add use_device() funcion

can switch to a specific device

rvc/configs/__init__.py

@@ -0,0 +1 @@
+from rvc.configs.config import Config

rvc/configs/config.py

@@ -105,18 +105,27 @@ class Config:
     def has_xpu() -> bool:
         return hasattr(torch, "xpu") and torch.xpu.is_available()
 
-    def use_fp32_config(self) -> None:
+    def params_config(self) -> tuple:
-        for config_file, data in self.json_config.items():
+        if self.gpu_mem is not None and self.gpu_mem <= 4:
-            try:
+            x_pad = 1
-                data["train"]["fp16_run"] = False
+            x_query = 5
-                with open(config_file, "w") as json_file:
+            x_center = 30
-                    json.dump(data, json_file, indent=4)
+            x_max = 32
-            except Exception as e:
+        elif self.is_half:
-                logger.info(f"Error updating {config_file}: {str(e)}")
+            # 6G PU_RAM conf
-        logger.info("overwrite configs.json")
+            x_pad = 3
+            x_query = 10
+            x_center = 60
+            x_max = 65
+        else:
+            # 5G GPU_RAM conf
+            x_pad = 1
+            x_query = 6
+            x_center = 38
+            x_max = 41
+        return x_pad, x_query, x_center, x_max
 
-    def device_config(self) -> tuple:
+    def use_cuda(self) -> None:
-        if torch.cuda.is_available():
         if self.has_xpu():
             self.device = self.instead = "xpu:0"
             self.is_half = True
@@ -136,46 +145,53 @@ class Config:
         else:
             logger.info(f"Found GPU {self.gpu_name}")
         self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
+            torch.cuda.get_device_properties(i_device).total_memory / 1024 / 1024 / 1024
-                / 1024
-                / 1024
-                / 1024
             + 0.4
         )
-        elif self.has_mps():
+
-            logger.info("No supported Nvidia GPU found")
+    def use_mps(self) -> None:
         self.device = self.instead = "mps"
         self.is_half = False
         self.use_fp32_config()
-        elif self.dml:
+        self.params_config()
+
+    def use_dml(self) -> None:
         import torch_directml
 
         self.device = torch_directml.device(torch_directml.default_device())
         self.is_half = False
+        self.params_config()
+
+    def use_cpu(self) -> None:
+        self.device = self.instead = "cpu"
+        self.is_half = False
+        self.use_fp32_config()
+        self.params_config()
+
+    def use_fp32_config(self) -> None:
+        for config_file, data in self.json_config.items():
+            try:
+                data["train"]["fp16_run"] = False
+                with open(config_file, "w") as json_file:
+                    json.dump(data, json_file, indent=4)
+            except Exception as e:
+                logger.info(f"Error updating {config_file}: {str(e)}")
+        logger.info("overwrite configs.json")
+
+    def device_config(self) -> tuple:
+        if torch.cuda.is_available():
+            self.use_cuda()
+        elif self.has_mps():
+            logger.info("No supported Nvidia GPU found")
+            self.use_mps()
+        elif self.dml:
+            self.use_dml()
         else:
             logger.info("No supported Nvidia GPU found")
             self.device = self.instead = "cpu"
             self.is_half = False
             self.use_fp32_config()
 
-        if self.gpu_mem is not None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-        elif self.is_half:
-            # 6G PU_RAM conf
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G GPU_RAM conf
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
         logger.info(f"Use {self.dml or self.instead} instead")
         logger.info(f"is_half:{self.is_half}, device:{self.device}")
-        return x_pad, x_query, x_center, x_max
+        return self.params_config()