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all models: removed options 'src_scale_mod', and 'sort samples by yaw as target'
If you want, you can manually remove unnecessary angles from src faceset after sort by yaw. Optimized sample generators (CPU workers). Now they consume less amount of RAM and work faster. added 4.2.other) data_src/dst util faceset pack.bat Packs /aligned/ samples into one /aligned/samples.pak file. After that, all faces will be deleted. 4.2.other) data_src/dst util faceset unpack.bat unpacks faces from /aligned/samples.pak to /aligned/ dir. After that, samples.pak will be deleted. Packed faceset load and work faster.
This commit is contained in:
Colombo
parent
8866dce22e
commit
50f892d57d
26 changed files with 577 additions and 433 deletions
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@ -5,10 +5,10 @@ import cv2
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import numpy as np
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from facelib import LandmarksProcessor
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from samplelib import (SampleGeneratorBase, SampleLoader, SampleProcessor,
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from samplelib import (SampleGeneratorBase, SampleHost, SampleProcessor,
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SampleType)
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from utils import iter_utils
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from utils import mp_utils
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'''
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arg
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@ -18,57 +18,46 @@ output_sample_types = [
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]
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'''
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class SampleGeneratorFace(SampleGeneratorBase):
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def __init__ (self, samples_path, debug=False, batch_size=1,
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sort_by_yaw=False,
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sort_by_yaw_target_samples_path=None,
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random_ct_samples_path=None,
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sample_process_options=SampleProcessor.Options(),
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output_sample_types=[],
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add_sample_idx=False,
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use_caching=False,
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generators_count=2,
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generators_random_seed=None,
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def __init__ (self, samples_path, debug=False, batch_size=1,
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random_ct_samples_path=None,
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sample_process_options=SampleProcessor.Options(),
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output_sample_types=[],
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add_sample_idx=False,
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**kwargs):
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super().__init__(samples_path, debug, batch_size)
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self.sample_process_options = sample_process_options
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self.output_sample_types = output_sample_types
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self.add_sample_idx = add_sample_idx
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if sort_by_yaw_target_samples_path is not None:
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self.sample_type = SampleType.FACE_YAW_SORTED_AS_TARGET
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elif sort_by_yaw:
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self.sample_type = SampleType.FACE_YAW_SORTED
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else:
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self.sample_type = SampleType.FACE
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samples_host = SampleHost.mp_host (SampleType.FACE, self.samples_path)
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self.samples_len = len(samples_host)
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if generators_random_seed is not None and len(generators_random_seed) != generators_count:
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raise ValueError("len(generators_random_seed) != generators_count")
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self.generators_random_seed = generators_random_seed
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samples = SampleLoader.load (self.sample_type, self.samples_path, sort_by_yaw_target_samples_path, use_caching=use_caching)
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np.random.shuffle(samples)
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self.samples_len = len(samples)
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if self.samples_len == 0:
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raise ValueError('No training data provided.')
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ct_samples = SampleLoader.load (SampleType.FACE, random_ct_samples_path, use_caching=use_caching) if random_ct_samples_path is not None else None
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index_host = mp_utils.IndexHost(self.samples_len)
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if random_ct_samples_path is not None:
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ct_samples_host = SampleHost.mp_host (SampleType.FACE, random_ct_samples_path)
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ct_index_host = mp_utils.IndexHost( len(ct_samples_host) )
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else:
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ct_samples_host = None
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ct_index_host = None
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if self.debug:
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self.generators_count = 1
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self.generators = [iter_utils.ThisThreadGenerator ( self.batch_func, (0, samples, ct_samples) )]
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self.generators = [iter_utils.ThisThreadGenerator ( self.batch_func, (samples_host.create_cli(), index_host.create_cli(), ct_samples_host.create_cli() if ct_index_host is not None else None, ct_index_host.create_cli() if ct_index_host is not None else None) )]
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else:
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self.generators_count = min ( generators_count, self.samples_len )
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self.generators = [iter_utils.SubprocessGenerator ( self.batch_func, (i, samples[i::self.generators_count], ct_samples ) ) for i in range(self.generators_count) ]
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self.generators_count = np.clip(multiprocessing.cpu_count(), 2, 4)
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self.generators = [iter_utils.SubprocessGenerator ( self.batch_func, (samples_host.create_cli(), index_host.create_cli(), ct_samples_host.create_cli() if ct_index_host is not None else None, ct_index_host.create_cli() if ct_index_host is not None else None), start_now=True ) for i in range(self.generators_count) ]
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self.generator_counter = -1
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#overridable
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def get_total_sample_count(self):
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return self.samples_len
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def __iter__(self):
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return self
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@ -77,83 +66,37 @@ class SampleGeneratorFace(SampleGeneratorBase):
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generator = self.generators[self.generator_counter % len(self.generators) ]
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return next(generator)
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def batch_func(self, param ):
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generator_id, samples, ct_samples = param
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if self.generators_random_seed is not None:
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np.random.seed ( self.generators_random_seed[generator_id] )
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samples_len = len(samples)
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samples_idxs = [*range(samples_len)]
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ct_samples_len = len(ct_samples) if ct_samples is not None else 0
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if self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
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if all ( [ samples[idx] == None for idx in samples_idxs] ):
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raise ValueError('Not enough training data. Gather more faces!')
