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| import os, numpy, torch, json | |
| from .parallelfolder import ParallelImageFolders | |
| from torchvision import transforms | |
| from torchvision.transforms.functional import to_tensor, normalize | |
| class FieldDef(object): | |
| def __init__(self, field, index, bitshift, bitmask, labels): | |
| self.field = field | |
| self.index = index | |
| self.bitshift = bitshift | |
| self.bitmask = bitmask | |
| self.labels = labels | |
| class MultiSegmentDataset(object): | |
| ''' | |
| Just like ClevrMulticlassDataset, but the second stream is a one-hot | |
| segmentation tensor rather than a flat one-hot presence vector. | |
| MultiSegmentDataset('dataset/clevrseg', | |
| imgdir='images/train/positive', | |
| segdir='images/train/segmentation') | |
| ''' | |
| def __init__(self, directory, transform=None, | |
| imgdir='img', segdir='seg', val=False, size=None): | |
| self.segdataset = ParallelImageFolders( | |
| [os.path.join(directory, imgdir), | |
| os.path.join(directory, segdir)], | |
| transform=transform) | |
| self.fields = [] | |
| with open(os.path.join(directory, 'labelnames.json'), 'r') as f: | |
| for defn in json.load(f): | |
| self.fields.append(FieldDef( | |
| defn['field'], defn['index'], defn['bitshift'], | |
| defn['bitmask'], defn['label'])) | |
| self.labels = ['-'] # Reserve label 0 to mean "no label" | |
| self.categories = [] | |
| self.label_category = [0] | |
| for fieldnum, f in enumerate(self.fields): | |
| self.categories.append(f.field) | |
| f.firstchannel = len(self.labels) | |
| f.channels = len(f.labels) - 1 | |
| for lab in f.labels[1:]: | |
| self.labels.append(lab) | |
| self.label_category.append(fieldnum) | |
| # Reserve 25% of the dataset for validation. | |
| first_val = int(len(self.segdataset) * 0.75) | |
| self.val = val | |
| self.first = first_val if val else 0 | |
| self.length = len(self.segdataset) - first_val if val else first_val | |
| # Truncate the dataset if requested. | |
| if size: | |
| self.length = min(size, self.length) | |
| def __len__(self): | |
| return self.length | |
| def __getitem__(self, index): | |
| img, segimg = self.segdataset[index + self.first] | |
| segin = numpy.array(segimg, numpy.uint8, copy=False) | |
| segout = torch.zeros(len(self.categories), | |
| segin.shape[0], segin.shape[1], dtype=torch.int64) | |
| for i, field in enumerate(self.fields): | |
| fielddata = ((torch.from_numpy(segin[:, :, field.index]) | |
| >> field.bitshift) & field.bitmask) | |
| segout[i] = field.firstchannel + fielddata - 1 | |
| bincount = numpy.bincount(segout.flatten(), | |
| minlength=len(self.labels)) | |
| return img, segout, bincount | |
| if __name__ == '__main__': | |
| ds = MultiSegmentDataset('dataset/clevrseg') | |
| print(ds[0]) | |
| import pdb; pdb.set_trace() | |