sbrl-github/research-rainfallradar
1
0
Fork 0
mirror of https://github.com/sbrl/research-rainfallradar synced 2024-11-22 01:12:59 +00:00
Code Issues Projects Releases Packages Wiki Activity

Move dataset parsing function to the right place

Browse source
This commit is contained in:
Starbeamrainbowlabs 2022-08-10 17:24:55 +01:00
parent 50f214450f
commit b52c7f89a7
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 27 additions and 50 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

56
aimodel/src/lib/dataset/dataset.py
View file

@ -9,38 +9,35 @@ import tensorflow as tf
from shuffle import shuffle
def parse_line(line):
if tf.strings.length(line) <= 0:
return None
try:
# Yes, this is really what the function is called that converts a string tensor to a regular python string.....
obj = json.loads(line.numpy())
except:
logger.warn("Ignoring invalid line.")
return None
rainfall = tf.constant(obj.rainfallradar, dtype=tf.float32)
waterdepth = tf.constant(obj.waterdepth, dtype=tf.float32)
# Inputs, dummy label since we'll be using semi-supervised contrastive learning
return rainfall, waterdepth
def make_dataset(filepaths, batch_size, shuffle_buffer_size=128, parallel_reads_multiplier=2):
return tf.data.TextLineDataset(
filenames=tf.data.Dataset.from_tensor_slices(filepaths).shuffle(len(filepaths), reshuffle_each_iteration=True),
compression_type=tf.constant("GZIP"),
num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier) # iowait can cause issues - especially on Viper
# TODO: Get rid of this tf.py_function call somehow, because it acquires the Python Global Interpreter lock, which prevents more than 1 thread to run at a time, and .map() uses threads....
).map(tf.py_function(parse_line), num_parallel_calls=tf.data.AUTOTUNE) \
.filter(lambda item : item is not None) \
.shuffle(1) \
# TO PARSE:
@tf.function
def parse_item(item):
parsed = tf.io.parse_single_example(item, features={
"rainfallradar": tf.io.FixedLenFeature([], tf.string),
"waterdepth": tf.io.FixedLenFeature([], tf.string)
})
rainfall = tf.io.parse_tensor(parsed["rainfallradar"], out_type=tf.float32)
water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32)
# TODO: The shape of the resulting tensor can't be statically determined, so we need to reshape here
# TODO: Any other additional parsing here, since multiple .map() calls are not optimal
return rainfall, water
def make_dataset(filenames, compression_type="GZIP", parallel_reads_multiplier=1.5, shuffle_buffer_size=128, batch_size=64):
return tf.data.TFRecordDataset(filenames,
compression_type=compression_type,
num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier)
).shuffle(shuffle_buffer_size) \
.map(parse_item, num_parallel_calls=tf.data.AUTOTUNE) \
.batch(batch_size) \
.prefetch(tf.data.AUTOTUNE)
def dataset(dirpath_input, batch_size=64, train_percentage=0.8):
def dataset(dirpath_input, batch_size=64, train_percentage=0.8, parallel_reads_multiplier=1.5):
filepaths = shuffle(list(filter(
lambda filepath: str(filepath).endswith(".jsonl.gz"),
lambda filepath: str(filepath).endswith(".tfrecord.gz"),
[ file.path for file in os.scandir(dirpath_input) ] # .path on a DirEntry object yields the absolute filepath
)))
filepaths_count = len(filepaths)
@ -49,8 +46,9 @@ def dataset(dirpath_input, batch_size=64, train_percentage=0.8):
filepaths_train = filepaths[:dataset_splitpoint]
filepaths_validate = filepaths[dataset_splitpoint:]
dataset_train = make_dataset(filepaths_train, batch_size)
dataset_validate = make_dataset(filepaths_validate, batch_size)
dataset_train = make_dataset(filepaths_train, batch_size=batch_size, parallel_reads_multiplier=parallel_reads_multiplier)
dataset_validate = make_dataset(filepaths_validate, batch_size=batch_size, parallel_reads_multiplier=parallel_reads_multiplier)
return dataset_train, dataset_validate

21
rainfallwrangler/src/lib/python/json2tfrecord.py
View file

@ -11,27 +11,6 @@ if not os.environ.get("NO_SILENCE"):
silence_tensorflow()
import tensorflow as tf
# TO PARSE:
@tf.function
def parse_item(item):
parsed = tf.io.parse_single_example(item, features={
"rainfallradar": tf.io.FixedLenFeature([], tf.string),
"waterdepth": tf.io.FixedLenFeature([], tf.string)
})
rainfall = tf.io.parse_tensor(parsed["rainfallradar"], out_type=tf.float32)
water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32)
# TODO: The shape of the resulting tensor can't be statically determined, so we need to reshape here
# TODO: Any other additional parsing here, since multiple .map() calls are not optimal
return rainfall, water
def parse_example(filenames, compression_type="GZIP", parallel_reads_multiplier=1.5):
return tf.data.TFRecordDataset(filenames,
compression_type=compression_type,
num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier)
).map(parse_item, num_parallel_calls=tf.data.AUTOTUNE)
def parse_args():
parser = argparse.ArgumentParser(description="Convert a generated .jsonl.gz file to a .tfrecord.gz file")
parser.add_argument("--input", "-i", help="Path to the input file to convert.", required=True)