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import os
import pandas as pd
import numpy as np
import bisect
from nowcasting import image
from nowcasting.mask import *
from nowcasting.config import cfg
from nowcasting.utils import *
import math
import json
def encode_month(month):
"""Encode the month into a vector
Parameters
----------
month : np.ndarray
(...,) int, between 1 and 12
Returns
-------
ret : np.ndarray
(..., 2) float
"""
angle = 2 * np.pi * month/12.0
ret = np.empty(shape=month.shape + (2,), dtype=np.float32)
ret[..., 0] = np.cos(angle)
ret[..., 1] = np.sin(angle)
return ret
def decode_month(code):
"""Decode the month code back to the month value
Parameters
----------
code : np.ndarray
(..., 2) float
Returns
-------
month : np.ndarray
(...,) int
"""
assert code.shape[-1] == 2
flag = code[..., 1] >= 0
arccos_res = np.arccos(code[..., 0])
angle = flag * arccos_res + (1 - flag) * (2 * np.pi - arccos_res)
month = angle / (2.0 * np.pi) * 12.0
month = np.round(month).astype(int)
return month
def get_valid_datetime_set():
valid_datetime_set = pickle.load(open(cfg.HKO_VALID_DATETIME_PATH, 'rb'))
return valid_datetime_set
def get_exclude_mask():
with np.load(os.path.join(cfg.HKO_DATA_BASE_PATH, 'mask_dat.npz')) as dat:
exclude_mask = dat['exclude_mask'][:]
return exclude_mask
def convert_datetime_to_filepath(date_time):
"""Convert datetime to the filepath
Parameters
----------
date_time : datetime.datetime
Returns
-------
ret : str
"""
ret = os.path.join("%04d" %date_time.year,
"%02d" %date_time.month,
"%02d" %date_time.day,
'RAD%02d%02d%02d%02d%02d00.png'
%(date_time.year - 2000, date_time.month, date_time.day,
date_time.hour, date_time.minute))
ret = os.path.join(cfg.HKO_PNG_PATH, ret)
return ret
def convert_datetime_to_maskpath(date_time):
"""Convert datetime to path of the mask
Parameters
----------
date_time : datetime.datetime
Returns
-------
ret : str
"""
ret = os.path.join("%04d" %date_time.year,
"%02d" %date_time.month,
"%02d" %date_time.day,
'RAD%02d%02d%02d%02d%02d00.mask'
%(date_time.year - 2000, date_time.month, date_time.day,
date_time.hour, date_time.minute))
ret = os.path.join(cfg.HKO_MASK_PATH, ret)
return ret
class HKOSimpleBuffer(object):
def __init__(self, df, max_buffer_length, width, height):
self._df = df
self._max_buffer_length = max_buffer_length
assert self._df.size > self._max_buffer_length
self._width = width
self._height = height
def reset(self):
self._datetime_keys = self._df.index[:self._max_buffer_length]
self._load()
def _load(self):
paths = []
for i in range(self._datetime_keys.size):
paths.append(convert_datetime_to_filepath(self._datetime_keys[i]))
self._frame_dat = image.quick_read_frames(path_list=paths,
im_h=self._height,
im_w=self._width,
grayscale=True)
self._frame_dat = self._frame_dat.reshape((self._max_buffer_length, 1,
self._height, self._width))
self._noise_mask_dat = np.zeros((self._datetime_keys.size, 1,
self._height, self._width),
dtype=np.uint8)
def get(self, timestamps):
"""timestamps must be sorted
Parameters
----------
timestamps
Returns
-------
"""
if not (timestamps[0] in self._datetime_keys and timestamps[-1] in self._datetime_keys):
read_begin_ind = self._df.index[self._df.index.get_loc(timestamps[0])]
read_end_ind = min(read_begin_ind + self._max_buffer_length, self._df.size)
assert self._df.index[read_end_ind - 1] >= timestamps[-1]
self._datetime_keys = self._df.index[read_begin_ind:read_end_ind]
self._load()
begin_ind = self._datetime_keys.get_loc(timestamps[0])
end_ind = self._datetime_keys.get_loc(timestamps[-1]) + 1
return self._frame_dat[begin_ind:end_ind, :, :, :],\
self._noise_mask_dat[begin_ind:end_ind, :, :, :]
def pad_hko_dat(frame_dat, mask_dat, batch_size):
if frame_dat.shape[1] < batch_size:
ret_frame_dat = np.zeros(shape=(frame_dat.shape[0], batch_size,
frame_dat.shape[2], frame_dat.shape[3], frame_dat.shape[4]),
dtype=frame_dat.dtype)
ret_mask_dat = np.zeros(shape=(mask_dat.shape[0], batch_size,
mask_dat.shape[2], mask_dat.shape[3], mask_dat.shape[4]),
dtype=mask_dat.dtype)
ret_frame_dat[:, :frame_dat.shape[1], ...] = frame_dat
ret_mask_dat[:, :frame_dat.shape[1], ...] = mask_dat
return ret_frame_dat, ret_mask_dat, frame_dat.shape[1]
else:
return frame_dat, mask_dat, batch_size
_exclude_mask = get_exclude_mask()
def precompute_mask(img):
if img.dtype == np.uint8:
threshold = round(cfg.HKO.ITERATOR.FILTER_RAINFALL_THRESHOLD * 255.0)
else:
threshold = cfg.HKO.ITERATOR.FILTER_RAINFALL_THRESHOLD
mask = np.zeros_like(img, dtype=bool)
mask[:] = np.broadcast_to((1 - _exclude_mask).astype(bool), shape=img.shape)
mask[np.logical_and(img < threshold,
img > 0)] = 0
return mask
class HKOIterator(object):
"""The iterator for HKO-7 dataset
"""
def __init__(self, pd_path, sample_mode, seq_len=30,
max_consecutive_missing=2, begin_ind=None, end_ind=None,
stride=None, width=None, height=None, base_freq='6min'):
"""Random sample: sample a random clip that will not violate the max_missing frame_num criteria
Sequent sample: sample a clip from the beginning of the time.
