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https://github.com/iperov/DeepFaceLab.git
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274 lines
11 KiB
Python
274 lines
11 KiB
Python
import copy
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import multiprocessing
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import traceback
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import cv2
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import numpy as np
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from core import mplib
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from core.joblib import SubprocessGenerator, ThisThreadGenerator
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from facelib import LandmarksProcessor
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from samplelib import (SampleGeneratorBase, SampleLoader, SampleProcessor,
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SampleType)
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'''
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arg
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output_sample_types = [
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[SampleProcessor.TypeFlags, size, (optional) {} opts ] ,
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...
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]
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'''
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class SampleGeneratorFacePerson(SampleGeneratorBase):
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def __init__ (self, samples_path, debug=False, batch_size=1,
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sample_process_options=SampleProcessor.Options(),
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output_sample_types=[],
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person_id_mode=1,
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**kwargs):
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super().__init__(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.person_id_mode = person_id_mode
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raise NotImplementedError("Currently SampleGeneratorFacePerson is not implemented.")
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samples_host = SampleLoader.mp_host (SampleType.FACE, samples_path)
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samples = samples_host.get_list()
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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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unique_person_names = { sample.person_name for sample in samples }
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persons_name_idxs = { person_name : [] for person_name in unique_person_names }
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for i,sample in enumerate(samples):
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persons_name_idxs[sample.person_name].append (i)
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indexes2D = [ persons_name_idxs[person_name] for person_name in unique_person_names ]
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index2d_host = mplib.Index2DHost(indexes2D)
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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, (samples_host.create_cli(), index2d_host.create_cli(),) )]
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else:
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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(), index2d_host.create_cli(),) ) for i in range(self.generators_count) ]
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self.generator_counter = -1
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def __iter__(self):
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return self
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def __next__(self):
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self.generator_counter += 1
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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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samples, index2d_host, = param
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bs = self.batch_size
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while True:
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person_idxs = index2d_host.get_1D(bs)
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samples_idxs = index2d_host.get_2D(person_idxs, 1)
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batches = None
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for n_batch in range(bs):
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person_id = person_idxs[n_batch]
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sample_idx = samples_idxs[n_batch][0]
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sample = samples[ sample_idx ]
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try:
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x, = SampleProcessor.process ([sample], self.sample_process_options, self.output_sample_types, self.debug)
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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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batches += [ [] ]
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i_person_id = 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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batches[i_person_id].append ( np.array([person_id]) )
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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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"""
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if self.person_id_mode==1:
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samples_len = len(samples)
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samples_idxs = [*range(samples_len)]
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shuffle_idxs = []
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elif self.person_id_mode==2:
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persons_count = len(samples)
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person_idxs = []
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for j in range(persons_count):
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for i in range(j+1,persons_count):
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person_idxs += [ [i,j] ]
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shuffle_person_idxs = []
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samples_idxs = [None]*persons_count
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shuffle_idxs = [None]*persons_count
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for i in range(persons_count):
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samples_idxs[i] = [*range(len(samples[i]))]
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shuffle_idxs[i] = []
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elif self.person_id_mode==3:
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persons_count = len(samples)
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person_idxs = [ *range(persons_count) ]
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shuffle_person_idxs = []
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samples_idxs = [None]*persons_count
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shuffle_idxs = [None]*persons_count
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for i in range(persons_count):
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samples_idxs[i] = [*range(len(samples[i]))]
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shuffle_idxs[i] = []
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if self.person_id_mode==2:
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if len(shuffle_person_idxs) == 0:
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shuffle_person_idxs = person_idxs.copy()
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np.random.shuffle(shuffle_person_idxs)
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person_ids = shuffle_person_idxs.pop()
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batches = None
