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resize rainfall to be 1/2 size of current

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Starbeamrainbowlabs 2022-09-01 18:47:07 +01:00
parent 8a86728b54
commit 3e4128c0a8
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 34 additions and 23 deletions
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49
aimodel/src/lib/dataset/dataset.py
View file

@ -6,40 +6,47 @@ from loguru import logger
import tensorflow as tf import tensorflow as tf
from ..io.readfile import readfile
from .shuffle import shuffle from .shuffle import shuffle
# TO PARSE: # TO PARSE:
@tf.function def parse_item(metadata):
def parse_item(item): def parse_item_inner(item):
parsed = tf.io.parse_single_example(item, features={ parsed = tf.io.parse_single_example(item, features={
"rainfallradar": tf.io.FixedLenFeature([], tf.string), "rainfallradar": tf.io.FixedLenFeature([], tf.string),
"waterdepth": tf.io.FixedLenFeature([], tf.string) "waterdepth": tf.io.FixedLenFeature([], tf.string)
}) })
rainfall = tf.io.parse_tensor(parsed["rainfallradar"], out_type=tf.float32) rainfall = tf.io.parse_tensor(parsed["rainfallradar"], out_type=tf.float32)
water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32) water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32)
# [channels, width, height] → [width, height, channels] - ref ConvNeXt does not support data_format=channels_first # [channels, width, height] → [width, height, channels] - ref ConvNeXt does not support data_format=channels_first
rainfall = tf.transpose(rainfall, [1, 2, 0]) rainfall = tf.transpose(rainfall, [1, 2, 0])
# [width, height] → [width, height, channels] # [width, height] → [width, height, channels]
water = tf.expand_dims(water, axis=-1) water = tf.expand_dims(water, axis=-1)
# TODO: The shape of the resulting tensor can't be statically determined, so we need to reshape here rainfall = tf.image.resize(rainfall, tf.constant(metadata.waterdepth))
print("DEBUG:dataset ITEM rainfall:shape", rainfall.shape, "water:shape", water.shape)
# TODO: Any other additional parsing here, since multiple .map() calls are not optimal
return ((rainfall, water), tf.ones(1))
def make_dataset(filenames, compression_type="GZIP", parallel_reads_multiplier=1.5, shuffle_buffer_size=128, batch_size=64): # TODO: The shape of the resulting tensor can't be statically determined, so we need to reshape here
print("DEBUG:dataset ITEM rainfall:shape", rainfall.shape, "water:shape", water.shape)
# TODO: Any other additional parsing here, since multiple .map() calls are not optimal
return ((rainfall, water), tf.ones(1))
return tf.function(parse_item_inner)
def make_dataset(filenames, metadata, compression_type="GZIP", parallel_reads_multiplier=1.5, shuffle_buffer_size=128, batch_size=64):
return tf.data.TFRecordDataset(filenames, return tf.data.TFRecordDataset(filenames,
compression_type=compression_type, compression_type=compression_type,
num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier) num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier)
).shuffle(shuffle_buffer_size) \ ).shuffle(shuffle_buffer_size) \
.map(parse_item, num_parallel_calls=tf.data.AUTOTUNE) \ .map(parse_item(metadata), num_parallel_calls=tf.data.AUTOTUNE) \
.batch(batch_size) \ .batch(batch_size) \
.prefetch(tf.data.AUTOTUNE) .prefetch(tf.data.AUTOTUNE)
def dataset(dirpath_input, batch_size=64, train_percentage=0.8, parallel_reads_multiplier=1.5): def dataset(dirpath_input, batch_size=64, train_percentage=0.8, parallel_reads_multiplier=1.5):
filepath_meta = os.path.join(dirpath_input, "metadata.json")
filepaths = shuffle(list(filter( filepaths = shuffle(list(filter(
lambda filepath: str(filepath).endswith(".tfrecord.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 [ file.path for file in os.scandir(dirpath_input) ] # .path on a DirEntry object yields the absolute filepath
@ -50,8 +57,10 @@ def dataset(dirpath_input, batch_size=64, train_percentage=0.8, parallel_reads_m
filepaths_train = filepaths[:dataset_splitpoint] filepaths_train = filepaths[:dataset_splitpoint]
filepaths_validate = filepaths[dataset_splitpoint:] filepaths_validate = filepaths[dataset_splitpoint:]
dataset_train = make_dataset(filepaths_train, batch_size=batch_size, parallel_reads_multiplier=parallel_reads_multiplier) metadata = json.loads(readfile(filepath_meta))
dataset_validate = make_dataset(filepaths_validate, batch_size=batch_size, parallel_reads_multiplier=parallel_reads_multiplier)
dataset_train = make_dataset(filepaths_train, metadata, batch_size=batch_size, parallel_reads_multiplier=parallel_reads_multiplier)
dataset_validate = make_dataset(filepaths_validate, metadata, batch_size=batch_size, parallel_reads_multiplier=parallel_reads_multiplier)
return dataset_train, dataset_validate #, filepaths return dataset_train, dataset_validate #, filepaths

2
rainfallwrangler/src/lib/python/json2tfrecord.py
View file

@ -37,6 +37,8 @@ def convert(filepath_in, filepath_out):
rainfall = tf.constant(obj["rainfallradar"], dtype=tf.float32) rainfall = tf.constant(obj["rainfallradar"], dtype=tf.float32)
water = tf.constant(obj["waterdepth"], dtype=tf.float32) water = tf.constant(obj["waterdepth"], dtype=tf.float32)
# TODO: cast float32 → divide by max_value → clip 0-1 (or -1 to +1? I don't know)
### ###
## 3: Print shape definitions (required when parsing) ## 3: Print shape definitions (required when parsing)
### ###