import numpy as np from nnlib import nnlib from models import ModelBase from facelib import FaceType from samples import * from utils.console_utils import * #SAE - Styled AutoEncoder class SAEModel(ModelBase): encoderH5 = 'encoder.h5' inter_BH5 = 'inter_B.h5' inter_ABH5 = 'inter_AB.h5' decoderH5 = 'decoder.h5' decodermH5 = 'decoderm.h5' decoder_srcH5 = 'decoder_src.h5' decoder_srcmH5 = 'decoder_srcm.h5' decoder_dstH5 = 'decoder_dst.h5' decoder_dstmH5 = 'decoder_dstm.h5' #override def onInitializeOptions(self, is_first_run, ask_override): default_resolution = 128 default_archi = 'liae' default_face_type = 'f' if is_first_run: self.options['resolution'] = input_int("Resolution (64,128 ?:help skip:128) : ", default_resolution, [64,128], help_message="More resolution requires more VRAM.") self.options['archi'] = input_str ("AE architecture (df, liae, ?:help skip:%s) : " % (default_archi) , default_archi, ['df','liae'], help_message="DF keeps faces more natural, while LIAE can fix overly different face shapes.").lower() self.options['lighter_encoder'] = input_bool ("Use lightweight encoder? (y/n, ?:help skip:n) : ", False, help_message="Lightweight encoder is 35% faster, requires less VRAM, sacrificing overall quality.") self.options['learn_mask'] = input_bool ("Learn mask? (y/n, ?:help skip:y) : ", True, help_message="Choose NO to reduce model size. In this case converter forced to use 'not predicted mask' that is not smooth as predicted. Styled SAE can learn without mask and produce same quality fake if you choose high blur value in converter.") else: self.options['resolution'] = self.options.get('resolution', default_resolution) self.options['archi'] = self.options.get('archi', default_archi) self.options['lighter_encoder'] = self.options.get('lighter_encoder', False) self.options['learn_mask'] = self.options.get('learn_mask', True) default_face_style_power = 10.0 if is_first_run or ask_override: default_face_style_power = default_face_style_power if is_first_run else self.options.get('face_style_power', default_face_style_power) self.options['face_style_power'] = np.clip ( input_number("Face style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_face_style_power), default_face_style_power, help_message="How fast NN will learn dst face style during generalization of src and dst faces. If style is learned good enough, set this value to 0.01 to prevent artifacts appearing."), 0.0, 100.0 ) else: self.options['face_style_power'] = self.options.get('face_style_power', default_face_style_power) default_bg_style_power = 10.0 if is_first_run or ask_override: default_bg_style_power = default_bg_style_power if is_first_run else self.options.get('bg_style_power', default_bg_style_power) self.options['bg_style_power'] = np.clip ( input_number("Background style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_bg_style_power), default_bg_style_power, help_message="How fast NN will learn dst background style during generalization of src and dst faces. If style is learned good enough, set this value to 0.1-0.3 to prevent artifacts appearing."), 0.0, 100.0 ) else: self.options['bg_style_power'] = self.options.get('bg_style_power', default_bg_style_power) default_ae_dims = 256 if self.options['archi'] == 'liae' else 512 default_ed_ch_dims = 42 if is_first_run: self.options['ae_dims'] = np.clip ( input_int("AutoEncoder dims (128-1024 ?:help skip:%d) : " % (default_ae_dims) , default_ae_dims, help_message="More dims are better, but requires more VRAM. You can fine-tune model size to fit your GPU." ), 