Everytime, the clips from {T_begin, T_begin + 6min, ..., T_begin + (seq_len-1) * 6min} will be used
The begin datetime will move forward by adding stride: T_begin += 6min * stride
Once the clips violates the maximum missing number criteria, the starting
point will be moved to the next datetime that does not violate the missing_frame criteria
Parameters
----------
pd_path : str
path of the saved pandas dataframe
sample_mode : str
Can be "random" or "sequent"
seq_len : int
max_consecutive_missing : int
The maximum consecutive missing frames
begin_ind : int
Index of the begin frame
end_ind : int
Index of the end frame
stride : int or None, optional
width : int or None, optional
height : int or None, optional
base_freq : str, optional
"""
if width is None:
width = cfg.HKO.ITERATOR.WIDTH
if height is None:
height = cfg.HKO.ITERATOR.HEIGHT
self._df = pd.read_pickle(pd_path)
self.set_begin_end(begin_ind=begin_ind, end_ind=end_ind)
self._df_index_set = frozenset([self._df.index[i] for i in range(self._df.size)])
self._exclude_mask = get_exclude_mask()
self._seq_len = seq_len
self._width = width
self._height = height
self._stride = stride
self._max_consecutive_missing = max_consecutive_missing
self._base_freq = base_freq
self._base_time_delta = pd.Timedelta(base_freq)
assert sample_mode in ["random", "sequent"], "Sample mode=%s is not supported" %sample_mode
self.sample_mode = sample_mode
if sample_mode == "sequent":
assert self._stride is not None
self._current_datetime = self.begin_time
self._buffer_mult = 6
self._buffer_datetime_keys = None
self._buffer_frame_dat = None
self._buffer_mask_dat = None
else:
self._max_buffer_length = None
def set_begin_end(self, begin_ind=None, end_ind=None):
self._begin_ind = 0 if begin_ind is None else begin_ind
self._end_ind = self.total_frame_num - 1 if end_ind is None else end_ind
@property
def total_frame_num(self):
return self._df.size
@property
def begin_time(self):
return self._df.index[self._begin_ind]
@property
def end_time(self):
return self._df.index[self._end_ind]
@property
def use_up(self):
if self.sample_mode == "random":
return False
else:
return self._current_datetime > self.end_time
def _next_exist_timestamp(self, timestamp):
next_ind = bisect.bisect_right(self._df.index, timestamp)
if next_ind >= self._df.size:
return None
else:
return self._df.index[bisect.bisect_right(self._df.index, timestamp)]
def _is_valid_clip(self, datetime_clip):
"""Check if the given datetime_clip is valid
Parameters
----------
datetime_clip :
Returns
-------
ret : bool
"""
missing_count = 0
for i in range(len(datetime_clip)):
if datetime_clip[i] not in self._df_index_set:
missing_count += 1
if missing_count > self._max_consecutive_missing or\
missing_count >= len(datetime_clip):
return False
else:
missing_count = 0
return True
def _load_frames(self, datetime_clips):
assert isinstance(datetime_clips, list)
for clip in datetime_clips:
assert len(clip) == self._seq_len
batch_size = len(datetime_clips)
frame_dat = np.zeros((self._seq_len, batch_size, 1, self._height, self._width),
dtype=np.uint8)
mask_dat = np.zeros((self._seq_len, batch_size, 1, self._height, self._width),
dtype=bool)
if self.sample_mode == "random":
paths = []
mask_paths = []
hit_inds = []
miss_inds = []
for i in range(self._seq_len):
for j in range(batch_size):
timestamp = datetime_clips[j][i]
if timestamp in self._df_index_set:
