def eval_on_batch(self, attr, traj, config):
if self.training:
entire_out, (local_out, local_length) = self(attr, traj, config)
else:
entire_out = self(attr, traj, config)
pred_dict, entire_loss = self.entire_estimate.eval_on_batch(
entire_out, attr['time'], config['time_mean'], config['time_std'])
if self.training:
# get the mean/std of each local path
mean, std = (self.kernel_size - 1) * config['time_gap_mean'], (
self.kernel_size - 1) * config['time_gap_std']
# get ground truth of each local path
local_label = utils.get_local_seq(traj['time_gap'],
self.kernel_size, mean, std)
local_loss = self.local_estimate.eval_on_batch(
local_out, local_length, local_label, mean, std)
return pred_dict, (
1 - self.alpha
) * entire_loss + self.alpha * local_loss ### According to eqn 8 of paper
else:
return pred_dict, entire_loss
def forward(self, traj, config):
lngs = torch.unsqueeze(traj['lngs'], dim=2)
lats = torch.unsqueeze(traj['lats'], dim=2)
states = self.state_em(traj['states'].long()) ### Optional, remove
locs = torch.cat((lngs, lats, states), dim=2) ### Remove states
# map the coords into 16-dim vector
locs = torch.tanh(self.process_coords(
locs)) ### size [batch, max len of trajectory in batch, 16]
locs = locs.permute(0, 2, 1)
conv_locs = F.elu(
self.conv(locs)
) ### size [batch, num_filter, max len of batch traj - kernel_size + 1]
conv_locs = conv_locs.permute(
0, 2, 1) ### size [batch, max len - kernel_size + 1, num_filter]
# calculate the dist for local paths
local_dist = utils.get_local_seq(traj['dist_gap'], self.kernel_size,
config['dist_gap_mean'],
config['dist_gap_std'])
local_dist = torch.unsqueeze(
local_dist,
dim=2) ### ### size [batch, max len - kernel_size + 1, 1]
conv_locs = torch.cat(
(conv_locs, local_dist),
dim=2) ### size [batch, max len - kernel_size + 1, num_filter + 1]
return conv_locs
def forward(self, traj, config):
lngs = torch.unsqueeze(traj['lngs'], dim = 2)
lats = torch.unsqueeze(traj['lats'], dim = 2)
roads = traj['roads'].long()
roads = self.road_em(roads)
locs = torch.cat((lngs, lats, roads), dim = 2)
# map the coords into 16-dim vector
locs = F.tanh(self.process_coords(locs))
locs = locs.permute(0, 2, 1)
conv_locs = F.elu(self.conv(locs)).permute(0, 2, 1)
# calculate the dist for local paths
local_dist = utils.get_local_seq(traj['dist_gap'], self.kernel_size, config['dist_gap_mean'], config['dist_gap_std'])
local_dist = torch.unsqueeze(local_dist, dim = 2)
conv_locs = torch.cat((conv_locs, local_dist), dim = 2)
return conv_locs
def eval_on_batch(self, attr, traj, config):
if self.training:
entire_out, (local_out, local_length) = self(attr, traj)
else:
entire_out = self(attr, traj)
pred_dict, entire_loss = self.entire_estimate.eval_on_batch(
entire_out, attr['time'], config['time_mean'],
config['time_std']) #entire_loss is scalar
if self.training:
mean, std = (self.kernel_size - 1) * config['time_gap_mean'], (
self.kernel_size - 1) * config['time_gap_std']
local_label = utils.get_local_seq(traj['time_gap'],
self.kernel_size, mean, std)
local_loss = self.loacl_estimate.eval_on_batch(
local_out, local_length, local_label, mean, std)
return pred_dict, (
1 - self.alpha) * entire_loss + self.alpha * local_loss
else:
return pred_dict, entire_loss
def forward(self, traj):
lngs = torch.unsqueeze(Variable(traj['lngs']),
dim=2) #[bs,seq_length,1]
lats = torch.unsqueeze(Variable(traj['lats']),
dim=2) #[bs,seq_length,1]
states = self.state_em(Variable(
traj['states']).long()) #[bs,seq_length,2]
locs = torch.cat((lngs, lats, states), dim=2) #[bs,seq_length,4]
locs = F.tanh(self.process_cooeds(locs)) #[bs,seq_length,16]
locs = locs.permute(0, 2, 1) #[bs,16,seq_length]
conv_locs = F.elu(self.conv(locs)).permute(
0, 2, 1) #[bs,conv(H),num_filters]
local_dist = utils.get_local_seq(Variable(traj['dist_gap']),
self.kernel_size, 'dist_gap_mean',
'dist_gap_std') #[bs,conv(H)]
local_dist = torch.unsqueeze(local_dist, dim=2) #[bs,conv(H),1]
conv_locs = torch.cat((conv_locs, local_dist),
dim=2) #[bs,conv(H),num_filters+1]
return conv_locs
