import numpy as np from nnlib import nnlib from models import ModelBase from facelib import FaceType from samples import * from utils.console_utils import * #U-net Face Morpher class UFMModel(ModelBase): encoderH5 = 'encoder.h5' decoder_srcH5 = 'decoder_src.h5' decoder_dstH5 = 'decoder_dst.h5' decoder_srcmH5 = 'decoder_srcm.h5' decoder_dstmH5 = 'decoder_dstm.h5' #override def onInitializeOptions(self, is_first_run, ask_for_session_options): default_resolution = 128 default_filters = 64 default_match_style = True default_face_type = 'f' if is_first_run: #first run self.options['resolution'] = input_int("Resolution (valid: 64,128,256, skip:128) : ", default_resolution, [64,128,256]) self.options['filters'] = np.clip ( input_int("Number of U-net filters (valid: 32-128, skip:64) : ", default_filters), 32, 128 ) self.options['match_style'] = input_bool ("Match style? (y/n skip:y) : ", default_match_style) self.options['face_type'] = input_str ("Half or Full face? (h/f, skip:f) : ", default_face_type, ['h','f']) else: #not first run self.options['resolution'] = self.options.get('resolution', default_resolution) self.options['filters'] = self.options.get('filters', default_filters) self.options['match_style'] = self.options.get('match_style', default_match_style) 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({2:1,3:2,4:6,5:8,6:16,7:24,8:32}) resolution = self.options['resolution'] bgr_shape = (resolution, resolution, 3) mask_shape = (resolution, resolution, 1) filters = self.options['filters'] if resolution == 64: lowest_dense = 512 elif resolution == 128: lowest_dense = 512 elif resolution == 256: lowest_dense = 256 self.encoder = modelify(UFMModel.EncFlow (ngf=filters, lowest_dense=lowest_dense)) (Input(bgr_shape)) dec_Inputs = [ Input(K.int_shape(x)[1:]) for x in self.encoder.outputs ] self.decoder_src = modelify(UFMModel.DecFlow (bgr_shape[2], ngf=filters)) (dec_Inputs) self.decoder_dst = modelify(UFMModel.DecFlow (bgr_shape[2], ngf=filters)) (dec_Inputs) self.decoder_srcm = modelify(UFMModel.DecFlow (mask_shape[2], ngf=filters//2)) (dec_Inputs) self.decoder_dstm = modelify(UFMModel.DecFlow (mask_shape[2], ngf=filters//2)) (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)) 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 = Input(bgr_shape) target_src = Input(bgr_shape) target_srcm = Input(mask_shape) warped_src_code = self.encoder (warped_src) pred_src_src = self.decoder_src(warped_src_code) pred_src_srcm = self.decoder_srcm(warped_src_code) warped_dst = Input(bgr_shape) target_dst = Input(bgr_shape) target_dstm = Input(mask_shape) warped_dst_code = self.encoder (warped_dst) pred_dst_dst = self.decoder_dst(warped_dst_code) pred_dst_dstm = self.decoder_dstm(warped_dst_code) pred_src_dst = self.decoder_src(warped_dst_code) pred_src_dstm = self.decoder_srcm(warped_dst_code) target_srcm_blurred = tf_gaussian_blur(resolution // 32)(target_srcm) target_srcm_sigm = target_srcm_blurred / 2.0 + 0.5 target_srcm_anti_sigm = 1.0 - target_srcm_sigm target_dstm_blurred = tf_gaussian_blur(resolution // 32)(target_dstm) target_dstm_sigm = target_dstm_blurred / 2.0 + 0.5 target_dstm_anti_sigm = 1.0 - target_dstm_sigm target_src_sigm = target_src+1 target_dst_sigm = target_dst+1 pred_src_src_sigm = pred_src_src+1 pred_dst_dst_sigm = pred_dst_dst+1 pred_src_dst_sigm = pred_src_dst+1 target_src_masked = target_src_sigm*target_srcm_sigm target_dst_masked = target_dst_sigm * target_dstm_sigm target_dst_anti_masked = target_dst_sigm * target_dstm_anti_sigm pred_src_src_masked = pred_src_src_sigm * target_srcm_sigm pred_dst_dst_masked = pred_dst_dst_sigm * target_dstm_sigm pred_src_dst_target_dst_masked = pred_src_dst_sigm * target_dstm_sigm pred_src_dst_target_dst_anti_masked = pred_src_dst_sigm * target_dstm_anti_sigm src_loss = K.mean( 100*K.square(tf_dssim(2.0)( target_src_masked, pred_src_src_masked )) ) if self.options['match_style']: src_loss += tf_style_loss(gaussian_blur_radius=resolution // 8, loss_weight=0.015)(pred_src_dst_target_dst_masked, target_dst_masked) src_loss += 0.05 * K.mean( tf_dssim(2.0)( pred_src_dst_target_dst_anti_masked, target_dst_anti_masked )) self.src_train = K.function ([warped_src, target_src, target_srcm, warped_dst, target_dst, target_dstm ],[src_loss], Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(src_loss, self.encoder.trainable_weights + self.decoder_src.trainable_weights) ) dst_loss = K.mean( 100*K.square(tf_dssim(2.0)( target_dst_masked, pred_dst_dst_masked )) ) self.dst_train = K.function ([warped_dst, target_dst, target_dstm],[dst_loss], Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(dst_loss, self.encoder.trainable_weights + self.decoder_dst.trainable_weights) ) src_mask_loss = K.mean(K.square(target_srcm-pred_src_srcm)) self.src_mask_train = K.function ([warped_src, target_srcm],[src_mask_loss], Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(src_mask_loss, self.encoder.trainable_weights + self.decoder_srcm.trainable_weights) ) dst_mask_loss = K.mean(K.square(target_dstm-pred_dst_dstm)) self.dst_mask_train = K.function ([warped_dst, target_dstm],[dst_mask_loss], Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(dst_mask_loss, self.encoder.trainable_weights + self.decoder_dstm.trainable_weights) ) self.AE_view = K.function ([warped_src, warped_dst],[pred_src_src, pred_src_srcm, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm]) self.AE_convert = K.function ([warped_dst],[pred_src_dst, pred_src_dstm]) 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(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] ] ), SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, sample_process_options=SampleProcessor.Options(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): self.save_weights_safe( [[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)], [self.decoder_srcm, self.get_strpath_storage_for_file(self.decoder_srcmH5)], [self.decoder_dstm, self.get_strpath_storage_for_file(self.decoder_dstmH5)] ] ) #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]) 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 = test_A D = test_B SS, SM, DD, DM, SD, SDM = self.AE_view ([test_A, test_B]) S, D, SS, SM, DD, DM, SD, SDM = [ x / 2 + 0.5 for x in [S, D, SS, SM, DD, DM, SD, SDM] ] SM, DM, SDM = [ np.repeat (x, (3,), -1) for x in [SM, DM, SDM] ] st = [] for i in range(0, len(test_A)): st.append ( np.concatenate ( ( S[i], SS[i], #SM[i], D[i], DD[i], #DM[i], SD[i], #SDM[i] ), axis=1) ) return [ ('U-net Face Morpher', np.concatenate ( st, axis=0 ) ) ] def predictor_func (self, face): face = face * 2.0 - 1.0 face_128_bgr = face[...,0:3] x, mx = [ (x[0] + 1.0) / 2.0 for x in self.AE_convert ( [ np.expand_dims(face_128_bgr,0) ] ) ] if self.options['match_style']: res = self.options['resolution'] s = int( res * 0.96875 ) mx = np.pad ( np.ones ( (s,s) ), (res-s) // 2 , mode='constant') mx = np.expand_dims(mx, -1) return np.concatenate ( (x,mx), -1 ) #override def get_converter(self, **in_options): from models import ConverterMasked if self.options['match_style']: base_erode_mask_modifier = 50 base_blur_mask_modifier = 50 else: base_erode_mask_modifier = 30 if self.options['face_type'] == 'f' else 100 base_blur_mask_modifier = 0 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, **in_options) @staticmethod def EncFlow(ngf=64, num_downs=4, lowest_dense=512): exec (nnlib.import_all(), locals(), globals()) use_bias = True def XNormalization(x): return InstanceNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x) def Conv2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=use_bias, kernel_initializer=RandomNormal(0, 0.02), bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None): return keras.layers.Conv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint ) def func(input): x = input result = [] for i in range(num_downs): x = LeakyReLU(0.1)(XNormalization(Conv2D( min(ngf* (2**i), ngf*8) , 5, 2, 'same')(x))) if i == 3: x_shape = K.int_shape(x)[1:] x = Reshape(x_shape)(Dense( np.prod(x_shape) )(Dense(lowest_dense)(Flatten()(x)))) result += [x] return result return func @staticmethod def DecFlow(output_nc, ngf=64, activation='tanh'): exec (nnlib.import_all(), locals(), globals()) use_bias = True def XNormalization(x): return InstanceNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x) def Conv2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=use_bias, kernel_initializer=RandomNormal(0, 0.02), bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None): return keras.layers.Conv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint ) def func(input): input_len = len(input) x = input[input_len-1] for i in range(input_len-1, -1, -1): x = SubpixelUpscaler()( LeakyReLU(0.1)(XNormalization(Conv2D( min(ngf* (2**i) *4, ngf*8 *4 ), 3, 1, 'same')(x))) ) if i != 0: x = Concatenate(axis=3)([ input[i-1] , x]) return Conv2D(output_nc, 3, 1, 'same', activation=activation)(x) return func Model = UFMModel