initial code to extract umdfaces.io dataset and train pose estimator

models/Model_DEV_FANSEG/Model.py

@@ -0,0 +1,101 @@
+import numpy as np
+
+from nnlib import nnlib
+from models import ModelBase
+from facelib import FaceType
+from facelib import FANSegmentator
+from samplelib import *
+from interact import interact as io
+
+class Model(ModelBase):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs, 
+                            ask_write_preview_history=False, 
+                            ask_target_iter=False,
+                            ask_sort_by_yaw=False,
+                            ask_random_flip=False,
+                            ask_src_scale_mod=False)
+    
+    #override
+    def onInitializeOptions(self, is_first_run, ask_override):            
+        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)
+     
+    #override
+    def onInitialize(self):
+        exec(nnlib.import_all(), locals(), globals())
+        self.set_vram_batch_requirements( {1.5:4} )
+
+        self.resolution = 256
+        self.face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF
+
+        
+        self.fan_seg = FANSegmentator(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:
+            f = SampleProcessor.TypeFlags
+            face_type = f.FACE_TYPE_FULL if self.options['face_type'] == 'f' else f.FACE_TYPE_HALF
+            
+            self.set_training_data_generators ([    
+                    SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size, 
+                            sample_process_options=SampleProcessor.Options(random_flip=True, motion_blur = [25, 1] ), 
+                            output_sample_types=[ [f.WARPED_TRANSFORMED | face_type | f.MODE_BGR_SHUFFLE | f.OPT_APPLY_MOTION_BLUR, self.resolution],
+                                                  [f.WARPED_TRANSFORMED | face_type | f.MODE_M | f.FACE_MASK_FULL, self.resolution]
+                                                ]),
+                                                
+                    SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, 
+                            sample_process_options=SampleProcessor.Options(random_flip=True ), 
+                            output_sample_types=[ [f.TRANSFORMED | face_type | f.MODE_BGR_SHUFFLE, self.resolution]
+                                                ])
+                                               ])
+                
+    #override
+    def onSave(self):        
+        self.fan_seg.save_weights()
+        
+    #override
+    def onTrainOneIter(self, generators_samples, generators_list):
+        target_src, target_src_mask = generators_samples[0]
+
+        loss,acc = self.fan_seg.train_on_batch( [target_src], [target_src_mask] )
+
+        return ( ('loss', loss), ('acc',acc))
+        
+    #override
+    def onGetPreview(self, sample):
+        test_A   = sample[0][0][0:4] #first 4 samples
+        test_B   = sample[1][0][0:4] #first 4 samples
+        
+        mAA = self.fan_seg.extract(test_A)
+        mBB = self.fan_seg.extract(test_B)
+
+        mAA = np.repeat ( mAA, (3,), -1)
+        mBB = np.repeat ( mBB, (3,), -1)
+        
+        st = []
+        for i in range(0, len(test_A)):
+            st.append ( np.concatenate ( (
+                test_A[i,:,:,0:3],
+                mAA[i],
+                test_A[i,:,:,0:3]*mAA[i],
+                ), axis=1) )
+                
+        st2 = []
+        for i in range(0, len(test_B)):
+            st2.append ( np.concatenate ( (
+                test_B[i,:,:,0:3],
+                mBB[i],
+                test_B[i,:,:,0:3]*mBB[i],
+                ), axis=1) )
+                
+        return [ ('training data', np.concatenate ( st, axis=0 ) ),
+                 ('evaluating data', np.concatenate ( st2, axis=0 ) ),
+                 ]