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model.py
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model.py
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import torch
from torch import nn
from utils import outputActivation
# The implementation of PiP architecture
class pipNet(nn.Module):
def __init__(self, args):
super(pipNet, self).__init__()
self.args = args
self.use_cuda = args.use_cuda
# Flag for output:
# -- Train-mode : Concatenate with true maneuver label.
# -- Test-mode : Concatenate with the predicted maneuver with the maximal probability.
self.train_output_flag = args.train_output_flag
self.use_planning = args.use_planning
self.use_fusion = args.use_fusion
# IO Setting
self.grid_size = args.grid_size
self.in_length = args.in_length
self.out_length = args.out_length
self.num_lat_classes = args.num_lat_classes
self.num_lon_classes = args.num_lon_classes
## Sizes of network layers
self.temporal_embedding_size = args.temporal_embedding_size
self.encoder_size = args.encoder_size
self.decoder_size = args.decoder_size
self.soc_conv_depth = args.soc_conv_depth
self.soc_conv2_depth = args.soc_conv2_depth
self.dynamics_encoding_size = args.dynamics_encoding_size
self.social_context_size = args.social_context_size
self.targ_enc_size = self.social_context_size + self.dynamics_encoding_size
self.fuse_enc_size = args.fuse_enc_size
self.fuse_conv1_size = 2 * self.fuse_enc_size
self.fuse_conv2_size = 4 * self.fuse_enc_size
# Activations:
self.leaky_relu = nn.LeakyReLU(0.1)
self.relu = nn.ReLU()
self.softmax = nn.Softmax(dim=1)
## Define network parameters
''' Convert traj to temporal embedding'''
self.temporalConv = nn.Conv1d(in_channels=2, out_channels=self.temporal_embedding_size, kernel_size=3, padding=1)
''' Encode the input temporal embedding '''
self.nbh_lstm = nn.LSTM(input_size=self.temporal_embedding_size, hidden_size=self.encoder_size, num_layers=1)
if self.use_planning:
self.plan_lstm = nn.LSTM(input_size=self.temporal_embedding_size, hidden_size=self.encoder_size, num_layers=1)
''' Encoded dynamic to dynamics_encoding_size'''
self.dyn_emb = nn.Linear(self.encoder_size, self.dynamics_encoding_size)
''' Convolutional Social Pooling on the planned vehicle and all nbrs vehicles '''
self.nbrs_conv_social = nn.Sequential(
nn.Conv2d(self.encoder_size, self.soc_conv_depth, 3),
self.leaky_relu,
nn.MaxPool2d((3, 3), stride=2),
nn.Conv2d(self.soc_conv_depth, self.soc_conv2_depth, (3, 1)),
self.leaky_relu
)
if self.use_planning:
self.plan_conv_social = nn.Sequential(
nn.Conv2d(self.encoder_size, self.soc_conv_depth, 3),
self.leaky_relu,
nn.MaxPool2d((3, 3), stride=2),
nn.Conv2d(self.soc_conv_depth, self.soc_conv2_depth, (3, 1)),
self.leaky_relu
)
self.pool_after_merge = nn.MaxPool2d((2, 2), padding=(1, 0))
else:
self.pool_after_merge = nn.MaxPool2d((2, 1), padding=(1, 0))
''' Target Fusion Module'''
if self.use_fusion:
''' Fused Structure'''
self.fcn_conv1 = nn.Conv2d(self.targ_enc_size, self.fuse_conv1_size, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(self.fuse_conv1_size)
self.fcn_pool1 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.fcn_conv2 = nn.Conv2d(self.fuse_conv1_size, self.fuse_conv2_size, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(self.fuse_conv2_size)
self.fcn_pool2 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.fcn_convTrans1 = nn.ConvTranspose2d(self.fuse_conv2_size, self.fuse_conv1_size, kernel_size=3, stride=2, padding=1)
self.back_bn1 = nn.BatchNorm2d(self.fuse_conv1_size)
self.fcn_convTrans2 = nn.ConvTranspose2d(self.fuse_conv1_size, self.fuse_enc_size, kernel_size=3, stride=2, padding=1)
self.back_bn2 = nn.BatchNorm2d(self.fuse_enc_size)
else:
self.fuse_enc_size = 0
''' Decoder LSTM'''
self.op_lat = nn.Linear(self.targ_enc_size + self.fuse_enc_size,
self.num_lat_classes) # output lateral maneuver.
self.op_lon = nn.Linear(self.targ_enc_size + self.fuse_enc_size,
self.num_lon_classes) # output longitudinal maneuver.
