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Normalization and transforms for batch_to_data class

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Si11ium
2020-06-15 15:14:07 +02:00
parent 0cff42f951
commit 76308888e0
1 changed files with 9 additions and 5 deletions
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14
point_toolset/point_io.py
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@@ -5,16 +5,17 @@ from torch_geometric.data import Data
class BatchToData(object):
def __init__(self):
def __init__(self, transforms=None):
super(BatchToData, self).__init__()
self.transforms = transforms if transforms else lambda x: x
def __call__(self, batch_x: torch.Tensor, batch_pos: torch.Tensor,
def __call__(self, batch_norm: torch.Tensor, batch_pos: torch.Tensor,
batch_y_l: Union[torch.Tensor, None] = None, batch_y_c: Union[torch.Tensor, None] = None):
# Convert to torch_geometric.data.Data type
# data = data.transpose(1, 2).contiguous()
batch_size, num_points, _ = batch_x.shape # (batch_size, num_points, 3)
batch_size, num_points, _ = batch_norm.shape # (batch_size, num_points, 3)
x = batch_x.reshape(batch_size * num_points, -1)
norm = batch_norm.reshape(batch_size * num_points, -1)
pos = batch_pos.reshape(batch_size * num_points, -1)
batch_y_l = batch_y_l.reshape(batch_size * num_points) if batch_y_l is not None else batch_y_l
batch_y_c = batch_y_c.reshape(batch_size * num_points) if batch_y_c is not None else batch_y_c
@@ -24,5 +25,8 @@ class BatchToData(object):
batch = batch.view(-1)
data = Data()
data.x, data.pos, data.batch, data.yl, data.yc = x, pos, batch, batch_y_l, batch_y_c
data.norm, data.pos, data.batch, data.yl, data.yc = norm, pos, batch, batch_y_l, batch_y_c
data = self.transforms(data)
return data