Update usage/interfere_cpu.py

usage/interfere_cpu.py

@@ -9,13 +9,9 @@ import numpy as np
 from torchvision import transforms
 from PIL import Image
 
-from transformers import undefined
-
 num_cls = 2
 classes = ['female', 'male']
 
-#############################
-# model struct
 def model_struct():
     model = torchvision.models.vgg16(pretrained=True) 
 
@@ -35,29 +31,6 @@ def model_struct():
         param.requires_grad = True
 
     return model
-
-
-#############################
-# graphic lib
-def dim(imgpath):
-    img = cv2.imread(imgpath, 1)
-    height, width, channels = img.shape
-    return height, width, channels
-
-def crop(imgfrom, imgto, x = 0, w = 64, y = 0, h = 64):
-    img = cv2.imread(imgfrom, 1)
-    img2 = img[y:y+h, x:x+w]
-    return cv2.imwrite(imgto, img2)
-
-def resize(imgfrom, imgto, width, height):
-    img = cv2.imread(imgfrom, 1)
-    img2 = cv2.resize(img, (width, height))
-    return cv2.imwrite(imgto, img2)
-
-def rgb32to24(imgfrom, imgto):
-    img = cv2.imread(imgfrom, 1)
-    img2 = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
-    return cv2.imwrite(imgto, img2)
     
 def cmpgraph_64x64(imgfrom, imgto):
     height, width, channels = dim(imgfrom)
@@ -104,12 +77,8 @@ def predictmain(model, filepath):
 
 
 if __name__ == '__main__':
-    # transfomer usage
-    model = undefined.from_pretrained("undefined")
-    model.load_adapter("DOFOFFICIAL/animeGender-dvgg-0.7", source="hf")
-
     # local usage
-    model = modelload("model_animeGender-dvgg-0.7.pth")
+    model = modelload("animeGender-dvgg-0.7.pth")
     
     # use your picture to interfere
     cmpgraph_64x64("path.png", "path(1).png")