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SAEHD: add 'Blur out mask' and 'Denoise DST faceset' options.
This commit is contained in:
iperov
parent
e41f87e682
commit
8e63666390
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@ -29,7 +29,7 @@ class SAEHDModel(ModelBase):
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yn_str = {True:'y',False:'n'}
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min_res = 64
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max_res = 640
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#default_usefp16 = self.options['use_fp16'] = self.load_or_def_option('use_fp16', False)
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default_resolution = self.options['resolution'] = self.load_or_def_option('resolution', 128)
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default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'f')
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@ -46,6 +46,8 @@ class SAEHDModel(ModelBase):
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default_masked_training = self.options['masked_training'] = self.load_or_def_option('masked_training', True)
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default_eyes_mouth_prio = self.options['eyes_mouth_prio'] = self.load_or_def_option('eyes_mouth_prio', False)
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default_uniform_yaw = self.options['uniform_yaw'] = self.load_or_def_option('uniform_yaw', False)
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default_blur_out_mask = self.options['blur_out_mask'] = self.load_or_def_option('blur_out_mask', False)
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default_dst_denoise = self.options['dst_denoise'] = self.load_or_def_option('dst_denoise', False)
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default_adabelief = self.options['adabelief'] = self.load_or_def_option('adabelief', True)
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@ -70,14 +72,14 @@ class SAEHDModel(ModelBase):
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self.ask_random_dst_flip()
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self.ask_batch_size(suggest_batch_size)
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#self.options['use_fp16'] = io.input_bool ("Use fp16", default_usefp16, help_message='Increases training/inference speed, reduces model size. Model may crash. Enable it after 1-5k iters.')
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if self.is_first_run():
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resolution = io.input_int("Resolution", default_resolution, add_info="64-640", help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16 and 32 for -d archi.")
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resolution = np.clip ( (resolution // 16) * 16, min_res, max_res)
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self.options['resolution'] = resolution
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self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf','head'], help_message="Half / mid face / full face / whole face / head. Half face has better resolution, but covers less area of cheeks. Mid face is 30% wider than half face. 'Whole face' covers full area of face include forehead. 'head' covers full head, but requires XSeg for src and dst faceset.").lower()
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while True:
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@ -138,11 +140,13 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.options['eyes_mouth_prio'] = io.input_bool ("Eyes and mouth priority", default_eyes_mouth_prio, help_message='Helps to fix eye problems during training like "alien eyes" and wrong eyes direction. Also makes the detail of the teeth higher.')
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self.options['uniform_yaw'] = io.input_bool ("Uniform yaw distribution of samples", default_uniform_yaw, help_message='Helps to fix blurry side faces due to small amount of them in the faceset.')
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self.options['blur_out_mask'] = io.input_bool ("Blur out mask", default_blur_out_mask, help_message='Blurs nearby area outside of applied face mask of training samples. The result is the background near the face is smoothed and less noticeable on swapped face. The exact xseg mask in src and dst faceset is required.')
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self.options['dst_denoise'] = io.input_bool ("Denoise DST faceset.", default_dst_denoise, help_message='Used in RTM(ReadyToMerge) training with RTM DST faceset. Removes high frequency noise keeping edges. Result is better face syncronization with any face. Can be enabled at any time.')
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default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0)
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default_gan_patch_size = self.options['gan_patch_size'] = self.load_or_def_option('gan_patch_size', self.options['resolution'] // 8)
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default_gan_dims = self.options['gan_dims'] = self.load_or_def_option('gan_dims', 16)
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if self.is_first_run() or ask_override:
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self.options['models_opt_on_gpu'] = io.input_bool ("Place models and optimizer on GPU", default_models_opt_on_gpu, help_message="When you train on one GPU, by default model and optimizer weights are placed on GPU to accelerate the process. You can place they on CPU to free up extra VRAM, thus set bigger dimensions.")
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@ -153,14 +157,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.options['random_warp'] = io.input_bool ("Enable random warp of samples", default_random_warp, help_message="Random warp is required to generalize facial expressions of both faces. When the face is trained enough, you can disable it to get extra sharpness and reduce subpixel shake for less amount of iterations.")
