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train.py
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train.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Aug 20 00:26:58 2017
@author: sakurai
"""
import os
import time
import copy
import matplotlib.pyplot as plt
import six
from tqdm import tqdm
import chainer
from chainer import optimizers
from chainer import cuda
from . import common
from .datasets import data_provider
from .models.modified_googlenet import ModifiedGoogLeNet
def train(main_script_path, func_train_one_batch, param_dict,
save_distance_matrix=False,):
script_filename = os.path.splitext(os.path.basename(main_script_path))[0]
chainer.config.train = False
device = 0
xp = chainer.cuda.cupy
config_parser = six.moves.configparser.ConfigParser()
config_parser.read('config')
log_dir_path = os.path.expanduser(config_parser.get('logs', 'dir_path'))
p = common.Logger(log_dir_path, **param_dict) # hyperparameters
##########################################################
# load database
##########################################################
streams = data_provider.get_streams(p.batch_size, dataset=p.dataset,
method=p.method)
stream_train, stream_train_eval, stream_test = streams
iter_train = stream_train.get_epoch_iterator()
##########################################################
# construct the model
##########################################################
model = ModifiedGoogLeNet(p.out_dim, p.normalize_output)
if device >= 0:
model.to_gpu()
model.cleargrads()
xp = model.xp
if p.optimizer == 'Adam':
optimizer = optimizers.Adam(p.learning_rate)
elif p.optimizer == 'RMSProp':
optimizer = optimizers.RMSprop(p.learning_rate)
else:
raise ValueError
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(p.l2_weight_decay))
print(p)
stop = False
logger = common.Logger(log_dir_path)
logger.soft_test_best = [0]
time_origin = time.time()
try:
for epoch in range(p.num_epochs):
time_begin = time.time()
epoch_losses = []
for i in tqdm(range(p.num_batches_per_epoch),
desc='# {}'.format(epoch)):
with chainer.using_config('train', True):
loss = func_train_one_batch(model, p, next(iter_train))
loss.backward()
optimizer.update()
model.cleargrads()
epoch_losses.append(loss.data)
del loss
loss_average = cuda.to_cpu(xp.array(
xp.hstack(epoch_losses).mean()))
# average accuracy and distance matrix for training data
D, soft, hard, retrieval = common.evaluate(
model, stream_train_eval.get_epoch_iterator(), p.distance_type,
return_distance_matrix=save_distance_matrix)
# average accuracy and distance matrix for testing data
D_test, soft_test, hard_test, retrieval_test = common.evaluate(
model, stream_test.get_epoch_iterator(), p.distance_type,
return_distance_matrix=save_distance_matrix)
time_end = time.time()
epoch_time = time_end - time_begin
total_time = time_end - time_origin
logger.epoch = epoch
logger.total_time = total_time
logger.loss_log.append(loss_average)
logger.train_log.append([soft[0], hard[0], retrieval[0]])
logger.test_log.append(
[soft_test[0], hard_test[0], retrieval_test[0]])
# retain the model if it scored the best test acc. ever
if soft_test[0] > logger.soft_test_best[0]:
logger.model_best = copy.deepcopy(model)
logger.optimizer_best = copy.deepcopy(optimizer)
logger.epoch_best = epoch
logger.D_best = D
logger.D_test_best = D_test
logger.soft_best = soft
logger.soft_test_best = soft_test
logger.hard_best = hard
logger.hard_test_best = hard_test
logger.retrieval_best = retrieval
logger.retrieval_test_best = retrieval_test
print("#", epoch)
print("time: {} ({})".format(epoch_time, total_time))
print("[train] loss:", loss_average)
print("[train] soft:", soft)
print("[train] hard:", hard)
print("[train] retr:", retrieval)
print("[test] soft:", soft_test)
print("[test] hard:", hard_test)
print("[test] retr:", retrieval_test)
print("[best] soft: {} (at # {})".format(logger.soft_test_best,
logger.epoch_best))
print(p)
# print norms of the weights
params = xp.hstack([xp.linalg.norm(param.data)
for param in model.params()]).tolist()
print("|W|", map(lambda param: float('%0.2f' % param), params))
print()
# Draw plots
if save_distance_matrix:
plt.figure(figsize=(8, 4))
plt.subplot(1, 2, 1)
mat = plt.matshow(D, fignum=0, cmap=plt.cm.gray)
plt.colorbar(mat, fraction=0.045)
plt.subplot(1, 2, 2)
mat = plt.matshow(D_test, fignum=0, cmap=plt.cm.gray)
plt.colorbar(mat, fraction=0.045)
plt.tight_layout()
plt.figure(figsize=(8, 4))
plt.subplot(1, 2, 1)
plt.plot(logger.loss_log, label="tr-loss")
plt.grid()
plt.legend(loc='best')
plt.subplot(1, 2, 2)
plt.plot(logger.train_log)
plt.plot(logger.test_log)
plt.grid()
plt.legend(["tr-soft", "tr-hard", "tr-retr",
"te-soft", "te-hard", "te-retr"],
bbox_to_anchor=(1.4, 1))
plt.ylim([0.0, 1.0])
plt.xlim([0, p.num_epochs])
plt.tight_layout()
plt.show()
plt.draw()
del D
del D_test
except KeyboardInterrupt:
stop = True
dir_name = "-".join([p.dataset, script_filename,
time.strftime("%Y%m%d%H%M%S"),
str(logger.soft_test_best[0])])
logger.save(dir_name)
p.save(dir_name)
print("total epochs: {} ({} [s])".format(logger.epoch, logger.total_time))
print("best test score (at # {})".format(logger.epoch_best))
print("[test] soft:", logger.soft_test_best)
print("[test] hard:", logger.hard_test_best)
print("[test] retr:", logger.retrieval_test_best)
print(str(p).replace(', ', '\n'))
print()
return stop