paths.append(convert_datetime_to_filepath(datetime_clips[j][i]))
mask_paths.append(convert_datetime_to_maskpath(datetime_clips[j][i]))
hit_inds.append([i, j])
else:
miss_inds.append([i, j])
hit_inds = np.array(hit_inds, dtype=int)
all_frame_dat = image.quick_read_frames(path_list=paths,
im_h=self._height,
im_w=self._width,
grayscale=True)
all_mask_dat = quick_read_masks(mask_paths)
frame_dat[hit_inds[:, 0], hit_inds[:, 1], :, :, :] = all_frame_dat
mask_dat[hit_inds[:, 0], hit_inds[:, 1], :, :, :] = all_mask_dat
else:
# Get the first_timestamp and the last_timestamp in the datetime_clips
first_timestamp = datetime_clips[-1][-1]
last_timestamp = datetime_clips[0][0]
for i in range(self._seq_len):
for j in range(batch_size):
timestamp = datetime_clips[j][i]
if timestamp in self._df_index_set:
first_timestamp = min(first_timestamp, timestamp)
last_timestamp = max(last_timestamp, timestamp)
if self._buffer_datetime_keys is None or\
not (first_timestamp in self._buffer_datetime_keys
and last_timestamp in self._buffer_datetime_keys):
read_begin_ind = self._df.index.get_loc(first_timestamp)
read_end_ind = self._df.index.get_loc(last_timestamp) + 1
read_end_ind = min(read_begin_ind +
self._buffer_mult * (read_end_ind - read_begin_ind),
self._df.size)
self._buffer_datetime_keys = self._df.index[read_begin_ind:read_end_ind]
# Fill in the buffer
paths = []
mask_paths = []
for i in range(self._buffer_datetime_keys.size):
paths.append(convert_datetime_to_filepath(self._buffer_datetime_keys[i]))
mask_paths.append(convert_datetime_to_maskpath(self._buffer_datetime_keys[i]))
self._buffer_frame_dat = image.quick_read_frames(path_list=paths,
im_h=self._height,
im_w=self._width,
grayscale=True)
self._buffer_mask_dat = quick_read_masks(mask_paths)
for i in range(self._seq_len):
for j in range(batch_size):
timestamp = datetime_clips[j][i]
if timestamp in self._df_index_set:
assert timestamp in self._buffer_datetime_keys
ind = self._buffer_datetime_keys.get_loc(timestamp)
frame_dat[i, j, :, :, :] = self._buffer_frame_dat[ind, :, :, :]
mask_dat[i, j, :, :, :] = self._buffer_mask_dat[ind, :, :, :]
return frame_dat, mask_dat
def reset(self, begin_ind=None, end_ind=None):
assert self.sample_mode == "sequent"
self.set_begin_end(begin_ind=begin_ind, end_ind=end_ind)
self._current_datetime = self.begin_time
def random_reset(self):
assert self.sample_mode == "sequent"
self.set_begin_end(begin_ind=np.random.randint(0,
self.total_frame_num -
5 * self._seq_len),
end_ind=None)
self._current_datetime = self.begin_time
def check_new_start(self):
assert self.sample_mode == "sequent"
datetime_clip = pd.date_range(start=self._current_datetime,
periods=self._seq_len,
freq=self._base_freq)
if self._is_valid_clip(datetime_clip):
return self._current_datetime == self.begin_time
else:
return True
def sample(self, batch_size, only_return_datetime=False):
"""Sample a minibatch from the hko7 dataset based on the given type and pd_file
Parameters
----------
batch_size : int
Batch size
only_return_datetime : bool
Whether to only return the datetimes
Returns
-------
frame_dat : np.ndarray
Shape: (seq_len, valid_batch_size, 1, height, width)
mask_dat : np.ndarray
Shape: (seq_len, valid_batch_size, 1, height, width)
datetime_clips : list
length should be valid_batch_size
new_start : bool
"""
if self.sample_mode == 'sequent':
if self.use_up:
raise ValueError("The HKOIterator has been used up!")
