init + inference.py патч

data/MBD/tps_grid_gen.py

@@ -0,0 +1,70 @@
+# encoding: utf-8
+
+import torch
+import itertools
+import torch.nn as nn
+from torch.autograd import Function, Variable
+
+class TPSGridGen(nn.Module):
+
+    def __init__(self, target_height, target_width, target_control_points):
+        super(TPSGridGen, self).__init__()
+        assert target_control_points.ndimension() == 2
+        assert target_control_points.size(1) == 2
+        N = target_control_points.size(0)
+        self.num_points = N
+        target_control_points = target_control_points.float()
+
+        # create padded kernel matrix
+        forward_kernel = torch.zeros(N + 3, N + 3)
+        target_control_partial_repr = self.compute_partial_repr(target_control_points, target_control_points)
+        forward_kernel[:N, :N].copy_(target_control_partial_repr)
+        forward_kernel[:N, -3].fill_(1)
+        forward_kernel[-3, :N].fill_(1)
+        forward_kernel[:N, -2:].copy_(target_control_points)
+        forward_kernel[-2:, :N].copy_(target_control_points.transpose(0, 1))
+        # compute inverse matrix
+        inverse_kernel = torch.inverse(forward_kernel)
+
+        # create target cordinate matrix
+        HW = target_height * target_width
+        target_coordinate = list(itertools.product(range(target_height), range(target_width)))
+        target_coordinate = torch.Tensor(target_coordinate) # HW x 2
+        Y, X = target_coordinate.split(1, dim = 1)
+        Y = Y * 2 / (target_height - 1) - 1
+        X = X * 2 / (target_width - 1) - 1
+        target_coordinate = torch.cat([X, Y], dim = 1) # convert from (y, x) to (x, y)
+        target_coordinate_partial_repr = self.compute_partial_repr(target_coordinate, target_control_points)
+        target_coordinate_repr = torch.cat([
+            target_coordinate_partial_repr, torch.ones(HW, 1), target_coordinate
+        ], dim = 1)
+
+        # register precomputed matrices
+        self.register_buffer('inverse_kernel', inverse_kernel)
+        self.register_buffer('padding_matrix', torch.zeros(3, 2))
+        self.register_buffer('target_coordinate_repr', target_coordinate_repr)
+
+    def forward(self, source_control_points):
+        assert source_control_points.ndimension() == 3
+        assert source_control_points.size(1) == self.num_points
+        assert source_control_points.size(2) == 2
+        batch_size = source_control_points.size(0)
+
+        Y = torch.cat([source_control_points, Variable(self.padding_matrix.expand(batch_size, 3, 2))], 1)
+        mapping_matrix = torch.matmul(Variable(self.inverse_kernel), Y)
+        source_coordinate = torch.matmul(Variable(self.target_coordinate_repr), mapping_matrix)
+        return source_coordinate
+    # phi(x1, x2) = r^2 * log(r), where r = ||x1 - x2||_2
+    def compute_partial_repr(self, input_points, control_points):
+        N = input_points.size(0)
+        M = control_points.size(0)
+        pairwise_diff = input_points.view(N, 1, 2) - control_points.view(1, M, 2)
+        # original implementation, very slow
+        # pairwise_dist = torch.sum(pairwise_diff ** 2, dim = 2) # square of distance
+        pairwise_diff_square = pairwise_diff * pairwise_diff
+        pairwise_dist = pairwise_diff_square[:, :, 0] + pairwise_diff_square[:, :, 1]
+        repr_matrix = 0.5 * pairwise_dist * torch.log(pairwise_dist)
+        # fix numerical error for 0 * log(0), substitute all nan with 0
+        mask = repr_matrix != repr_matrix
+        repr_matrix.masked_fill_(mask, 0)
+        return repr_matrix