added different schemas for the model types (currently, amp and saehd)

models/ModelBase.py

@@ -433,6 +433,10 @@ class ModelBase(object):
         #return predictor_func, predictor_input_shape, MergerConfig() for the model
         raise NotImplementedError
 
+    #overridable
+    def get_config_schema_path(self):
+        raise NotImplementedError
+
     def get_pretraining_data_path(self):
         return self.pretraining_data_path
 
@@ -493,7 +497,7 @@ class ModelBase(object):
         fun = self.get_strpath_configuration_path if not auto_gen else self.get_model_conf_path
 
         try:
-            with open(fun(), 'r') as file, open(models.get_config_schema_path(), 'r') as schema:
+            with open(fun(), 'r') as file, open(self.get_config_schema_path(), 'r') as schema:
                 data = yaml.safe_load(file)
                 validate(data, yaml.safe_load(schema))
         except FileNotFoundError:

models/Model_AMP/Model.py

@@ -11,6 +11,8 @@ from models import ModelBase
 from samplelib import *
 from core.cv2ex import *
 
+from pathlib import Path
+
 class AMPModel(ModelBase):
 
     #override
@@ -812,4 +814,10 @@ class AMPModel(ModelBase):
         import merger
         return predictor_morph, (self.options['resolution'], self.options['resolution'], 3), merger.MergerConfigMasked(face_type=self.face_type, default_mode = 'overlay')
 
+    #override
+    def get_config_schema_path(self):
+        config_path = Path(__file__).parent.absolute() / Path("config_schema.json")
+        return config_path
+
+
 Model = AMPModel

models/Model_AMP/config_schema.json

@@ -4,14 +4,15 @@
   "definitions": {
     "dfl_config": {
       "type": "object",
-      "additionalProperties": false,
+      "additionalProperties": true,
       "properties": {
         "use_fp16": {
           "type": "boolean"
         },
-        "archi": {
+        "morph_factor": {
-          "type": "string",
+          "type": "number",
-          "pattern": "^(df|liae)-(\\b(?!\\w*(\\w)\\w*\\1)[udtc]+\\b)+|^(df|liae)$"
+          "minimum":0.0,
+          "maximum":1.0
         },
         "resolution": {
           "type": "integer",
@@ -44,6 +45,12 @@
           "maximum": 256,
           "multipleOf": 2
         },
+        "inter_dims": {
+          "type": "integer",
+          "minimum": 32,
+          "maximum": 2048,
+          "multipleOf": 2
+        },
         "d_dims": {
           "type": "integer",
           "minimum": 16,
@@ -214,7 +221,6 @@
       "required": [
         "adabelief",
         "ae_dims",
-        "archi",
         "autobackup_hour",
         "background_power",
         "batch_size",
@@ -225,6 +231,8 @@
         "d_dims",
         "d_mask_dims",
         "e_dims",
+        "inter_dims",
+        "morph_factor",
         "eyes_prio",
         "face_style_power",
         "face_type",

models/Model_SAEHD/Model.py

@@ -10,6 +10,8 @@ from facelib import FaceType
 from models import ModelBase
 from samplelib import *
 
+from pathlib import Path
+
 class SAEHDModel(ModelBase):
 
     #override
@@ -1058,4 +1060,9 @@ class SAEHDModel(ModelBase):
         import merger
         return self.predictor_func, (self.options['resolution'], self.options['resolution'], 3), merger.MergerConfigMasked(face_type=self.face_type, default_mode = 'overlay')
 
+    #override
+    def get_config_schema_path(self):
+        config_path = Path(__file__).parent.absolute() / Path("config_schema.json")
+        return config_path
+
 Model = SAEHDModel

models/__init__.py

@@ -1,11 +1,7 @@
 from .ModelBase import ModelBase
-from pathlib import Path
 
 def import_model(model_class_name):
     module = __import__('Model_'+model_class_name, globals(), locals(), [], 1)
     return getattr(module, 'Model')
 
 
-def get_config_schema_path():
-    config_path = Path(__file__).parent.absolute() / Path("config_schema.json")
-    return config_path