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https://github.com/open-mmlab/mmselfsup.git
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66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
from openselfsup.utils import build_from_cfg
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import torch
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from PIL import Image
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from torchvision.transforms import Compose, RandomCrop
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import torchvision.transforms.functional as TF
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from .registry import DATASETS, PIPELINES
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from .base import BaseDataset
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def image_to_patches(img):
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"""Crop split_per_side x split_per_side patches from input image.
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Args:
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img (PIL Image): input image.
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Returns:
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list[PIL Image]: A list of cropped patches.
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"""
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split_per_side = 3 # split of patches per image side
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patch_jitter = 21 # jitter of each patch from each grid
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h, w = img.size
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h_grid = h // split_per_side
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w_grid = w // split_per_side
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h_patch = h_grid - patch_jitter
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w_patch = w_grid - patch_jitter
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assert h_patch > 0 and w_patch > 0
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patches = []
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for i in range(split_per_side):
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for j in range(split_per_side):
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p = TF.crop(img, i * h_grid, j * w_grid, h_grid, w_grid)
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p = RandomCrop((h_patch, w_patch))(p)
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patches.append(p)
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return patches
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@DATASETS.register_module
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class RelativeLocDataset(BaseDataset):
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"""Dataset for relative patch location.
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"""
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def __init__(self, data_source, pipeline, format_pipeline):
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super(RelativeLocDataset, self).__init__(data_source, pipeline)
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format_pipeline = [build_from_cfg(p, PIPELINES) for p in format_pipeline]
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self.format_pipeline = Compose(format_pipeline)
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def __getitem__(self, idx):
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img = self.data_source.get_sample(idx)
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assert isinstance(img, Image.Image), \
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'The output from the data source must be an Image, got: {}. \
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Please ensure that the list file does not contain labels.'.format(
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type(img))
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img = self.pipeline(img)
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patches = image_to_patches(img)
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patches = [self.format_pipeline(p) for p in patches]
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perms = []
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# create a list of patch pairs
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[perms.append(torch.cat((patches[i], patches[4]), dim=0)) for i in range(9) if i != 4]
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# create corresponding labels for patch pairs
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patch_labels = torch.LongTensor([0, 1, 2, 3, 4, 5, 6, 7])
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return dict(img=torch.stack(perms), patch_label=patch_labels) # 8(2C)HW, 8
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def evaluate(self, scores, keyword, logger=None):
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raise NotImplemented
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