0.2.1 (#27)

* Fix backend env var parsing

* 0.2.1

* Fix getting cpu mode

backend/nodes/pytorch_nodes.py

@@ -13,20 +13,15 @@
 
 from .node_base import NodeBase
 from .node_factory import NodeFactory
-from .properties.inputs.file_inputs import DirectoryInput, PthFileInput, TorchFileInput
+from .properties.inputs.file_inputs import (DirectoryInput, PthFileInput,
+                                            TorchFileInput)
 from .properties.inputs.generic_inputs import SliderInput, TextInput
 from .properties.inputs.numpy_inputs import ImageInput
-from .properties.inputs.pytorch_inputs import (
-    ModelInput,
-    StateDictInput,
-    TorchScriptInput,
-)
+from .properties.inputs.pytorch_inputs import (ModelInput, StateDictInput,
+                                               TorchScriptInput)
 from .properties.outputs.numpy_outputs import ImageOutput
-from .properties.outputs.pytorch_outputs import (
-    ModelOutput,
-    StateDictOutput,
-    TorchScriptOutput,
-)
+from .properties.outputs.pytorch_outputs import (ModelOutput, StateDictOutput,
+                                                 TorchScriptOutput)
 from .utils.architecture.RRDB import RRDBNet as ESRGAN
 from .utils.architecture.SPSR import SPSRNet as SPSR
 from .utils.architecture.SRVGG import SRVGGNetCompact as RealESRGANv2
@@ -38,7 +33,9 @@ def check_env():
         "cuda" if torch.cuda.is_available() and os.environ["device"] != "cpu" else "cpu"
     )
 
-    if bool(os.environ["isFp16"]):
+    logger.info(f"Using device: {os.environ['device']}")
+
+    if os.environ["isFp16"] == "True":
         if os.environ["device"] == "cpu":
             torch.set_default_tensor_type(torch.HalfTensor)
         elif os.environ["device"] == "cuda":
@@ -162,7 +159,7 @@ def run(self, model: torch.nn.Module, img: np.ndarray) -> np.ndarray:
         # Borrowed from iNNfer
         logger.info("Converting image to tensor")
         img_tensor = np2tensor(img, change_range=True)
-        if bool(os.environ["isFp16"]):
+        if os.environ["isFp16"] == "True":
             model = model.half()
         logger.info("Upscaling image")
         t_out, _ = auto_split_process(

backend/nodes/utils/utils.py

@@ -208,7 +208,7 @@ def auto_split_process(
     if max_depth is None or max_depth == current_depth:
         try:
             d_img = lr_img.to(torch.device(os.environ["device"]))
-            if bool(os.environ["isFp16"]):
+            if os.environ["isFp16"] == "True":
                 d_img = d_img.half()
             result = model(d_img).detach().cpu()
             del d_img

backend/run.py

@@ -95,10 +95,11 @@ async def run(request: Request):
             full_data = request.json
             logger.info(full_data)
             nodes_list = full_data["data"]
-            os.environ["device"] = "cpu" if bool(full_data["isCpu"]) else "cuda"
+            os.environ["device"] = "cpu" if full_data["isCpu"] else "cuda"
             os.environ["isFp16"] = (
-                "False" if bool(full_data["isCpu"]) else str(full_data["isFp16"])
+                "False" if full_data["isCpu"] else str(full_data["isFp16"])
             )
+            logger.info(f"Using device: {os.environ['device']}")
             os.environ["resolutionX"] = str(full_data["resolutionX"])
             os.environ["resolutionY"] = str(full_data["resolutionY"])
             executor = Executor(nodes_list, app.loop, queue)

package.json

@@ -1,7 +1,7 @@
 {
   "name": "chainner",
   "productName": "chaiNNer",
-  "version": "0.2.0",
+  "version": "0.2.1",
   "description": "A flowchart based image processing GUI",
   "main": ".webpack/main",
   "scripts": {