/
runner.py
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/
runner.py
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import logging
import os
import numpy as np
from model import Model
from iterator import Iterator
from clip_iterator import Clip_Iterator
import config as c
from utils import config_log, save_png
from utils import normalize_frames
from evaluation import Evaluator
class Runner(object):
def __init__(self, para_tuple=None, mode="train"):
self.para_tuple = para_tuple
self.model = Model(para_tuple, mode=mode)
if not para_tuple:
self.model.init_params()
def train(self):
step = 0
train_iter = Clip_Iterator(c.TRAIN_DIR_CLIPS)
while step < c.MAX_ITER:
data = train_iter.sample_clips(batch_size=c.BATCH_SIZE)
in_data = data[:, :c.IN_SEQ, ...]
if c.IN_CHANEL == 3:
gt_data = data[:, c.IN_SEQ:c.IN_SEQ + c.OUT_SEQ, :, :, 1:-1]
elif c.IN_CHANEL == 1:
gt_data = data[:, c.IN_SEQ:c.IN_SEQ + c.OUT_SEQ, ...]
else:
raise NotImplementedError
if c.NORMALIZE:
in_data = normalize_frames(in_data)
gt_data = normalize_frames(gt_data)
mse, mae, gdl = self.model.train_step(in_data, gt_data)
logging.info(f"Iter {step}: \n\t mse:{mse} \n\t mae:{mae} \n\t gdl:{gdl}")
if (step + 1) % c.SAVE_ITER == 0:
self.model.save_model()
if (step + 1) % c.VALID_ITER == 0:
self.valid_clips(step)
step += 1
def valid_clips(self, step):
test_iter = Clip_Iterator(c.VALID_DIR_CLIPS)
evaluator = Evaluator(step)
i = 0
for data in test_iter.sample_valid(c.BATCH_SIZE):
in_data = data[:, :c.IN_SEQ, ...]
if c.IN_CHANEL == 3:
gt_data = data[:, c.IN_SEQ:c.IN_SEQ + c.OUT_SEQ, :, :, 1:-1]
elif c.IN_CHANEL == 1:
gt_data = data[:, c.IN_SEQ:c.IN_SEQ + c.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.model.valid_step(in_data, gt_data)
evaluator.evaluate(gt_data, pred)
logging.info(f"Iter {step} {i}: \n\t mse:{mse} \n\t mae:{mae} \n\t gdl:{gdl}")
i += 1
evaluator.done()
def run_benchmark(self, iter, mode="Valid"):
if mode == "Valid":
time_interval = c.RAINY_VALID
stride = 20
else:
time_interval = c.RAINY_TEST
stride = 1
test_iter = Iterator(time_interval=time_interval,
sample_mode="sequent",
seq_len=c.IN_SEQ + c.OUT_SEQ,
stride=1)
evaluator = Evaluator(iter)
i = 1
while not test_iter.use_up:
data, date_clip, *_ = test_iter.sample(batch_size=c.BATCH_SIZE)
in_data = np.zeros(shape=(c.BATCH_SIZE, c.IN_SEQ, c.H, c.W, c.IN_CHANEL))
gt_data = np.zeros(shape=(c.BATCH_SIZE, c.OUT_SEQ, c.H, c.W, 1))
if type(data) == type([]):
break
in_data[...] = data[:, :c.IN_SEQ, ...]
if c.IN_CHANEL == 3:
gt_data[...] = data[:, c.IN_SEQ:c.IN_SEQ + c.OUT_SEQ, :, :, 1:-1]
elif c.IN_CHANEL == 1:
gt_data[...] = data[:, c.IN_SEQ:c.IN_SEQ + c.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)
mse, mae, gdl, pred = self.model.valid_step(in_data, gt_data)
evaluator.evaluate(gt_data, pred)
logging.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(c.BATCH_SIZE):
predict_date = date_clip[b][c.IN_SEQ]
logging.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()
def test(para, iter, mode="Test"):
model = Runner(para, mode=mode)
model.run_benchmark(iter, mode=mode)
if __name__ == '__main__':
config_log()
# paras = ("first_try", "94999")
paras = None
runner = Runner(paras)
runner.train()