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update == 04.20.2019 == (#242)

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* superb improved fanseg

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* added FANseg extractor for src and dst faces to use it in training

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* update to 'partial' func

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* trained FANSeg_256_full_face.h5,
new experimental models: AVATAR, RecycleGAN

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* fix for TCC mode cards(tesla), was conflict with plaidML initialization.

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* update manuals

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iperov 2019-04-20 08:23:08 +04:00 • committed by GitHub
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commit 046649e6be
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94
models/Model_FANSegmentator/Model.py
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@ -1,94 +0,0 @@
import numpy as np
from nnlib import nnlib
from models import ModelBase
from facelib import FaceType
from facelib import FANSegmentator
from samplelib import *
from interact import interact as io
class Model(ModelBase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs,
ask_write_preview_history=False,
ask_target_iter=False,
ask_sort_by_yaw=False,
ask_random_flip=False,
ask_src_scale_mod=False)
#override
def onInitialize(self):
exec(nnlib.import_all(), locals(), globals())
self.set_vram_batch_requirements( {1.5:4} )
self.resolution = 256
self.face_type = FaceType.FULL
self.fan_seg = FANSegmentator(self.resolution,
FaceType.toString(self.face_type),
load_weights=not self.is_first_run(),
weights_file_root=self.get_model_root_path(),
training=True)
if self.is_training_mode:
f = SampleProcessor.TypeFlags
f_type = f.FACE_TYPE_FULL
self.set_training_data_generators ([
SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=True, motion_blur = [25, 1], normalize_tanh = True ),
output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE | f.OPT_APPLY_MOTION_BLUR, self.resolution],
[f.TRANSFORMED | f_type | f.MODE_M | f.FACE_MASK_FULL, self.resolution]
]),
SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=True, normalize_tanh = True ),
output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE, self.resolution]
])
])
#override
def onSave(self):
self.fan_seg.save_weights()
#override
def onTrainOneIter(self, generators_samples, generators_list):
target_src, target_src_mask = generators_samples[0]
loss = self.fan_seg.train_on_batch( [target_src], [target_src_mask] )
return ( ('loss', loss), )
#override
def onGetPreview(self, sample):
test_A = sample[0][0][0:4] #first 4 samples
test_B = sample[1][0][0:4] #first 4 samples
mAA = self.fan_seg.extract_from_bgr([test_A])
mBB = self.fan_seg.extract_from_bgr([test_B])
test_A, test_B, = [ np.clip( (x + 1.0)/2.0, 0.0, 1.0) for x in [test_A, test_B] ]
mAA = np.repeat ( mAA, (3,), -1)
mBB = np.repeat ( mBB, (3,), -1)
st = []
for i in range(0, len(test_A)):
st.append ( np.concatenate ( (
test_A[i,:,:,0:3],
mAA[i],
test_A[i,:,:,0:3]*mAA[i],
), axis=1) )
st2 = []
for i in range(0, len(test_B)):
st2.append ( np.concatenate ( (
test_B[i,:,:,0:3],
mBB[i],
test_B[i,:,:,0:3]*mBB[i],
), axis=1) )
return [ ('FANSegmentator', np.concatenate ( st, axis=0 ) ),
('never seen', np.concatenate ( st2, axis=0 ) ),
]