from functools import partial import cv2 import numpy as np from facelib import FaceType from interact import interact as io from mathlib import get_power_of_two from models import ModelBase from nnlib import nnlib, FUNIT from samplelib import * class FUNITModel(ModelBase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs, 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['resolution'] = io.input_int("Resolution ( 128,224 ?:help skip:128) : ", 128, [128,224]) else: self.options['resolution'] = self.options.get('resolution', 128) 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="").lower() else: self.options['face_type'] = self.options.get('face_type', default_face_type) if (is_first_run or ask_override) and 'tensorflow' in self.device_config.backend: def_optimizer_mode = self.options.get('optimizer_mode', 1) self.options['optimizer_mode'] = io.input_int ("Optimizer mode? ( 1,2,3 ?:help skip:%d) : " % (def_optimizer_mode), def_optimizer_mode, help_message="1 - no changes. 2 - allows you to train x2 bigger network consuming RAM. 3 - allows you to train x3 bigger network consuming huge amount of RAM and slower, depends on CPU power.") else: self.options['optimizer_mode'] = self.options.get('optimizer_mode', 1) #override def onInitialize(self, batch_size=-1, **in_options): exec(nnlib.code_import_all, locals(), globals()) self.set_vram_batch_requirements({4:16}) resolution = self.options['resolution'] face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF person_id_max_count = SampleGeneratorFace.get_person_id_max_count(self.training_data_src_path) self.model = FUNIT( face_type_str=FaceType.toString(face_type), batch_size=self.batch_size, encoder_nf=64, encoder_downs=2, encoder_res_blk=2, class_downs=4, class_nf=64, class_latent=64, mlp_blks=2, dis_nf=64, dis_res_blks=10, num_classes=person_id_max_count, subpixel_decoder=True, initialize_weights=self.is_first_run(), is_training=self.is_training_mode, tf_cpu_mode=self.options['optimizer_mode']-1 ) if not self.is_first_run(): self.load_weights_safe(self.model.get_model_filename_list()) if self.is_training_mode: t = SampleProcessor.Types face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF output_sample_types=[ {'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'resolution':128, 'normalize_tanh':True} ] 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), output_sample_types=output_sample_types, person_id_mode=True ), SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size, sample_process_options=SampleProcessor.Options(random_flip=True), output_sample_types=output_sample_types, person_id_mode=True ), 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=output_sample_types, person_id_mode=True ), 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=output_sample_types, person_id_mode=True ), ]) #override def get_model_filename_list(self): return self.model.get_model_filename_list() #override def onSave(self): self.save_weights_safe(self.model.get_model_filename_list()) #override def onTrainOneIter(self, generators_samples, generators_list): xa,la = generators_samples[0] xb,lb = generators_samples[1] G_loss, D_loss = self.model.train(xa,la,xb,lb) return ( ('G_loss', G_loss), ('D_loss', D_loss), ) #override def onGetPreview(self, generators_samples): xa = generators_samples[0][0] xb = generators_samples[1][0] ta = generators_samples[2][0] tb = generators_samples[3][0] view_samples = min(4, xa.shape[0]) lines_train = [] lines_test = [] for i in range(view_samples): s_xa = self.model.get_average_class_code([ xa[i:i+1] ])[0][None,...] s_xb = self.model.get_average_class_code([ xb[i:i+1] ])[0][None,...] s_ta = self.model.get_average_class_code([ ta[i:i+1] ])[0][None,...] s_tb = self.model.get_average_class_code([ tb[i:i+1] ])[0][None,...] xaxa = self.model.convert ([ xa[i:i+1], s_xa ] )[0][0] xbxb = self.model.convert ([ xb[i:i+1], s_xb ] )[0][0] xaxb = self.model.convert ([ xa[i:i+1], s_xb ] )[0][0] xbxa = self.model.convert ([ xb[i:i+1], s_xa ] )[0][0] tata = self.model.convert ([ ta[i:i+1], s_ta ] )[0][0] tbtb = self.model.convert ([ tb[i:i+1], s_tb ] )[0][0] tatb = self.model.convert ([ ta[i:i+1], s_tb ] )[0][0] tbta = self.model.convert ([ tb[i:i+1], s_ta ] )[0][0] line_train = [ xa[i], xaxa, xb[i], xbxb, xaxb, xbxa ] line_test = [ ta[i], tata, tb[i], tbtb, tatb, tbta ] lines_train += [ np.concatenate([ np.clip(x/2+0.5,0,1) for x in line_train], axis=1) ] lines_test += [ np.concatenate([ np.clip(x/2+0.5,0,1) for x in line_test ], axis=1) ] lines_train = np.concatenate ( lines_train, axis=0 ) lines_test = np.concatenate ( lines_test, axis=0 ) return [ ('TRAIN', lines_train ), ('TEST', lines_test) ] def predictor_func (self, face=None, dummy_predict=False): if dummy_predict: self.model.convert ([ np.zeros ( (1, self.options['resolution'], self.options['resolution'], 3), dtype=np.float32 ), self.average_class_code ]) else: bgr, = self.model.convert ([ face[np.newaxis,...]*2-1, self.average_class_code ]) return bgr[0] / 2 + 0.5 #override def get_ConverterConfig(self): face_type = FaceType.FULL import converters return self.predictor_func, (self.options['resolution'], self.options['resolution'], 3), converters.ConverterConfigMasked(face_type=face_type, default_mode = 1, clip_hborder_mask_per=0.0625 if (face_type == FaceType.FULL) else 0, ) Model = FUNITModel