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Merge 60 into 55 and then 55 into master
USE THE DATALOADER I HAVE. JUST SAVE ON CPU AND LOAD TO GPU EVERY BATCH put the result of the tiling function on CPU if necessary
LOAD CKPT FROM HERE USE PRETRAINED TO OBTAIN GOOD SEGMENTATION ld-results-bucket/merfish_june25_v7
graphclustering 65 #TODO: Compute median density of connected components so that resolution parameter is about 1 self.reference_density = AUCH
NAMEDTUPLE 151
#TODO: this might be too slow. Eliminate torch.bincount. new_dict = self.params new_dict["filter_by_size"] = (min_size, max_size) new_membership = old_2_new[self.membership] return self._replace(membership=new_membership, params=new_dict, sizes=torch.bincount(new_membership))
The text was updated successfully, but these errors were encountered:
dalessioluca
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Merge 60 into 55 and then 55 into master
USE THE DATALOADER I HAVE.
JUST SAVE ON CPU AND LOAD TO GPU EVERY BATCH
put the result of the tiling function on CPU if necessary
LOAD CKPT FROM HERE USE PRETRAINED TO OBTAIN GOOD SEGMENTATION
ld-results-bucket/merfish_june25_v7
graphclustering 65
#TODO: Compute median density of connected components so that resolution parameter is about 1
self.reference_density = AUCH
NAMEDTUPLE 151
#TODO: this might be too slow. Eliminate torch.bincount.
new_dict = self.params
new_dict["filter_by_size"] = (min_size, max_size)
new_membership = old_2_new[self.membership]
return self._replace(membership=new_membership, params=new_dict, sizes=torch.bincount(new_membership))
The text was updated successfully, but these errors were encountered: