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mainAE.py
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mainAE.py
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# -*- coding: utf-8 -*-
import models, data
from utils.visualize import Visualizer
from config import opt
import torch as t, numpy as np
import warnings
from torch.utils.data import DataLoader
def train(f='train'):
if f=='train':
opt.tr_va_te = 0
# model
model = getattr(models, opt.model)(opt = opt)
if opt.load_model_path:
model.load(opt.load_model_path)
model.to(opt.device)
model.train()
# data
train_data = data.datasets(opt).getData()
train_dataloader = DataLoader(train_data, opt.batch_size, shuffle = True)
# certerion & optimzer
certerion = t.nn.MSELoss()
optimizer = t.optim.Adam(model.parameters(),
lr = opt.lr,
weight_decay = opt.weight_decay)
# losses
epoch_losses_train = [] # np.zeros(opt.max_epoch)
epoch_losses_test = []
#train
for epoch in range(opt.max_epoch):
# print('model : ',opt.model,', epoch : ',epoch)
temp_loss_train = []
for _, (d_t, ts) in enumerate(train_dataloader):
# print(_, end=' ')
input_data = d_t[0].to(opt.device)
target_data = d_t[1].to(opt.device)
optimizer.zero_grad()
input_data = input_data.permute(1,0,2)
target_data = target_data.permute(1,0,2)
# print(input_data.size(), target_data.size())
output_data, output_former = model(input_data)
loss_train = certerion(input_data, output_data)
loss_train.backward()
optimizer.step()
temp_loss_train.append(loss_train.item())
epoch_losses_train.append(np.mean(temp_loss_train))
# TODO(ljx): print
if epoch % 50 == 25:
print('model:',opt.model,' ,epoch:',epoch, 'train loss:',epoch_losses_train[-1], opt.lr)
if epoch < 3:
continue
if epoch_losses_train[-1] > epoch_losses_train[-2]:
opt.lr = opt.lr * opt.lr_decay
path = model.save(opt)
return epoch_losses_train, epoch_losses_test, path
def pre(f='pre'):
opt.tr_va_te = opt.train_test_pre
model = getattr(models, opt.model)(opt = opt).eval()
if opt.load_model_path:
model.load(opt.load_model_path)
model.to(opt.device)
# print('\n\n', list(model.out_sigma.named_parameters()), '\n\n')
return pre_(model)
def pre_(model):
test_data = data.datasets(opt).getData()
N = test_data.__len__()
if opt.num > N :
warnings.warn('Warning: data is not long enough, data(%d) num(%d)' % (N, opt.num))
start_index = 0
all_input_data = None
all_output_data = None
all_target_data = None
all_ts = None
loss = []
index = start_index
while index - start_index < opt.num:
def datatype(test_data, index):
d_t, ts = test_data[index]
i = d_t[0].to(opt.device).unsqueeze(0).permute(1,0,2)
t = d_t[1].to(opt.device).unsqueeze(0).permute(1,0,2)
return i, t, ts
input_data, target_data, ts = datatype(test_data, index)
output_data, _ = model(input_data)
# print(input_data.shape, target_data.shape,output_data.shape)
temp_loss = t.nn.MSELoss()(input_data, output_data).item()
loss.append(temp_loss)
def tensor2numpy(i_, o_, t_):
return i_.cpu().detach().numpy(), \
o_.cpu().detach().numpy(), \
t_.cpu().detach().numpy()
i_, o_, t_ = tensor2numpy(input_data, output_data, target_data)
all_input_data = i_ if all_input_data is None else np.concatenate([all_input_data, i_])
all_output_data = o_ if all_output_data is None else np.concatenate([all_output_data, o_])
all_target_data = t_ if all_target_data is None else np.concatenate([all_target_data, t_])
all_ts = ts if all_ts is None else (t.cat((all_ts[0], ts[0]), dim = 0), t.cat((all_ts[1], ts[1]), dim = 0))
index += opt.future
print(all_input_data.shape, all_output_data.shape, all_target_data.shape)
all_ts = (all_ts[0].unsqueeze(0), all_ts[1].unsqueeze(0))
# Visualizer().drawTest(([], all_output_data, all_input_data), all_ts, drawLot = True)
import matplotlib.pyplot as plt
plt.figure()
plt.plot(all_output_data[:100, 0, 0])
plt.plot(all_input_data[:100, 0, 0])
plt.show()
evaluation(t.from_numpy(all_output_data), t.from_numpy(all_input_data), 0)
return loss, None, None
def evaluation(output, target, batch):
numerator = t.sqrt(t.mean(t.pow(output[:,batch,:]-target[:,batch,:],2)))
denominator_u = t.sqrt(t.mean(t.pow(target[:,batch,:],2))) * t.sqrt(t.mean(t.pow(output[:,batch,:],2)))
MSE = t.mean(t.pow(output[:,batch,:]-target[:,batch,:],2)).item()
U = t.div(numerator, denominator_u).item()
print('model: ',opt.model)
print('MSE: ',MSE)
print('U: ',U)
def help():
pass
def LetsGo(kwargs, fun):
if kwargs is not None:
opt._parse(kwargs)
opt.input_size = opt._input_kv[opt.data]
opt.output_size = opt._output_kv[opt.data]
opt.needLog = opt._needLog_kv[opt.data]
epoch_losses_train, epoch_losses_test, path = fun()
print('path : ',path)
if len(epoch_losses_train) > 1:
if epoch_losses_train:
print('epoch_losses_train')
Visualizer().drawEpochLoss(epoch_losses_train[10:])
# if epoch_losses_test:
# print('epoch_losses_test')
# Visualizer().drawEpochLoss(epoch_losses_test[10:])
# print('\n','epoch_losses_train:\n',epoch_losses_train)
# print('\n','epoch_losses_train:\n',epoch_losses_test)
return path
if __name__ == '__main__':
t.set_default_tensor_type('torch.DoubleTensor')
'''
AutoEncoder_NINO_0116113812, AutoEncoder_NINO_0117104946
'''
m_path = 'checkpoints/AutoEncoder_NINO_0122203529.pth'
m_path = None
'''
multi
'''
m_model = 'AutoEncoder'
m_data = 'NINO' # NINO, Yahoo, Wecar, Aircraft, BJpm
m_lr = 0.001
m_num = 80
mm = {'load_model_path':m_path, 'model':m_model, 'data':m_data,
'lr':m_lr, 'num':m_num}
LetsGo(mm, pre) # train, pre
opt._parse(printconfig = True)
def main_run(run_opt, run_data):
t.set_default_tensor_type('torch.DoubleTensor')
print('\nAutoEncoder')
opt = run_opt
m_path = None
m_model = 'AutoEncoder'
m_data = run_data # NINO, Yahoo, Wecar, Aircraft, BJpm
m_lr = 0.001
m_num = 80
mm = {'load_model_path':m_path, 'model':m_model, 'data':m_data,
'lr':m_lr, 'num':m_num}
newpath = LetsGo(mm, train)
opt._parse(printconfig = True)
return newpath
def main_run_pre(run_opt, run_data):
t.set_default_tensor_type('torch.DoubleTensor')
print('\nAutoEncoder')
opt = run_opt
m_path = opt.model_list_path[0]
m_model = 'AutoEncoder'
m_data = run_data # NINO, Yahoo, Wecar, Aircraft, BJpm
m_lr = 0.001
m_num = 80
mm = {'load_model_path':m_path, 'model':m_model, 'data':m_data,
'lr':m_lr, 'num':m_num}
newpath = LetsGo(mm, pre)
# opt._parse(printconfig = True)
return newpath