# load_img模块importPIL.ImageasImageimporttorchimporttorchvision.transformsastransforms img_size=512iftorch.cuda.is_available()else128#根据设备选择改变后项数大小 defload_img(img_path):#图像读入 img=Image.open(img_path).convert(
if cfg.DEBUG: roidb = copy.deepcopy(self.COCO.loadImgs(image_ids))[:100] else: roidb = copy.deepcopy(self.COCO.loadImgs(image_ids)) # 填充roidb还缺少的元素构成完整的roidb结构,暂用空值代替 for entry in roidb: self._prep_roidb_entry(entry) if gt: # Include ground-truth object annota...
img=img.unsqueeze(0)returnimgdeftest_pth(): img=load_img() model=AlexNet() model.load_state_dict(torch.load("alexnet.pth")) model.eval() outs=model(img)print(outs)deftest_onnx(): img=load_img() session= onnxruntime.InferenceSession('alexnet.onnx') inputs={session.get_inputs()[0...
dynamic img size support in vit, deit, eva improved to support resize from non-square patch grids, thanks https://github.com/wojtke Oct 31, 2024 Add a set of new very well trained ResNet & ResNet-V2 18/34 (basic block) weights. See https://huggingface.co/blog/rwightman/resnet-tric...
img_folder = root + '/test_cifar10/' fp = open(file_annotation,'r') data_dict = json.load(fp) #如果图像数和标签数不匹配说明数据集标注生成有问题,报错提示 assert len(data_dict['images'])==len(data_dict['categories']) num_data = len(data_dict['images']) ...
img2 = plt.imread('003.jpg') img = [img1, img2]foriinimg: plt.imshow(i) plt.show() 使用cv2进行图像的读取 在cv2库中,需要用到的函数有: imread(文件地址):读取地址处的文件图像 imshow('窗口名称', 图像数组):将图像数组显示出来,但必须结合waitKey()使用,否则无法显示图像 ...
Imagefrom torchvision import transformsimport cnndef imshow(img): img = img / 2 + 0.5 npimg = img.numpy() plt.imshow(np.transpose(npimg, (1, 2, 0))) plt.show()model = torch.load("params/cnn_imgs_02.pkl")img_path= "imgs/05.jpg"img_np = Image.open(img_path)t...
import torch img = torch.from_numpy(img_arr) out = img.permute(2, 0, 1) out.shape outs:torch.Size([3, 512, 512]) 除了处理一张图像,我们更多的时候需要加载一批图像。毕竟我们的深度学习模型不想总盯着一个美女看,它喜欢看各种各样的美女,除了lena还有luna,sherry,cherry等等,这样它才能总结出美...
data[item] img = self.loader(img) img = self.transform(img) return img, label def __len__(self): return len(self.data) 里面有2个比较重要的函数: __getitem__是真正读取数据的地方,迭代器通过索引来读取数据集中数据,因此只需要这一个方法中加入读取数据的相关功能即可。在这个函数里面,我们对第...
img = gluoncvth.utils.load_image( gluoncvth.utils.download(url, filename)).unsqueeze(0) # Make prediction output = model.evaluate(img) predict =torch.max(output, 1)[1].cpu().numpy() + 1 # Get color pallete for visualization mask = gluoncvth.utils.get_mask_pallete(predict, 'ade20k'...