sync ms

app.py

@@ -38,7 +38,7 @@ def _L(zh_txt: str):
 
 def infer(input_img: str, checkpoint_file: str):
     status = "Success"
-    outstr = None
+    outstr = ""
     try:
         model = Model()
         model.restore(f"{MODEL_DIR}/{checkpoint_file}")

model.py

@@ -7,6 +7,7 @@ import torch.nn as nn
 
 class Model(torch.jit.ScriptModule):
     CHECKPOINT_FILENAME_PATTERN = "model-{}.pth"
+
     __constants__ = [
         "_hidden1",
         "_hidden2",
@@ -30,6 +31,7 @@ class Model(torch.jit.ScriptModule):
 
     def __init__(self):
         super(Model, self).__init__()
+
         self._hidden1 = nn.Sequential(
             nn.Conv2d(in_channels=3, out_channels=48, kernel_size=5, padding=2),
             nn.BatchNorm2d(num_features=48),
@@ -88,6 +90,7 @@ class Model(torch.jit.ScriptModule):
         )
         self._hidden9 = nn.Sequential(nn.Linear(192 * 7 * 7, 3072), nn.ReLU())
         self._hidden10 = nn.Sequential(nn.Linear(3072, 3072), nn.ReLU())
+
         self._digit_length = nn.Sequential(nn.Linear(3072, 7))
         self._digit1 = nn.Sequential(nn.Linear(3072, 11))
         self._digit2 = nn.Sequential(nn.Linear(3072, 11))
@@ -108,12 +111,14 @@ class Model(torch.jit.ScriptModule):
         x = x.view(x.size(0), 192 * 7 * 7)
         x = self._hidden9(x)
         x = self._hidden10(x)
+
         length_logits = self._digit_length(x)
         digit1_logits = self._digit1(x)
         digit2_logits = self._digit2(x)
         digit3_logits = self._digit3(x)
         digit4_logits = self._digit4(x)
         digit5_logits = self._digit5(x)
+
         return (
             length_logits,
             digit1_logits,
@@ -147,6 +152,10 @@ class Model(torch.jit.ScriptModule):
 
     def restore(self, path_to_checkpoint_file):
         self.load_state_dict(
-            torch.load(path_to_checkpoint_file, map_location=torch.device("cpu"))
+            torch.load(
+                path_to_checkpoint_file,
+                map_location=torch.device("cpu"),
+            )
         )
-        return int(path_to_checkpoint_file.split("model-")[-1][:-4])
+        step = int(path_to_checkpoint_file.split("model-")[-1][:-4])
+        return step