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interfere.py

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+# This file is provided by DOF Studio on huggingface.co
+# with model animeGender-dvgg-0.7.
+# Link: https://huggingface.co/DOFOFFICIAL/animeGender-dvgg-0.7
+
+import cv2
+import torch
+import torchvision
+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) 
+
+    last_dim = len(model.classifier) - 1
+    num_fc = model.classifier[last_dim].in_features 
+    
+    model.classifier[last_dim] = torch.nn.Linear(num_fc, num_cls)
+
+    model.classifier.append(torch.nn.Softmax())
+
+    for param in model.parameters():
+        param.requires_grad = False
+
+    for param in model.classifier[last_dim].parameters():
+        param.requires_grad = True
+    for param in model.classifier[last_dim + 1].parameters():
+        param.requires_grad = True
+
+    model = model.cuda() #GPU
+
+    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)
+    img = cv2.imread(imgfrom, 1)
+    img2 = []
+    if height > width:
+        hnew = int(np.round(64 / width * height))
+        wnew = 64
+        img2 = cv2.resize(img, (wnew, hnew))
+        img2 = img2[0:64, 0:64]
+    elif width > height:
+        wnew = int(np.round(64 / height * width))
+        hnew = 64
+        img2 = cv2.resize(img, (wnew, hnew))
+        img2 = img2[0:64, 0:64]
+    else:
+        img2 = cv2.resize(img, (64,64))
+    img3 = cv2.cvtColor(img2, cv2.COLOR_BGRA2BGR)
+    return cv2.imwrite(imgto, img3)
+    
+
+#############################
+# model usage
+def predict_class(img_path, model):
+    img = Image.open(img_path)
+    transform = transforms.Compose([transforms.ToTensor()])
+    img = transform(img).cuda()
+    img = torch.unsqueeze(img, dim=0)
+    out = model(img)
+    max = torch.max(out).item()
+    pmax = torch.max(out, 1)[1].item()
+    cls = classes[pmax]
+    print('This is ' + cls + ' with confidence of ' + str(np.round(max, 3)))
+
+def modelload(modelpath):
+    device = torch.device('cuda')
+    model = model_struct()
+    model.to(device)
+    model.eval()
+    save = torch.load(modelpath)
+    model.load_state_dict(save)
+    return model
+
+def predictmain(model, filepath):
+    img = filepath
+    predict_class(img, model)
+
+
+if __name__ == '__main__':
+    # transfomer usage
+    model = undefined.from_pretrained("undefined")
+    model.load_adapter("DOFOFFICIAL/animeGender-dvgg-0.7", source="hf")
+    model.to("cuda")
+    model.eval()
+
+    # local usage
+    model = modelload("model_animeGender-dvgg-0.7.pth")
+    
+    # use your picture to interfere
+    cmpgraph_64x64("path.png", "path(1).png")
+    predictmain(model, "path(1).png")

interfere_cpu.py

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+# This file is provided by DOF Studio on huggingface.co
+# with model animeGender-dvgg-0.7.
+# Link: https://huggingface.co/DOFOFFICIAL/animeGender-dvgg-0.7
+
+import cv2
+import torch
+import torchvision
+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) 
+
+    last_dim = len(model.classifier) - 1
+    num_fc = model.classifier[last_dim].in_features 
+    
+    model.classifier[last_dim] = torch.nn.Linear(num_fc, num_cls)
+
+    model.classifier.append(torch.nn.Softmax())
+
+    for param in model.parameters():
+        param.requires_grad = False
+
+    for param in model.classifier[last_dim].parameters():
+        param.requires_grad = True
+    for param in model.classifier[last_dim + 1].parameters():
+        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)
+    img = cv2.imread(imgfrom, 1)
+    img2 = []
+    if height > width:
+        hnew = int(np.round(64 / width * height))
+        wnew = 64
+        img2 = cv2.resize(img, (wnew, hnew))
+        img2 = img2[0:64, 0:64]
+    elif width > height:
+        wnew = int(np.round(64 / height * width))
+        hnew = 64
+        img2 = cv2.resize(img, (wnew, hnew))
+        img2 = img2[0:64, 0:64]
+    else:
+        img2 = cv2.resize(img, (64,64))
+    img3 = cv2.cvtColor(img2, cv2.COLOR_BGRA2BGR)
+    return cv2.imwrite(imgto, img3)
+    
+
+#############################
+# model usage
+def predict_class(img_path, model):
+    img = Image.open(img_path)
+    transform = transforms.Compose([transforms.ToTensor()])
+    img = transform(img)
+    img = torch.unsqueeze(img, dim=0)
+    out = model(img)
+    max = torch.max(out).item()
+    pmax = torch.max(out, 1)[1].item()
+    cls = classes[pmax]
+    print('This is ' + cls + ' with confidence of ' + str(np.round(max, 3)))
+
+def modelload(modelpath):
+    model = model_struct()
+    save = torch.load(modelpath)
+    model.load_state_dict(save)
+    return model
+
+def predictmain(model, filepath):
+    img = filepath
+    predict_class(img, model)
+
+
+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")
+    
+    # use your picture to interfere
+    cmpgraph_64x64("path.png", "path(1).png")
+    predictmain(model, "path(1).png")