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if self.sample_type == SampleType.FACE:
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shuffle_idxs = []
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elif self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
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shuffle_idxs = []
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shuffle_idxs_2D = [[]]*samples_len
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while True:
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def batch_func(self, param ):
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samples, index_host, ct_samples, ct_index_host = param
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bs = self.batch_size
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while True:
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batches = None
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for n_batch in range(self.batch_size):
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while True:
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sample = None
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if self.sample_type == SampleType.FACE:
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if len(shuffle_idxs) == 0:
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shuffle_idxs = samples_idxs.copy()
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np.random.shuffle(shuffle_idxs)
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indexes = index_host.get(bs)
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ct_indexes = ct_index_host.get(bs) if ct_samples is not None else None
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idx = shuffle_idxs.pop()
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sample = samples[ idx ]
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for n_batch in range(bs):
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sample = samples[ indexes[n_batch] ]
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ct_sample = ct_samples[ ct_indexes[n_batch] ] if ct_samples is not None else None
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elif self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
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if len(shuffle_idxs) == 0:
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shuffle_idxs = samples_idxs.copy()
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np.random.shuffle(shuffle_idxs)
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try:
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x, = SampleProcessor.process ([sample], self.sample_process_options, self.output_sample_types, self.debug, ct_sample=ct_sample)
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except:
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raise Exception ("Exception occured in sample %s. Error: %s" % (sample.filename, traceback.format_exc() ) )
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idx = shuffle_idxs.pop()
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if samples[idx] != None:
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if len(shuffle_idxs_2D[idx]) == 0:
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a = shuffle_idxs_2D[idx] = [ *range(len(samples[idx])) ]
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np.random.shuffle (a)
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if batches is None:
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batches = [ [] for _ in range(len(x)) ]
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if self.add_sample_idx:
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batches += [ [] ]
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i_sample_idx = len(batches)-1
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idx2 = shuffle_idxs_2D[idx].pop()
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sample = samples[idx][idx2]
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idx = (idx << 16) | (idx2 & 0xFFFF)
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if sample is not None:
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try:
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ct_sample=None
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if ct_samples is not None:
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ct_sample=ct_samples[np.random.randint(ct_samples_len)]
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x, = SampleProcessor.process ([sample], self.sample_process_options, self.output_sample_types, self.debug, ct_sample=ct_sample)
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except:
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raise Exception ("Exception occured in sample %s. Error: %s" % (sample.filename, traceback.format_exc() ) )
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if batches is None:
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batches = [ [] for _ in range(len(x)) ]
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if self.add_sample_idx:
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batches += [ [] ]
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i_sample_idx = len(batches)-1
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for i in range(len(x)):
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batches[i].append ( x[i] )
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if self.add_sample_idx:
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batches[i_sample_idx].append (idx)
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break
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for i in range(len(x)):
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batches[i].append ( x[i] )
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if self.add_sample_idx:
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batches[i_sample_idx].append (idx)
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yield [ np.array(batch) for batch in batches]
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@staticmethod
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def get_person_id_max_count(samples_path):
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return SampleLoader.get_person_id_max_count(samples_path)
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return SampleHost.get_person_id_max_count(samples_path)
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