import multiprocessing import operator from functools import partial import numpy as np from core import mathlib from core.interact import interact as io from core.leras import nn from facelib import FaceType, TernausNet from models import ModelBase from samplelib import * class FANSegModel(ModelBase): #override def on_initialize_options(self): device_config = nn.getCurrentDeviceConfig() yn_str = {True:'y',False:'n'} #default_resolution = 256 #default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'f') ask_override = self.ask_override() if self.is_first_run() or ask_override: self.ask_autobackup_hour() self.ask_target_iter() self.ask_batch_size(24) #if self.is_first_run(): #resolution = io.input_int("Resolution", default_resolution, add_info="64-512") #resolution = np.clip ( (resolution // 16) * 16, 64, 512) #self.options['resolution'] = resolution #self.options['face_type'] = io.input_str ("Face type", default_face_type, ['f']).lower() #override def on_initialize(self): device_config = nn.getCurrentDeviceConfig() nn.initialize(data_format="NHWC") tf = nn.tf device_config = nn.getCurrentDeviceConfig() devices = device_config.devices self.resolution = resolution = 256#self.options['resolution'] #self.face_type = {'h' : FaceType.HALF, # 'mf' : FaceType.MID_FULL, # 'f' : FaceType.FULL, # 'wf' : FaceType.WHOLE_FACE}[ self.options['face_type'] ] self.face_type = FaceType.FULL place_model_on_cpu = len(devices) == 0 models_opt_device = '/CPU:0' if place_model_on_cpu else '/GPU:0' bgr_shape = nn.get4Dshape(resolution,resolution,3) mask_shape = nn.get4Dshape(resolution,resolution,1) # Initializing model classes self.model = TernausNet(f'{self.model_name}_FANSeg', resolution, FaceType.toString(self.face_type), load_weights=not self.is_first_run(), weights_file_root=self.get_model_root_path(), training=True, place_model_on_cpu=place_model_on_cpu) if self.is_training: # Adjust batch size for multiple GPU gpu_count = max(1, len(devices) ) bs_per_gpu = max(1, self.get_batch_size() // gpu_count) self.set_batch_size( gpu_count*bs_per_gpu) # Compute losses per GPU gpu_pred_list = [] gpu_losses = [] gpu_loss_gvs = [] for gpu_id in range(gpu_count): with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ): with tf.device(f'/CPU:0'): # slice on CPU, otherwise all batch data will be transfered to GPU first batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu ) gpu_input_t = self.model.input_t [batch_slice,:,:,:] gpu_target_t = self.model.target_t [batch_slice,:,:,:] # process model tensors gpu_pred_logits_t, gpu_pred_t = self.model.net([gpu_input_t]) gpu_pred_list.append(gpu_pred_t) gpu_loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=gpu_target_t, logits=gpu_pred_logits_t), axis=[1,2,3]) gpu_losses += [gpu_loss] gpu_loss_gvs += [ nn.tf_gradients ( gpu_loss, self.model.net_weights ) ] # Average losses and gradients, and create optimizer update ops with tf.device (models_opt_device): pred = nn.tf_concat(gpu_pred_list, 0) loss = tf.reduce_mean(gpu_losses) loss_gv_op = self.model.opt.get_update_op (nn.tf_average_gv_list (gpu_loss_gvs)) # Initializing training and view functions def train(input_np, target_np): l, _ = nn.tf_sess.run ( [loss, loss_gv_op], feed_dict={self.model.input_t :input_np, self.model.target_t :target_np }) return l self.train = train def view(input_np): return nn.tf_sess.run ( [pred], feed_dict={self.model.input_t :input_np}) self.view = view # initializing sample generators training_data_src_path = self.training_data_src_path training_data_dst_path = self.training_data_dst_path cpu_count = min(multiprocessing.cpu_count(), 8) src_generators_count = cpu_count // 2 dst_generators_count = cpu_count // 2 src_generators_count = int(src_generators_count * 1.5) src_generator = SampleGeneratorFace(training_data_src_path, random_ct_samples_path=training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(), sample_process_options=SampleProcessor.Options(random_flip=True), output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'ct_mode':'lct', 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'motion_blur':(25, 5), 'gaussian_blur':(25,5), 'data_format':nn.data_format, 'resolution': resolution}, {'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution}, ], generators_count=src_generators_count ) dst_generator = SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(), sample_process_options=SampleProcessor.Options(random_flip=True), output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'motion_blur':(25, 5), 'gaussian_blur':(25,5), 'data_format':nn.data_format, 'resolution': resolution}, ], generators_count=dst_generators_count, raise_on_no_data=False ) if not dst_generator.is_initialized(): io.log_info(f"\nTo view the model on unseen faces, place any aligned faces in {training_data_dst_path}.\n") self.set_training_data_generators ([src_generator, dst_generator]) #override def get_model_filename_list(self): return self.model.model_filename_list #override def onSave(self): self.model.save_weights() #override def onTrainOneIter(self): source_np, target_np = self.generate_next_samples()[0] loss = self.train (source_np, target_np) return ( ('loss', loss ), ) #override def onGetPreview(self, samples): n_samples = min(4, self.get_batch_size(), 800 // self.resolution ) src_samples, dst_samples = samples source_np, target_np = src_samples S, TM, SM, = [ np.clip(x, 0.0, 1.0) for x in ([source_np,target_np] + self.view (source_np) ) ] TM, SM, = [ np.repeat (x, (3,), -1) for x in [TM, SM] ] green_bg = np.tile( np.array([0,1,0], dtype=np.float32)[None,None,...], (self.resolution,self.resolution,1) ) result = [] st = [] for i in range(n_samples): ar = S[i]*TM[i]+ green_bg*(1-TM[i]), SM[i], S[i]*SM[i] + green_bg*(1-SM[i]) #todo green bg st.append ( np.concatenate ( ar, axis=1) ) result += [ ('FANSeg training faces', np.concatenate (st, axis=0 )), ] if len(dst_samples) != 0: dst_np, = dst_samples D, DM, = [ np.clip(x, 0.0, 1.0) for x in ([dst_np] + self.view (dst_np) ) ] DM, = [ np.repeat (x, (3,), -1) for x in [DM] ] st = [] for i in range(n_samples): ar = D[i], DM[i], D[i]*DM[i]+ green_bg*(1-DM[i]) #todo green bg st.append ( np.concatenate ( ar, axis=1) ) result += [ ('FANSeg unseen faces', np.concatenate (st, axis=0 )), ] return result Model = FANSegModel