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for n_batch in range(self.batch_size):
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if self.person_id_mode==1:
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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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idx = shuffle_idxs.pop()
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sample = samples[ idx ]
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try:
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x, = SampleProcessor.process ([sample], self.sample_process_options, self.output_sample_types, self.debug)
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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 type(x) != tuple and type(x) != list:
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raise Exception('SampleProcessor.process returns NOT tuple/list')
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if batches is None:
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batches = [ [] for _ in range(len(x)) ]
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batches += [ [] ]
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i_person_id = 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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batches[i_person_id].append ( np.array([sample.person_id]) )
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elif self.person_id_mode==2:
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person_id1, person_id2 = person_ids
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if len(shuffle_idxs[person_id1]) == 0:
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shuffle_idxs[person_id1] = samples_idxs[person_id1].copy()
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np.random.shuffle(shuffle_idxs[person_id1])
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idx = shuffle_idxs[person_id1].pop()
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sample1 = samples[person_id1][idx]
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if len(shuffle_idxs[person_id2]) == 0:
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shuffle_idxs[person_id2] = samples_idxs[person_id2].copy()
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np.random.shuffle(shuffle_idxs[person_id2])
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idx = shuffle_idxs[person_id2].pop()
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sample2 = samples[person_id2][idx]
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if sample1 is not None and sample2 is not None:
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try:
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x1, = SampleProcessor.process ([sample1], self.sample_process_options, self.output_sample_types, self.debug)
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except:
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raise Exception ("Exception occured in sample %s. Error: %s" % (sample1.filename, traceback.format_exc() ) )
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try:
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x2, = SampleProcessor.process ([sample2], self.sample_process_options, self.output_sample_types, self.debug)
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except:
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raise Exception ("Exception occured in sample %s. Error: %s" % (sample2.filename, traceback.format_exc() ) )
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x1_len = len(x1)
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if batches is None:
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batches = [ [] for _ in range(x1_len) ]
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batches += [ [] ]
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i_person_id1 = len(batches)-1
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batches += [ [] for _ in range(len(x2)) ]
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batches += [ [] ]
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i_person_id2 = len(batches)-1
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for i in range(x1_len):
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batches[i].append ( x1[i] )
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for i in range(len(x2)):
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batches[x1_len+1+i].append ( x2[i] )
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batches[i_person_id1].append ( np.array([sample1.person_id]) )
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batches[i_person_id2].append ( np.array([sample2.person_id]) )
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elif self.person_id_mode==3:
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if len(shuffle_person_idxs) == 0:
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shuffle_person_idxs = person_idxs.copy()
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np.random.shuffle(shuffle_person_idxs)
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person_id = shuffle_person_idxs.pop()
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if len(shuffle_idxs[person_id]) == 0:
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shuffle_idxs[person_id] = samples_idxs[person_id].copy()
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np.random.shuffle(shuffle_idxs[person_id])
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idx = shuffle_idxs[person_id].pop()
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sample1 = samples[person_id][idx]
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if len(shuffle_idxs[person_id]) == 0:
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shuffle_idxs[person_id] = samples_idxs[person_id].copy()
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np.random.shuffle(shuffle_idxs[person_id])
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idx = shuffle_idxs[person_id].pop()
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sample2 = samples[person_id][idx]
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if sample1 is not None and sample2 is not None:
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try:
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x1, = SampleProcessor.process ([sample1], self.sample_process_options, self.output_sample_types, self.debug)
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except:
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raise Exception ("Exception occured in sample %s. Error: %s" % (sample1.filename, traceback.format_exc() ) )
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try:
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x2, = SampleProcessor.process ([sample2], self.sample_process_options, self.output_sample_types, self.debug)
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except:
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raise Exception ("Exception occured in sample %s. Error: %s" % (sample2.filename, traceback.format_exc() ) )
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x1_len = len(x1)
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if batches is None:
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batches = [ [] for _ in range(x1_len) ]
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batches += [ [] ]
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i_person_id1 = len(batches)-1
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batches += [ [] for _ in range(len(x2)) ]
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batches += [ [] ]
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i_person_id2 = len(batches)-1
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for i in range(x1_len):
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batches[i].append ( x1[i] )
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for i in range(len(x2)):
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batches[x1_len+1+i].append ( x2[i] )
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batches[i_person_id1].append ( np.array([sample1.person_id]) )
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batches[i_person_id2].append ( np.array([sample2.person_id]) )
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"""
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