def __init__(self, inArgs):
torch.manual_seed(inArgs['dsId']) #seed becomes 'dataset Id'
torch.cuda.manual_seed(inArgs['dsId'])
self.args = {}
self.args["batch_size"] = inArgs["batch_size"]
self.args["pretrainEpochs"] = inArgs["pretrainEpochs"]
self.args["trainEpochs"] = inArgs["trainEpochs"]
self.args['cuda'] = inArgs["cuda"]
self.args['device'] = inArgs['device']
self.args['modelLoc'] = inArgs[
'modelLoc'] #Ben: the location of 'trained model'
self.args["optim"] = inArgs["optim"]
# Network Arguments
self.args['dropout_prob'] = inArgs["dropout"]
self.args['encoder_size'] = 64
self.args['decoder_size'] = 128
self.args['in_length'] = inArgs[
'input_size'] #Ben: INPUT (history length)
self.args['out_length'] = inArgs[
'output_size'] #Ben: OUTPUT (output seq length)
self.args['grid_size'] = (13, 3)
self.args['upp_grid_size'] = (7, 3)
self.args['soc_conv_depth'] = 64
self.args['conv_3x1_depth'] = 16
self.args['dyn_embedding_size'] = 32
self.args['input_embedding_size'] = 32
self.args['num_lat_classes'] = 3
self.args['num_lon_classes'] = 2
self.args['use_maneuvers'] = inArgs["maneuvers"]
self.args['ours'] = (inArgs["predAlgo"] == "Traphic")
self.args['nll_only'] = inArgs['nll_only']
self.args["learning_rate"] = inArgs["lr"]
self.args["predAlgo"] = inArgs["predAlgo"] #TraPHic
self.args["w_decay"] = inArgs['w_decay']
# currentDT = datetime.datetime.now()
# self.args['name'] = "{}_{}_model.tar".format(inArgs["predAlgo"], currentDT.strftime("%Y_%m_%d_%H_%M"))
self.args['name'] = inArgs['name_temp'].format(self.args["predAlgo"],
inArgs['dset'])
self.args["pretrain_loss"] = inArgs['pretrain_loss']
self.args['train_loss'] = inArgs['train_loss']
self.args['dir'] = inArgs['dir']
self.args['raw_dir'] = inArgs['raw_dir']
self.args['dsId'] = inArgs['dsId']
self.args['log_dir'] = inArgs['log_dir']
self.args['tensorboard'] = inArgs['tensorboard']
self.args['class_type'] = inArgs['class_type']
if self.args["predAlgo"] == "Traphic": # Ben: Declare the network
self.net = traphicNet(self.args)
else:
self.net = highwayNet(self.args)
if self.args['cuda']:
self.net = self.net.cuda(self.args['device'])
def __init__(self, inArgs):
self.args = {}
self.args["batch_size"] = inArgs["batch_size"]
self.args["pretrainEpochs"] = inArgs["pretrainEpochs"]
self.args["trainEpochs"] = inArgs["trainEpochs"]
self.args['cuda'] = inArgs["cuda"]
# args['cuda'] = False
self.args['device'] = inArgs['device']
self.args['modelLoc'] = inArgs['modelLoc']
self.args["optim"] = inArgs["optim"]
# Network Arguments
self.args['dropout_prob'] = inArgs["dropout"]
self.args['encoder_size'] = 64
self.args['decoder_size'] = 128
self.args['in_length'] = inArgs['input_size']
self.args['out_length'] = inArgs['output_size']
self.args['grid_size'] = (13, 3)
self.args['upp_grid_size'] = (7, 3)
self.args['soc_conv_depth'] = 64
self.args['conv_3x1_depth'] = 16
self.args['dyn_embedding_size'] = 32
self.args['input_embedding_size'] = 32
self.args['num_lat_classes'] = 3
self.args['num_lon_classes'] = 2
self.args['use_maneuvers'] = inArgs["maneuvers"]
self.args['ours'] = (inArgs["predAlgo"] == "Traphic")
self.args['nll_only'] = True
self.args["learning_rate"] = inArgs["lr"]
self.args["predAlgo"] = inArgs["predAlgo"]
# currentDT = datetime.datetime.now()
# self.args['name'] = "{}_{}_model.tar".format(inArgs["predAlgo"], currentDT.strftime("%Y_%m_%d_%H_%M"))
self.args['name'] = inArgs['name_temp'].format(self.args["predAlgo"],
inArgs['dset'])
self.args["pretrain_loss"] = inArgs['pretrain_loss']
self.args['train_loss'] = inArgs['train_loss']
self.args['dir'] = inArgs['dir']
self.args['raw_dir'] = inArgs['raw_dir']
self.args['dsId'] = 0
self.args['log_dir'] = inArgs['log_dir']
self.args['tensorboard'] = inArgs['tensorboard']
if self.args["predAlgo"] == "Traphic":
self.net = traphicNet(self.args)
else:
self.net = highwayNet(self.args)
if self.args['cuda']:
self.net = self.net.cuda(self.args['device'])
print(self.net)
def evaluate(self, viewArgs, thread=None):
if thread:
thread.signalCanvas("\n[INFO] Evaluation started...")
