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https://github.com/iperov/DeepFaceLab.git
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92 lines
3.3 KiB
Python
92 lines
3.3 KiB
Python
import pickle
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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 samplelib import (SampleGeneratorBase, SampleHost, SampleProcessor,
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SampleType)
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from utils import iter_utils
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'''
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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 SampleGeneratorFaceTemporal(SampleGeneratorBase):
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def __init__ (self, samples_path, debug, batch_size, temporal_image_count, sample_process_options=SampleProcessor.Options(), output_sample_types=[], generators_count=2, **kwargs):
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super().__init__(samples_path, debug, batch_size)
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self.temporal_image_count = temporal_image_count
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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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if self.debug:
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self.generators_count = 1
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else:
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self.generators_count = generators_count
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samples = SampleHost.load (SampleType.FACE_TEMPORAL_SORTED, self.samples_path)
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samples_len = len(samples)
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if samples_len == 0:
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raise ValueError('No training data provided.')
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pickled_samples = pickle.dumps(samples, 4)
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if self.debug:
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self.generators = [iter_utils.ThisThreadGenerator ( self.batch_func, (0, pickled_samples) )]
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else:
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self.generators = [iter_utils.SubprocessGenerator ( self.batch_func, (i, pickled_samples) ) 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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generator_id, pickled_samples = param
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samples = pickle.loads(pickled_samples)
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samples_len = len(samples)
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mult_max = 1
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l = samples_len - ( (self.temporal_image_count)*mult_max - (mult_max-1) )
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samples_idxs = [ *range(l+1) ]
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if len(samples_idxs) - self.temporal_image_count < 0:
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raise ValueError('Not enough samples to fit temporal line.')
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shuffle_idxs = []
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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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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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temporal_samples = []
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mult = np.random.randint(mult_max)+1
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for i in range( self.temporal_image_count ):
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sample = samples[ idx+i*mult ]
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try:
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temporal_samples += SampleProcessor.process ([sample], self.sample_process_options, self.output_sample_types, self.debug)[0]
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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(temporal_samples)) ]
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for i in range(len(temporal_samples)):
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batches[i].append ( temporal_samples[i] )
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yield [ np.array(batch) for batch in batches]
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