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8484060e01
Enable autobackup? (y/n ?:help skip:%s) : Autobackup model files with preview every hour for last 15 hours. Latest backup located in model/<>_autobackups/01 SAE: added option only for CUDA builds: Enable gradient clipping? (y/n, ?:help skip:%s) : Gradient clipping reduces chance of model collapse, sacrificing speed of training.
122 lines
No EOL
5.4 KiB
Python
122 lines
No EOL
5.4 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 PoseEstimator
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from samplelib import *
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from interact import interact as io
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import imagelib
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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_enable_autobackup=False,
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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 onInitializeOptions(self, is_first_run, ask_override):
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yn_str = {True:'y',False:'n'}
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default_face_type = 'f'
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if is_first_run:
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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()
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else:
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self.options['face_type'] = self.options.get('face_type', default_face_type)
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def_train_bgr = self.options.get('train_bgr', True)
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if is_first_run or ask_override:
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self.options['train_bgr'] = io.input_bool ("Train bgr? (y/n, ?:help skip: %s) : " % (yn_str[def_train_bgr]), def_train_bgr)
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else:
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self.options['train_bgr'] = self.options.get('train_bgr', def_train_bgr)
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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( {4:64} )
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self.resolution = 128
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self.face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF
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self.pose_est = PoseEstimator(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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t = SampleProcessor.Types
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face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF
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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, generators_count=4,
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sample_process_options=SampleProcessor.Options( rotation_range=[0,0] ), #random_flip=True,
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output_sample_types=[ {'types': (t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_BGR_SHUFFLE), 'resolution':self.resolution, 'motion_blur':(25, 1) },
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{'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR_SHUFFLE), 'resolution':self.resolution },
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{'types': (t.IMG_PITCH_YAW_ROLL,)}
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]),
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SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, generators_count=4,
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sample_process_options=SampleProcessor.Options( rotation_range=[0,0] ), #random_flip=True,
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output_sample_types=[ {'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'resolution':self.resolution },
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{'types': (t.IMG_PITCH_YAW_ROLL,)}
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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.pose_est.save_weights()
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#override
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def onTrainOneIter(self, generators_samples, generators_list):
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target_srcw, target_src, pitch_yaw_roll = generators_samples[0]
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bgr_loss, pyr_loss = self.pose_est.train_on_batch( target_srcw, target_src, pitch_yaw_roll, skip_bgr_train=not self.options['train_bgr'] )
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return ( ('bgr_loss', bgr_loss), ('pyr_loss', pyr_loss), )
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#override
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def onGetPreview(self, generators_samples):
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test_src = generators_samples[0][1][0:4] #first 4 samples
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test_pyr_src = generators_samples[0][2][0:4]
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test_dst = generators_samples[1][0][0:4]
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test_pyr_dst = generators_samples[1][1][0:4]
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h,w,c = self.resolution,self.resolution,3
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h_line = 13
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result = []
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for name, img, pyr in [ ['training data', test_src, test_pyr_src], \
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['evaluating data',test_dst, test_pyr_dst] ]:
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bgr_pred, pyr_pred = self.pose_est.extract(img)
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hor_imgs = []
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for i in range(len(img)):
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img_info = np.ones ( (h,w,c) ) * 0.1
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i_pyr = pyr[i]
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i_pyr_pred = pyr_pred[i]
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lines = ["%.4f %.4f %.4f" % (i_pyr[0],i_pyr[1],i_pyr[2]),
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"%.4f %.4f %.4f" % (i_pyr_pred[0],i_pyr_pred[1],i_pyr_pred[2]) ]
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lines_count = len(lines)
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for ln in range(lines_count):
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img_info[ ln*h_line:(ln+1)*h_line, 0:w] += \
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imagelib.get_text_image ( (h_line,w,c), lines[ln], color=[0.8]*c )
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hor_imgs.append ( np.concatenate ( (
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img[i,:,:,0:3],
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bgr_pred[i],
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img_info
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), axis=1) )
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result += [ (name, np.concatenate (hor_imgs, axis=0)) ]
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return result |