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import json
import os
import numpy as np
from nowcasting.config import cfg
from nowcasting.helpers.visualization import save_hko_movie
from nowcasting.hko_iterator import HKOIterator
from nowcasting.hko_evaluation import HKOEvaluation
class HKOBenchmarkEnv(object):
"""The Benchmark environment for the HKO7 Dataset
There are two settings for the Benchmark, the "fixed" setting and the "online" setting.
In the "fixed" setting, pre-defined input sequences that have the same length will be
fed into the model for prediction.
This setting tests the model's ability to use the instant past to predict the future.
In the "online" setting, M frames will be given each time and the forecasting model
is required to predict the next K frames every stride steps.
If the begin_new_episode flag is turned on, a new episode has begun, which means that the current received images have no relationship with the previous images.
If the need_upload_prediction flag is turned on, the model is required to predict the
This setting tests both the model's ability to adapt in an online fashion and
the ability to capture the long-term dependency.
The input frame will be missing in some timestamps.
To run the benchmark in the fixed setting:
env = HKOBenchmarkEnv(...)
while not env.done:
# Get the observation
in_frame_dat, in_mask_dat, in_datetime_clips, out_datetime_clips, begin_new_episode =
env.get_observation(batch_size=batch_size)
# Running your algorithm to get the prediction
prediction = ...
# Upload prediction to the environment
env.upload_prediction(prediction)
"""
def __init__(self,
pd_path,
save_dir="hko7_benchmark",
mode="fixed"):
assert mode == "fixed" or mode == "online"
self._pd_path = pd_path
self._save_dir = save_dir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
self._mode = mode
self._out_seq_len = cfg.HKO.BENCHMARK.OUT_LEN
self._stride = cfg.HKO.BENCHMARK.STRIDE
if mode == "fixed":
self._in_seq_len = cfg.HKO.BENCHMARK.IN_LEN
else:
self._in_seq_len = cfg.HKO.BENCHMARK.STRIDE
self._hko_iter = HKOIterator(pd_path=pd_path,
sample_mode="sequent",
seq_len=self._in_seq_len + self._out_seq_len,
stride=self._stride)
self._stat_dict = self._get_benchmark_stat()
self._begin_new_episode = True
self._received_pred_seq_num = 0
self._need_upload_prediction = False
#TODO Save some predictions
self._save_seq_inds = set(np.arange(1, cfg.HKO.BENCHMARK.VISUALIZE_SEQ_NUM + 1) * \
(self._stat_dict['pred_seq_num'] //
cfg.HKO.BENCHMARK.VISUALIZE_SEQ_NUM))
self._all_eval = HKOEvaluation(seq_len=self._out_seq_len, use_central=False)
# The seq_ids that will be saved as a gif
# Holder of the inner data
self._in_frame_dat = None
self._in_mask_dat = None
self._in_datetime_clips = None
self._out_frame_dat = None
self._out_mask_dat = None
self._out_datetime_clips = None
def reset(self):
self._hko_iter.reset()
self._all_eval.clear_all()
self._begin_new_episode = True
self._received_pred_seq_num = 0
self._need_upload_prediction = False
@property
def _fingerprint(self):
pd_file_name = os.path.splitext(os.path.basename(self._pd_path))[0]
if self._mode == "fixed":
fingerprint = pd_file_name + "_in" + str(self._in_seq_len)\
+ "_out" + str(self._out_seq_len) + "_stride" + str(self._stride)\
+ "_" + self._mode
else:
fingerprint = pd_file_name + "_out" + str(self._out_seq_len)\
+ "_stride" + str(self._stride)\
+ "_" + self._mode
return fingerprint
@property
def _stat_filepath(self):
filename = self._fingerprint + ".json"
return os.path.join(cfg.HKO.BENCHMARK.STAT_PATH, filename)
def _get_benchmark_stat(self):
"""Get the general statistics of the benchmark
Returns
-------
stat_dict : dict
'pred_seq_num' --> Total number of predictions the model needs to make
"""
if os.path.exists(self._stat_filepath):
stat_dict = json.load(open(self._stat_filepath))
else:
seq_num = 0
episode_num = 0
episode_start_datetime = []
while not self._hko_iter.use_up:
if self._mode == "fixed":
datetime_clips, new_start =\
self._hko_iter.sample(batch_size=1024, only_return_datetime=True)
if len(datetime_clips) == 0:
continue
seq_num += len(datetime_clips)
episode_num += len(datetime_clips)
elif self._mode == "online":
datetime_clips, new_start = \
self._hko_iter.sample(batch_size=1, only_return_datetime=True)
if len(datetime_clips) == 0:
continue
episode_num += new_start
if new_start:
episode_start_datetime.append(datetime_clips[0][0].strftime('%Y%m%d%H%M'))
if self._stride != 1:
seq_num += 1
else:
seq_num += 1
print(self._fingerprint, seq_num, episode_num)
self._hko_iter.reset()
stat_dict = {'pred_seq_num': seq_num,
'episode_num': episode_num,
'episode_start_datetime': episode_start_datetime}
json.dump(stat_dict, open(self._stat_filepath, 'w'), indent=3)
return stat_dict
@property
def done(self):
return self._received_pred_seq_num >= self._stat_dict["pred_seq_num"]
def get_observation(self, batch_size=1):
"""
Parameters
----------
batch_size : int
Returns
-------
in_frame_dat : np.ndarray
Will be between 0 and 1
in_datetime_clips : list
out_datetime_clips : list
begin_new_episode : bool
need_upload_prediction : bool
"""
if self._mode == "online":
assert batch_size == 1
assert not self._need_upload_prediction
assert not self.done
assert not self._hko_iter.use_up
while True:
frame_dat, mask_dat, datetime_clips, new_start =\
self._hko_iter.sample(batch_size=batch_size, only_return_datetime=False)
if len(datetime_clips) == 0:
continue
else:
break
frame_dat = frame_dat.astype(np.float32) / 255.0
self._need_upload_prediction = True
if self._mode == "online":
self._begin_new_episode = new_start
if new_start and self._stride == 1:
self._need_upload_prediction = False
else:
self._begin_new_episode = True
self._in_datetime_clips = [ele[:self._in_seq_len] for ele in datetime_clips]
self._out_datetime_clips = [ele[self._in_seq_len:(self._in_seq_len +
self._out_seq_len)]
for ele in datetime_clips]
self._in_frame_dat = frame_dat[:self._in_seq_len, ...]
