From 1c8845d558b9a857a58e625036d08a588d1d1e26 Mon Sep 17 00:00:00 2001
From: iperov <lepersorium@gmail.com>
Date: Sat, 16 Mar 2019 20:57:51 +0400
Subject: [PATCH] model fansegmentator for developing purposes

---
 models/Model_FANSegmentator/Model.py    | 105 ++++++++++++++++++++++++
 models/Model_FANSegmentator/__init__.py |   1 +
 2 files changed, 106 insertions(+)
 create mode 100644 models/Model_FANSegmentator/Model.py
 create mode 100644 models/Model_FANSegmentator/__init__.py

diff --git a/models/Model_FANSegmentator/Model.py b/models/Model_FANSegmentator/Model.py
new file mode 100644
index 0000000..fe7a900
--- /dev/null
+++ b/models/Model_FANSegmentator/Model.py
@@ -0,0 +1,105 @@
+import numpy as np
+
+from nnlib import nnlib
+from models import ModelBase
+from facelib import FaceType
+from facelib import FANSegmentator
+from samples import *
+from interact import interact as io
+
+class Model(ModelBase):
+
+    
+    #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
+        
+        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() )
+
+        if self.is_training_mode:
+            f = SampleProcessor.TypeFlags
+            f_type = f.FACE_ALIGN_FULL #if self.face_type == FaceType.FULL else f.FACE_ALIGN_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=self.random_flip, normalize_tanh = True, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ), 
+                            output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR, self.resolution],
+                                                  [f.TRANSFORMED | f_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=self.random_flip, normalize_tanh = True, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ), 
+                            output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR, 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 = self.fan_seg.train_on_batch( [target_src], [target_src_mask] )
+
+        return ( ('loss', loss), )
+        
+    #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_from_bgr([test_A])
+        mBB = self.fan_seg.extract_from_bgr([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 [ ('FANSegmentator', np.concatenate ( st, axis=0 ) ),
+                 ('never seen', np.concatenate ( st2, axis=0 ) ),
+                 ]
+
+    def predictor_func (self, face):
+        
+        face_64_bgr = face[...,0:3]
+        face_64_mask = np.expand_dims(face[...,3],-1)
+        
+        x, mx = self.src_view ( [ np.expand_dims(face_64_bgr,0) ] )
+        x, mx = x[0], mx[0]     
+        
+        return np.concatenate ( (x,mx), -1 )
+
+    #override
+    def get_converter(self):
+        from converters import ConverterMasked
+        return ConverterMasked(self.predictor_func,
+                               predictor_input_size=64, 
+                               output_size=64, 
+                               face_type=FaceType.HALF, 
+                               base_erode_mask_modifier=100,
+                               base_blur_mask_modifier=100)
+        
+   
\ No newline at end of file
diff --git a/models/Model_FANSegmentator/__init__.py b/models/Model_FANSegmentator/__init__.py
new file mode 100644
index 0000000..cdb3fe7
--- /dev/null
+++ b/models/Model_FANSegmentator/__init__.py
@@ -0,0 +1 @@
+from .Model import Model
\ No newline at end of file