chore: refactor code quality issues

.deepsource.toml

@@ -0,0 +1,10 @@
+version = 1
+
+exclude_patterns = ["samplelib/**"]
+
+[[analyzers]]
+name = "python"
+enabled = true
+
+  [analyzers.meta]
+  runtime_version = "3.x.x"

XSegEditor/XSegEditor.py

@@ -1206,7 +1206,7 @@ class MainWindow(QXMainWindow):
         self.image_paths_done = []
         self.image_paths = image_paths
         self.image_paths_has_ie_polys = image_paths_has_ie_polys
-        self.set_has_ie_polys_count ( len([ 1 for x in self.image_paths_has_ie_polys if self.image_paths_has_ie_polys[x] == True]) )
+        self.set_has_ie_polys_count ( len([ 1 for x in self.image_paths_has_ie_polys if self.image_paths_has_ie_polys[x] is True]) )
         self.loading_frame.hide()
         self.loading_frame = None
 

core/cv2ex.py

@@ -24,7 +24,7 @@ def cv2_imread(filename, flags=cv2.IMREAD_UNCHANGED, loader_func=None, verbose=T
 
 def cv2_imwrite(filename, img, *args):
     ret, buf = cv2.imencode( Path(filename).suffix, img, *args)
-    if ret == True:
+    if ret is True:
         try:
             with open(filename, "wb") as stream:
                 stream.write( buf )

core/imagelib/warp.py

@@ -37,7 +37,7 @@ def gen_warp_params (w, flip, rotation_range=[-10,10], scale_range=[-0.5, 0.5],
     random_transform_mat = cv2.getRotationMatrix2D((w // 2, w // 2), rotation, scale)
     random_transform_mat[:, 2] += (tx*w, ty*w)
 
-    params = dict()
+    params = {}
     params['mapx'] = mapx
     params['mapy'] = mapy
     params['rmat'] = random_transform_mat

core/joblib/SubprocessorBase.py

@@ -90,7 +90,7 @@ class Subprocessor(object):
 
         # disable pickling
         def __getstate__(self):
-            return dict()
+            return {}
         def __setstate__(self, d):
             self.__dict__.update(d)
 

core/leras/models/ModelBase.py

@@ -22,8 +22,7 @@ class ModelBase(nn.Saveable):
             for subname in layer.keys():
                 sublayer = layer[subname]
                 self._build_sub(sublayer, f"{name}_{subname}")
-        elif isinstance (layer, nn.LayerBase) or \
+        elif isinstance (layer, (nn.LayerBase, ModelBase)):
-                isinstance (layer, ModelBase):
 
             if layer.name is None:
                 layer.name = name

core/leras/ops/__init__.py

@@ -16,8 +16,7 @@ def batch_set_value(tuples):
             feed_dict = {}
 
             for x, value in tuples:
-                if isinstance(value, nn.tf.Operation) or \
+                if isinstance(value, (nn.tf.Operation, nn.tf.Variable)):
-                    isinstance(value, nn.tf.Variable):
                     assign_ops.append(value)
                 else:
                     value = np.asarray(value, dtype=x.dtype.as_numpy_dtype)

core/mplib/__init__.py

@@ -48,7 +48,7 @@ class IndexHost():
 
     # disable pickling
     def __getstate__(self):
-        return dict()
+        return {}
     def __setstate__(self, d):
         self.__dict__.update(d)
 
@@ -137,7 +137,7 @@ class Index2DHost():
 
     # disable pickling
     def __getstate__(self):
-        return dict()
+        return {}
     def __setstate__(self, d):
         self.__dict__.update(d)
 
@@ -203,7 +203,7 @@ class ListHost():
 
     # disable pickling
     def __getstate__(self):
-        return dict()
+        return {}
     def __setstate__(self, d):
         self.__dict__.update(d)
 
@@ -278,7 +278,7 @@ class DictHost():
 
     # disable pickling
     def __getstate__(self):
-        return dict()
+        return {}
     def __setstate__(self, d):
         self.__dict__.update(d)
 

core/qtex/QSubprocessor.py

@@ -91,7 +91,7 @@ class QSubprocessor(object):
 
