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"""Spatial Transformer Network."""
from einops.layers.torch import Rearrange
import torch
from torch import nn
from torch import Tensor
import torch.nn.functional as F
class SpatialTransformerNetwork(nn.Module):
"""A network with differentiable attention.
Network that learns how to perform spatial transformations on the input image in order to enhance the
geometric invariance of the model.
# TODO: add arguments to make it more general.
"""
def __init__(self) -> None:
super().__init__()
# Initialize the identity transformation and its weights and biases.
linear = nn.Linear(32, 3 * 2)
linear.weight.data.zero_()
linear.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))
self.theta = nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=8, kernel_size=7),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=8, out_channels=10, kernel_size=5),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.ReLU(inplace=True),
Rearrange("b c h w -> b (c h w)", h=3, w=3),
nn.Linear(in_features=10 * 3 * 3, out_features=32),
nn.ReLU(inplace=True),
linear,
Rearrange("b (row col) -> b row col", row=2, col=3),
)
def forward(self, x: Tensor) -> Tensor:
"""The spatial transformation."""
grid = F.affine_grid(self.theta(x), x.shape)
return F.grid_sample(x, grid, align_corners=False)
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