128, 1024 ) self.options['ed_ch_dims'] = np.clip ( input_int("Encoder/Decoder dims per channel (21-85 ?:help skip:%d) : " % (default_ed_ch_dims) , default_ed_ch_dims, help_message="More dims are better, but requires more VRAM. You can fine-tune model size to fit your GPU." ), 21, 85 ) self.options['face_type'] = 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['ae_dims'] = self.options.get('ae_dims', default_ae_dims) self.options['ed_ch_dims'] = self.options.get('ed_ch_dims', default_ed_ch_dims) self.options['face_type'] = self.options.get('face_type', default_face_type) #override def onInitialize(self, **in_options): exec(nnlib.import_all(), locals(), globals()) self.set_vram_batch_requirements({2:1,3:2,4:3,5:6,6:8,7:12,8:16}) resolution = self.options['resolution'] ae_dims = self.options['ae_dims'] ed_ch_dims = self.options['ed_ch_dims'] adapt_k_size = False bgr_shape = (resolution, resolution, 3) mask_shape = (resolution, resolution, 1) warped_src = Input(bgr_shape) target_src = Input(bgr_shape) target_srcm = Input(mask_shape) warped_dst = Input(bgr_shape) target_dst = Input(bgr_shape) target_dstm = Input(mask_shape) if self.options['archi'] == 'liae': self.encoder = modelify(SAEModel.LIAEEncFlow(resolution, adapt_k_size, self.options['lighter_encoder'], ed_ch_dims=ed_ch_dims) ) (Input(bgr_shape)) enc_output_Inputs = [ Input(K.int_shape(x)[1:]) for x in self.encoder.outputs ] self.inter_B = modelify(SAEModel.LIAEInterFlow(resolution, ae_dims=ae_dims)) (enc_output_Inputs) self.inter_AB = modelify(SAEModel.LIAEInterFlow(resolution, ae_dims=ae_dims)) (enc_output_Inputs) inter_output_Inputs = [ Input( np.array(K.int_shape(x)[1:])*(1,1,2) ) for x in self.inter_B.outputs ] self.decoder = modelify(SAEModel.LIAEDecFlow (bgr_shape[2],ed_ch_dims=ed_ch_dims//2)) (inter_output_Inputs) if self.options['learn_mask']: self.decoderm = modelify(SAEModel.LIAEDecFlow (mask_shape[2],ed_ch_dims=int(ed_ch_dims/1.5) )) (inter_output_Inputs) if not self.is_first_run(): self.encoder.load_weights (self.get_strpath_storage_for_file(self.encoderH5)) self.inter_B.load_weights (self.get_strpath_storage_for_file(self.inter_BH5)) self.inter_AB.load_weights (self.get_strpath_storage_for_file(self.inter_ABH5)) self.decoder.load_weights (self.get_strpath_storage_for_file(self.decoderH5)) if self.options['learn_mask']: self.decoderm.load_weights (self.get_strpath_storage_for_file(self.decodermH5)) warped_src_code = self.encoder (warped_src) warped_src_inter_AB_code = self.inter_AB (warped_src_code) warped_src_inter_code = Concatenate()([warped_src_inter_AB_code,warped_src_inter_AB_code]) pred_src_src = self.decoder(warped_src_inter_code) if self.options['learn_mask']: pred_src_srcm = self.decoderm(warped_src_inter_code) warped_dst_code = self.encoder (warped_dst) warped_dst_inter_B_code = self.inter_B (warped_dst_code) warped_dst_inter_AB_code = self.inter_AB (warped_dst_code) warped_dst_inter_code = Concatenate()([warped_dst_inter_B_code,warped_dst_inter_AB_code]) pred_dst_dst = self.decoder(warped_dst_inter_code) if self.options['learn_mask']: pred_dst_dstm = self.decoderm(warped_dst_inter_code) warped_src_dst_inter_code = Concatenate()([warped_dst_inter_AB_code,warped_dst_inter_AB_code]) pred_src_dst = self.decoder(warped_src_dst_inter_code) if self.options['learn_mask']: pred_src_dstm = self.decoderm(warped_src_dst_inter_code) else: self.encoder = modelify(SAEModel.DFEncFlow(resolution, adapt_k_size, self.options['lighter_encoder'], ae_dims=ae_dims, ed_ch_dims=ed_ch_dims) ) (Input(bgr_shape)) dec_Inputs = [ Input(K.int_shape(x)[1:]) for x in self.encoder.outputs ] self.decoder_src = modelify(SAEModel.DFDecFlow (bgr_shape[2],ed_ch_dims=ed_ch_dims//2)) (dec_Inputs) self.decoder_dst = modelify(SAEModel.DFDecFlow (bgr_shape[2],ed_ch_dims=ed_ch_dims//2)) (dec_Inputs) if self.options['learn_mask']: self.decoder_srcm = modelify(SAEModel.DFDecFlow (mask_shape[2],ed_ch_dims=int(ed_ch_dims/1.5))) (dec_Inputs) self.decoder_dstm = modelify(SAEModel.DFDecFlow (mask_shape[2],ed_ch_dims=int(ed_ch_dims/1.5))) (dec_Inputs) if not self.is_first_run(): self.encoder.load_weights (self.get_strpath_storage_for_file(self.encoderH5)) self.decoder_src.load_weights (self.get_strpath_storage_for_file(self.decoder_srcH5)) self.decoder_dst.load_weights (self.get_strpath_storage_for_file(self.decoder_dstH5)) if self.options['learn_mask']: self.decoder_srcm.load_weights (self.get_strpath_storage_for_file(self.decoder_srcmH5)) self.decoder_dstm.load_weights (self.get_strpath_storage_for_file(self.decoder_dstmH5)) warped_src_code = self.encoder (warped_src) warped_dst_code = self.encoder (warped_dst) pred_src_src = self.decoder_src(warped_src_code) pred_dst_dst = self.decoder_dst(warped_dst_code) pred_src_dst = self.decoder_src(warped_dst_code) if self.options['learn_mask']: pred_src_srcm = self.decoder_srcm(warped_src_code) pred_dst_dstm = self.decoder_dstm(warped_dst_code) pred_src_dstm = self.decoder_srcm(warped_dst_code) ms_count = len(pred_src_src) target_src_ar = [ target_src if i == 0 else tf.image.resize_bicubic( target_src, (resolution // (2**i) ,)*2 ) for i in range(ms_count-1, -1, -1)] target_srcm_ar = [ target_srcm if i == 0 else tf.image.resize_bicubic( target_srcm, (resolution // (2**i) ,)*2 ) for i in range(ms_count-1, -1, -1)] target_dst_ar = [ target_dst if i == 0 else tf.image.resize_bicubic( target_dst, (resolution // (2**i) ,)*2 ) for i in range(ms_count-1, -1, -1)] target_dstm_ar = [ target_dstm if i == 0 else tf.image.resize_bicubic( target_dstm, (resolution // (2**i) ,)*2 ) for i in range(ms_count-1, -1, -1)] target_srcm_blurred_ar = [ tf_gaussian_blur( max(1, x.get_shape().as_list()[1] // 32) )(x) for x in target_srcm_ar] target_srcm_sigm_ar = [ x / 2.0 + 0.5 for x in target_srcm_blurred_ar] target_srcm_anti_sigm_ar = [ 1.0 - x for x in target_srcm_sigm_ar] target_dstm_blurred_ar = [ tf_gaussian_blur( max(1, x.get_shape().as_list()[1] // 32) )(x) for x in target_dstm_ar] target_dstm_sigm_ar = [ x / 2.0 + 0.5 for x in target_dstm_blurred_ar] target_dstm_anti_sigm_ar = [ 1.0 - x for x in target_dstm_sigm_ar] target_src_sigm_ar = [ x + 1 for x in target_src_ar] target_dst_sigm_ar = [ x + 1 for x in target_dst_ar] pred_src_src_sigm_ar = [ x + 1 for x in pred_src_src] pred_dst_dst_sigm_ar = [ x + 1 for x in pred_dst_dst] pred_src_dst_sigm_ar = [ x + 1 for x in pred_src_dst] target_src_masked_ar = [ target_src_sigm_ar[i]*target_srcm_sigm_ar[i] for i in range(len(target_src_sigm_ar))] target_dst_masked_ar = [ target_dst_sigm_ar[i]*target_dstm_sigm_ar[i] for i in range(len(target_dst_sigm_ar))] target_dst_anti_masked_ar = [ target_dst_sigm_ar[i]*target_dstm_anti_sigm_ar[i] for i in range(len(target_dst_sigm_ar))] psd_target_dst_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))] psd_target_dst_anti_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_anti_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))] if self.is_training_mode: def optimizer(): return Adam(lr=5e-5, beta_1=0.5, beta_2=0.999) if self.options['archi'] == 'liae': src_loss_train_weights = self.encoder.