# 需要导入模块: import network [as 别名]# 或者: from network importNetwork[as 别名]defmain(args):paths = Dataset(args.dataset_path)['abspath'] print('%d images to load.'% len(paths))assert(len(paths)>0)# Load model files and config filenetwork =Network() network.load_model(args.mode...
关于MSDNet的源代码链接在上⼀篇学习笔记⾥已经贴出，本篇笔记主要是关于代码中dynamic⽅式如何实现的⼀个详细介绍，也就是所谓的budgeted batch classification ⽂中所描述两种评估⽅法：budgeted batch，anytime ⽂中所描述的budgeted batch是对于⼀个batch中的众多样本来说，共⽤⼀个固定的资源限制，...
'vgg_1.weights.pth')# 载入modelmodel=models.vgg16()# we do not specify ``weights``, i.e. create untrained modelmodel.load_state_dict(torch.load('vgg_1.weights.pth'))model.eval()
# 需要导入模块: import model [as 别名]# 或者: from model importNetworkCIFAR[as 别名]defmain():image_shape = [3, image_size, image_size] devices = os.getenv("CUDA_VISIBLE_DEVICES")or""devices_num = len(devices.split(",")) logging.info("args = %s", args) genotype = eval("genotyp...
model.eval() with torch.no_grad(): output_mc = [] for mc_run in range(args.num_monte_carlo): logits = model(x_test) probs = torch.nn.functional.softmax(logits, dim=-1) output_mc.append(probs) output = torch.stack(output_mc) pred_mean = output.mean(dim=0) y_pred = torch...
align = NaiveDlib(args.dlibFaceMean, args.dlibFacePredictor) net = openface.TorchWrap(args.networkModel, imgDim=args.imgDim, cuda=args.cuda) # `img` is a numpy matrix containing the RGB pixels of the image. bb = align.getLargestFaceBoundingBox(img) alignedFace = align.alignImg("affine"...
Args: model: the DML model train_set: the features of the training set triplets: the generated triplets epochs: the training epochs batch_sz: the batch size """ print '\nStart of DML Training' print '===' n_batch = triplets.shape[0] / batch_sz + 1 for i in xrange(epochs): np...
model.cuda()ifargs.model:ifos.path.isfile(args.model):print("=> loading checkpoint '{}'".format(args.model)) checkpoint = torch.load(args.model) args.start_epoch = checkpoint['epoch'] best_prec1 = checkpoint['best_prec1'] model.load_state_dict(checkpoint['state_dict'])print("=> lo...
self.sample_num =100self.batch_size = args.batch_size self.save_dir = args.save_dir self.result_dir = args.result_dir self.dataset = args.dataset self.log_dir = args.log_dir self.gpu_mode = args.gpu_mode self.model_name = args.gan_type ...
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