def interpolated_sum(self, cnns, coords, grids):
X = coords[:, :, 0]
Y = coords[:, :, 1]
cnn_outs = []
for i in range(len(grids)):
grid = grids[i]
Xs = X * grid
X0 = torch.floor(Xs)
X1 = X0 + 1
Ys = Y * grid
Y0 = torch.floor(Ys)
Y1 = Y0 + 1
w_00 = (X1 - Xs) * (Y1 - Ys)
w_01 = (X1 - Xs) * (Ys - Y0)
w_10 = (Xs - X0) * (Y1 - Ys)
w_11 = (Xs - X0) * (Ys - Y0)
X0 = torch.clamp(X0, 0, grid - 1)
X1 = torch.clamp(X1, 0, grid - 1)
Y0 = torch.clamp(Y0, 0, grid - 1)
Y1 = torch.clamp(Y1, 0, grid - 1)
N1_id = X0 + Y0 * grid
N2_id = X0 + Y1 * grid
N3_id = X1 + Y0 * grid
N4_id = X1 + Y1 * grid
M_00 = utils.gather_feature(N1_id, cnns[i])
M_01 = utils.gather_feature(N2_id, cnns[i])
M_10 = utils.gather_feature(N3_id, cnns[i])
M_11 = utils.gather_feature(N4_id, cnns[i])
cnn_out = w_00.unsqueeze(2) * M_00 + \
w_01.unsqueeze(2) * M_01 + \
w_10.unsqueeze(2) * M_10 + \
w_11.unsqueeze(2) * M_11
cnn_outs.append(cnn_out)
concat_features = torch.cat(cnn_outs, dim=2)
return concat_features
def sampling(self, ids, features):
cnn_out_feature = []
for i in range(ids.size()[1]):
id = ids[:, i, :]
cnn_out = utils.gather_feature(id, features[i])
cnn_out_feature.append(cnn_out)
concat_features = torch.cat(cnn_out_feature, dim=2)
return concat_features