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Processor.py
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Processor.py
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'''
Author: Pu Zhang
Date: 2019/7/1
'''
from utils import *
import time
import torch.nn as nn
import yaml
class Processor():
def __init__(self, args):
self.args=args
Dataloader=DataLoader_bytrajec2
self.dataloader = Dataloader(args)
model=import_class(args.model)
self.net = model(args)
self.set_optimizer()
self.load_model()
# self.load_weights_from_srlstm()
# self.parameters_update_seton_secondSR()
# Uncomment if train the second SR layer
if self.args.using_cuda:
self.net=self.net.cuda()
else:
self.net=self.net.cpu()
print(self.net)
self.net_file = open(os.path.join(self.args.model_dir, 'net.txt'), 'a+')
self.net_file.write(str(self.net))
self.net_file.close()
self.log_file_curve = open(os.path.join(self.args.model_dir, 'log_curve.txt'), 'a+')
def parameters_update_seton(self):
for p in self.net.parameters():
p.requires_grad=True
def parameters_update_seton_secondSR(self):
for p in self.net.parameters():
p.requires_grad=True
self.net.cell.weight_ih.requires_grad = False
self.net.cell.bias_ih.requires_grad = False
self.net.cell.weight_hh.requires_grad = False
self.net.cell.bias_hh.requires_grad = False
self.net.inputLayer.weight.requires_grad = False
self.net.inputLayer.bias.requires_grad = False
self.net.outputLayer.weight.requires_grad = False
self.net.outputLayer.bias.requires_grad = False
self.net.gcn.ngate.MLP[0].bias.requires_grad = False
self.net.gcn.ngate.MLP[0].weight.requires_grad = False
self.net.gcn.relativeLayer.MLP[0].weight.requires_grad = False
self.net.gcn.relativeLayer.MLP[0].bias.requires_grad = False
self.net.gcn.W_nei.MLP[0].weight.requires_grad = False
self.net.gcn.WAr.MLP[0].weight.requires_grad = False
def parameters_update_seton(self):
for p in self.net.parameters():
p.requires_grad=True
def load_weights_from_srlstm(self):
if self.args.pretrain_load > 0:
self.args.model_save_path = self.args.save_dir + '/' + self.args.pretrain_model + '/' + self.args.pretrain_model + '_' +\
str(self.args.pretrain_load) + '.tar'
if os.path.isfile(self.args.model_save_path):
print('Loading checkpoint')
checkpoint = torch.load(self.args.model_save_path)#,map_location={'cuda:0': 'cuda:0'})
saved_weights=checkpoint['state_dict']
self.net.inputLayer.weight.data=nn.Parameter(saved_weights['inputLayer.weight'])
self.net.inputLayer.bias.data = nn.Parameter(saved_weights['inputLayer.bias'])
self.net.cell.weight_ih.data=nn.Parameter(saved_weights['cell.weight_ih'])
self.net.cell.bias_ih.data = nn.Parameter(saved_weights['cell.bias_ih'])
self.net.cell.weight_hh.data=nn.Parameter(saved_weights['cell.weight_hh'])
self.net.cell.bias_hh.data = nn.Parameter(saved_weights['cell.bias_hh'])
self.net.gcn.ngate.MLP[0].weight.data=nn.Parameter(saved_weights['gcn.ngate.MLP.0.weight'])
self.net.gcn.ngate.MLP[0].bias.data=nn.Parameter(saved_weights['gcn.ngate.MLP.0.bias'])
self.net.gcn.WAr.MLP[0].weight.data=nn.Parameter(saved_weights['gcn.WAr.MLP.0.weight'])
self.net.gcn.W_nei.MLP[0].weight.data=nn.Parameter(saved_weights['gcn.W_nei.MLP.0.weight'])
self.net.gcn.relativeLayer.MLP[0].weight.data=nn.Parameter(saved_weights['gcn.relativeLayer.MLP.0.weight'])
self.net.gcn.relativeLayer.MLP[0].bias.data=nn.Parameter(saved_weights['gcn.relativeLayer.MLP.0.bias'])
self.net.outputLayer.weight.data=nn.Parameter(saved_weights['outputLayer.weight'])
self.net.outputLayer.bias.data = nn.Parameter(saved_weights['outputLayer.bias'])
self.parameters_update_seton_secondSR()
def save_model(self,epoch):
model_path= self.args.save_dir + '/' + self.args.train_model + '/' + self.args.train_model + '_' +\
str(epoch) + '.tar'
torch.save({
'epoch': epoch,
'state_dict': self.net.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict()
}, model_path)
def load_model(self):
if self.args.load_model > 0:
self.args.model_save_path = self.args.save_dir + '/' + self.args.train_model + '/' + self.args.train_model + '_' + \
str(self.args.load_model) + '.tar'
if os.path.isfile(self.args.model_save_path):
print('Loading checkpoint')
checkpoint = torch.load(self.args.model_save_path,map_location={'cuda:0': 'cuda:'+str(self.args.gpu)})
model_epoch = checkpoint['epoch']
self.net.load_state_dict(checkpoint['state_dict'])
print('Loaded checkpoint at epoch', model_epoch)
def load_model_epoch(self,epoch):
if epoch > 0:
self.args.model_save_path = self.args.save_dir + '/' + self.args.train_model + '/' + self.args.train_model + '_' +\
str(epoch) + '.tar'
if os.path.isfile(self.args.model_save_path):
print('Loading checkpoint')
checkpoint = torch.load(self.args.model_save_path)#,map_location={'cuda:2': 'cuda:0'})
model_epoch = checkpoint['epoch']
self.net.load_state_dict(checkpoint['state_dict'])
print('Loaded checkpoint at epoch', model_epoch)
def set_optimizer(self):
self.optimizer = torch.optim.Adam(self.net.parameters(),lr=self.args.learning_rate)
self.criterion = nn.MSELoss(reduce=False)
def playtest(self):
print('Testing begin')
test_error, test_final_error, _, _, _ = self.test_epoch(self.args.load_model)
print('Set: {}, epoch: {:.5f},test_error: {:.5f} test_final_error: {:.5f}'.format(self.args.test_set,self.args.load_model,test_error,test_final_error))
def playtrain(self):
print('Training begin')
find_result=[]
test_error, test_final_error=0,0
for epoch in range(self.args.num_epochs):
train_loss,_=self.train_epoch(epoch)
val_error,val_final,_,_,_= self.val_epoch(epoch)
#test
if epoch > self.args.start_test:
test_error, test_final_error,_,look,_ = self.test_epoch(epoch)
self.save_model(epoch)
#log files
self.log_file_curve.write(str(epoch) + ',' + str(train_loss) + ',' + str(
val_error) + ',' + str(val_final) + ','+str(test_error) + ',' + str(test_final_error) + '\n')
if epoch%10==0:
self.log_file_curve.close()
self.log_file_curve = open(os.path.join(self.args.model_dir, 'log_curve.txt'), 'a+')
#console log
print('----epoch {}, train_loss={:.5f}, valid_error={:.3f}, valid_final={:.3f},test_error={:.3f},valid_final={:.3f}'
.format(epoch, train_loss,val_error, val_final,test_error,test_final_error))
def smaller(self,A,Aepoch,B,Bepoch):
if A<B:
return A,Aepoch
else:
return B,Bepoch
def train_epoch(self,epoch):
self.dataloader.reset_batch_pointer(set='train', valid=False)
loss_epoch=0
v1_sum,v2_sum,v3_sum=0,0,0
for batch in range(self.dataloader.trainbatchnums):
start = time.time()
inputs,batch_id = self.dataloader.get_train_batch(batch)
inputs = tuple([torch.Tensor(i) for i in inputs])
inputs = tuple([i.cuda() for i in inputs])
loss=torch.zeros(1).cuda()
batch_abs, batch_norm, shift_value, seq_list, nei_list, nei_num, batch_pednum = inputs
inputs_fw = batch_abs[:-1], batch_norm[:-1], shift_value[:-1], seq_list[:-1], nei_list[:-1], nei_num[:-1], batch_pednum[:-1]
self.net.zero_grad()
outputs, _, _ ,look= self.net.forward(inputs_fw,iftest=False)
v1, v2, v3=look
v1_sum+=v1
v2_sum+=v2
v3_sum+=v3
lossmask,num=getLossMask(outputs, seq_list[0],seq_list[1:],using_cuda=self.args.using_cuda)
loss_o=torch.sum(self.criterion(outputs, batch_norm[1:,:,:2]),dim=2)
loss += torch.sum(loss_o*lossmask)/num
loss_epoch+=loss.item()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.net.parameters(), self.args.clip)
self.optimizer.step()
end= time.time()
if batch%self.args.show_step==0 and self.args.ifshow_detail:
