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train.py
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import os
import time
from options import TrainOptions, TestOptions
from framework import SketchModel
from utils import load_data
from writer import Writer
from evaluate import run_eval
import numpy as np
# import torchsnooper
def run_train(train_params=None, test_params=None):
opt = TrainOptions().parse(train_params)
testopt = TestOptions().parse(test_params)
testopt.timestamp = opt.timestamp
testopt.batch_size = opt.batch_size
# model init
model = SketchModel(opt)
model.print_detail()
writer = Writer(opt)
# data load
trainDataloader = load_data(opt, datasetType='train', shuffle=opt.shuffle)
trainDataloader_2 = load_data(opt, datasetType='train', shuffle=True)
trainDataloader_3 = load_data(opt, datasetType='train', shuffle=True)
testDataloader = load_data(opt, datasetType='test')
# train epoches
# with torchsnooper.snoop():
ii = 0
min_test_avgloss = 100
min_test_avgloss_epoch = 0
best_p_metric = 0
best_p_metric_epoch = 0
best_c_metric = 0
best_c_metric_epoch = 0
for epoch in range(opt.epoch):
data_last = None
for i, (data, data_2, data_3) in enumerate(zip(trainDataloader, trainDataloader_2, trainDataloader_3)):
if i == 0:
data_last = data_3
else:
model.step_pi(data_2, data_last, data_3)
data_last = data_3
model.step_theta(data)
if ii % opt.plot_freq == 0:
writer.plot_train_loss(model.loss, ii)
if ii % opt.print_freq == 0:
writer.print_train_loss(epoch, i, model.loss)
ii += 1
model.update_learning_rate()
if opt.plot_weights:
writer.plot_model_wts(model, epoch)
# test
if epoch % opt.run_test_freq == 0:
model.save_network('latest')
loss_avg, P_metric, C_metric = run_eval(
opt=testopt,
loader=testDataloader,
dataset='test',
write_result=False)
writer.print_test_loss(epoch, 0, loss_avg)
writer.plot_test_loss(loss_avg, epoch)
writer.print_eval_metric(epoch, P_metric, C_metric)
writer.plot_eval_metric(epoch, P_metric, C_metric)
if loss_avg < min_test_avgloss:
min_test_avgloss = loss_avg
min_test_avgloss_epoch = epoch
print('saving the model at the end of epoch {} with test best avgLoss {}'.format(epoch, min_test_avgloss))
model.save_network('bestloss')
if C_metric > best_c_metric:
best_c_metric = C_metric
best_c_metric_epoch = epoch
print('saving the model at the end of epoch {} with test best C_metric {}'.format(epoch, best_c_metric))
model.save_network('best_c')
if P_metric > best_p_metric:
best_p_metric = P_metric
best_p_metric_epoch = epoch
print('saving the model at the end of epoch {} with test best P_metric {}'.format(epoch, best_p_metric))
model.save_network('best_p')
testopt.which_epoch = 'latest'
testopt.metric_way = 'wlen'
loss_avg, P_metric, C_metric = run_eval(
opt=testopt,
loader=testDataloader,
dataset='test',
write_result=False)
testopt.which_epoch = 'bestloss'
testopt.metric_way = 'wlen'
loss_avg_2, P_metric_2, C_metric_2 = run_eval(
opt=testopt,
loader=testDataloader,
dataset='test',
write_result=False)
testopt.which_epoch = 'best_c'
testopt.metric_way = 'wlen'
loss_avg_3, P_metric_3, C_metric_3 = run_eval(
opt=testopt,
loader=testDataloader,
dataset='test',
write_result=False)
testopt.which_epoch = 'best_p'
testopt.metric_way = 'wlen'
loss_avg_4, P_metric_4, C_metric_4 = run_eval(
opt=testopt,
loader=testDataloader,
dataset='test',
write_result=False)
record_list = {
'p_metric': round(P_metric*100, 2),
'c_metric': round(C_metric*100, 2),
'loss_avg': round(loss_avg, 4),
'best_epoch': min_test_avgloss_epoch,
'p_metric_2': round(P_metric_2*100, 2),
'c_metric_2': round(C_metric_2*100, 2),
'loss_avg_2': round(loss_avg_2, 4),
'best_c_epoch': best_c_metric_epoch,
'p_metric_3': round(P_metric_3*100, 2),
'c_metric_3': round(C_metric_3*100, 2),
'loss_avg_3': round(loss_avg_3, 4),
'best_p_epoch': best_p_metric_epoch,
'p_metric_4': round(P_metric_4*100, 2),
'c_metric_4': round(C_metric_4*100, 2),
'loss_avg_4': round(loss_avg_4, 4),
}
writer.train_record(record_list=record_list)
writer.close()
return record_list, opt.timestamp, opt.creativity_alpha, opt.creativity_beta
if __name__ == "__main__":
import warnings
warnings.filterwarnings("ignore")
record_list, _, creativity_alpha, creativity_beta = run_train()
print(record_list)
print("creativity_alpha: ", creativity_alpha)
print("creativity_beta: ", creativity_beta)