| import json |
| import os |
| import random |
|
|
| from torch.utils.data import Dataset |
|
|
| from PIL import Image |
| from PIL import ImageFile |
| ImageFile.LOAD_TRUNCATED_IMAGES = True |
| Image.MAX_IMAGE_PIXELS = None |
|
|
| from .utils import pre_caption |
| import os,glob |
|
|
| import torch |
| import numpy as np |
|
|
| class pretrain_dataset(Dataset): |
| def __init__(self, ann_file, transform, class_num = 4585, root = ''): |
|
|
| self.ann = [] |
| for f in ann_file: |
| print('loading '+f) |
| ann = json.load(open(f,'r')) |
| self.ann += ann |
| |
| self.transform = transform |
| self.class_num = class_num |
| self.root = root |
|
|
| |
| def __len__(self): |
| return len(self.ann) |
| |
| def __getitem__(self, index): |
| |
| ann = self.ann[index] |
|
|
| image_path_use = os.path.join(self.root, ann['image_path']) |
| image = Image.open(image_path_use).convert('RGB') |
| image = self.transform(image) |
|
|
| |
| if ann.get('union_label_id') is not None: |
| num = ann['union_label_id'] |
| image_tag = np.zeros([self.class_num]) |
| image_tag[num] = 1 |
| image_tag = torch.tensor(image_tag, dtype = torch.long) |
| else: |
| image_tag = None |
|
|
| caption_index = np.random.randint(0, len(ann['caption'])) |
|
|
| caption = pre_caption(ann['caption'][caption_index],30) |
|
|
| num = ann['parse_label_id'][caption_index] |
| parse_tag = np.zeros([self.class_num]) |
| parse_tag[num] = 1 |
| parse_tag = torch.tensor(parse_tag, dtype = torch.long) |
|
|
| return image, caption, image_tag, parse_tag |
| |
|
|
| class finetune_dataset(Dataset): |
| def __init__(self, ann_file, transform, transform_224, class_num = 4585, root = ''): |
|
|
| self.ann = [] |
| for f in ann_file: |
| print('loading '+f) |
| ann = json.load(open(f,'r')) |
| self.ann += ann |
| |
| self.transform = transform |
| self.transform_224 = transform_224 |
| self.class_num = class_num |
| self.root = root |
|
|
| |
| def __len__(self): |
| return len(self.ann) |
| |
| def __getitem__(self, index): |
| |
| ann = self.ann[index] |
|
|
| image_path_use = os.path.join(self.root, ann['image_path']) |
| image = Image.open(image_path_use).convert('RGB') |
| image = self.transform(image) |
|
|
| image_224 = Image.open(image_path_use).convert('RGB') |
| image_224 = self.transform_224(image_224) |
|
|
| |
| if ann.get('union_label_id') is not None: |
| num = ann['union_label_id'] |
| image_tag = np.zeros([self.class_num]) |
| image_tag[num] = 1 |
| image_tag = torch.tensor(image_tag, dtype = torch.long) |
| else: |
| image_tag = None |
|
|
| caption_index = np.random.randint(0, len(ann['caption'])) |
|
|
| caption = pre_caption(ann['caption'][caption_index],30) |
|
|
| num = ann['parse_label_id'][caption_index] |
| parse_tag = np.zeros([self.class_num]) |
| parse_tag[num] = 1 |
| parse_tag = torch.tensor(parse_tag, dtype = torch.long) |
|
|
| return image, image_224, caption, image_tag, parse_tag |
| |
|
|
