mirror of
https://github.com/iperov/DeepFaceLab.git
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94 lines
3.8 KiB
Python
94 lines
3.8 KiB
Python
import numpy as np
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from nnlib import nnlib
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from models import ModelBase
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from facelib import FaceType
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from facelib import FANSegmentator
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from samples import *
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from interact import interact as io
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class Model(ModelBase):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs,
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ask_write_preview_history=False,
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ask_target_iter=False,
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ask_sort_by_yaw=False,
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ask_random_flip=False,
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ask_src_scale_mod=False)
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#override
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def onInitialize(self):
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exec(nnlib.import_all(), locals(), globals())
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self.set_vram_batch_requirements( {1.5:4} )
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self.resolution = 256
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self.face_type = FaceType.FULL
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self.fan_seg = FANSegmentator(self.resolution,
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FaceType.toString(self.face_type),
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load_weights=not self.is_first_run(),
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weights_file_root=self.get_model_root_path(),
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training=True)
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if self.is_training_mode:
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f = SampleProcessor.TypeFlags
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f_type = f.FACE_ALIGN_FULL
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self.set_training_data_generators ([
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SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=True, normalize_tanh = True ),
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output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE, self.resolution],
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[f.TRANSFORMED | f_type | f.MODE_M | f.FACE_MASK_FULL, self.resolution]
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]),
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SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=True, normalize_tanh = True ),
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output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE, self.resolution]
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])
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])
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#override
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def onSave(self):
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self.fan_seg.save_weights()
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#override
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def onTrainOneIter(self, generators_samples, generators_list):
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target_src, target_src_mask = generators_samples[0]
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loss = self.fan_seg.train_on_batch( [target_src], [target_src_mask] )
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return ( ('loss', loss), )
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#override
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def onGetPreview(self, sample):
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test_A = sample[0][0][0:4] #first 4 samples
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test_B = sample[1][0][0:4] #first 4 samples
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mAA = self.fan_seg.extract_from_bgr([test_A])
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mBB = self.fan_seg.extract_from_bgr([test_B])
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test_A, test_B, = [ np.clip( (x + 1.0)/2.0, 0.0, 1.0) for x in [test_A, test_B] ]
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mAA = np.repeat ( mAA, (3,), -1)
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mBB = np.repeat ( mBB, (3,), -1)
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st = []
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for i in range(0, len(test_A)):
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st.append ( np.concatenate ( (
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test_A[i,:,:,0:3],
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mAA[i],
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test_A[i,:,:,0:3]*mAA[i],
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), axis=1) )
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st2 = []
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for i in range(0, len(test_B)):
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st2.append ( np.concatenate ( (
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test_B[i,:,:,0:3],
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mBB[i],
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test_B[i,:,:,0:3]*mBB[i],
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), axis=1) )
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return [ ('FANSegmentator', np.concatenate ( st, axis=0 ) ),
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('never seen', np.concatenate ( st2, axis=0 ) ),
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]
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