def get_list_dataset(pair_type):
ele_list = dataset[pair_type][:len(dataset[pair_type]) //
batch_size * batch_size]
def load(idx):
o = {
'input':
np.stack((dataset['patches'][v].astype(np.float32) -
dataset['mean'][v]) / 256.0 for v in idx),
'target':
1 if pair_type == 'matches' else -1
}
o['input'] = torch.from_numpy(o['input'])
o['target'] = torch.LongTensor([o['target']])
o['input'] = o['input'].float()
o['target'] = o['target'].float()
o['input'] = o['input'].cuda()
o['target'] = o['target'].cuda()
return o
ele_list = list(map(load, ele_list))
ds = ListDataset(elem_list=ele_list)
# ds = ds.transform({'input': torch.from_numpy, 'target': lambda x: torch.LongTensor([x])})
# ds = ds.transform({'input': torch.from_numpy, 'target': lambda x: torch.Cudaha([x])})
return ds.batch(policy='include-last', batchsize=batch_size // 2)
def get_list_dataset(pair_type):
ds = ListDataset(elem_list=dataset[pair_type],
load=lambda idx: {'input': np.stack((dataset['patches'][v].astype(np.float32)
- dataset['mean'][v]) / 256.0 for v in idx),
'target': 1 if pair_type == 'matches' else -1})
ds = ds.transform({'input': torch.from_numpy, 'target': lambda x: torch.LongTensor([x])})
return ds.batch(policy='include-last', batchsize=batch_size // 2)
def get_list_dataset(pair_type):
ds = ListDataset(elem_list=dataset[pair_type],
load=lambda idx: {
'input':
np.stack((dataset['patches'][v].astype(np.float32)
- dataset['mean'][v]) / 256.0
for v in idx),
'target':
1 if pair_type == 'matches' else -1
})
ds = ds.transform({
'input': torch.from_numpy,
'target': lambda x: torch.LongTensor([x])
})
return ds.batch(policy='include-last', batchsize=batch_size // 2)