/
avg_runner.py
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/
avg_runner.py
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import datetime
import os
from config import c, cfg_from_file
import numpy as np
import tensorflow as tf
from clip_iterator import Clip_Iterator
from discriminator import Discriminator
from evaluation import Evaluator
from generator import Generator
from iterator import Iterator
from utility.notifier import Notifier
from utility.simplified_log import Logger
from utils import normalize_frames, save_png, crop_img
flags = tf.flags
flags.DEFINE_string('device', '0,1', '显卡')
flags.DEFINE_string('config', '/extend/gru_tf_data/0916_ensemble/cfg0.yml', '配置文件')
flags.DEFINE_string('restore', None, '参数')
flags.DEFINE_string('mode', 'train', '模式')
flags.DEFINE_string('iter', '-1', '测试参数轮数')
class AVGRunner:
def __init__(self, restore_path, mode="train"):
self.notifier = Notifier()
self.logger = Logger(path=c.SAVE_PATH,
name=f"train{datetime.datetime.now().strftime('%Y%m%d%H%M')}.log")
self.global_step = 0
self.num_steps = c.MAX_ITER
tf_config = tf.ConfigProto(allow_soft_placement=True)
tf_config.gpu_options.allow_growth = True
self.sess = tf.Session(config=tf_config)
self.summary_writer = tf.summary.FileWriter(c.SAVE_SUMMARY, graph=self.sess.graph)
if mode == "train":
self._out_seq = c.OUT_SEQ
self._h = c.H
self._w = c.W
else:
# self._batch = 1
self._out_seq = c.PREDICT_LENGTH
self._h = c.PREDICTION_H
self._w = c.PREDICTION_W
self._in_seq = c.IN_SEQ
self._batch = c.BATCH_SIZE
self.g_model = Generator(self.sess, self.summary_writer, mode=mode)
if c.ADVERSARIAL and mode == "train":
self.d_model = Discriminator(self.sess, self.summary_writer)
else:
self.d_model = None
self.saver = tf.train.Saver(max_to_keep=0)
if restore_path is not None:
self.saver.restore(self.sess, restore_path)
else:
self.sess.run(tf.global_variables_initializer())
def get_train_batch(self, iterator):
data, *_ = iterator.sample(batch_size=self._batch)
in_data = data[:, :self._in_seq, :, :, :]
if c.IN_CHANEL == 3:
gt_data = data[:, self._in_seq:self._in_seq + self._out_seq, :, :, :]
elif c.IN_CHANEL == 1:
gt_data = data[:, self._in_seq:self._in_seq + self._out_seq, :, :, :]
else:
raise NotImplementedError
if c.NORMALIZE:
in_data = normalize_frames(in_data)
gt_data = normalize_frames(gt_data)
in_data = crop_img(in_data)
gt_data = crop_img(gt_data)
return in_data, gt_data
def train(self):
train_iter = Iterator(time_interval=c.RAINY_TRAIN,
sample_mode="random",
seq_len=self._in_seq + self._out_seq,
stride=1)
while self.global_step < c.MAX_ITER:
if c.ADVERSARIAL and self.global_step > c.ADV_INVOLVE:
print("start d_model")
in_data, gt_data = self.get_train_batch(train_iter)
d_loss, *_ = self.d_model.train_step(in_data, gt_data, self.g_model)
else:
d_loss = 0
in_data, gt_data = self.get_train_batch(train_iter)
g_loss, mse, gd_loss, global_step = self.g_model.train_step(in_data, gt_data, self.d_model)
self.global_step = global_step
self.logger.info(f"Iter {self.global_step}: \n\t "
f"g_loss: {g_loss:.4f} \n\t"
f"mse: {mse:.4f} \n\t "
f"mse_real: {gd_loss:.4f} \n\t"
f"d_loss: {d_loss:.4f}")
if (self.global_step + 1) % c.SAVE_ITER == 0:
self.save_model()
if (self.global_step + 1) % c.VALID_ITER == 0:
self.run_benchmark(global_step, mode="Valid")
def valid(self):
test_iter = Clip_Iterator(c.VALID_DIR_CLIPS)
evaluator = Evaluator(self.global_step)
i = 0
for data in test_iter.sample_valid(self._batch):
in_data = data[:, :self._in_seq, ...]
