start implementing driver for train_predict, but not finished yet

aimodel/src/lib/dataset/dataset.py

@@ -52,7 +52,7 @@ def make_dataset(filepaths, metadata, shape_watch_desired=[100,100], compression
 	
 	dataset = tf.data.TFRecordDataset(filepaths,
 		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) if parallel_reads_multiplier > 0 else None
 	)
 	if shuffle:
 		dataset = dataset.shuffle(shuffle_buffer_size)
@@ -88,6 +88,17 @@ def dataset(dirpath_input, batch_size=64, train_percentage=0.8, parallel_reads_m
 	return dataset_train, dataset_validate #, filepaths
 
 def dataset_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) if os.path.isdir(dirpath_input) else [ dirpath_input ]
 	
 	return make_dataset(

aimodel/src/lib/dataset/dataset_segmenter.py

@@ -49,7 +49,7 @@ def make_dataset(filepaths, metadata, shape_water_desired=[100,100], water_thres
 	
 	dataset = tf.data.TFRecordDataset(filepaths,
 		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) if parallel_reads_multiplier > 0 else None
 	)
 	if shuffle:
 		dataset = dataset.shuffle(shuffle_buffer_size)
@@ -85,6 +85,18 @@ def dataset_segmenter(dirpath_input, batch_size=64, train_percentage=0.8, parall
 	return dataset_train, dataset_validate #, filepaths
 
 def dataset_predict(dirpath_input, parallel_reads_multiplier=1.5, prefetch=True, water_threshold=0.1):
+	"""Creates a tf.data.Dataset() for prediction using the image segmentation head 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.
+		water_threshold (float, optional): The water depth threshold to consider cells to contain water, in metres. Defaults to 0.1.
+
+	Returns:
+		tf.data.Dataset: A tensorflow Dataset for the given input files.
+	"""
 	filepaths = get_filepaths(dirpath_input) if os.path.isdir(dirpath_input) else [ dirpath_input ]
 	