datetime_clips = []
new_start = False
for i in range(batch_size):
while not self.use_up:
datetime_clip = pd.date_range(start=self._current_datetime,
periods=self._seq_len,
freq=self._base_freq)
if self._is_valid_clip(datetime_clip):
new_start = new_start or (self._current_datetime == self.begin_time)
datetime_clips.append(datetime_clip)
self._current_datetime += self._stride * self._base_time_delta
break
else:
new_start = True
self._current_datetime =\
self._next_exist_timestamp(timestamp=self._current_datetime)
if self._current_datetime is None:
# This indicates that there is no timestamp left,
# We point the current_datetime to be the next timestamp of self.end_time
self._current_datetime = self.end_time + self._base_time_delta
break
continue
new_start = None if batch_size != 1 else new_start
if only_return_datetime:
return datetime_clips, new_start
else:
assert only_return_datetime is False
datetime_clips = []
new_start = None
for i in range(batch_size):
while True:
rand_ind = np.random.randint(0, self._df.size, 1)[0]
random_datetime = self._df.index[rand_ind]
datetime_clip = pd.date_range(start=random_datetime,
periods=self._seq_len,
freq=self._base_freq)
if self._is_valid_clip(datetime_clip):
datetime_clips.append(datetime_clip)
break
frame_dat, mask_dat = self._load_frames(datetime_clips=datetime_clips)
return frame_dat, mask_dat, datetime_clips, new_start
# Simple test for the performance of the HKO iterator.
if __name__ == '__main__':
np.random.seed(123)
import time
import cProfile, pstats
from nowcasting.config import cfg
from nowcasting.helpers.visualization import save_hko_gif, save_hko_movie
minibatch_size = 32
seq_len = 30
train_hko_iter = HKOIterator(pd_path=cfg.HKO_PD.RAINY_TRAIN,
sample_mode="random",
seq_len=seq_len)
valid_hko_iter = HKOIterator(pd_path=cfg.HKO_PD.RAINY_VALID,
sample_mode="sequent",
seq_len=seq_len,
stride=5)
test_hko_iter = HKOIterator(pd_path=cfg.HKO_PD.RAINY_TEST,
sample_mode="sequent",
seq_len=seq_len,
stride=5)
repeat_time = 3
pr = cProfile.Profile()
pr.enable()
begin = time.time()
for i in range(repeat_time):
sample_sequence, sample_mask, sample_datetime_clips, new_start =\
train_hko_iter.sample(batch_size=minibatch_size)
end = time.time()
pr.disable()
ps = pstats.Stats(pr).sort_stats('cumulative')
ps.print_stats(20)
print("Train Data Sample FPS: %f" % (minibatch_size * seq_len
* repeat_time / float(end - begin)))
begin = time.time()
for i in range(repeat_time):
sample_sequence, sample_mask, sample_datetimes, new_start =\
valid_hko_iter.sample(batch_size=minibatch_size)
end = time.time()
print("Valid Data Sample FPS: %f" % (minibatch_size * seq_len
* repeat_time / float(end - begin)))
begin = time.time()
for i in range(repeat_time):
sample_sequence, sample_mask, sample_datetimes, new_start =\
test_hko_iter.sample(batch_size=minibatch_size)
end = time.time()
print("Test Data Sample FPS: %f" %(minibatch_size * seq_len
* repeat_time / float(end-begin)))
code = encode_month(np.arange(1, 13))
month = decode_month(code)
print(code)
print(month.T)
train_time = 0
for i in range(30):
train_batch, train_mask, sample_datetimes, new_start = \
train_hko_iter.sample(batch_size=minibatch_size)
name_str = 'train_' + str(i) + '_' + sample_datetimes[0][0].strftime('%Y%m%d%H%M')
save_hko_movie(train_batch[:, 0, 0, :, :],
sample_datetimes[0],
train_mask[:, 0, 0, :, :],
masked=False,
save_path=name_str + '.mp4')
tic = time.time()
save_hko_movie(train_batch[:, 0, 0, :, :],
sample_datetimes[0],
train_mask[:, 0, 0, :, :],
masked=True,
save_path=name_str + '_filtered.mp4')
toc = time.time()
save_hko_movie(train_mask[:, 0, 0, :, :].astype(np.uint8) * 255,
sample_datetimes[0],
None,
masked=False,
save_path=name_str + '_mask.mp4')
print('train, time:', toc - tic)
valid_time = 0
while not valid_hko_iter.use_up:
valid_batch, valid_mask, sample_datetimes, new_start =\
valid_hko_iter.sample(batch_size=minibatch_size)
if valid_batch.shape[1] == 0:
break
name_str = 'valid_' + str(valid_time) + '_' + sample_datetimes[0][0].strftime('%Y%m%d%H%M')
save_hko_movie(valid_batch[:, 0, 0, :, :],
sample_datetimes[0],
valid_mask[:, 0, 0, :, :],
masked=False,
save_path=name_str + '.mp4')
tic = time.time()
save_hko_movie(valid_batch[:, 0, 0, :, :],
sample_datetimes[0],
valid_mask[:, 0, 0, :, :],
masked=True,
save_path=name_str + '_filtered.mp4')
toc = time.time()
save_hko_movie(valid_mask[:, 0, 0, :, :].astype(np.uint8) * 255,
sample_datetimes[0],
None,
masked=False,
save_path=name_str + '_mask.mp4')
print('valid, time:', toc - tic)
print(valid_batch.shape[1])
valid_time += 1
print(valid_time)
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