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| import pickle | |
| from types import new_class | |
| import torch | |
| import numpy as np | |
| import os | |
| import json | |
| from os.path import join, dirname, isdir, isfile, expanduser, realpath, basename | |
| from random import shuffle, seed as set_seed | |
| from PIL import Image | |
| from itertools import combinations | |
| from torchvision import transforms | |
| from torchvision.transforms.transforms import Resize | |
| from datasets.utils import blend_image_segmentation | |
| from general_utils import get_from_repository | |
| COCO_CLASSES = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'} | |
| class COCOWrapper(object): | |
| def __init__(self, split, fold=0, image_size=400, aug=None, mask='separate', negative_prob=0, | |
| with_class_label=False): | |
| super().__init__() | |
| self.mask = mask | |
| self.with_class_label = with_class_label | |
| self.negative_prob = negative_prob | |
| from third_party.hsnet.data.coco import DatasetCOCO | |
| get_from_repository('COCO-20i', ['COCO-20i.tar']) | |
| foldpath = join(dirname(__file__), '../third_party/hsnet/data/splits/coco/%s/fold%d.pkl') | |
| def build_img_metadata_classwise(self): | |
| with open(foldpath % (self.split, self.fold), 'rb') as f: | |
| img_metadata_classwise = pickle.load(f) | |
| return img_metadata_classwise | |
| DatasetCOCO.build_img_metadata_classwise = build_img_metadata_classwise | |
| # DatasetCOCO.read_mask = read_mask | |
| mean = [0.485, 0.456, 0.406] | |
| std = [0.229, 0.224, 0.225] | |
| transform = transforms.Compose([ | |
| transforms.Resize((image_size, image_size)), | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean, std) | |
| ]) | |
| self.coco = DatasetCOCO(expanduser('~/datasets/COCO-20i/'), fold, transform, split, 1, False) | |
| self.all_classes = [self.coco.class_ids] | |
| self.coco.base_path = join(expanduser('~/datasets/COCO-20i')) | |
| def __len__(self): | |
| return len(self.coco) | |
| def __getitem__(self, i): | |
| sample = self.coco[i] | |
| label_name = COCO_CLASSES[int(sample['class_id'])] | |
| img_s, seg_s = sample['support_imgs'][0], sample['support_masks'][0] | |
| if self.negative_prob > 0 and torch.rand(1).item() < self.negative_prob: | |
| new_class_id = sample['class_id'] | |
| while new_class_id == sample['class_id']: | |
| sample2 = self.coco[torch.randint(0, len(self), (1,)).item()] | |
| new_class_id = sample2['class_id'] | |
| img_s = sample2['support_imgs'][0] | |
| seg_s = torch.zeros_like(seg_s) | |
| mask = self.mask | |
| if mask == 'separate': | |
| supp = (img_s, seg_s) | |
| elif mask == 'text_label': | |
| # DEPRECATED | |
| supp = [int(sample['class_id'])] | |
| elif mask == 'text': | |
| supp = [label_name] | |
| else: | |
| if mask.startswith('text_and_'): | |
| mask = mask[9:] | |
| label_add = [label_name] | |
| else: | |
| label_add = [] | |
| supp = label_add + blend_image_segmentation(img_s, seg_s, mode=mask) | |
| if self.with_class_label: | |
| label = (torch.zeros(0), sample['class_id'],) | |
| else: | |
| label = (torch.zeros(0), ) | |
| return (sample['query_img'],) + tuple(supp), (sample['query_mask'].unsqueeze(0),) + label |