 	return make_dataset(

aimodel/src/subcommands/train_predict.py

@@ -0,0 +1,121 @@
+import io
+import json
+import os
+import sys
+import argparse
+import re
+
+from loguru import logger
+import tensorflow as tf
+import numpy as np
+
+from lib.io.writefile import writefile
+from lib.io.handle_open import handle_open
+from lib.ai.RainfallWaterContraster import RainfallWaterContraster
+from lib.dataset.dataset import dataset_predict
+from lib.io.find_paramsjson import find_paramsjson
+from lib.io.readfile import readfile
+from lib.vis.embeddings import vis_embeddings
+
+
+MODE_JSONL = 1
+MODE_PNG = 2
+
+def parse_args():
+	parser = argparse.ArgumentParser(description="Output water depth image segmentation maps using a given pretrained image segmentation model.")
+	# parser.add_argument("--config", "-c", help="Filepath to the TOML config file to load.", required=True)
+	parser.add_argument("--input", "-i", help="Path to input directory containing the .tfrecord(.gz) files to predict for. If a single file is passed instead, then only that file will be converted.", required=True)
+	parser.add_argument("--output", "-o", help="Path to output file to write output to. If the file extension .png is used instead of .jsonl.gz, then an image is written instead (+d is replaced with the item index).")
+	parser.add_argument("--records-per-file", help="Optional, only valid with the .jsonl.gz file extension. If specified, this limits the number of records written to each file. When using this option, you MUST have the string '+d' (without quotes) somewhere in your output filepath.", type=int)
+	parser.add_argument("--checkpoint", "-c", help="Checkpoint file to load model weights from.", required=True)
+	parser.add_argument("--params", "-p", help="Optional. The file containing the model hyperparameters (usually called 'params.json'). If not specified, it's location will be determined automatically.")
+	parser.add_argument("--reads-multiplier", help="Optional. The multiplier for the number of files we should read from at once. Defaults to 1.5, which means read ceil(NUMBER_OF_CORES * 1.5). Set to a higher number of systems with high read latency to avoid starving the GPU of data.")
+	
+	return parser
+
+def run(args):
+	
+	# Note that we do NOT check to see if the checkpoint file exists, because Tensorflow/Keras requires that we pass the stem instead of the actual index file..... :-/
+	
+	if (not hasattr(args, "params")) or args.params == None:
+		args.params = find_paramsjson(args.checkpoint)
+	if (not hasattr(args, "read_multiplier")) or args.read_multiplier == None:
+		args.read_multiplier = 1.5
+	if (not hasattr(args, "records_per_file")) or args.records_per_file == None:
+		args.records_per_file = 0 # 0 = unlimited
+	if (not hasattr(args, "output")) or args.output == None:
+		args.output = "-"
+	
+	if not os.path.exists(args.params):
+		raise Exception(f"Error: The specified filepath params.json hyperparameters ('{args.params}) does not exist.")
+	if not os.path.exists(args.checkpoint):
+		raise Exception(f"Error: The specified filepath to the checkpoint to load ('{args.checkpoint}) does not exist.")
+	
+	if args.records_per_file > 0 and args.output.endswith(".jsonl.gz"):
+		dirpath_output=os.path.dirname(args.output)
+		if not os.path.exists(dirpath_output):
+			os.mkdir(dirpath_output)
+	
+	
+	ai = RainfallWaterContraster.from_checkpoint(args.checkpoint, **json.loads(readfile(args.params)))
+	
+	sys.stderr.write(f"\n\n>>> This is TensorFlow {tf.__version__}\n\n\n")
+	
+	# Note that if using a directory of input files, the output order is NOT GUARANTEED TO BE THE SAME. In fact, it probably won't be.
+	dataset = dataset_predict(
+		dirpath_input=args.input,
+		parallel_reads_multiplier=args.read_multiplier
+	)
+	
+	# for items in dataset_train.repeat(10):
+	# 	print("ITEMS", len(items))
+	# 	print("LEFT", [ item.shape for item in items[0] ])
+	# print("ITEMS DONE")
+	# exit(0)
+	
+	
+	
+	logger.info("Output mode is "+("PNG" if output_mode == MODE_PNG else "JSONL"))
+	logger.info(f"Records per file: {args.records_per_file}")
+	
+	
+	do_jsonl(args, ai, dataset, write_mode)
+	
+	sys.stderr.write(">>> Complete\n")
+
+def do_jsonl(args, ai, dataset):
+	output_mode = MODE_PNG if args.output.endswith(".png") else MODE_JSONL
+	write_mode = "wt" if args.output.endswith(".gz") else "w"
+	
+	handle = sys.stdout
+	filepath_metadata = None
+	if args.output != "-":
+		handle = handle_open(
+			args.output if args.records_per_file <= 0 else args.output.replace("+d", str(0)),
+			write_mode
+		)
+		filepath_metadata = os.path.join(os.path.dirname(args.output), "metadata.json")
+	
+	logger.info(f"filepath_output: {args.output}")
+	logger.info(f"filepath_params: {filepath_metadata}")
+	
+	i = 0
+	i_file = i
+	files_done = 0
+	for step_rainfall, step_water in ai.embed(dataset):
+		if args.records_per_file > 0 and i_file > args.records_per_file:
+			files_done += 1
+			i_file = 0
+			handle.close()
+			logger.info(f"PROGRESS:file {files_done}")
+			handle = handle_open(args.output.replace("+d", str(files_done+1)), write_mode, handle_mode=output_mode)
+		
+		handle.write(json.dumps(step_rainfall.numpy().tolist(), separators=(',', ':'))+"\n") # Ref https://stackoverflow.com/a/64710892/1460422
+		
+		if i == 0 or i % 100 == 0:
+			sys.stderr.write(f"[pretrain:predict] STEP {i}\r")
+		
+		i += 1
+		i_file += 1
+
+	handle.close()