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trainer.py
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165 lines (145 loc) · 6.21 KB
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import torch.optim as optim
import math
import util
import torch
import numpy as np
class Trainer_D():
def __init__(self, model, lrate, lrate2, wdecay, clip, step_size, seq_out_len, scaler, device, cl=True):
self.scaler = scaler
self.model = model
self.model.to(device)
ignored_params = list(map(id, model.stae.parameters()))
base_params = filter(lambda p: id(p) not in ignored_params, model.parameters())
self.optimizer = optim.Adam([{'params': base_params},
{'params': model.stae.parameters(), 'lr': lrate2}], lr=lrate, weight_decay=wdecay)
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(self.optimizer, mode='min', factor=0.1, patience=5, eps=1e-8, verbose=True)
self.loss = util.masked_mae
self.clip = clip
self.step = step_size
self.iter = 1
self.task_level = 1
self.seq_out_len = seq_out_len
self.cl = cl
def train(self, input, real_val):
self.model.train()
self.optimizer.zero_grad()
output = self.model(input)
real = torch.unsqueeze(real_val, dim=1)
predict = self.scaler.inverse_transform(output)
if self.iter % self.step == 0 and self.task_level <= self.seq_out_len:
self.task_level +=1
if self.cl:
loss = self.loss(predict[:, :, :, :self.task_level], real[:, :, :, :self.task_level], 0.0)
else:
loss = self.loss(predict, real, 0.0)
# loss = self.loss(predict, real, 0.0)
loss.backward()
if self.clip is not None:
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip)
self.optimizer.step()
# mae = util.masked_mae(predict,real,0.0).item()
mape = util.masked_mape(predict, real, 0.0).item()
rmse = util.masked_rmse(predict, real, 0.0).item()
self.iter += 1
return loss.item(), mape, rmse
def eval(self, input, real_val):
self.model.eval()
output = self.model(input)
real = torch.unsqueeze(real_val, dim=1)
predict = self.scaler.inverse_transform(output)
loss = self.loss(predict, real, 0.0)
mape = util.masked_mape(predict, real, 0.0).item()
rmse = util.masked_rmse(predict, real, 0.0).item()
return loss.item(), mape, rmse
class AETrainer():
def __init__(self, model, lrate, wdecay, clip, step_size, seq_out_len, scaler, device, cl=True):
self.scaler = scaler
self.model = model
self.model.to(device)
self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
self.loss = util.masked_mae
self.clip = clip
self.step = step_size
self.iter = 1
self.task_level = 1
self.seq_out_len = seq_out_len
self.cl = cl
def train(self, input):
self.model.train()
self.optimizer.zero_grad()
# input = nn.functional.pad(input,(1,0,0,0))
output = self.model(input)
# output = output.transpose(1,3)
#output = [batch_size,12,num_nodes,1]
real = input[:, :1, :, :]
predict = self.scaler.inverse_transform(output)
real = self.scaler.inverse_transform(real)
#print('--------------predict-------------------')
#print(predict)
#print('--------------real-------------------')
#print(real)
loss = self.loss(predict, real, 0.0)
loss.backward()
if self.clip is not None:
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip)
self.optimizer.step()
# mae = util.masked_mae(predict,real,0.0).item()
mape = util.masked_mape(predict, real, 0.0).item()
rmse = util.masked_rmse(predict, real, 0.0).item()
self.iter += 1
return loss.item(), mape, rmse
def eval(self, input):
self.model.eval()
# input = nn.functional.pad(input,(1,0,0,0))
output = self.model(input)
# output = output.transpose(1,3)
#output = [batch_size,12,num_nodes,1]
real = input[:, :1, :, :]
predict = self.scaler.inverse_transform(output)
real = self.scaler.inverse_transform(real)
loss = self.loss(predict, real, 0.0)
mape = util.masked_mape(predict, real, 0.0).item()
rmse = util.masked_rmse(predict, real, 0.0).item()
return loss.item(), mape, rmse
class Optim(object):
def _makeOptimizer(self):
if self.method == 'sgd':
self.optimizer = optim.SGD(self.params, lr=self.lr, weight_decay=self.lr_decay)
elif self.method == 'adagrad':
self.optimizer = optim.Adagrad(self.params, lr=self.lr, weight_decay=self.lr_decay)
elif self.method == 'adadelta':
self.optimizer = optim.Adadelta(self.params, lr=self.lr, weight_decay=self.lr_decay)
elif self.method == 'adam':
self.optimizer = optim.Adam(self.params, lr=self.lr, weight_decay=self.lr_decay)
else:
raise RuntimeError("Invalid optim method: " + self.method)
def __init__(self, params, method, lr, clip, lr_decay=1, start_decay_at=None):
self.params = params # careful: params may be a generator
self.last_ppl = None
self.lr = lr
self.clip = clip
self.method = method
self.lr_decay = lr_decay
self.start_decay_at = start_decay_at
self.start_decay = False
self._makeOptimizer()
def step(self):
# Compute gradients norm.
grad_norm = 0
if self.clip is not None:
torch.nn.utils.clip_grad_norm_(self.params, self.clip)
self.optimizer.step()
return grad_norm
# decay learning rate if val perf does not improve or we hit the start_decay_at limit
def updateLearningRate(self, ppl, epoch):
if self.start_decay_at is not None and epoch >= self.start_decay_at:
self.start_decay = True
if self.last_ppl is not None and ppl > self.last_ppl:
self.start_decay = True
if self.start_decay:
self.lr = self.lr * self.lr_decay
print("Decaying learning rate to %g" % self.lr)
#only decay for one epoch
self.start_decay = False
self.last_ppl = ppl
self._makeOptimizer()