self._out_frame_dat = frame_dat[self._in_seq_len:(self._in_seq_len + self._out_seq_len),
...]
self._in_mask_dat = mask_dat[:self._in_seq_len, ...]
self._out_mask_dat = mask_dat[self._in_seq_len:(self._in_seq_len + self._out_seq_len), ...]
return self._in_frame_dat,\
self._in_datetime_clips,\
self._out_datetime_clips,\
self._begin_new_episode, \
self._need_upload_prediction
def upload_prediction(self, prediction):
"""
Parameters
----------
prediction : np.ndarray
"""
assert self._need_upload_prediction, "Must call get_observation first!" \
" Also, check the value of need_upload_predction" \
" after calling"
self._need_upload_prediction = False
received_seq_inds = range(self._received_pred_seq_num,
self._received_pred_seq_num + prediction.shape[1])
save_ind_set = set(received_seq_inds).intersection(self._save_seq_inds)
if len(save_ind_set) > 0:
assert len(save_ind_set) == 1
ind = save_ind_set.pop()
ind -= self._received_pred_seq_num
if not os.path.exists(os.path.join(self._save_dir, self._fingerprint)):
os.makedirs(os.path.join(self._save_dir, self._fingerprint))
print("Saving prediction videos to %s" % os.path.join(self._save_dir,
self._fingerprint))
save_hko_movie(im_dat=self._in_frame_dat[:, ind, 0, ...],
mask_dat=self._in_mask_dat[:, ind, 0, :, :],
datetime_list=self._in_datetime_clips[ind],
save_path=os.path.join(self._save_dir, self._fingerprint,
"%s_in.mp4" %
self._in_datetime_clips[ind][0]
.strftime('%Y%m%d%H%M')))
save_hko_movie(im_dat=self._out_frame_dat[:, ind, 0, ...],
mask_dat=self._out_mask_dat[:, ind, 0, :, :],
masked=False,
datetime_list=self._out_datetime_clips[ind],
save_path=os.path.join(self._save_dir, self._fingerprint,
"%s_out.mp4" %
self._in_datetime_clips[ind][0]
.strftime('%Y%m%d%H%M')))
save_hko_movie(im_dat=prediction[:, ind, 0, ...],
mask_dat=self._out_mask_dat[:, ind, 0, :, :],
masked=False,
datetime_list=self._out_datetime_clips[ind],
save_path=os.path.join(self._save_dir, self._fingerprint,
"%s_pred.mp4" %
self._in_datetime_clips[ind][0]
.strftime('%Y%m%d%H%M')))
self._received_pred_seq_num += prediction.shape[1]
if self._mode == "online":
if self._stride == 1:
assert not self._begin_new_episode
self._all_eval.update(gt=self._out_frame_dat,
pred=prediction,
mask=self._out_mask_dat,
start_datetimes=[ele[0] for ele in self._out_datetime_clips])
def print_stat_readable(self):
self._all_eval.print_stat_readable(prefix="Received:%d " %self._received_pred_seq_num)
def save_eval(self):
assert self._received_pred_seq_num == self._stat_dict['pred_seq_num'],\
"Must upload all the predictions to the testbed!"
print("Saving evaluation result to %s" %os.path.join(self._save_dir, self._fingerprint))
self._all_eval.save(prefix=os.path.join(self._save_dir, self._fingerprint, "eval_all"))
if __name__ == '__main__':
env_fixed = HKOBenchmarkEnv(pd_path=cfg.HKO_PD.RAINY_TEST, mode="fixed")
env_online = HKOBenchmarkEnv(pd_path=cfg.HKO_PD.RAINY_TEST, mode="online")
print("Fixed Rainy Test SeqNum:", env_fixed._stat_dict['pred_seq_num'])
print("Online Rainy Test SeqNum:", env_online._stat_dict['pred_seq_num'])
env_fixed = HKOBenchmarkEnv(pd_path=cfg.HKO_PD.RAINY_VALID, mode="fixed")
env_online = HKOBenchmarkEnv(pd_path=cfg.HKO_PD.RAINY_VALID, mode="online")
print("Fixed Rainy Valid SeqNum:", env_fixed._stat_dict['pred_seq_num'])
print("Online Rainy Valid SeqNum:", env_online._stat_dict['pred_seq_num'])
env_fixed = HKOBenchmarkEnv(pd_path=cfg.HKO_PD.ALL_15, mode="fixed")
env_online = HKOBenchmarkEnv(pd_path=cfg.HKO_PD.ALL_15, mode="online")
print("Fixed Rainy2015 ALL SeqNum:", env_fixed._stat_dict['pred_seq_num'])
print("Online Rainy2015 ALL SeqNum:", env_online._stat_dict['pred_seq_num'])
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