self.dec_lstm = nn.LSTM(input_size=self.targ_enc_size + self.fuse_enc_size + self.num_lat_classes + self.num_lon_classes,
hidden_size=self.decoder_size)
''' Output layers '''
self.op = nn.Linear(self.decoder_size, 5)
def forward(self, nbsHist, nbsMask, planFut, planMask, targsHist, targsEncMask, lat_enc, lon_enc):
''' Forward target vehicle's dynamic'''
dyn_enc = self.leaky_relu(self.temporalConv(targsHist.permute(1,2,0)))
_, (dyn_enc, _) = self.nbh_lstm(dyn_enc.permute(2,0,1))
dyn_enc = self.leaky_relu( self.dyn_emb(dyn_enc.view(dyn_enc.shape[1],dyn_enc.shape[2])) )
''' Forward neighbour vehicles'''
nbrs_enc = self.leaky_relu(self.temporalConv(nbsHist.permute(1, 2, 0)))
_, (nbrs_enc, _) = self.nbh_lstm(nbrs_enc.permute(2, 0, 1))
nbrs_enc = nbrs_enc.view(nbrs_enc.shape[1], nbrs_enc.shape[2])
''' Masked neighbour vehicles'''
nbrs_grid = torch.zeros_like(nbsMask).float()
nbrs_grid = nbrs_grid.masked_scatter_(nbsMask, nbrs_enc)
nbrs_grid = nbrs_grid.permute(0,3,2,1)
nbrs_grid = self.nbrs_conv_social(nbrs_grid)
if self.use_planning:
''' Forward planned vehicle'''
plan_enc = self.leaky_relu(self.temporalConv(planFut.permute(1, 2, 0)))
_, (plan_enc, _) = self.plan_lstm(plan_enc.permute(2, 0, 1))
plan_enc = plan_enc.view(plan_enc.shape[1], plan_enc.shape[2])
''' Masked planned vehicle'''
plan_grid = torch.zeros_like(planMask).float()
plan_grid = plan_grid.masked_scatter_(planMask, plan_enc)
plan_grid = plan_grid.permute(0, 3, 2, 1)
plan_grid = self.plan_conv_social(plan_grid)
''' Merge neighbour and planned vehicle'''
merge_grid = torch.cat((nbrs_grid, plan_grid), dim=3)
merge_grid = self.pool_after_merge(merge_grid)
else:
merge_grid = self.pool_after_merge(nbrs_grid)
social_context = merge_grid.view(-1, self.social_context_size)
'''Concatenate social_context (neighbors + ego's planing) and dyn_enc, then place into the targsEncMask '''
target_enc = torch.cat((social_context, dyn_enc),1)
target_grid = torch.zeros_like(targsEncMask).float()
target_grid = target_grid.masked_scatter_(targsEncMask, target_enc)
if self.use_fusion:
'''Fully Convolutional network to get a grid to be fused'''
fuse_conv1 = self.relu(self.fcn_conv1(target_grid.permute(0, 3, 2, 1)))
fuse_conv1 = self.bn1(fuse_conv1)
fuse_conv1 = self.fcn_pool1(fuse_conv1)
fuse_conv2 = self.relu(self.fcn_conv2(fuse_conv1))
fuse_conv2 = self.bn2(fuse_conv2)
fuse_conv2 = self.fcn_pool2(fuse_conv2)
# Encoder / Decoder #
fuse_trans1 = self.relu(self.fcn_convTrans1(fuse_conv2))
fuse_trans1 = self.back_bn1(fuse_trans1+fuse_conv1)
fuse_trans2 = self.relu(self.fcn_convTrans2(fuse_trans1))
fuse_trans2 = self.back_bn2(fuse_trans2)
# Extract the location with targets
fuse_grid_mask = targsEncMask[:,:,:,0:self.fuse_enc_size]
fuse_grid = torch.zeros_like(fuse_grid_mask).float()
fuse_grid = fuse_grid.masked_scatter_(fuse_grid_mask, fuse_trans2.permute(0, 3, 2, 1))
'''Finally, Integrate everything together'''
enc_rows_mark = targsEncMask[:,:,:,0].view(-1)
enc_rows = [i for i in range(len(enc_rows_mark)) if enc_rows_mark[i]]
enc = torch.cat([target_grid, fuse_grid], dim=3)
enc = enc.view(-1, self.fuse_enc_size+self.targ_enc_size)
enc = enc[enc_rows, :]
else:
enc = target_enc
'''Maneuver recognition'''
lat_pred = self.softmax(self.op_lat(enc))
lon_pred = self.softmax(self.op_lon(enc))
if self.train_output_flag:
enc = torch.cat((enc, lat_enc, lon_enc), 1)
fut_pred = self.decode(enc)
return fut_pred, lat_pred, lon_pred
else:
fut_pred = []
for k in range(self.num_lon_classes):
for l in range(self.num_lat_classes):
lat_enc_tmp = torch.zeros_like(lat_enc)
lon_enc_tmp = torch.zeros_like(lon_enc)
lat_enc_tmp[:, l] = 1
lon_enc_tmp[:, k] = 1
# Concatenate maneuver label before feeding to decoder
enc_tmp = torch.cat((enc, lat_enc_tmp, lon_enc_tmp), 1)
fut_pred.append(self.decode(enc_tmp))
return fut_pred, lat_pred, lon_pred
def decode(self,enc):
enc = enc.repeat(self.out_length, 1, 1)
h_dec, _ = self.dec_lstm(enc)
h_dec = h_dec.permute(1, 0, 2)
fut_pred = self.op(h_dec)
fut_pred = fut_pred.permute(1, 0, 2)
fut_pred = outputActivation(fut_pred)
return fut_pred