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self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 5.0", help_message="Forces the neural network to learn small details of the face. Enable it only when the face is trained enough with lr_dropout(on) and random_warp(off), and don't disable. The higher the value, the higher the chances of artifacts. Typical fine value is 0.1"), 0.0, 5.0 )
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if self.options['gan_power'] != 0.0:
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if self.options['gan_power'] != 0.0:
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gan_patch_size = np.clip ( io.input_int("GAN patch size", default_gan_patch_size, add_info="3-640", help_message="The higher patch size, the higher the quality, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is resolution / 8." ), 3, 640 )
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self.options['gan_patch_size'] = gan_patch_size
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gan_dims = np.clip ( io.input_int("GAN dimensions", default_gan_dims, add_info="4-512", help_message="The dimensions of the GAN network. The higher dimensions, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is 16." ), 4, 512 )
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self.options['gan_dims'] = gan_dims
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if 'df' in self.options['archi']:
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self.options['true_face_power'] = np.clip ( io.input_number ("'True face' power.", default_true_face_power, add_info="0.0000 .. 1.0", help_message="Experimental option. Discriminates result face to be more like src face. Higher value - stronger discrimination. Typical value is 0.01 . Comparison - https://i.imgur.com/czScS9q.png"), 0.0, 1.0 )
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else:
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@ -176,7 +180,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if self.options['pretrain'] and self.get_pretraining_data_path() is None:
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raise Exception("pretraining_data_path is not defined")
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self.gan_model_changed = (default_gan_patch_size != self.options['gan_patch_size']) or (default_gan_dims != self.options['gan_dims'])
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self.pretrain_just_disabled = (default_pretrain == True and self.options['pretrain'] == False)
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@ -198,7 +202,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if 'eyes_prio' in self.options:
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self.options.pop('eyes_prio')
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eyes_mouth_prio = self.options['eyes_mouth_prio']
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archi_split = self.options['archi'].split('-')
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@ -207,7 +211,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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archi_type, archi_opts = archi_split
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elif len(archi_split) == 1:
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archi_type, archi_opts = archi_split[0], None
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self.archi_type = archi_type
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ae_dims = self.options['ae_dims']
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@ -219,16 +223,18 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.set_iter(0)
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adabelief = self.options['adabelief']
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use_fp16 = False
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if self.is_exporting:
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use_fp16 = io.input_bool ("Export quantized?", False, help_message='Makes the exported model faster. If you have problems, disable this option.')
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self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power']
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random_warp = False if self.pretrain else self.options['random_warp']
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random_src_flip = self.random_src_flip if not self.pretrain else True
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random_dst_flip = self.random_dst_flip if not self.pretrain else True
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blur_out_mask = self.options['blur_out_mask']
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dst_denoise = self.options['dst_denoise']
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if self.pretrain:
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self.options_show_override['gan_power'] = 0.0
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self.options_show_override['random_warp'] = False
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@ -241,8 +247,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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ct_mode = self.options['ct_mode']
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if ct_mode == 'none':
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ct_mode = None
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models_opt_on_gpu = False if len(devices) == 0 else self.options['models_opt_on_gpu']
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models_opt_device = nn.tf_default_device_name if models_opt_on_gpu and self.is_training else '/CPU:0'
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optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
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@ -356,7 +362,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_G_loss_gvs = []
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gpu_D_code_loss_gvs = []
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gpu_D_src_dst_loss_gvs = []
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for gpu_id in range(gpu_count):
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with tf.device( f'/{devices[gpu_id].tf_dev_type}:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
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with tf.device(f'/CPU:0'):
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@ -371,6 +377,22 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_target_dstm = self.target_dstm[batch_slice,:,:,:]
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gpu_target_dstm_em = self.target_dstm_em[batch_slice,:,:,:]
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gpu_target_srcm_anti = 1-gpu_target_srcm
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gpu_target_dstm_anti = 1-gpu_target_dstm