args = self.getPredArgs(viewArgs)
args['eval'] = True
predAlgo = viewArgs["predAlgo"]
optimSelection = viewArgs["optim"]
if predAlgo == "Traphic":
if thread:
thread.signalCanvas(
"\n[INFO]: Using Traphic for the saved model")
args['train_flag'] = False
args["ours"] = True
net = traphicNet(args)
elif predAlgo == "Social GAN":
sganEval(sgan_args, thread)
return
elif predAlgo == "Social-LSTM":
print(predAlgo)
elif predAlgo == "Social Conv":
if thread:
thread.signalCanvas(
"\n[INFO]: Using Convolutional Social Pooling")
args['train_flag'] = False
net = highwayNet(args)
net.eval()
d = os.path.join(args['modelLoc'])
if thread:
thread.signalCanvas(d)
if os.path.exists(d):
net.load_state_dict(torch.load(d))
if thread:
thread.signalCanvas("\n[INFO]: model loaded")
else:
if thread:
thread.signalCanvas("\n[INFO]: can not find model to evaluate")
if args["cuda"]:
if thread:
thread.signalCanvas("\n[INFO]: Using CUDA")
net.cuda()
if optimSelection == "Adam":
optim = torch.optim.Adam(net.parameters(),
lr=args['learning_rate'])
if thread:
thread.signalCanvas("\n[INFO]: Optimizer: \n" + str(optim))
else:
if thread:
thread.signalCanvas("\n[INFO]: NOT YET IMPLEMENTED")
return
crossEnt = torch.nn.BCELoss()
if thread:
thread.signalCanvas("\n[INFO]: Loss: \n" + str(crossEnt))
# TODO: More hardcodes
dataset_name = viewArgs["dir"].split('/')[2]
prediction_data_path = 'model/Prediction/data/{}'.format(dataset_name)
trSet_path = os.path.join(prediction_data_path, "TrainSet.npy")
valSet_path = os.path.join(prediction_data_path, "ValSet.npy")
tstSet_path = os.path.join(prediction_data_path, "TestSet.npy")
trSet = ngsimDataset(trSet_path)
trDataloader = DataLoader(trSet,
batch_size=args['batch_size'],
shuffle=True,
num_workers=8,
collate_fn=trSet.collate_fn)
testSet = ngsimDataset(valSet_path)
testDataloader = DataLoader(testSet,
batch_size=args['batch_size'],
shuffle=True,
num_workers=8,
collate_fn=testSet.collate_fn)
valSet = ngsimDataset(tstSet_path)
valDataloader = DataLoader(valSet,
batch_size=args['batch_size'],
shuffle=True,
num_workers=8,
collate_fn=valSet.collate_fn)
if predAlgo == "Traphic":
args["ours"] = True
engine = TraphicEngine(net, optim, trDataloader, testDataloader,
args, thread)
elif predAlgo == "Social Conv":
engine = SocialEngine(net, optim, trDataloader, testDataloader,
args, thread)
else:
if thread:
thread.signalCanvas("\n[INFO]: NOT YET IMPLEMENTED")
if thread:
thread.signalCanvas(
"\n[INFO]: *** Evaluating Prediction Model ***")
engine.eval(testDataloader)
def train(self, viewArgs, thread=None):
if thread:
thread.signalCanvas("\n[INFO] Training started...")
args = self.getPredArgs(viewArgs)
args['eval'] = False
predAlgo = viewArgs["predAlgo"]
optimSelection = viewArgs["optim"]
if predAlgo == "Traphic":
if thread:
thread.signalCanvas("\n[INFO]: Using TRAPHIC model")
args["ours"] = True
args['train_flag'] = True
net = traphicNet(args)
elif predAlgo == "Social GAN":
sgan_args.num_epochs = int(args["pretrainEpochs"]) + int(
args["trainEpochs"])
sgan_args.batch_size = args["batch_size"]
sgan_args.dropout = args['dropout_prob']
sgan_args.g_learning_rate = args["learning_rate"]
sgan_args.g_learning_rate = args["learning_rate"]
if thread:
thread.signalCanvas("\n[INFO]: Using Sgan model")
thread.signalCanvas(
"\n[INFO]: *** Training Prediction Model ***")
sganTrain(sgan_args, thread)
return
elif predAlgo == "Social-LSTM":
print(predAlgo)
elif predAlgo == "Social Conv":
if thread:
thread.signalCanvas(
"\n[INFO]: Using Convolutional Social Pooling")
args['train_flag'] = True
net = highwayNet(args)
if args["cuda"]:
if thread:
thread.signalCanvas("\n[INFO]: Using CUDA")
net.cuda()
if optimSelection == "Adam":
optim = torch.optim.Adam(net.parameters(),
lr=args['learning_rate'])
if thread:
thread.signalCanvas("\n[INFO]: Optimizer: \n" + str(optim))
else:
if thread:
thread.signalCanvas("\n[INFO]: NOT YET IMPLEMENTED")
return
crossEnt = torch.nn.BCELoss()
if thread:
thread.signalCanvas("\n[INFO]: Loss: \n" + str(crossEnt))
# name of
dataset_name = viewArgs["dir"].split('/')[2]
prediction_data_path = 'model/Prediction/data/{}'.format(dataset_name)
trSet_path = os.path.join(prediction_data_path, "TrainSet.npy")
valSet_path = os.path.join(prediction_data_path, "ValSet.npy")
trSet = ngsimDataset(trSet_path)
valSet = ngsimDataset(valSet_path)
trDataloader = DataLoader(trSet,
batch_size=args['batch_size'],
shuffle=True,
num_workers=8,
collate_fn=trSet.collate_fn)
valDataloader = DataLoader(valSet,
batch_size=args['batch_size'],
shuffle=True,
num_workers=8,
collate_fn=valSet.collate_fn)
if predAlgo == "Traphic":
args["ours"] = True
engine = TraphicEngine(net, optim, trDataloader, valDataloader,
args, thread)
elif predAlgo == "Social Conv":
engine = SocialEngine(net, optim, trDataloader, valDataloader,
args, thread)
else:
if thread:
thread.signalCanvas("\n[INFO]: NOT YET IMPLEMENTED")
if thread:
thread.signalCanvas("\n[INFO]: *** Training Prediction Model ***")
engine.start()
