我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用torchvision.models.vgg19()。
def __init__(self, num_classes, pretrained=False, bn_after_act=False, bn_before_act=False): super(Vgg19, self).__init__() self.pretrained = pretrained self.bn_before_act = bn_before_act self.bn_after_act = bn_after_act model = models.vgg19(pretrained = pretrained) self.features = model.features self.fc17 = nn.Linear(512 * 7 * 7, 4096) self.bn17 = nn.BatchNorm1d(4096) self.fc18 = nn.Linear(4096, 4096) self.bn18 = nn.BatchNorm1d(4096) self.fc19 = nn.Linear(4096, num_classes) self._initialize_weights()
def def_netF(): vgg19 = M.vgg19() vgg19.load_state_dict(torch.load('vgg19.pth')) vgg19.classifier = nn.Sequential( *list(vgg19.classifier.children())[:2] ) for param in vgg19.parameters(): param.requires_grad = False return vgg19
def contentFunc(self): conv_3_3_layer = 14 cnn = models.vgg19(pretrained=True).features cnn = cnn.cuda() model = nn.Sequential() model = model.cuda() for i,layer in enumerate(list(cnn)): model.add_module(str(i),layer) if i == conv_3_3_layer: break return model
def get_vgg19(num_classes, pretrained): net = models.vgg19() if pretrained: net.load_state_dict(torch.load(pretrained_vgg19_path)) net.classifier = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, num_classes), ) return net
def __init__(self): """Select conv1_1 ~ conv5_1 activation maps.""" super(VGGNet, self).__init__() self.select = ['0', '5', '10', '19', '28'] self.vgg = models.vgg19(pretrained=True).features
def load_model(): model = models.vgg19(pretrained=True) model.eval() if use_cuda: model.cuda() for p in model.features.parameters(): p.requires_grad = False for p in model.classifier.parameters(): p.requires_grad = False return model
def __init__(self): super(VGGNet, self).__init__() self.vgg = models.vgg19(pretrained=True)
def getNetwork(args): if (args.net_type == 'alexnet'): net = models.alexnet(pretrained=args.finetune) file_name = 'alexnet' elif (args.net_type == 'vggnet'): if(args.depth == 11): net = models.vgg11(pretrained=args.finetune) elif(args.depth == 13): net = models.vgg13(pretrained=args.finetune) elif(args.depth == 16): net = models.vgg16(pretrained=args.finetune) elif(args.depth == 19): net = models.vgg19(pretrained=args.finetune) else: print('Error : VGGnet should have depth of either [11, 13, 16, 19]') sys.exit(1) file_name = 'vgg-%s' %(args.depth) elif (args.net_type == 'resnet'): net = resnet(args.finetune, args.depth) file_name = 'resnet-%s' %(args.depth) else: print('Error : Network should be either [alexnet / vggnet / resnet]') sys.exit(1) return net, file_name
def vgg19(num_classes=1000, pretrained='imagenet'): """VGG 19-layer model (configuration "E") """ model = models.vgg19(pretrained=False) if pretrained is not None: settings = pretrained_settings['vgg19'][pretrained] model = load_pretrained(model, num_classes, settings) model = modify_vggs(model) return model
def main(): args = parse_args() if not os.path.exists(args.out_dir): os.makedirs(args.out_dir) target_layer_names = ['35'] target_index = None # Prepare input image if args.img: img = cv2.imread(args.img, 1) else: img = misc.face() img = np.float32(cv2.resize(img, (224, 224))) / 255 preprocessed_img = preprocess_image(img, args.cuda) model = vgg19(pretrained=True) if args.cuda: model.cuda() # Prediction output = model(preprocessed_img) pred_index = np.argmax(output.data.cpu().numpy()) print('Prediction: {}'.format(IMAGENET_LABELS[pred_index])) # Prepare grad cam grad_cam = GradCam( pretrained_model=model, target_layer_names=target_layer_names, cuda=args.cuda) # Compute grad cam mask = grad_cam(preprocessed_img, target_index) save_cam_image(img, mask, os.path.join(args.out_dir, 'grad_cam.jpg')) print('Saved Grad-CAM image') # Reload preprocessed image preprocessed_img = preprocess_image(img) # Compute guided backpropagation guided_backprop = GuidedBackpropGrad( pretrained_model=model, cuda=args.cuda) guided_backprop_saliency = guided_backprop(preprocessed_img, index=target_index) cam_mask = np.zeros(guided_backprop_saliency.shape) for i in range(guided_backprop_saliency.shape[0]): cam_mask[i, :, :] = mask cam_guided_backprop = np.multiply(cam_mask, guided_backprop_saliency) save_as_gray_image( cam_guided_backprop, os.path.join(args.out_dir, 'guided_grad_cam.jpg')) print('Saved Guided Grad-CAM image')
