def forward(self, data1):
data1.input = F.elu(self.conv1(data1.adj, data1.input))
size, start = self.pool_args(32, 24)
data2, c1 = sparse_voxel_max_pool(data1, size, start, transform)
data2.input = F.elu(self.conv2(data2.adj, data2.input))
size, start = self.pool_args(8, 3)
data3, c2 = sparse_voxel_max_pool(data2, size, start, transform)
data3.input = F.elu(self.conv2(data3.adj, data3.input))
size, start = self.pool_args(4, 2)
data4, c3 = sparse_voxel_max_pool(data3, size, start, transform)
data4.input = F.dropout(data4.input, training=self.training)
data4.input = F.elu(self.fc1(data4.input))
x = [upscale(data4.input, c3), self.skip3(data3.input)]
data3.input = F.elu(self.conv4(data3.adj, torch.cat(x, dim=1)))
x = [upscale(data3.input, c2), self.skip2(data2.input)]
data2.input = F.elu(self.conv5(data2.adj, torch.cat(x, dim=1)))
x = [upscale(data2.input, c1), self.skip1(data1.input)]
data1.input = F.elu(self.conv6(data1.adj, torch.cat(x, dim=1)))
data1.input = F.dropout(data1.input, training=self.training)
data1.input = self.fc2(data1.input)
return F.log_softmax(data1.input, dim=1)
def forward(self, data):
data.input = F.elu(self.conv1(data.adj, data.input))
size, start = self.pool_args(64, 16)
data, _ = sparse_voxel_max_pool(data, size, start, transform)
data.input = F.elu(self.conv2(data.adj, data.input))
size, start = self.pool_args(32, 8)
data, _ = sparse_voxel_max_pool(data, size, start, transform)
data.input = F.elu(self.conv3(data.adj, data.input))
size, start = self.pool_args(16, 4)
data, _ = sparse_voxel_max_pool(data, size, start, transform)
data.input = F.elu(self.conv4(data.adj, data.input))
size, start = self.pool_args(8, 2)
data, _ = sparse_voxel_max_pool(data, size, start, transform)
data.input = F.elu(self.conv5(data.adj, data.input))
size, start = self.pool_args(4, 1)
data, _ = sparse_voxel_max_pool(data, size, start, transform)
data.input = F.elu(self.conv6(data.adj, data.input))
data, _ = dense_voxel_max_pool(data, 1, -0.5, 1.5, transform)
x = data.input.view(-1, 8 * 128)
x = F.dropout(x, training=self.training)
x = self.fc1(x)
return F.log_softmax(x, dim=1)
def forward(self, data):
f1 = F.relu(self.conv1(data.adj, data.input))
f1 = F.relu(self.conv12(data.adj, f1))
batch = Variable(data.batch.view(-1, 1).expand(data.batch.size(0), 64))
f1_res = scatter_mean(batch, f1)
size, start = self.pool_args(32, 8)
data2, _ = sparse_voxel_max_pool(
data, size, start, transform, weight=f1[:, 0])
f2 = F.relu(self.conv2(data2.adj, data2.input))
f2 = F.relu(self.conv22(data2.adj, f2))
batch = Variable(
data2.batch.view(-1, 1).expand(data2.batch.size(0), 64))
f2_res = scatter_mean(batch, f2)
size, start = self.pool_args(16, 4)
data3, _ = sparse_voxel_max_pool(
data2, size, start, transform, weight=f2[:, 0])
f3 = F.relu(self.conv3(data3.adj, data3.input))
f3 = F.relu(self.conv32(data3.adj, f3))
batch = Variable(
data3.batch.view(-1, 1).expand(data3.batch.size(0), 64))
f3_res = scatter_mean(batch, f3)
size, start = self.pool_args(8, 2)
data4, _ = sparse_voxel_max_pool(
data3, size, start, transform, weight=f3[:, 0])
f4 = F.relu(self.conv4(data4.adj, data4.input))
f4 = F.relu(self.conv42(data4.adj, f4))
batch = Variable(
data4.batch.view(-1, 1).expand(data4.batch.size(0), 64))
f4_res = scatter_mean(batch, f4)
size, start = self.pool_args(4, 1)
data5, _ = sparse_voxel_max_pool(
data4, size, start, transform, weight=f4[:, 0])
f5 = F.relu(self.conv5(data5.adj, data5.input))
f5 = F.relu(self.conv52(data5.adj, f5))
batch = Variable(
data5.batch.view(-1, 1).expand(data5.batch.size(0), 64))
f5_res = scatter_mean(batch, f5)
x = torch.cat([f1_res, f2_res, f3_res, f4_res, f5_res], dim=1)
# data.input = F.relu(self.conv5(data.adj, data.input))
# data, _ = dense_voxel_max_pool(data, 1, -0.5, 1.5)
x = x.view(-1, self.fc1.weight.size(1))
x = F.dropout(x, training=self.training)
x = self.fc1(x)
return F.log_softmax(x, dim=1)
def forward(self, data):
data.input = F.elu(self.conv1(data.adj, data.input))
data, _ = sparse_voxel_max_pool(data, 5, 0, CartesianAdj())
data.input = F.elu(self.conv2(data.adj, data.input))