if c.IN_CHANEL == 3:
gt_data = data[:, self._in_seq:self._in_seq + self._out_seq, :, :, 1:-1]
elif c.IN_CHANEL == 1:
gt_data = data[:, self._in_seq:self._in_seq + self._out_seq, ...]
else:
raise NotImplementedError
if c.NORMALIZE:
in_data = normalize_frames(in_data)
gt_data = normalize_frames(gt_data)
mse, mae, gdl, pred = self.g_model.valid_step(in_data, gt_data)
evaluator.evaluate(gt_data, pred)
self.logger.info(f"Iter {self.global_step} {i}: \n\t "
f"mse:{mse:.4f} \n\t "
f"mae:{mae:.4f} \n\t "
f"gdl:{gdl:.4f}")
i += 1
evaluator.done()
def save_model(self):
from os.path import join
save_path = self.saver.save(self.sess,
join(c.SAVE_MODEL, "model.ckpt"),
global_step=self.global_step)
self.logger.info("Model saved in path: %s" % save_path)
def run_benchmark(self, iter, mode="Test"):
if mode == "Valid":
time_interval = c.RAINY_VALID
stride = 5
else:
time_interval = c.RAINY_TEST
stride = 1
test_iter = Iterator(time_interval=time_interval,
sample_mode="sequent",
seq_len=self._in_seq + self._out_seq,
stride=1)
evaluator = Evaluator(iter, length=self._out_seq, mode=mode)
i = 1
while not test_iter.use_up:
data, date_clip, *_ = test_iter.sample(batch_size=self._batch)
in_data = np.zeros(shape=(self._batch, self._in_seq, self._h, self._w, c.IN_CHANEL))
gt_data = np.zeros(shape=(self._batch, self._out_seq, self._h, self._w, 1))
if type(data) == type([]):
break
in_data[...] = data[:, :self._in_seq, :, :, :]
if c.IN_CHANEL == 3:
gt_data[...] = data[:, self._in_seq:self._in_seq + self._out_seq, :, :, :]
elif c.IN_CHANEL == 1:
gt_data[...] = data[:, self._in_seq:self._in_seq + self._out_seq, :, :, :]
else:
raise NotImplementedError
# in_date = date_clip[0][:c.IN_SEQ]
if c.NORMALIZE:
in_data = normalize_frames(in_data)
gt_data = normalize_frames(gt_data)
in_data = crop_img(in_data)
gt_data = crop_img(gt_data)
mse, mae, gdl, pred = self.g_model.valid_step(in_data, gt_data)
evaluator.evaluate(gt_data, pred)
self.logger.info(f"Iter {iter} {i}: \n\t mse:{mse} \n\t mae:{mae} \n\t gdl:{gdl}")
i += 1
if i % stride == 0:
if c.IN_CHANEL == 3:
in_data = in_data[:, :, :, :, 1:-1]
for b in range(self._batch):
predict_date = date_clip[b][self._in_seq - 1]
self.logger.info(f"Save {predict_date} results")
if mode == "Valid":
save_path = os.path.join(c.SAVE_VALID, str(iter), predict_date.strftime("%Y%m%d%H%M"))
else:
save_path = os.path.join(c.SAVE_TEST, str(iter), predict_date.strftime("%Y%m%d%H%M"))
path = os.path.join(save_path, "in")
save_png(in_data[b], path)
path = os.path.join(save_path, "pred")
save_png(pred[b], path)
path = os.path.join(save_path, "out")
save_png(gt_data[b], path)
evaluator.done()
self.notifier.eval(iter, evaluator.result_path)
def test(para, iter, mode="Test"):
model = AVGRunner(para, mode)
model.run_benchmark(iter, mode=mode)
if __name__ == '__main__':
FLAGS = tf.flags.FLAGS
device = FLAGS.device
config = FLAGS.config
paras = FLAGS.restore
mode = FLAGS.mode
step = FLAGS.iter
os.environ['CUDA_VISIBLE_DEVICES'] = device
cfg_from_file(config)
print(c.SAVE_PATH)
print(device, config, paras)
runner = AVGRunner(paras, mode)
try:
if mode == 'train':
runner.train()
else:
runner.run_benchmark(step, mode=mode)
except Exception as e:
runner.notifier.send("Something wrong\n" + str(e))
runner.logger.error(str(e))
else:
runner.notifier.send("Done")