
if self.add_sample_idx:
batches[-1].append (idx)
break
yield [ np.array(batch) for batch in batches]
def batch_func(self, generator_id):
samples = self.generator_samples[generator_id]
data_len = len(samples)
if data_len == 0:
raise ValueError('No training data provided.')
if self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
if all([x == None for x in samples]):
raise ValueError(
'Not enough training data. Gather more faces!')
if self.sample_type == SampleType.FACE:
shuffle_idxs = []
elif self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
shuffle_idxs = []
shuffle_idxs_2D = [[]] * data_len
while True:
batches = None
for n_batch in range(self.batch_size):
while True:
sample = None
if self.sample_type == SampleType.FACE:
if len(shuffle_idxs) == 0:
shuffle_idxs = random.sample(
range(data_len), data_len)
idx = shuffle_idxs.pop()
sample = samples[idx]
elif self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
if len(shuffle_idxs) == 0:
shuffle_idxs = random.sample(
range(data_len), data_len)
idx = shuffle_idxs.pop()
if samples[idx] != None:
if len(shuffle_idxs_2D[idx]) == 0:
shuffle_idxs_2D[idx] = random.sample(
range(len(samples[idx])),
len(samples[idx]))
idx2 = shuffle_idxs_2D[idx].pop()
sample = samples[idx][idx2]
if sample is not None:
try:
x = SampleProcessor.process(
sample, self.sample_process_options,
self.output_sample_types, self.debug)
except:
raise Exception(
"Exception occured in sample %s. Error: %s" %
(sample.filename, traceback.format_exc()))
if type(x) != tuple and type(x) != list:
raise Exception(
'SampleProcessor.process returns NOT tuple/list'
)
if batches is None:
batches = [[] for _ in range(len(x))]
for i in range(len(x)):
batches[i].append(x[i])
break
yield [np.array(batch) for batch in batches]