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if blur_out_mask:
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#gpu_target_src = gpu_target_src*gpu_target_srcm_blur + nn.gaussian_blur(gpu_target_src, resolution // 32)*gpu_target_srcm_anti_blur
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#gpu_target_dst = gpu_target_dst*gpu_target_dstm_blur + nn.gaussian_blur(gpu_target_dst, resolution // 32)*gpu_target_dstm_anti_blur
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bg_blur_div = 128
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gpu_target_src = gpu_target_src*gpu_target_srcm + \
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tf.math.divide_no_nan(nn.gaussian_blur(gpu_target_src*gpu_target_srcm_anti, resolution / bg_blur_div),
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(1-nn.gaussian_blur(gpu_target_srcm, resolution / bg_blur_div) ) ) * gpu_target_srcm_anti
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gpu_target_dst = gpu_target_dst*gpu_target_dstm + \
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tf.math.divide_no_nan(nn.gaussian_blur(gpu_target_dst*gpu_target_dstm_anti, resolution / bg_blur_div),
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(1-nn.gaussian_blur(gpu_target_dstm, resolution / bg_blur_div)) ) * gpu_target_dstm_anti
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# process model tensors
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if 'df' in archi_type:
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gpu_src_code = self.inter(self.encoder(gpu_warped_src))
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@ -408,7 +430,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_target_dstm_style_blur = gpu_target_dstm_blur #default style mask is 0.5 on boundary
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gpu_target_dstm_blur = tf.clip_by_value(gpu_target_dstm_blur, 0, 0.5) * 2
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gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
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gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
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gpu_target_dst_style_masked = gpu_target_dst*gpu_target_dstm_style_blur
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gpu_target_dst_style_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_style_blur)
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@ -503,14 +525,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_G_loss += gan_power*(DLoss(gpu_pred_src_src_d_ones, gpu_pred_src_src_d) + \
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DLoss(gpu_pred_src_src_d2_ones, gpu_pred_src_src_d2))
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if masked_training:
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# Minimal src-src-bg rec with total_variation_mse to suppress random bright dots from gan
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gpu_G_loss += 0.000001*nn.total_variation_mse(gpu_pred_src_src)
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gpu_G_loss += 0.02*tf.reduce_mean(tf.square(gpu_pred_src_src_anti_masked-gpu_target_src_anti_masked),axis=[1,2,3] )
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gpu_G_loss_gvs += [ nn.gradients ( gpu_G_loss, self.src_dst_trainable_weights ) ]
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@ -620,10 +642,10 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if do_init:
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model.init_weights()
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###############
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# initializing sample generators
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if self.is_training:
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training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path()
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@ -641,7 +663,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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SampleGeneratorFace(training_data_src_path, random_ct_samples_path=random_ct_samples_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(random_flip=random_src_flip),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , '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},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.EYES_MOUTH, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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@ -651,7 +673,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(random_flip=random_dst_flip),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'denoise_filter' : dst_denoise, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , '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},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.EYES_MOUTH, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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@ -664,20 +687,20 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if self.pretrain_just_disabled:
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self.update_sample_for_preview(force_new=True)
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def export_dfm (self):
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output_path=self.get_strpath_storage_for_file('model.dfm')
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io.log_info(f'Dumping .dfm to {output_path}')
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tf = nn.tf
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nn.set_data_format('NCHW')
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with tf.device (nn.tf_default_device_name):
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warped_dst = tf.placeholder (nn.floatx, (None, self.resolution, self.resolution, 3), name='in_face')
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warped_dst = tf.transpose(warped_dst, (0,3,1,2))
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if 'df' in self.archi_type:
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gpu_dst_code = self.inter(self.encoder(warped_dst))
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gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
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@ -692,21 +715,21 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
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_, gpu_pred_dst_dstm = self.decoder(gpu_dst_code)
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gpu_pred_src_dst = tf.transpose(gpu_pred_src_dst, (0,2,3,1))
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gpu_pred_dst_dstm = tf.transpose(gpu_pred_dst_dstm, (0,2,3,1))
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gpu_pred_src_dstm = tf.transpose(gpu_pred_src_dstm, (0,2,3,1))