print(
'train-{}/{} (epoch {}), train_loss = {:.5f}, time/batch = {:.5f}, value1={:.5f},value2={:.5f},value3={:.5f}'.format(batch,self.dataloader.trainbatchnums,
epoch,
loss.item(), end - start,v1,v2,v3))
train_loss_epoch = loss_epoch / self.dataloader.trainbatchnums
v1_sum=v1_sum / self.dataloader.trainbatchnums
v2_sum=v2_sum / self.dataloader.trainbatchnums
v3_sum=v3_sum / self.dataloader.trainbatchnums
return train_loss_epoch,(v1_sum,v2_sum,v3_sum)
def val_epoch(self, epoch):
if self.dataloader.val_fraction==0:
return 0,0,0
self.dataloader.reset_batch_pointer(set='train', valid=True)
error_epoch,final_error_epoch = 0,0
error_cnt_epoch,final_error_cnt_epoch = 1e-5,1e-5
v1_sum,v2_sum,v3_sum=0,0,0
v1,v2,v3=0,0,0
for batch in range(self.dataloader.valbatchnums):
inputs,batch_id = self.dataloader.get_val_batch(batch)
inputs = tuple([torch.Tensor(i) for i in inputs])
inputs = tuple([i.cuda() for i in inputs])
batch_abs, batch_norm, shift_value, seq_list, nei_list, nei_num, batch_pednum = inputs
inputs_fw = batch_abs[:-1], batch_norm[:-1], shift_value[:-1], seq_list[:-1], nei_list[:-1], nei_num[:-1], batch_pednum[:-1]
forward = self.net.forward
outputs_infer, _, _, look = forward(inputs_fw, iftest=True)
lossmask, num = getLossMask(outputs_infer, seq_list[0], seq_list[1:], using_cuda=self.args.using_cuda)
error, error_cnt, final_error, final_error_cnt, _ = L2forTest(outputs_infer, batch_norm[1:, :, :2],
self.args.obs_length, lossmask)
v1, v2, v3=look
v1_sum+=v1
v2_sum+=v2
v3_sum+=v3
error_epoch += error
error_cnt_epoch += error_cnt
final_error_epoch+=final_error
final_error_cnt_epoch+=final_error_cnt
v1_sum=v1_sum / self.dataloader.valbatchnums
v2_sum=v2_sum / self.dataloader.valbatchnums
v3_sum=v3_sum / self.dataloader.valbatchnums
val_error=error_epoch/error_cnt_epoch
final_error=final_error_epoch/final_error_cnt_epoch
return val_error,final_error,0,0,(v1_sum,v2_sum,v3_sum)
def test_epoch(self,epoch):
self.dataloader.reset_batch_pointer(set='test')
error_epoch,final_error_epoch,error_nl_epoch = 0,0,0
error_cnt_epoch,final_error_cnt_epoch,error_nl_cnt_epoch= 1e-5,1e-5,1e-5
value1_sum,value2_sum,value3_sum=0,0,0
for batch in range(self.dataloader.testbatchnums):
if batch%100==0:
print('testing batch',batch,self.dataloader.testbatchnums)
inputs, batch_id = self.dataloader.get_test_batch(batch)
inputs = tuple([torch.Tensor(i) for i in inputs])
if self.args.using_cuda:
inputs = tuple([i.cuda() for i in inputs])
batch_abs, batch_norm, shift_value, seq_list, nei_list, nei_num, batch_pednum = inputs
inputs_fw = batch_abs[:-1], batch_norm[:-1], shift_value[:-1], seq_list[:-1], nei_list[:-1], nei_num[:-1], batch_pednum[:-1]
forward = self.net.forward
self.net.zero_grad()
outputs_infer, _, _, look = forward(inputs_fw, iftest=True)
value1, value2, value3 = look
value1_sum += value1
value2_sum += value2
value3_sum += value3
lossmask, num = getLossMask(outputs_infer, seq_list[0], seq_list[1:], using_cuda=self.args.using_cuda)
error, error_cnt, final_error, final_error_cnt, error_nl,error_nl_cnt,_ = L2forTest_nl(outputs_infer, batch_norm[1:, :, :2],
self.args.obs_length, lossmask,seq_list[1:],nl_thred=0)
error_epoch += error
error_cnt_epoch += error_cnt
final_error_epoch += final_error
final_error_cnt_epoch += final_error_cnt
error_nl_epoch+=error_nl
error_nl_cnt_epoch+=error_nl_cnt
value1_sum=value1_sum / self.dataloader.testbatchnums
value2_sum=value2_sum / self.dataloader.testbatchnums
value3_sum=value3_sum / self.dataloader.testbatchnums
return error_epoch / error_cnt_epoch, final_error_epoch / final_error_cnt_epoch, \
error_nl_epoch/error_nl_cnt_epoch, (value1_sum,value2_sum,value3_sum),error_cnt_epoch