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights dst_loss_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights if self.options['learn_mask']: src_mask_loss_train_weights = self.encoder.trainable_weights + self.inter_AB.trainable_weights + self.decoderm.trainable_weights dst_mask_loss_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoderm.trainable_weights else: src_loss_train_weights = self.encoder.trainable_weights + self.decoder_src.trainable_weights dst_loss_train_weights = self.encoder.trainable_weights + self.decoder_dst.trainable_weights if self.options['learn_mask']: src_mask_loss_train_weights = self.encoder.trainable_weights + self.decoder_srcm.trainable_weights dst_mask_loss_train_weights = self.encoder.trainable_weights + self.decoder_dstm.trainable_weights src_loss = sum([ K.mean( 100*K.square(tf_dssim(2.0)( target_src_masked_ar[i], pred_src_src_sigm_ar[i] * target_srcm_sigm_ar[i] ) )) for i in range(len(target_src_masked_ar)) ]) if self.options['face_style_power'] != 0: face_style_power = self.options['face_style_power'] / 100.0 src_loss += tf_style_loss(gaussian_blur_radius=resolution // 8, loss_weight=0.2*face_style_power)( psd_target_dst_masked_ar[-1], target_dst_masked_ar[-1] ) if self.options['bg_style_power'] != 0: bg_style_power = self.options['bg_style_power'] / 100.0 src_loss += K.mean( (100*bg_style_power)*K.square(tf_dssim(2.0)( psd_target_dst_anti_masked_ar[-1], target_dst_anti_masked_ar[-1] ))) self.src_train = K.function ([warped_src, target_src, target_srcm, warped_dst, target_dst, target_dstm ],[src_loss], optimizer().get_updates(src_loss, src_loss_train_weights) ) dst_loss = sum([ K.mean( 100*K.square(tf_dssim(2.0)( target_dst_masked_ar[i], pred_dst_dst_sigm_ar[i] * target_dstm_sigm_ar[i] ) )) for i in range(len(target_dst_masked_ar)) ]) self.dst_train = K.function ([warped_dst, target_dst, target_dstm ],[dst_loss], optimizer().get_updates(dst_loss, dst_loss_train_weights) ) if self.options['learn_mask']: src_mask_loss = sum([ K.mean(K.square(target_srcm_ar[i]-pred_src_srcm[i])) for i in range(len(target_srcm_ar)) ]) self.src_mask_train = K.function ([warped_src, target_srcm],[src_mask_loss], optimizer().get_updates(src_mask_loss, src_mask_loss_train_weights) ) dst_mask_loss = sum([ K.mean(K.square(target_dstm_ar[i]-pred_dst_dstm[i])) for i in range(len(target_dstm_ar)) ]) self.dst_mask_train = K.function ([warped_dst, target_dstm],[dst_mask_loss], optimizer().get_updates(dst_mask_loss, dst_mask_loss_train_weights) ) self.AE_view = K.function ([warped_src, warped_dst], [pred_src_src[-1], pred_dst_dst[-1], pred_src_dst[-1] ] ) else: if self.options['learn_mask']: self.AE_convert = K.function ([warped_dst],[ pred_src_dst[-1], pred_src_dstm[-1] ]) else: self.AE_convert = K.function ([warped_dst],[ pred_src_dst[-1] ]) if self.is_training_mode: f = SampleProcessor.TypeFlags face_type = f.FACE_ALIGN_FULL if self.options['face_type'] == 'f' else f.FACE_ALIGN_HALF self.set_training_data_generators ([ SampleGeneratorFace(self.training_data_src_path, sort_by_yaw_target_samples_path=self.training_data_dst_path if self.sort_by_yaw else None, debug=self.is_debug(), batch_size=self.batch_size, sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, normalize_tanh = True, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ), output_sample_types=[ [f.WARPED_TRANSFORMED | face_type | f.MODE_BGR, resolution], [f.TRANSFORMED | face_type | f.MODE_BGR, resolution], [f.TRANSFORMED | face_type | f.MODE_M | f.FACE_MASK_FULL, resolution] ] ), SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, normalize_tanh = True), output_sample_types=[ [f.WARPED_TRANSFORMED | face_type | f.MODE_BGR, resolution], [f.TRANSFORMED | face_type | f.MODE_BGR, resolution], [f.TRANSFORMED | face_type | f.MODE_M | f.FACE_MASK_FULL, resolution] ] ) ]) #override def onSave(self): if self.options['archi'] == 'liae': ar = [[self.encoder, self.get_strpath_storage_for_file(self.encoderH5)], [self.inter_B, self.get_strpath_storage_for_file(self.inter_BH5)], [self.inter_AB, self.get_strpath_storage_for_file(self.inter_ABH5)], [self.decoder, self.get_strpath_storage_for_file(self.decoderH5)] ] if self.options['learn_mask']: ar += [ [self.decoderm, self.get_strpath_storage_for_file(self.decodermH5)] ] else: ar = [[self.encoder, self.get_strpath_storage_for_file(self.encoderH5)], [self.decoder_src, self.get_strpath_storage_for_file(self.decoder_srcH5)], [self.decoder_dst, self.get_strpath_storage_for_file(self.decoder_dstH5)] ] if self.options['learn_mask']: ar += [ [self.decoder_srcm, self.get_strpath_storage_for_file(self.decoder_srcmH5)], [self.decoder_dstm, self.get_strpath_storage_for_file(self.decoder_dstmH5)] ] self.save_weights_safe(ar) #override def onTrainOneEpoch(self, sample): warped_src, target_src, target_src_mask = sample[0] warped_dst, target_dst, target_dst_mask = sample[1] src_loss, = self.src_train ([warped_src, target_src, target_src_mask, warped_dst, target_dst, target_dst_mask]) dst_loss, = self.dst_train ([warped_dst, target_dst, target_dst_mask]) if self.options['learn_mask']: src_mask_loss, = self.src_mask_train ([warped_src, target_src_mask]) dst_mask_loss, = self.dst_mask_train ([warped_dst, target_dst_mask]) return ( ('src_loss', src_loss), ('dst_loss', dst_loss) ) #override def onGetPreview(self, sample): test_A = sample[0][1][0:4] #first 4 samples test_A_m = sample[0][2][0:4] #first 4 samples test_B = sample[1][1][0:4] test_B_m = sample[1][2][0:4] S, D, SS, DD, SD, = [ x / 2 + 0.5 for x in ([test_A,test_B] + self.AE_view ([test_A, test_B]) ) ] #SM, DM, SDM = [ np.repeat (x, (3,), -1) for x in [SM, DM, SDM] ] st_x3 = [] for i in range(0, len(test_A)): st_x3.append ( np.concatenate ( ( S[i], SS[i], #SM[i], D[i], DD[i], #DM[i], SD[i], #SDM[i] ), axis=1) ) return [ ('SAE', np.concatenate (st_x3, axis=0 )), ] def predictor_func (self, face): face_tanh = face * 2.0 - 1.0 face_bgr = face_tanh[...,0:3] prd = [ (x[0] + 1.0) / 2.0 for x in self.AE_convert ( [ np.expand_dims(face_bgr,0) ] ) ] if not self.options['learn_mask']: prd += [ np.expand_dims(face[...,3],-1) ] return np.concatenate ( [prd[0], prd[1]], -1 ) #override def get_converter(self, **in_options): from models import ConverterMasked base_erode_mask_modifier = 40 if self.options['face_type'] == 'f' else 100 base_blur_mask_modifier = 10 if self.options['face_type'] == 'f' else 100 face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF return ConverterMasked(self.predictor_func, predictor_input_size=self.options['resolution'], output_size=self.options['resolution'], face_type=face_type, base_erode_mask_modifier=base_erode_mask_modifier, base_blur_mask_modifier=base_blur_mask_modifier, clip_border_mask_per=0.03125, **in_options) @staticmethod def LIAEEncFlow(resolution, adapt_k_size, light_enc, ed_ch_dims=42): exec (nnlib.import_all(), locals(), globals()) k_size = resolution // 16 + 1 if adapt_k_size else 5 strides = resolution // 32 if adapt_k_size else 2 def downscale (dim): def func(x): return LeakyReLU(0.1)(Conv2D(dim, k_size, strides=strides, padding='same')(x)) return func def downscale_sep (dim): def func(x): return