data, _ = sparse_voxel_max_pool(data, 7, 0, CartesianAdj())
data.input = F.elu(self.conv3(data.adj, data.input))
data, _ = dense_voxel_max_pool(data, 14, 0, 28)
x = data.input.contiguous().view(-1, self.fc1.weight.size(1))
x = F.elu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=1)
def forward(self, data):
ones = Variable(data.input.data.new(data.input.size(0), 1).fill_(1))
data.input = F.elu(self.conv1(data.adj, data.input))
data1, cluster1 = sparse_voxel_max_pool(data, (1 / 64), 0, trans)
x = torch.cat([data1.input, ones[:data1.input.size(0)]], dim=1)
x = F.elu(self.conv2(data1.adj, x))
x = self.conv22(data1.adj, x)
data1.input = F.elu(x + self.skip1(data1.input))
data2, cluster2 = sparse_voxel_max_pool(data1, (1 / 32), 0, trans)
x = torch.cat((data2.input, ones[:data2.input.size(0), :]), dim=1)
x = F.elu(self.conv3(data2.adj, x))
x = self.conv32(data2.adj, x)
data2.input = F.elu(x + data2.input)
data3, cluster3 = sparse_voxel_max_pool(data2, (1 / 16), 0, trans)
x = torch.cat([data3.input, ones[:data3.input.size(0)]], dim=1)
x = F.elu(self.conv4(data3.adj, x))
x = self.conv42(data3.adj, x)
data3.input = F.elu(x + data3.input)
data4, cluster4 = sparse_voxel_max_pool(data3, (1 / 8), 0, trans)
x = torch.cat([data4.input, ones[:data4.input.size(0)]], dim=1)
x = F.elu(self.conv5(data4.adj, x))
x = self.conv52(data4.adj, x)
data4.input = F.elu(x + self.skip2(data4.input))
data3.input = torch.cat(
[upscale(data4.input, cluster4),
self.fc3(data3.input)], dim=1)
data3.input = F.elu(self.convRPN1(data3.adj, data3.input))
# data3.input = F.elu(self.conv52(data3.adj, data3.input))
data2.input = torch.cat(
[upscale(data3.input, cluster3),
self.fc2(data2.input)], dim=1)
data2.input = F.elu(self.convRPN2(data2.adj, data2.input))
data2.input = self.convRPN4(data2.adj, data2.input)
# data1.input = torch.cat(
# [upscale(data2.input, cluster2), self.fc1(data1.input)], dim=1)
# data1.input = F.elu(self.convRPN3(data1.adj, data1.input))
# data1.input = self.convRPN4(data1.adj, data1.input)
class_out = data2.input[:, :num_classes *
num_anchors].contiguous().view(-1, 2)
bb_out = data2.input[:, num_classes * num_anchors:].contiguous().view(
-1, 7)
return F.log_softmax(class_out, dim=1), bb_out, data2.pos, data2
def forward(self, data):
data.input = F.relu(self.conv1(data.adj, data.input))
# att1 = self.att1(data.input)
size, start = self.pool_args(32, 8)
data, _ = sparse_voxel_max_pool(data,
size,
start,
transform,
weight=data.input[:, 0])
data.input = F.relu(self.conv2(data.adj, data.input))
# att2 = self.att2(data.input)
size, start = self.pool_args(16, 4)
data, _ = sparse_voxel_max_pool(data,
size,
start,
transform,
weight=data.input[:, 0])
data.input = F.relu(self.conv3(data.adj, data.input))
# att3 = self.att3(data.input)
size, start = self.pool_args(8, 2)
data, _ = sparse_voxel_max_pool(data,
size,
start,
transform,
weight=data.input[:, 0])
data.input = F.relu(self.conv4(data.adj, data.input))
# att4 = self.att4(data.input)
size, start = self.pool_args(4, 1)
data, _ = sparse_voxel_max_pool(data,
size,
start,
transform,
weight=data.input[:, 0])
data.input = F.relu(self.conv5(data.adj, data.input))
data, _ = dense_voxel_max_pool(data, 1, -0.5, 1.5)
x = data.input.view(-1, self.fc1.weight.size(1))
x = F.dropout(x, training=self.training)
x = self.fc1(x)
return F.log_softmax(x, dim=1)
def forward(self, data):
# data.input = F.elu(self.conv1(data.adj, data.input))
size, start = self.pool_args(13, 5)
data, _ = sparse_voxel_max_pool(data, size, start, CartesianAdj())
data.input = F.elu(self.conv1(data.adj, data.input))
size, start = self.pool_args(7, 3)
data, _ = sparse_voxel_max_pool(data, size, start, CartesianAdj())
data.input = F.elu(self.conv2(data.adj, data.input))
size, start = self.pool_args(5, 2)
data, _ = sparse_voxel_max_pool(data, size, start, CartesianAdj())
data.input = F.elu(self.conv3(data.adj, data.input))
data, _ = dense_voxel_max_pool(data, 1 / 2, 0, 1)
x = data.input.view(-1, self.fc1.weight.size(1))
x = F.elu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=1)