mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-05 20:42:11 -07:00
61472cdaf7
removed support of extracted(aligned) PNG faces. Use old builds to convert from PNG to JPG. fanseg model file in facelib/ is renamed
95 lines
No EOL
3.3 KiB
Python
95 lines
No EOL
3.3 KiB
Python
import os
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import pickle
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from functools import partial
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from pathlib import Path
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import cv2
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import numpy as np
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from core.interact import interact as io
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from core.leras import nn
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class DFLSegNet(object):
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VERSION = 1
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def __init__ (self, name,
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resolution,
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load_weights=True,
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weights_file_root=None,
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training=False,
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place_model_on_cpu=False,
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run_on_cpu=False,
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optimizer=None,
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data_format="NHWC"):
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nn.initialize(data_format=data_format)
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tf = nn.tf
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self.weights_file_root = Path(weights_file_root) if weights_file_root is not None else Path(__file__).parent
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with tf.device ('/CPU:0'):
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#Place holders on CPU
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self.input_t = tf.placeholder (nn.floatx, nn.get4Dshape(resolution,resolution,3) )
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self.target_t = tf.placeholder (nn.floatx, nn.get4Dshape(resolution,resolution,1) )
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# Initializing model classes
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archi = nn.DFLSegnetArchi()
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with tf.device ('/CPU:0' if place_model_on_cpu else '/GPU:0'):
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self.enc = archi.Encoder(3, 64, name='Encoder')
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self.dec = archi.Decoder(64, 1, name='Decoder')
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self.enc_dec_weights = self.enc.get_weights()+self.dec.get_weights()
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model_name = f'{name}_{resolution}'
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self.model_filename_list = [ [self.enc, f'{model_name}_enc.npy'],
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[self.dec, f'{model_name}_dec.npy'],
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]
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if training:
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if optimizer is None:
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raise ValueError("Optimizer should be provided for training mode.")
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self.opt = optimizer
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self.opt.initialize_variables (self.enc_dec_weights, vars_on_cpu=place_model_on_cpu)
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self.model_filename_list += [ [self.opt, f'{model_name}_opt.npy' ] ]
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else:
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with tf.device ('/CPU:0' if run_on_cpu else '/GPU:0'):
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_, pred = self.dec(self.enc(self.input_t))
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def net_run(input_np):
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return nn.tf_sess.run ( [pred], feed_dict={self.input_t :input_np})[0]
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self.net_run = net_run
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# Loading/initializing all models/optimizers weights
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for model, filename in self.model_filename_list:
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do_init = not load_weights
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if not do_init:
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do_init = not model.load_weights( self.weights_file_root / filename )
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if do_init:
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model.init_weights()
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def flow(self, x):
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return self.dec(self.enc(x))
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def get_weights(self):
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return self.enc_dec_weights
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def save_weights(self):
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for model, filename in io.progress_bar_generator(self.model_filename_list, "Saving", leave=False):
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model.save_weights( self.weights_file_root / filename )
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def extract (self, input_image):
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input_shape_len = len(input_image.shape)
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if input_shape_len == 3:
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input_image = input_image[None,...]
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result = np.clip ( self.net_run(input_image), 0, 1.0 )
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result[result < 0.1] = 0 #get rid of noise
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if input_shape_len == 3:
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result = result[0]
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return result |