update

app.py

@@ -4,6 +4,8 @@ import requests
 import os
 import subprocess
 from typing import Union
+
+from sympy import E
 from pth2onnx import convert_pth_to_onnx
 import re
 import time
@@ -33,7 +35,11 @@ def convertmnn(onnx_path: str, mnn_path: str, fp16=False):
     param = ['mnnconvert', '-f', 'ONNX', '--modelFile', onnx_path, '--MNNModel', mnn_path, '--bizCode', 'biz', '--info', '--detectSparseSpeedUp']
     if fp16:
         param.append('--fp16')
-    subprocess.run(param, check=True)
+    try:
+        subprocess.run(param, check=True)
+    except Exception as e:
+        print(f"转换 MNN 模型时出错: {e}")
+
 
 def download_file(url: str, save_path: str):
     response = requests.get(url)
@@ -381,6 +387,7 @@ with gr.Blocks() as demo:
             [model_type_opt[0], "https://github.com/Kim2091/Kim2091-Models/releases/download/2x-AnimeSharpV4/2x-AnimeSharpV4_RCAN_fp16_op17.onnx"],
             [model_type_opt[0], "https://drive.google.com/uc?export=download&confirm=1&id=1PeqL1ikJbBJbVzvlqvtb4d7QdSW7BzrQ"],
             [model_type_opt[0], "https://drive.google.com/file/d/1maYmC5yyzWCC42X5O0HeDuepsLFh7AV4/view?usp=drive_link"],
+            [model_type_opt[0], "https://objectstorage.us-phoenix-1.oraclecloud.com/n/ax6ygfvpvzka/b/open-modeldb-files/o/1x-MangaJPEGHQ.pth"],
         ]
         example_input = gr.Examples(examples=examples, inputs=[input_type, url_input], label='示例模型链接')
     

mnnsr.py

@@ -81,14 +81,15 @@ def modelTest_for_gradio(modelPath, image_path, tilesize = 0, backend = 3):
     session = net.createSession(config)
     
     print("Run on backendtype: %d \n" % net.getSessionInfo(session, 2))
-
     # 读取图像
     image = cv2.imread(image_path)
     if image.ndim == 2:
         # 为了方便处理，先将其扩展为3维数组 (height, width, 1)
-        print("extend dims")
-        image = np.expand_dims(image, axis=-1)
-    image_channel = image.shape[2]
+        # print("extend dims, image.shape=", image.shape)
+        image_channel = 1
+        # image = np.expand_dims(image, axis=-1)
+    else:
+        image_channel = image.shape[2]
     
     image = cv2.resize(image, (tilesize, tilesize))
 
@@ -116,10 +117,11 @@ def modelTest_for_gradio(modelPath, image_path, tilesize = 0, backend = 3):
 
     # 显示图像(在Gradio中不需要)
     # display(Image(data=cv2.imencode('.jpg', image)[1].tobytes()))
-    
+    # print("image.shape=", image.shape)
     image = image/255.0
     #preprocess it
-    image = image.transpose((2, 0, 1))
+    if model_channel>=3:
+        image = image.transpose((2, 0, 1))
     #change numpy data type as np.float32 to match tensor's format
     image = image.astype(np.float32)
     #cv2 read shape is NHWC, Tensor's need is NCHW,transpose it