train_mono_predict: initial implementation

aimodel/src/lib/dataset/dataset_mono.py

@@ -109,7 +109,7 @@ def dataset_mono(dirpath_input, train_percentage=0.8, **kwargs):
 	
 	return dataset_train, dataset_validate #, filepaths
 
-def dataset_mono_predict(dirpath_input, parallel_reads_multiplier=1.5, prefetch=True):
+def dataset_mono_predict(dirpath_input):
 	"""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!!
 	
@@ -126,10 +126,9 @@ def dataset_mono_predict(dirpath_input, parallel_reads_multiplier=1.5, prefetch=
 	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
+		shuffle=False, #even with shuffle=False we're not gonna get them all in the same order since we're reading in parallel
+		**kwargs
 	)
 
 if __name__ == "__main__":

aimodel/src/subcommands/train_mono_predict.py

@@ -0,0 +1,189 @@
+import io
+import json
+import os
+import sys
+import argparse
+import re
+
+from loguru import logger
+import tensorflow as tf
+from lib.dataset.batched_iterator import batched_iterator
+
+from lib.io.handle_open import handle_open
+from lib.ai.RainfallWaterMono import RainfallWaterMono
+from lib.dataset.dataset_mono import dataset_mono_predict
+from lib.io.find_paramsjson import find_paramsjson
+from lib.io.readfile import readfile
+from lib.vis.segmentation_plot import segmentation_plot
+
+
+MODE_JSONL = 1
+MODE_PNG = 2
+
+def parse_args():
+	parser = argparse.ArgumentParser(description="Output water depth image segmentation maps using a given pretrained mono 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 0. When using this start with 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. SETTING THIS WILL SCRAMBLE THE ORDER OF THE DATASET.")
+	parser.add_argument("--model-code", help="A description of the model used to predict the data. Will be inserted in the title of png plots.")
+	parser.add_argument("--log", help="Optional. If specified when the file extension is .jsonl[.gz], then this chooses what is logged. Specify a comma separated list of values. Possible values: rainfall_actual, water_actual, water_predict. Default: rainfall_actual,water_actual,water_predict.")
+	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 args.params == None:
+		logger.error("Error: Failed to find params.json. Please ensure it's either in the same directory as the checkpoint or 1 level above")
+		return
+	if (not hasattr(args, "read_multiplier")) or args.read_multiplier == None:
+		args.read_multiplier = 0
+	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 hasattr(args, "model_code")) or args.model_code == None:
+		args.model_code = ""
+	if (not hasattr(args, "log")) or args.log == None:
+		args.log = "rainfall_actual,water_actual,water_predict"
+	
+	args.log = args.log.strip().split(",")
+	
+	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)
+	
+	
+	model_params = json.loads(readfile(args.params))
+	ai = RainfallWaterMono.from_checkpoint(args.checkpoint, **model_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)
+	
+	
+	output_mode = MODE_PNG if args.output.endswith(".png") else MODE_JSONL
+	logger.info("Output mode is "+("PNG" if output_mode == MODE_PNG else "JSONL"))
+	logger.info(f"Records per file: {args.records_per_file}")
+	
+	if output_mode == MODE_JSONL:
+		do_jsonl(args, ai, dataset, model_params)
+	else:
+		do_png(args, ai, dataset, model_params)
+	
+	sys.stderr.write(">>> Complete\n")
+
+def do_png(args, ai, dataset, model_params):
+	if not os.path.exists(os.path.dirname(args.output)):
+		os.mkdir(os.path.dirname(args.output))
+	
+	i = 0
+	gen = batched_iterator(dataset, tensors_in_item=2, batch_size=model_params["batch_size"])
+	for item in gen:
+		rainfall, water = item
+		
+		water_predict_batch = ai.embed(rainfall)
+		water = tf.unstack(water, axis=0)
+		
+		i_batch = 0
+		for water_predict in water_predict_batch:
+			# [ width, height, softmax_probabilities ] → [ batch, width, height ]
+			water_predict = tf.math.argmax(water_predict, axis=-1) 
+			# [ width, height ]
+			water_actual = tf.squeeze(water[i_batch])
+			
+			segmentation_plot(
+				water_actual, water_predict,
+				args.model_code,
+				args.output.replace("+d", str(i))
+			)
+			
+			i_batch += 1
+			i += 1
+			
+			if i % 100 == 0:
+				sys.stderr.write(f"Processed {i} items\r")
+
+def do_jsonl(args, ai, dataset, model_params):
+	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 batch in batched_iterator(dataset, tensors_in_item=2, batch_size=model_params["batch_size"]):
+		rainfall_actual_batch, water_actual_batch = batch
+		
+		water_predict_batch = ai.embed(rainfall_actual_batch)
+		water_actual_batch = tf.unstack(water_actual_batch, axis=0)
+		rainfall_actual_batch = tf.unstack(rainfall_actual_batch, axis=0)
+		
+		i_batch = 0
+		for water_predict in water_predict_batch:
+			# [ width, height, softmax_probabilities ] → [ batch, width, height ]
+			# water_predict = tf.math.argmax(water_predict, axis=-1) 
+			# [ width, height ]
+			water_actual = tf.squeeze(water_actual_batch[i_batch])
+			
+			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)
+			
+			item_obj = {}
+			if "rainfall_actual" in args.log:
+				item_obj["rainfall_actual"] = rainfall_actual_batch[i_batch].numpy().tolist()
+			if "water_actual" in args.log:
+				item_obj["water_actual"] = water_actual.numpy().tolist()
+			if "water_predict" in args.log:
+				item_obj["water_predict"] = water_predict.numpy().tolist()
+			
+			handle.write(json.dumps(item_obj, 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_batch += 1
+			
+		
+		
+		i += 1
+		i_file += 1
+
+	handle.close()