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import pickle |
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from types import new_class |
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import torch |
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import numpy as np |
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import os |
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import json |
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from os.path import join, dirname, isdir, isfile, expanduser, realpath, basename |
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from random import shuffle, seed as set_seed |
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from PIL import Image |
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from itertools import combinations |
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from torchvision import transforms |
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from torchvision.transforms.transforms import Resize |
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from datasets.utils import blend_image_segmentation |
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from general_utils import get_from_repository |
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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'} |
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class COCOWrapper(object): |
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def __init__(self, split, fold=0, image_size=400, aug=None, mask='separate', negative_prob=0, |
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with_class_label=False): |
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super().__init__() |
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self.mask = mask |
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self.with_class_label = with_class_label |
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self.negative_prob = negative_prob |
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from third_party.hsnet.data.coco import DatasetCOCO |
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get_from_repository('COCO-20i', ['COCO-20i.tar']) |
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foldpath = join(dirname(__file__), '../third_party/hsnet/data/splits/coco/%s/fold%d.pkl') |
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def build_img_metadata_classwise(self): |
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with open(foldpath % (self.split, self.fold), 'rb') as f: |
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img_metadata_classwise = pickle.load(f) |
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return img_metadata_classwise |
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DatasetCOCO.build_img_metadata_classwise = build_img_metadata_classwise |
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mean = [0.485, 0.456, 0.406] |
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std = [0.229, 0.224, 0.225] |
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transform = transforms.Compose([ |
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transforms.Resize((image_size, image_size)), |
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transforms.ToTensor(), |
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transforms.Normalize(mean, std) |
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]) |
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self.coco = DatasetCOCO(expanduser('~/datasets/COCO-20i/'), fold, transform, split, 1, False) |
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self.all_classes = [self.coco.class_ids] |
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self.coco.base_path = join(expanduser('~/datasets/COCO-20i')) |
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def __len__(self): |
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return len(self.coco) |
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def __getitem__(self, i): |
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sample = self.coco[i] |
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label_name = COCO_CLASSES[int(sample['class_id'])] |
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img_s, seg_s = sample['support_imgs'][0], sample['support_masks'][0] |
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if self.negative_prob > 0 and torch.rand(1).item() < self.negative_prob: |
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new_class_id = sample['class_id'] |
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while new_class_id == sample['class_id']: |
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sample2 = self.coco[torch.randint(0, len(self), (1,)).item()] |
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new_class_id = sample2['class_id'] |
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img_s = sample2['support_imgs'][0] |
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seg_s = torch.zeros_like(seg_s) |
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mask = self.mask |
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if mask == 'separate': |
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supp = (img_s, seg_s) |
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elif mask == 'text_label': |
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supp = [int(sample['class_id'])] |
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elif mask == 'text': |
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supp = [label_name] |
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else: |
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if mask.startswith('text_and_'): |
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mask = mask[9:] |
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label_add = [label_name] |
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else: |
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label_add = [] |
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supp = label_add + blend_image_segmentation(img_s, seg_s, mode=mask) |
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if self.with_class_label: |
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label = (torch.zeros(0), sample['class_id'],) |
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else: |
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label = (torch.zeros(0), ) |
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return (sample['query_img'],) + tuple(supp), (sample['query_mask'].unsqueeze(0),) + label |
