Update app.py

app.py

@@ -8,6 +8,16 @@ from ftplib import FTP
 from spandrel import ImageModelDescriptor, ModelLoader
 import torch
 import subprocess
+import spandrel
+import ncnn
+import MNN
+
+print("Torch",torch.__version__)
+print("Gradio",gr.__version__)
+print("Spandrel", spandrel.__version__)
+print("NCNN", ncnn.__version__)
+print("MNN", MNN.__version__)
+
 
 # 定义 downloaded_files 变量
 downloaded_files = {}
@@ -233,18 +243,24 @@ def start_process(input_file, input_url, input2, shape0_str, shape1_str, output_
 
             # 获取输出
             example_output = traced_torch_model(example_input)
-            width_ratio = example_output.shape[2] / example_input.shape[2]
-            print_log(task_id, input2, "获得缩放倍率="+ str(width_ratio)+", 输出shape="+str(list(example_output.shape)), "完成")
-            log+= ("获得缩放倍率="+str(width_ratio)+", 输出shape="+str(list(example_output.shape))+"\n")
-            yield [], log
+            if isinstance(example_output, torch.Tensor):
+                width_ratio = example_output.shape[2] / example_input.shape[2]
+                print_log(task_id, input2, "获得缩放倍率="+ str(width_ratio)+", 输出shape="+str(list(example_output.shape)), "完成")
+                log+= ("获得缩放倍率="+str(width_ratio)+", 输出shape="+str(list(example_output.shape))+"\n")
+                yield [], log
+            else:
+                print_log(task_id, input2, "Traced torch model输出" + type(example_output), "错误")
+                log+="Traced torch model输出" + type(example_output)+ "错误\n"
+                yield [], log
 
+        scale = int(width_ratio)
 
         # 转换为 ONNX 模型
         if "ONNX" in output_type or "NCNN" in output_type or "MNN" in output_type:
-            if str(width_ratio) in input2 or width_ratio >0:
+            if str(scale) in input2 or scale <1:
                 onnx_path = output_base + ".onnx"
             else:
-                onnx_path = output_base + "-x" + str(int(width_ratio)) + ".onnx"
+                onnx_path = output_base + "-x" + str(scale) + ".onnx"
             if os.path.exists(onnx_path):
                 print_log(task_id, input2, "转换为ONNX模型", "跳过")
                 log += "跳过转换为ONNX模型\n"
@@ -259,10 +275,10 @@ def start_process(input_file, input_url, input2, shape0_str, shape1_str, output_
         # 转换为 mnn 模型
         if "MNN" in output_type:
             
-            if str(width_ratio) in input2:
+            if str(scale) in input2 or scale < 1:
                 mnn_path = output_base + ".mnn"
             else:
-                mnn_path = output_base + "-x" + str(width_ratio) + ".mnn"
+                mnn_path = output_base + "-x" + str(scale) + ".mnn"
 
             mnn_config = ""
             if "Fixed" in output_type and input_tensor0 is not None:
@@ -300,17 +316,17 @@ def start_process(input_file, input_url, input2, shape0_str, shape1_str, output_
                                 yield [], log
                     returncode = process.poll()
                     if returncode != 0:
-                        print_log(task_id, input2, f"转换为MNN模型，返回码: {returncode}，命令: {mnn_command} ", "失败")
+                        print_log(task_id, input2, f"转换为MNN模型，返回码: {returncode}，命令: {mnn_command} ", "错误")
                         log += f"执行mnn命令失败，返回码: {returncode}，命令: {mnn_command} \n"
                     else:
-                        log += f"执行命令成功: {mnn_command} \n"
+                        log += f"执行mnn命令成功: {mnn_command} \n"
                 except Exception as e:
-                    log += f"执行命令: {mnn_command} 失败，错误信息: {str(e)}\n"
+                    log += f"执行mnn命令: {mnn_command} 失败，错误信息: {str(e)}\n"
                     print_log(task_id, input2, f"转换为MNN模型，错误信息: {str(e)}", "错误")
 
         if "NCNN" in output_type:
-            print_log(task_id, input2, "执行命令" + command, "开始")
-            log += "执行命令…\n"
+            print_log(task_id, input2, "执行ncnn命令" + command, "开始")
+            log += "执行ncnn命令…\n"
             yield [], log
 
             try:
@@ -328,12 +344,12 @@ def start_process(input_file, input_url, input2, shape0_str, shape1_str, output_
                             yield [], log
                 returncode = process.poll()
                 if returncode != 0:
-                    log += f"执行命令失败，返回码: {returncode}，命令: {command} \n"
+                    log += f"执行ncnn命令失败，返回码: {returncode}，命令: {command} \n"
                     print_log(task_id, input2, f"返回码: {returncode}，命令: {command} ", "失败")
                 else:
-                    log += f"执行命令成功: {command} \n"
+                    log += f"执行ncnn命令成功: {command} \n"
             except Exception as e:
-                log += f"执行命令: {command} 失败，错误信息: {str(e)}\n"
+                log += f"执行ncnn命令: {command} 失败，错误信息: {str(e)}\n"
                 print_log(task_id, input2, f"错误信息: {str(e)}", "错误")
                 
             # 查找 output_folder 目录下以 .ncnn.bin 和 .ncnn.param 结尾的文件
@@ -350,7 +366,6 @@ def start_process(input_file, input_url, input2, shape0_str, shape1_str, output_
                 # 压缩包内文件夹名称
                 zip_folder_name = f"models-{input2}"
                 # 重命名后的文件名
-                scale = int(width_ratio)
                 new_bin_name = f"x{scale}.bin"
                 new_param_name = f"x{scale}.param"
                 # 创建压缩包