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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()
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