implement ability to embed & plot pretrained embeddings

aimodel/src/lib/ai/RainfallWaterContraster.py

@@ -89,7 +89,6 @@ class RainfallWaterContraster(object):
 		)
 	
 	def embed(self, dataset):
-		result = []
 		i_batch = -1
 		for batch in dataset:
 			i_batch += 1
@@ -98,10 +97,9 @@ class RainfallWaterContraster(object):
 			
 			rainfall = tf.unstack(rainfall, axis=0)
 			water = tf.unstack(water, axis=0)
+			for step in zip(rainfall, water):
+				yield step
 		
-			result.extend(zip(rainfall, water))
-		
-		return result
 	
 	def embed_rainfall(self, dataset):
 		result = []

aimodel/src/lib/dataset/dataset.py

@@ -43,10 +43,10 @@ def parse_item(metadata, shape_water_desired):
 	
 	return tf.function(parse_item_inner)
 
-def make_dataset(filenames, metadata, shape_watch_desired=[100,100], compression_type="GZIP", parallel_reads_multiplier=1.5, shuffle_buffer_size=128, batch_size=64):
+def make_dataset(filepaths, metadata, shape_watch_desired=[100,100], compression_type="GZIP", parallel_reads_multiplier=1.5, shuffle_buffer_size=128, batch_size=64):
 	if "NO_PREFETCH" in os.environ:
 		logger.info("disabling data prefetching.")
-	return tf.data.TFRecordDataset(filenames,
+	return tf.data.TFRecordDataset(filepaths,
 		compression_type=compression_type,
 		num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier)
 	).shuffle(shuffle_buffer_size) \
@@ -55,11 +55,14 @@ def make_dataset(filenames, metadata, shape_watch_desired=[100,100], compression
 		.prefetch(0 if "NO_PREFETCH" in os.environ else tf.data.AUTOTUNE)
 
 
-def dataset(dirpath_input, batch_size=64, train_percentage=0.8, parallel_reads_multiplier=1.5):
+def get_filepaths(dirpath_input):
-	filepaths = shuffle(list(filter(
+	return shuffle(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
 	)))
+
+def dataset(dirpath_input, batch_size=64, train_percentage=0.8, parallel_reads_multiplier=1.5):
+	filepaths = get_filepaths(dirpath_input)
 	filepaths_count = len(filepaths)
 	dataset_splitpoint = math.floor(filepaths_count * train_percentage)
 	
@@ -73,9 +76,18 @@ def dataset(dirpath_input, batch_size=64, train_percentage=0.8, parallel_reads_m
 	
 	return dataset_train, dataset_validate #, filepaths
 
-def dataset_predict():
+def dataset_predict(dirpath_input, batch_size=64, parallel_reads_multiplier=1.5):
-	raise NotImplementedError("Not implemented yet")
+	filepaths = get_filepaths(dirpath_input)
+	filepaths_count = len(filepaths)
+	for i in range(len(filepaths)):
+		filepaths.append(filepaths[-1])
 	
+	return make_dataset(
+		filepaths=filepaths,
+		metadata=read_metadata(dirpath_input),
+		batch_size=batch_size,
+		parallel_reads_multiplier=parallel_reads_multiplier
+	), filepaths[0:filepaths_count], filepaths_count
 
 if __name__ == "__main__":
 	ds_train, ds_validate = dataset("/mnt/research-data/main/rainfallwater_records-viperfinal/")

aimodel/src/lib/io/handle_open.py

@@ -0,0 +1,9 @@
+import io
+import gzip
+
+
+def handle_open(filepath, mode):
+	if filepath.endswith(".gz"):
+		return gzip.open(filepath, mode)
+	else:
+		return io.open(filepath, mode)

aimodel/src/subcommands/pretrain_plot.py

@@ -0,0 +1,59 @@
+import json
+import os
+import sys
+import argparse
+
+from loguru import logger
+import tensorflow as tf
+import numpy as np
+
+from lib.io.handle_open import handle_open
+from lib.vis.embeddings import vis_embeddings
+
+def parse_args():
+	parser = argparse.ArgumentParser(description="Plot embeddings predicted by the contrastive learning pretrained model with UMAP.")
+	# 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 file containing the content to plot.", required=True)
+	parser.add_argument("--output", "-o", help="Path to output file to write the resulting image to.", required=True)
+	parser.add_argument("--only-gpu",
+		help="If the GPU is not available, exit with an error (useful on shared HPC systems to avoid running out of memory & affecting other users)", action="store_true")
+	
+	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 os.path.exists(args.input):
+		raise Exception(f"Error: The specified input filepath ('{args.input}) does not exist.")
+	
+	filepath_input = args.input
+	
+	stem, ext = os.path.splitext(args.output)
+	filepath_output_rainfall = f"{stem}-rainfall.{ext}"
+	filepath_output_water = f"{stem}-water.{ext}"
+	
+	
+	sys.stderr.write(f"\n\n>>> This is TensorFlow {tf.__version__}\n\n\n")
+	
+	embeddings = []
+	with handle_open(filepath_input, "w") as handle:
+		for line in handle:
+			obj = json.loads(line)
+			embeddings.append(obj["rainfall"])
+	
+	logger.info(">>> Plotting rainfall with UMAP\n")
+	vis_embeddings(filepath_output_rainfall, np.array(embeddings))
+	
+	
+	embeddings = []
+	with handle_open(filepath_input, "w") as handle:
+		for line in handle:
+			obj = json.loads(line)
+			embeddings.append(obj["water"])
+	
+	logger.info(">>> Plotting water with UMAP\n")
+	vis_embeddings(filepath_output_water, np.array(embeddings))
+	
+	sys.stderr.write(">>> Complete\n")

aimodel/src/subcommands/pretrain_predict.py

@@ -52,12 +52,11 @@ def run(args):
 	
 	sys.stderr.write(f"\n\n>>> This is TensorFlow {tf.__version__}\n\n\n")
 	
-	dataset_train, filepaths, filepaths_length = dataset_predict(
+	dataset = dataset_predict(
 		dirpath_input=args.input,
 		batch_size=ai.batch_size,
 		parallel_reads_multiplier=args.read_multiplier
 	)
-	filepaths = filepaths[0:filepaths_length]
 	
 	# for items in dataset_train.repeat(10):
 	# 	print("ITEMS", len(items))
@@ -69,18 +68,17 @@ def run(args):
 	if filepath_output != "-":
 		handle = io.open(filepath_output, "w")
 	
-	embeddings = ai.embed(dataset_train)[0:filepaths_length] # Trim off the padding
+	for rainfall, water in ai.embed(dataset):
-	result = list(zip(filepaths, embeddings))
-	for filepath, embedding in result:
 		handle.write(json.dumps({
-			"filepath": filepath,
+			"rainfall": rainfall.numpy().tolist(),
-			"embedding": embedding.numpy().tolist()
+			"water": water.numpy().tolist()
 		}, separators=(',', ':'))+"\n") # Ref https://stackoverflow.com/a/64710892/1460422
 	
 	if filepath_output != "-":
+		handle.close()
+		
 		sys.stderr.write(">>> Plotting with UMAP\n")
 		filepath_output_umap = os.path.splitext(filepath_output)[0]+'.png'
-		labels = [ os.path.basename(os.path.dirname(filepath)) for filepath in filepaths ]
+		vis_embeddings(filepath_output_umap, np.array([ embedding.numpy() for embedding in embeddings ]))
-		vis_embeddings(filepath_output_umap, np.array([ embedding.numpy() for embedding in embeddings ]), np.array(labels))
 	
 	sys.stderr.write(">>> Complete\n")