mirror of
https://github.com/iperov/DeepFaceLab.git
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0c2e1c3944
Maximum resolution is increased to 640. ‘hd’ archi is removed. ‘hd’ was experimental archi created to remove subpixel shake, but ‘lr_dropout’ and ‘disable random warping’ do that better. ‘uhd’ is renamed to ‘-u’ dfuhd and liaeuhd will be automatically renamed to df-u and liae-u in existing models. Added new experimental archi (key -d) which doubles the resolution using the same computation cost. It is mean same configs will be x2 faster, or for example you can set 448 resolution and it will train as 224. Strongly recommended not to train from scratch and use pretrained models. New archi naming: 'df' keeps more identity-preserved face. 'liae' can fix overly different face shapes. '-u' increased likeness of the face. '-d' (experimental) doubling the resolution using the same computation cost Examples: df, liae, df-d, df-ud, liae-ud, ... Improved GAN training (GAN_power option). It was used for dst model, but actually we don’t need it for dst. Instead, a second src GAN model with x2 smaller patch size was added, so the overall quality for hi-res models should be higher. Added option ‘Uniform yaw distribution of samples (y/n)’: Helps to fix blurry side faces due to small amount of them in the faceset. Quick96: Now based on df-ud archi and 20% faster. XSeg trainer: Improved sample generator. Now it randomly adds the background from other samples. Result is reduced chance of random mask noise on the area outside the face. Now you can specify ‘batch_size’ in range 2-16. Reduced size of samples with applied XSeg mask. Thus size of packed samples with applied xseg mask is also reduced.
216 lines
No EOL
10 KiB
Python
216 lines
No EOL
10 KiB
Python
import multiprocessing
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import operator
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from functools import partial
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import numpy as np
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from core import mathlib
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from core.interact import interact as io
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from core.leras import nn
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from facelib import FaceType, XSegNet
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from models import ModelBase
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from samplelib import *
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class XSegModel(ModelBase):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, force_model_class_name='XSeg', **kwargs)
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#override
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def on_initialize_options(self):
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ask_override = self.ask_override()
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if not self.is_first_run() and ask_override:
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if io.input_bool(f"Restart training?", False, help_message="Reset model weights and start training from scratch."):
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self.set_iter(0)
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default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'wf')
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if self.is_first_run():
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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. Choose the same as your deepfake model.").lower()
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if self.is_first_run() or ask_override:
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self.ask_batch_size(4, range=[2,16])
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#override
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def on_initialize(self):
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device_config = nn.getCurrentDeviceConfig()
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self.model_data_format = "NCHW" if len(device_config.devices) != 0 and not self.is_debug() else "NHWC"
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nn.initialize(data_format=self.model_data_format)
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tf = nn.tf
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device_config = nn.getCurrentDeviceConfig()
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devices = device_config.devices
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self.resolution = resolution = 256
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self.face_type = {'h' : FaceType.HALF,
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'mf' : FaceType.MID_FULL,
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'f' : FaceType.FULL,
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'wf' : FaceType.WHOLE_FACE,
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'head' : FaceType.HEAD}[ self.options['face_type'] ]
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place_model_on_cpu = len(devices) == 0
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models_opt_device = '/CPU:0' if place_model_on_cpu else '/GPU:0'
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bgr_shape = nn.get4Dshape(resolution,resolution,3)
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mask_shape = nn.get4Dshape(resolution,resolution,1)
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# Initializing model classes
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self.model = XSegNet(name='XSeg',
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resolution=resolution,
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load_weights=not self.is_first_run(),
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weights_file_root=self.get_model_root_path(),
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training=True,
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place_model_on_cpu=place_model_on_cpu,
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optimizer=nn.RMSprop(lr=0.0001, lr_dropout=0.3, name='opt'),
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data_format=nn.data_format)
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if self.is_training:
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# Adjust batch size for multiple GPU
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gpu_count = max(1, len(devices) )
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bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
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self.set_batch_size( gpu_count*bs_per_gpu)
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# Compute losses per GPU
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gpu_pred_list = []
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gpu_losses = []
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gpu_loss_gvs = []
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for gpu_id in range(gpu_count):
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with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
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with tf.device(f'/CPU:0'):
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# slice on CPU, otherwise all batch data will be transfered to GPU first
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batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
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gpu_input_t = self.model.input_t [batch_slice,:,:,:]
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gpu_target_t = self.model.target_t [batch_slice,:,:,:]
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# process model tensors
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gpu_pred_logits_t, gpu_pred_t = self.model.flow(gpu_input_t)
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gpu_pred_list.append(gpu_pred_t)
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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])
