update

app.py

@@ -154,33 +154,33 @@ async def _process_model(model_input: Union[str, gr.File], tilesize: int, output
 
 with gr.Blocks() as demo:
     gr.Markdown("# 模型转换工具")
-    # with gr.Row():
-    with gr.Column():
-        input_type = gr.Radio(['模型链接', '上传模型文件'], label='输入类型')
-        url_input = gr.Textbox(label='模型链接')
-        file_input = gr.File(label='上传模型文件', visible=False)
+    with gr.Row():
+        with gr.Column():
+            input_type = gr.Radio(['模型链接', '上传模型文件'], label='输入类型')
+            url_input = gr.Textbox(label='模型链接')
+            file_input = gr.File(label='上传模型文件', visible=False)
 
-        
-        def show_input(input_type):
-            if input_type == '模型链接':
-                return gr.update(visible=True), gr.update(visible=False)
-            else:
-                return gr.update(visible=False), gr.update(visible=True)
-        
-        input_type.change(show_input, inputs=input_type, outputs=[url_input, file_input])
-        
-        tilesize = gr.Number(label="Tilesize", value=0, precision=0)
-        # 添加fp16和try_run复选框
-        fp16 = gr.Checkbox(label="FP16", value=False)
-        try_run = gr.Checkbox(label="测试mnn推理", value=False)
-        convert_btn = gr.Button("开始转换")
-    with gr.Column():
-        log_box = gr.Textbox(label="转换日志", lines=10, interactive=False)
-    # with gr.Row():
-        onnx_output = gr.File(label="ONNX 模型输出")
-        mnn_output = gr.File(label="MNN 模型输出")
-
-    async def process_model(input_type, url_input, file_input, tilesize, fp16, try_run, log_box):
+            
+            def show_input(input_type):
+                if input_type == '模型链接':
+                    return gr.update(visible=True), gr.update(visible=False)
+                else:
+                    return gr.update(visible=False), gr.update(visible=True)
+            
+            input_type.change(show_input, inputs=input_type, outputs=[url_input, file_input])
+            
+            tilesize = gr.Number(label="Tilesize", value=0, precision=0)
+            # 添加fp16和try_run复选框
+            fp16 = gr.Checkbox(label="FP16", value=False)
+            try_run = gr.Checkbox(label="测试mnn推理", value=False)
+            convert_btn = gr.Button("开始转换")
+        with gr.Column():
+        # with gr.Row():
+            log_box = gr.Textbox(label="转换日志", lines=10, interactive=False)
+            onnx_output = gr.File(label="ONNX 模型输出")
+            mnn_output = gr.File(label="MNN 模型输出")
+
+    async def process_model(input_type, url_input, file_input, tilesize, fp16, try_run):
         global task_counter
         task_counter += 1
         output_dir = os.path.join(os.getcwd(), f"output_{task_counter}")
@@ -225,7 +225,7 @@ with gr.Blocks() as demo:
 
     convert_btn.click(
         process_model,
-        inputs=[input_type, url_input, file_input, tilesize, log_box],
+        inputs=[input_type, url_input, file_input, tilesize, fp16, try_run],
         outputs=[onnx_output, mnn_output, log_box],
         api_name="convert_model"
     )

pth2onnx.py

@@ -73,7 +73,7 @@ def convert_pth_to_onnx(pth_path: str, onnx_path: str=None, channel:int=0, tiles
             print(f'文件名 {filename} 包含匹配模式。')
         else:
             base_path = f"{base_path}-x{scale}"
-        
+        print("final use_fp16", str(use_fp16) )
         onnx_path = base_path + ("-Grayscale" if channel==1 else "") + ("-fp16.onnx" if use_fp16 else ".onnx")
         
     # 处理相对路径情况