dataset_mono: simplify param passing, onehot+threshold water depth data

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])