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gpu_losses += [gpu_loss]
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gpu_loss_gvs += [ nn.gradients ( gpu_loss, self.model.get_weights() ) ]
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# Average losses and gradients, and create optimizer update ops
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with tf.device (models_opt_device):
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pred = nn.concat(gpu_pred_list, 0)
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loss = tf.reduce_mean(gpu_losses)
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loss_gv_op = self.model.opt.get_update_op (nn.average_gv_list (gpu_loss_gvs))
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# Initializing training and view functions
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def train(input_np, target_np):
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l, _ = nn.tf_sess.run ( [loss, loss_gv_op], feed_dict={self.model.input_t :input_np, self.model.target_t :target_np })
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return l
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self.train = train
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def view(input_np):
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return nn.tf_sess.run ( [pred], feed_dict={self.model.input_t :input_np})
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self.view = view
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# initializing sample generators
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cpu_count = min(multiprocessing.cpu_count(), 8)
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src_dst_generators_count = cpu_count // 2
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src_generators_count = cpu_count // 2
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dst_generators_count = cpu_count // 2
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srcdst_generator = SampleGeneratorFaceXSeg([self.training_data_src_path, self.training_data_dst_path],
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debug=self.is_debug(),
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batch_size=self.get_batch_size(),
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resolution=resolution,
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face_type=self.face_type,
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generators_count=src_dst_generators_count,
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data_format=nn.data_format)
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src_generator = SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(random_flip=False),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':False, 'channel_type' : SampleProcessor.ChannelType.BGR, 'border_replicate':False, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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generators_count=src_generators_count,
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raise_on_no_data=False )
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dst_generator = SampleGeneratorFace(self.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=False),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':False, 'channel_type' : SampleProcessor.ChannelType.BGR, 'border_replicate':False, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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generators_count=dst_generators_count,
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raise_on_no_data=False )
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self.set_training_data_generators ([srcdst_generator, src_generator, dst_generator])
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#override
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def get_model_filename_list(self):
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return self.model.model_filename_list
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#override
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def onSave(self):
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self.model.save_weights()
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#override
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def onTrainOneIter(self):
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image_np, mask_np = self.generate_next_samples()[0]
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loss = self.train (image_np, mask_np)
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return ( ('loss', loss ), )
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#override
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def onGetPreview(self, samples):
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n_samples = min(4, self.get_batch_size(), 800 // self.resolution )
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srcdst_samples, src_samples, dst_samples = samples
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image_np, mask_np = srcdst_samples
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I, M, IM, = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([image_np,mask_np] + self.view (image_np) ) ]
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M, IM, = [ np.repeat (x, (3,), -1) for x in [M, IM] ]
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green_bg = np.tile( np.array([0,1,0], dtype=np.float32)[None,None,...], (self.resolution,self.resolution,1) )
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result = []
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st = []
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for i in range(n_samples):
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ar = I[i]*M[i]+0.5*I[i]*(1-M[i])+0.5*green_bg*(1-M[i]), IM[i], I[i]*IM[i]+0.5*I[i]*(1-IM[i]) + 0.5*green_bg*(1-IM[i])
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st.append ( np.concatenate ( ar, axis=1) )
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result += [ ('XSeg training faces', np.concatenate (st, axis=0 )), ]
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if len(src_samples) != 0:
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src_np, = src_samples
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D, DM, = [ np.clip(nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([src_np] + self.view (src_np) ) ]
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DM, = [ np.repeat (x, (3,), -1) for x in [DM] ]
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st = []
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for i in range(n_samples):
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ar = D[i], DM[i], D[i]*DM[i] + 0.5*D[i]*(1-DM[i]) + 0.5*green_bg*(1-DM[i])
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st.append ( np.concatenate ( ar, axis=1) )
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result += [ ('XSeg src faces', np.concatenate (st, axis=0 )), ]
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if len(dst_samples) != 0:
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dst_np, = dst_samples
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D, DM, = [ np.clip(nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([dst_np] + self.view (dst_np) ) ]
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DM, = [ np.repeat (x, (3,), -1) for x in [DM] ]
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st = []
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for i in range(n_samples):
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ar = D[i], DM[i], D[i]*DM[i] + 0.5*D[i]*(1-DM[i]) + 0.5*green_bg*(1-DM[i])
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st.append ( np.concatenate ( ar, axis=1) )
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result += [ ('XSeg dst faces', np.concatenate (st, axis=0 )), ]
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return result
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Model = XSegModel |