From 8ac5159adc4c6e152d75dcccc60d06de03a78c4c Mon Sep 17 00:00:00 2001 From: Starbeamrainbowlabs Date: Fri, 11 Nov 2022 18:23:50 +0000 Subject: [PATCH] dataset_mono: simplify param passing, onehot+threshold water depth data --- aimodel/src/lib/dataset/dataset_mono.py | 124 ++++++++++++++++++++++++ 1 file changed, 124 insertions(+) create mode 100644 aimodel/src/lib/dataset/dataset_mono.py diff --git a/aimodel/src/lib/dataset/dataset_mono.py b/aimodel/src/lib/dataset/dataset_mono.py new file mode 100644 index 0000000..e392b59 --- /dev/null +++ b/aimodel/src/lib/dataset/dataset_mono.py @@ -0,0 +1,124 @@ +import os +import math +import json + +from loguru import logger + +import tensorflow as tf + +from lib.dataset.read_metadata import read_metadata + +from ..io.readfile import readfile +from .shuffle import shuffle + + + +# TO PARSE: +def parse_item(metadata, shape_water_desired=[100,100], water_threshold=0.1, water_bins=2): + water_width_source, water_height_source = metadata["waterdepth"] + water_width_target, water_height_target = shape_water_desired + water_offset_x = math.ceil((water_width_source - water_width_target) / 2) + water_offset_y = math.ceil((water_height_source - water_height_target) / 2) + def parse_item_inner(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) + # [channels, width, height] → [width, height, channels] - ref ConvNeXt does not support data_format=channels_first + + rainfall = tf.reshape(rainfall, tf.constant(metadata["rainfallradar"], dtype=tf.int32)) + water = tf.reshape(water, tf.constant(metadata["waterdepth"], dtype=tf.int32)) + + rainfall = tf.transpose(rainfall, [1, 2, 0]) # channels_first → channels_last + # rainfall = tf.image.resize(rainfall, tf.cast(tf.constant(metadata["rainfallradar"]) / 2, dtype=tf.int32)) + + water = tf.expand_dims(water, axis=-1) # [width, height] → [width, height, channels] + water = tf.image.crop_to_bounding_box(water, water_offset_x, water_offset_y, water_width_target, water_height_target) + + + water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.int32) + water = tf.one_hot(water, water_bins, axis=-1, dtype=tf.int32) + + + print("DEBUG:dataset ITEM rainfall:shape", rainfall.shape, "water:shape", water.shape) + return rainfall, water + + return tf.function(parse_item_inner) + +def make_dataset(filepaths, compression_type="GZIP", parallel_reads_multiplier=1.5, shuffle_buffer_size=128, batch_size=64, prefetch=True, shuffle=True, **kwargs): + if "NO_PREFETCH" in os.environ: + logger.info("disabling data prefetching.") + + dataset = tf.data.TFRecordDataset(filepaths, + compression_type=compression_type, + num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier) if parallel_reads_multiplier > 0 else None + ) + if shuffle: + dataset = dataset.shuffle(shuffle_buffer_size) + dataset = dataset.map(parse_item(**kwargs), num_parallel_calls=tf.data.AUTOTUNE) + + if batch_size != None: + dataset = dataset.batch(batch_size, drop_remainder=True) + if prefetch: + dataset = dataset.prefetch(0 if "NO_PREFETCH" in os.environ else tf.data.AUTOTUNE) + + return dataset + + +def get_filepaths(dirpath_input, do_shuffle=True): + result = list(filter( + 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 + )) + if do_shuffle: + result = shuffle(result) + else: + result = sorted(result, key=lambda filepath: int(os.path.basename(filepath).split(".", 1)[0])) + + return result + +def dataset_mono(dirpath_input, train_percentage=0.8, **kwargs): + filepaths = get_filepaths(dirpath_input) + filepaths_count = len(filepaths) + dataset_splitpoint = math.floor(filepaths_count * train_percentage) + + filepaths_train = filepaths[:dataset_splitpoint] + filepaths_validate = filepaths[dataset_splitpoint:] + + metadata = read_metadata(dirpath_input) + + dataset_train = make_dataset(filepaths_train, metadata=metadata, **kwargs) + dataset_validate = make_dataset(filepaths_validate, metadata=metadata, **kwargs) + + return dataset_train, dataset_validate #, filepaths + +def dataset_mono_predict(dirpath_input, parallel_reads_multiplier=1.5, prefetch=True): + """Creates a tf.data.Dataset() for prediction using the contrastive learning model. + Note that this WILL MANGLE THE ORDERING if you set parallel_reads_multiplier to anything other than 0!! + + Args: + dirpath_input (string): The path to the directory containing the input (.tfrecord.gz) files + parallel_reads_multiplier (float, optional): The number of files to read in parallel. Defaults to 1.5. + prefetch (bool, optional): Whether to prefetch data into memory or not. Defaults to True. + + Returns: + tf.data.Dataset: A tensorflow Dataset for the given input files. + """ + filepaths = get_filepaths(dirpath_input, do_shuffle=False) if os.path.isdir(dirpath_input) else [ dirpath_input ] + + return make_dataset( + filepaths=filepaths, + metadata=read_metadata(dirpath_input), + parallel_reads_multiplier=parallel_reads_multiplier, + batch_size=None, + prefetch=prefetch, + shuffle=False #even with shuffle=False we're not gonna get them all in the same order since we're reading in parallel + ) + +if __name__ == "__main__": + ds_train, ds_validate = dataset_mono("/mnt/research-data/main/rainfallwater_records-viperfinal/") + for thing in ds_validate(): + as_str = str(thing) + print(thing[:200]) \ No newline at end of file