         # disable pickling
         def __getstate__(self):
-            return dict()
+            return {}
         def __setstate__(self, d):
             self.__dict__.update(d)
 

facelib/FaceType.py

@@ -34,4 +34,4 @@ to_string_dict = { FaceType.HALF : 'half_face',
                    FaceType.MARK_ONLY :'mark_only',  
                  }
 
-from_string_dict = { to_string_dict[x] : x for x in to_string_dict.keys() }  
+from_string_dict = { to_string_dict[x] : x for x in to_string_dict }  

facelib/S3FDExtractor.py

@@ -246,7 +246,7 @@ class S3FDExtractor(object):
         return bboxlist
 
     def refine_nms(self, dets, thresh):
-        keep = list()
+        keep = []
         if len(dets) == 0:
             return keep
 

merger/InteractiveMergerSubprocessor.py

@@ -531,7 +531,7 @@ class InteractiveMergerSubprocessor(Subprocessor):
                 self.process_remain_frames = False
 
         return (self.is_interactive and self.is_interactive_quitting) or \
-               (not self.is_interactive and self.process_remain_frames == False)
+               (not self.is_interactive and self.process_remain_frames is False)
 
 
     #override

merger/MergerConfig.py

@@ -33,7 +33,7 @@ class MergerConfig(object):
     #overridable
     def ask_settings(self):
         s = """Choose sharpen mode: \n"""
-        for key in self.sharpen_dict.keys():
+        for key in self.sharpen_dict:
             s += f"""({key}) {self.sharpen_dict[key]}\n"""
         io.log_info(s)
         self.sharpen_mode = io.input_int ("", 0, valid_list=self.sharpen_dict.keys(), help_message="Enhance details by applying sharpen filter.")
@@ -79,7 +79,7 @@ mode_dict = {0:'original',
              5:'raw-rgb',
              6:'raw-predict'}
 
-mode_str_dict = { mode_dict[key] : key for key in mode_dict.keys() }
+mode_str_dict = { mode_dict[key] : key for key in mode_dict }
 
 mask_mode_dict = {1:'dst',
                   2:'learned-prd',
@@ -189,7 +189,7 @@ class MergerConfigMasked(MergerConfig):
 
     def ask_settings(self):
         s = """Choose mode: \n"""
-        for key in mode_dict.keys():
+        for key in mode_dict:
             s += f"""({key}) {mode_dict[key]}\n"""
         io.log_info(s)
         mode = io.input_int ("", mode_str_dict.get(self.default_mode, 1) )
@@ -204,7 +204,7 @@ class MergerConfigMasked(MergerConfig):
                 self.hist_match_threshold = np.clip ( io.input_int("Hist match threshold", 255, add_info="0..255"), 0, 255)
 
         s = """Choose mask mode: \n"""
-        for key in mask_mode_dict.keys():
+        for key in mask_mode_dict:
             s += f"""({key}) {mask_mode_dict[key]}\n"""
         io.log_info(s)
         self.mask_mode = io.input_int ("", 1, valid_list=mask_mode_dict.keys() )

models/Model_Quick96/Model.py

@@ -232,7 +232,7 @@ class QModel(ModelBase):
 
             self.set_training_data_generators ([
                     SampleGeneratorFace(training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
-                        sample_process_options=SampleProcessor.Options(random_flip=True if self.pretrain else False),
+                        sample_process_options=SampleProcessor.Options(random_flip=bool(self.pretrain)),
                         output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':True,  'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR,                                                           'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
                                                 {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR,                                                           'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
                                                 {'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G,   'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution}
@@ -240,7 +240,7 @@ class QModel(ModelBase):
                         generators_count=src_generators_count ),
 
                     SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
-                        sample_process_options=SampleProcessor.Options(random_flip=True if self.pretrain else False),
+                        sample_process_options=SampleProcessor.Options(random_flip=bool(self.pretrain)),
                         output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':True,  'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR,                                                           'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
                                                 {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR,                                                           'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
                                                 {'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G,   'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution}

models/Model_SAEHD/Model.py

@@ -177,7 +177,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
         
         self.gan_model_changed = (default_gan_patch_size != self.options['gan_patch_size']) or (default_gan_dims != self.options['gan_dims'])
 
-        self.pretrain_just_disabled = (default_pretrain == True and self.options['pretrain'] == False)
+        self.pretrain_just_disabled = (default_pretrain is True and self.options['pretrain'] == False)
 
     #override
     def on_initialize(self):