fix

train_model.py

@@ -76,7 +76,8 @@ def encode(batch: [(torch.tensor, str)], in_alphabet, max_len):
 
 
 def encode_str(batch: [(str, str)], in_alphabet, max_len):
-    batch = [(torch.tensor([in_alphabet[letter] for letter in in_str], dtype=torch.int), out_str) for in_str, out_str in batch]
+    batch = [(torch.tensor([in_alphabet[letter] for letter in in_str], dtype=torch.int), out_str) for in_str, out_str in
+             batch]
     return encode(batch, in_alphabet, max_len)
 
 
@@ -192,12 +193,13 @@ def cfg():
                  'u', 'v', 'w', 'x', 'y', 'z']
 
 
-def signature(model,in_alphabet,max_len):
+def signature(model, in_alphabet, max_len):
     mock_x = [('abc', 'xyz')]
     mock_text, _ = encode_str(mock_x, in_alphabet, max_len)
     mock_y = model(mock_text)
     return mlflow.models.signature.infer_signature(mock_text, mock_y)
 
+
 @ex.automain
 def run(kernel_size, hidden_layers, data_file, epochs, teacher_forcing_probability, learning_rate, batch_size, max_len,
         total_out_len, model_file, out_lookup, in_lookup, mode):
@@ -227,20 +229,13 @@ def run(kernel_size, hidden_layers, data_file, epochs, teacher_forcing_probabili
 
         cnn = CNN(kernel_size=kernel_size, hidden_layers=hidden_layers, channels=max_len, embedding_size=max_len,
                   in_alphabet=in_alphabet, max_len=max_len).to(device)
-        if os.path.isfile(model_file):
-            cnn.load_state_dict(torch.load(model_file, map_location=torch.device('cpu')))
-        else:
-            if mode == 'train':
-                train_model(cnn, learning_rate, in_alphabet, max_len, data, epochs, batch_size)
-                torch.save(cnn.state_dict(), model_file)
-                ex.add_artifact(model_file)
-
-                mlflow.pytorch.log_model(cnn, "cnn-model", registered_model_name="PhoneticEdDistEmbeddings",
-                                         signature=signature(cnn,in_alphabet, max_len))
-                log_artifacts(model_file)
-            else:
-                print(model_file + " missing!")
-                exit(2)
+        if mode == 'train':
+            train_model(cnn, learning_rate, in_alphabet, max_len, data, epochs, batch_size)
+            torch.save(cnn.state_dict(), model_file)
+            ex.add_artifact(model_file)
+            log_artifacts(model_file)
+            mlflow.pytorch.log_model(cnn, "cnn-model", registered_model_name="PhoneticEdDistEmbeddings",
+                                     signature=signature(cnn, in_alphabet, max_len))
 
         if mode == 'eval':
             cnn.eval()