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exp_dynamic_conditional.py
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import os
import torch
from exp.exp_basic import Exp_Basic
from models.model_factory import get_model
from data_provider.data_factory import get_data
from utils.loss import L2Loss
import matplotlib.pyplot as plt
from utils.visual import visual
import numpy as np
class Exp_Dynamic_Conditional(Exp_Basic):
def __init__(self, args):
super(Exp_Dynamic_Conditional, self).__init__(args)
def vali(self):
myloss = L2Loss(size_average=False)
test_l2_full = 0
self.model.eval()
with torch.no_grad():
for x, time, fx, yy in self.test_loader:
x, time, fx, yy = x.cuda(), time.cuda(), fx.cuda(), yy.cuda()
for t in range(self.args.T_out):
input_T = time[:, t:t + 1].reshape(x.shape[0], 1)
if self.args.fun_dim == 0:
fx = None
im = self.model(x, fx=fx, T=input_T)
if t == 0:
pred = im
else:
pred = torch.cat((pred, im), -1)
if self.args.normalize:
pred = self.dataset.y_normalizer.decode(pred)
test_l2_full += myloss(pred.reshape(x.shape[0], -1), yy.reshape(x.shape[0], -1)).item()
test_loss_full = test_l2_full / (self.args.ntest)
return test_loss_full
def train(self):
if self.args.optimizer == 'AdamW':
optimizer = torch.optim.AdamW(self.model.parameters(), lr=self.args.lr, weight_decay=self.args.weight_decay)
elif self.args.optimizer == 'Adam':
optimizer = torch.optim.Adam(self.model.parameters(), lr=self.args.lr, weight_decay=self.args.weight_decay)
else:
raise ValueError('Optimizer only AdamW or Adam')
if self.args.scheduler == 'OneCycleLR':
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=self.args.lr, epochs=self.args.epochs,
steps_per_epoch=len(self.train_loader),
pct_start=self.args.pct_start)
elif self.args.scheduler == 'CosineAnnealingLR':
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=self.args.epochs)
elif self.args.scheduler == 'StepLR':
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=self.args.step_size, gamma=self.args.gamma)
myloss = L2Loss(size_average=False)
for ep in range(self.args.epochs):
self.model.train()
train_l2_step = 0
for pos, time, fx, yy in self.train_loader:
x, time, fx, yy = pos.cuda(), time.cuda(), fx.cuda(), yy.cuda()
for t in range(self.args.T_out):
y = yy[..., self.args.out_dim * t:self.args.out_dim * (t + 1)]
input_T = time[:, t:t + 1].reshape(x.shape[0], 1)
if self.args.fun_dim == 0:
fx = None
im = self.model(x, fx=fx, T=input_T)
loss = myloss(im.reshape(x.shape[0], -1), y.reshape(x.shape[0], -1))
train_l2_step += loss.item()
optimizer.zero_grad()
loss.backward()
if self.args.max_grad_norm is not None:
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.max_grad_norm)
optimizer.step()
if self.args.scheduler == 'OneCycleLR':
scheduler.step()
if self.args.scheduler == 'CosineAnnealingLR' or self.args.scheduler == 'StepLR':
scheduler.step()
train_loss_step = train_l2_step / (self.args.ntrain * float(self.args.T_out))
print("Epoch {} Train loss step : {:.5f} ".format(ep, train_loss_step))
test_loss_full = self.vali()
print("Epoch {} Test loss full : {:.5f}".format(ep, test_loss_full))
if ep % 100 == 0:
if not os.path.exists('./checkpoints'):
os.makedirs('./checkpoints')
print('save models')
torch.save(self.model.state_dict(), os.path.join('./checkpoints', self.args.save_name + '.pt'))
if not os.path.exists('./checkpoints'):
os.makedirs('./checkpoints')
print('final save models')
torch.save(self.model.state_dict(), os.path.join('./checkpoints', self.args.save_name + '.pt'))
def test(self):
self.model.load_state_dict(torch.load("./checkpoints/" + self.args.save_name + ".pt"))
self.model.eval()
if not os.path.exists('./results/' + self.args.save_name + '/'):
os.makedirs('./results/' + self.args.save_name + '/')
rel_err = 0.0
id = 0
myloss = L2Loss(size_average=False)
with torch.no_grad():
for x, time, fx, yy in self.test_loader:
id += 1
x, time, fx, yy = x.cuda(), time.cuda(), fx.cuda(), yy.cuda() # x : B, 4096, 2 fx : B, 4096 y : B, 4096, T
for t in range(self.args.T_out):
input_T = time[:, t:t + 1].reshape(x.shape[0], 1) # B,step
if self.args.fun_dim == 0:
fx = None
im = self.model(x, fx=fx, T=input_T)
if t == 0:
pred = im
else:
pred = torch.cat((pred, im), -1)
if self.args.normalize:
pred = self.dataset.y_normalizer.decode(pred)
rel_err += myloss(pred.reshape(x.shape[0], -1), yy.reshape(x.shape[0], -1)).item()
if id < self.args.vis_num:
print('visual: ', id)
visual(yy[:, :, -4:-2], torch.sqrt(yy[:, :, -1:] ** 2 + yy[:, :, -2:-1] ** 2),
torch.sqrt(pred[:, :, -1:] ** 2 + pred[:, :, -2:-1] ** 2), self.args, id)
rel_err /= self.args.ntest
print("rel_err:{}".format(rel_err))