LeakyReLU(0.1)(SeparableConv2D(dim, k_size, strides=strides, padding='same')(x)) return func def upscale (dim): def func(x): return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, 3, strides=1, padding='same')(x))) return func def func(input): ed_dims = K.int_shape(input)[-1]*ed_ch_dims x = input x = downscale(ed_dims)(x) if not light_enc: x = downscale(ed_dims*2)(x) x = downscale(ed_dims*4)(x) x = downscale(ed_dims*8)(x) else: x = downscale_sep(ed_dims*2)(x) x = downscale(ed_dims*4)(x) x = downscale_sep(ed_dims*8)(x) x = Flatten()(x) return x return func @staticmethod def LIAEInterFlow(resolution, ae_dims=256): exec (nnlib.import_all(), locals(), globals()) lowest_dense_res=resolution // 16 def upscale (dim): def func(x): return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, 3, strides=1, padding='same')(x))) return func def func(input): x = input[0] x = Dense(ae_dims)(x) x = Dense(lowest_dense_res * lowest_dense_res * ae_dims*2)(x) x = Reshape((lowest_dense_res, lowest_dense_res, ae_dims*2))(x) x = upscale(ae_dims*2)(x) return x return func @staticmethod def LIAEDecFlow(output_nc,ed_ch_dims=21,activation='tanh'): exec (nnlib.import_all(), locals(), globals()) ed_dims = output_nc * ed_ch_dims def upscale (dim): def func(x): return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, 3, strides=1, padding='same')(x))) return func def to_bgr (): def func(x): return Conv2D(output_nc, kernel_size=5, padding='same', activation='tanh')(x) return func def func(input): x = input[0] x1 = upscale(ed_dims*8)( x ) x1_bgr = to_bgr() ( x1 ) x2 = upscale(ed_dims*4)( x1 ) x2_bgr = to_bgr() ( x2 ) x3 = upscale(ed_dims*2)( x2 ) x3_bgr = to_bgr() ( x3 ) return [ x1_bgr, x2_bgr, x3_bgr ] return func @staticmethod def DFEncFlow(resolution, adapt_k_size, light_enc, ae_dims=512, ed_ch_dims=42): exec (nnlib.import_all(), locals(), globals()) k_size = resolution // 16 + 1 if adapt_k_size else 5 strides = resolution // 32 if adapt_k_size else 2 lowest_dense_res = resolution // 16 def downscale (dim): def func(x): return LeakyReLU(0.1)(Conv2D(dim, k_size, strides=strides, padding='same')(x)) return func def downscale_sep (dim): def func(x): return LeakyReLU(0.1)(SeparableConv2D(dim, k_size, strides=strides, padding='same')(x)) return func def upscale (dim): def func(x): return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, 3, strides=1, padding='same')(x))) return func def func(input): x = input ed_dims = K.int_shape(input)[-1]*ed_ch_dims x = downscale(ed_dims)(x) if not light_enc: x = downscale(ed_dims*2)(x) x = downscale(ed_dims*4)(x) x = downscale(ed_dims*8)(x) else: x = downscale_sep(ed_dims*2)(x) x = downscale_sep(ed_dims*4)(x) x = downscale_sep(ed_dims*8)(x) x = Dense(ae_dims)(Flatten()(x)) x = Dense(lowest_dense_res * lowest_dense_res * ae_dims)(x) x = Reshape((lowest_dense_res, lowest_dense_res, ae_dims))(x) x = upscale(ae_dims)(x) return x return func @staticmethod def DFDecFlow(output_nc, ed_ch_dims=21): exec (nnlib.import_all(), locals(), globals()) ed_dims = output_nc * ed_ch_dims def upscale (dim): def func(x): return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, 3, strides=1, padding='same')(x))) return func def to_bgr (): def func(x): return Conv2D(output_nc, kernel_size=5, padding='same', activation='tanh')(x) return func def func(input): x = input[0] x1 = upscale(ed_dims*8)( x ) x1_bgr = to_bgr() ( x1 ) x2 = upscale(ed_dims*4)( x1 ) x2_bgr = to_bgr() ( x2 ) x3 = upscale(ed_dims*2)( x2 ) x3_bgr = to_bgr() ( x3 ) return [ x1_bgr, x2_bgr, x3_bgr ] return func Model = SAEModel