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initial code to extract umdfaces.io dataset and train pose estimator

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iperov 2019-04-23 08:14:09 +04:00
parent 51a917facc
commit e58197ca22
18 changed files with 437 additions and 57 deletions
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101
models/Model_FANSEG/Model.py
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@ -1,101 +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 onInitializeOptions(self, is_first_run, ask_override):
default_face_type = 'f'
if is_first_run:
self.options['face_type'] = io.input_str ("Half or Full face? (h/f, ?:help skip:f) : ", default_face_type, ['h','f'], help_message="Half face has better resolution, but covers less area of cheeks.").lower()
else:
self.options['face_type'] = self.options.get('face_type', default_face_type)
#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 if self.options['face_type'] == 'f' else FaceType.HALF
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
face_type = f.FACE_TYPE_FULL if self.options['face_type'] == 'f' else f.FACE_TYPE_HALF
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] ),
output_sample_types=[ [f.WARPED_TRANSFORMED | face_type | f.MODE_BGR_SHUFFLE | f.OPT_APPLY_MOTION_BLUR, self.resolution],
[f.WARPED_TRANSFORMED | face_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 ),
output_sample_types=[ [f.TRANSFORMED | face_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,acc = self.fan_seg.train_on_batch( [target_src], [target_src_mask] )
return ( ('loss', loss), ('acc',acc))
#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(test_A)
mBB = self.fan_seg.extract(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 [ ('training data', np.concatenate ( st, axis=0 ) ),
('evaluating data', np.concatenate ( st2, axis=0 ) ),
]