Class ValDataset

dataset.py:206–255

class ValDataset(BaseDataset):
    def __init__(self, root_dataset, odgt, opt, **kwargs):
        super(ValDataset, self).__init__(odgt, opt, **kwargs)
        self.root_dataset = root_dataset

    def __getitem__(self, index):
        this_record = self.list_sample[index]
        # load image and label
        image_path = os.path.join(self.root_dataset, this_record['fpath_img'])
        segm_path = os.path.join(self.root_dataset, this_record['fpath_segm'])
        img = Image.open(image_path).convert('RGB')
        segm = Image.open(segm_path)
        assert(segm.mode == "L")
        assert(img.size[0] == segm.size[0])
        assert(img.size[1] == segm.size[1])

        ori_width, ori_height = img.size

        img_resized_list = []
        for this_short_size in self.imgSizes:
            # calculate target height and width
            scale = min(this_short_size / float(min(ori_height, ori_width)),
                        self.imgMaxSize / float(max(ori_height, ori_width)))
            target_height, target_width = int(ori_height * scale), int(ori_width * scale)

            # to avoid rounding in network
            target_width = self.round2nearest_multiple(target_width, self.padding_constant)
            target_height = self.round2nearest_multiple(target_height, self.padding_constant)

            # resize images
            img_resized = imresize(img, (target_width, target_height), interp='bilinear')

            # image transform, to torch float tensor 3xHxW
            img_resized = self.img_transform(img_resized)
            img_resized = torch.unsqueeze(img_resized, 0)
            img_resized_list.append(img_resized)

        # segm transform, to torch long tensor HxW
        segm = self.segm_transform(segm)
        batch_segms = torch.unsqueeze(segm, 0)

        output = dict()
        output['img_ori'] = np.array(img)
        output['img_data'] = [x.contiguous() for x in img_resized_list]
        output['seg_label'] = batch_segms.contiguous()
        output['info'] = this_record['fpath_img']
        return output

    def __len__(self):
        return self.num_sample


class TestDataset(BaseDataset):