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tf.identity(gpu_pred_dst_dstm, name='out_face_mask')
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tf.identity(gpu_pred_src_dst, name='out_celeb_face')
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tf.identity(gpu_pred_src_dstm, name='out_celeb_face_mask')
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tf.identity(gpu_pred_src_dstm, name='out_celeb_face_mask')
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output_graph_def = tf.graph_util.convert_variables_to_constants(
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nn.tf_sess,
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tf.get_default_graph().as_graph_def(),
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nn.tf_sess,
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tf.get_default_graph().as_graph_def(),
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['out_face_mask','out_celeb_face','out_celeb_face_mask']
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)
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)
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import tf2onnx
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with tf.device("/CPU:0"):
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model_proto, _ = tf2onnx.convert._convert_common(
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@ -716,7 +739,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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output_names=['out_face_mask:0','out_celeb_face:0','out_celeb_face_mask:0'],
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opset=13,
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output_path=output_path)
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#override
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def get_model_filename_list(self):
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return self.model_filename_list
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@ -739,27 +762,28 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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bs = self.get_batch_size()
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( (warped_src, target_src, target_srcm, target_srcm_em), \
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(warped_dst, target_dst, target_dstm, target_dstm_em) ) = self.generate_next_samples()
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(warped_dst, target_dst, target_dst_train, target_dstm, target_dstm_em) ) = self.generate_next_samples()
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src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
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src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst_train, target_dstm, target_dstm_em)
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for i in range(bs):
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self.last_src_samples_loss.append ( (target_src[i], target_srcm[i], target_srcm_em[i], src_loss[i] ) )
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self.last_dst_samples_loss.append ( (target_dst[i], target_dstm[i], target_dstm_em[i], dst_loss[i] ) )
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self.last_src_samples_loss.append ( (src_loss[i], target_src[i], target_srcm[i], target_srcm_em[i],) )
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self.last_dst_samples_loss.append ( (dst_loss[i], target_dst[i], target_dst_train[i], target_dstm[i], target_dstm_em[i],) )
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|
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if len(self.last_src_samples_loss) >= bs*16:
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src_samples_loss = sorted(self.last_src_samples_loss, key=operator.itemgetter(3), reverse=True)
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dst_samples_loss = sorted(self.last_dst_samples_loss, key=operator.itemgetter(3), reverse=True)
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src_samples_loss = sorted(self.last_src_samples_loss, key=operator.itemgetter(0), reverse=True)
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dst_samples_loss = sorted(self.last_dst_samples_loss, key=operator.itemgetter(0), reverse=True)
|
||||
|
||||
target_src = np.stack( [ x[0] for x in src_samples_loss[:bs] ] )
|
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target_srcm = np.stack( [ x[1] for x in src_samples_loss[:bs] ] )
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target_srcm_em = np.stack( [ x[2] for x in src_samples_loss[:bs] ] )
|
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target_src = np.stack( [ x[1] for x in src_samples_loss[:bs] ] )
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target_srcm = np.stack( [ x[2] for x in src_samples_loss[:bs] ] )
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target_srcm_em = np.stack( [ x[3] for x in src_samples_loss[:bs] ] )
|
||||
|
||||
target_dst = np.stack( [ x[0] for x in dst_samples_loss[:bs] ] )
|
||||
target_dstm = np.stack( [ x[1] for x in dst_samples_loss[:bs] ] )
|
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target_dstm_em = np.stack( [ x[2] for x in dst_samples_loss[:bs] ] )
|
||||
target_dst = np.stack( [ x[1] for x in dst_samples_loss[:bs] ] )
|
||||
target_dst_train = np.stack( [ x[2] for x in dst_samples_loss[:bs] ] )
|
||||
target_dstm = np.stack( [ x[3] for x in dst_samples_loss[:bs] ] )
|
||||
target_dstm_em = np.stack( [ x[4] for x in dst_samples_loss[:bs] ] )
|
||||
|
||||
src_loss, dst_loss = self.src_dst_train (target_src, target_src, target_srcm, target_srcm_em, target_dst, target_dst, target_dstm, target_dstm_em)
|
||||
src_loss, dst_loss = self.src_dst_train (target_src, target_src, target_srcm, target_srcm_em, target_dst, target_dst_train, target_dstm, target_dstm_em)
|
||||
self.last_src_samples_loss = []
|
||||
self.last_dst_samples_loss = []
|
||||
|
||||
|
|
@ -767,14 +791,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
|
|||
self.D_train (warped_src, warped_dst)
|
||||
|
||||
if self.gan_power != 0:
|
||||
self.D_src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
|
||||
self.D_src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst_train, target_dstm, target_dstm_em)
|
||||
|
||||
return ( ('src_loss', np.mean(src_loss) ), ('dst_loss', np.mean(dst_loss) ), )
|
||||
|
||||
#override
|
||||
def onGetPreview(self, samples, for_history=False):
|
||||
( (warped_src, target_src, target_srcm, target_srcm_em),
|
||||
(warped_dst, target_dst, target_dstm, target_dstm_em) ) = samples
|
||||
(warped_dst, target_dst, target_dst_train, target_dstm, target_dstm_em) ) = samples
|
||||
|
||||
S, D, SS, DD, DDM, SD, SDM = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst) ) ]
|
||||
DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ]
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user