DeepFaceLab/models/Model_DEV_POSEEST/Model.py

import numpy as np

from nnlib import nnlib
from models import ModelBase
from facelib import FaceType
from facelib import PoseEstimator
from samplelib import *
from interact import interact as io
import imagelib

class Model(ModelBase):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs,
                            ask_enable_autobackup=False,
                            ask_write_preview_history=False,
                            ask_target_iter=False,
                            ask_random_flip=False)

    #override
    def onInitializeOptions(self, is_first_run, ask_override):
        yn_str = {True:'y',False:'n'}

        default_face_type = 'f'
        if is_first_run:
            self.options['face_type'] = io.input_str ("Half or Full face? (h/f, ?:help skip:f) : ", default_face_type, ['h','f'], help_message="Half face has better resolution, but covers less area of cheeks.").lower()
        else:
            self.options['face_type'] = self.options.get('face_type', default_face_type)

        def_train_bgr = self.options.get('train_bgr', True)
        if is_first_run or ask_override:
            self.options['train_bgr'] = io.input_bool ("Train bgr? (y/n, ?:help skip: %s) : " % (yn_str[def_train_bgr]), def_train_bgr)
        else:
            self.options['train_bgr'] = self.options.get('train_bgr', def_train_bgr)

    #override
    def onInitialize(self):
        exec(nnlib.import_all(), locals(), globals())
        self.set_vram_batch_requirements( {4:64} )

        self.resolution = 128
        self.face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF


        self.pose_est = PoseEstimator(self.resolution,
                                      FaceType.toString(self.face_type),
                                      load_weights=not self.is_first_run(),
                                      weights_file_root=self.get_model_root_path(),
                                      training=True)

        if self.is_training_mode:
            t = SampleProcessor.Types
            face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF

            self.set_training_data_generators ([
                    SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size, generators_count=4,
                            sample_process_options=SampleProcessor.Options( rotation_range=[0,0] ), #random_flip=True,
                            output_sample_types=[ {'types': (t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_BGR_SHUFFLE), 'resolution':self.resolution, 'motion_blur':(25, 1) },
                                                  {'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR_SHUFFLE), 'resolution':self.resolution },
                                                  {'types': (t.IMG_PITCH_YAW_ROLL,)}
                                                ]),

                    SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, generators_count=4,
                            sample_process_options=SampleProcessor.Options( rotation_range=[0,0] ), #random_flip=True,
                            output_sample_types=[ {'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'resolution':self.resolution },
                                                  {'types': (t.IMG_PITCH_YAW_ROLL,)}
                                                ])
                                            ])

    #override
    def onSave(self):
        self.pose_est.save_weights()

    #override
    def onTrainOneIter(self, generators_samples, generators_list):
        target_srcw, target_src, pitch_yaw_roll = generators_samples[0]

        bgr_loss, pyr_loss = self.pose_est.train_on_batch( target_srcw, target_src, pitch_yaw_roll, skip_bgr_train=not self.options['train_bgr'] )

        return ( ('bgr_loss', bgr_loss), ('pyr_loss', pyr_loss), )

    #override
    def onGetPreview(self, generators_samples):
        test_src     = generators_samples[0][1][0:4] #first 4 samples
        test_pyr_src = generators_samples[0][2][0:4]
        test_dst     = generators_samples[1][0][0:4]
        test_pyr_dst = generators_samples[1][1][0:4]

        h,w,c = self.resolution,self.resolution,3
        h_line = 13

        result = []
        for name, img, pyr in [ ['training data', test_src, test_pyr_src],  \
                                ['evaluating data',test_dst, test_pyr_dst] ]:
            bgr_pred, pyr_pred = self.pose_est.extract(img)
            
            hor_imgs = []
            for i in range(len(img)):
                img_info = np.ones ( (h,w,c) ) * 0.1
                
                i_pyr = pyr[i]
                i_pyr_pred = pyr_pred[i]
                lines = ["%.4f %.4f %.4f" % (i_pyr[0],i_pyr[1],i_pyr[2]),
                         "%.4f %.4f %.4f" % (i_pyr_pred[0],i_pyr_pred[1],i_pyr_pred[2]) ]

                lines_count = len(lines)
                for ln in range(lines_count):
                    img_info[ ln*h_line:(ln+1)*h_line, 0:w] += \
                        imagelib.get_text_image (  (h_line,w,c), lines[ln], color=[0.8]*c )

                hor_imgs.append ( np.concatenate ( (
                    img[i,:,:,0:3],
                    bgr_pred[i],
                    img_info
                    ), axis=1) )


            result += [ (name, np.concatenate (hor_imgs, axis=0)) ]

        return result