ai: implement saving only the rainfall encoder

aimodel/src/lib/ai/RainfallWaterContraster.py

@@ -36,12 +36,12 @@ class RainfallWaterContraster(object):
 			self.filepath_summary = os.path.join(self.dir_output, "summary.txt")
 			
 			writefile(self.filepath_summary, "") # Empty the file ahead of time
-			self.model = self.make_model()
+			self.make_model()
 			
 			summarywriter(self.model, self.filepath_summary, append=True)
 			writefile(os.path.join(self.dir_output, "params.json"), json.dumps(self.get_config()))
 		else:	
-			self.model = self.load_model(filepath_checkpoint)
+			self.load_model(filepath_checkpoint)
 	
 	def get_config(self):
 		return {
@@ -59,7 +59,7 @@ class RainfallWaterContraster(object):
 	
 	
 	def make_model(self):
-		return model_rainfallwater_contrastive(
+		self.model, self.model_predict = model_rainfallwater_contrastive(
 			batch_size=self.batch_size,
 			summary_file=self.filepath_summary,
 			**self.kwargs
@@ -72,7 +72,7 @@ class RainfallWaterContraster(object):
 		filepath_checkpoint (string): The filepath to load the saved model from.
 		"""
 		
-		return tf.keras.models.load_model(filepath_checkpoint, custom_objects={
+		self.model_predict = tf.keras.models.load_model(filepath_checkpoint, custom_objects={
 			"LayerContrastiveEncoder": LayerContrastiveEncoder,
 			"LayerCheeseMultipleOut": LayerCheeseMultipleOut
 		})
@@ -84,7 +84,8 @@ class RainfallWaterContraster(object):
 			dataset_train,
 			validation_data=dataset_validate,
 			epochs=self.epochs,
-			callbacks=make_callbacks(self.dir_output)
+			callbacks=make_callbacks(self.dir_output, self.model_predict),
+			steps_per_epoch=10 # For testing
 		)
 	
 	def embed(self, dataset):
@@ -92,7 +93,7 @@ class RainfallWaterContraster(object):
 		i_batch = -1
 		for batch in dataset:
 			i_batch += 1
-			result_batch = self.model(batch[0])
+			result_batch = self.model(batch[0]) # ((rainfall, water), dummy_label)
 			rainfall, water = tf.unstack(result_batch, axis=-2)
 			
 			rainfall = tf.unstack(rainfall, axis=0)
@@ -101,3 +102,10 @@ class RainfallWaterContraster(object):
 			result.extend(zip(rainfall, water))
 		
 		return result
+	
+	def embed_rainfall(self, dataset):
+		result = []
+		for batch in dataset:
+			result_batch = self.model_predict(batch)
+			result.extend(tf.unstack(result_batch, axis=0))
+		return result

aimodel/src/lib/ai/components/CallbackCustomModelCheckpoint.py

@@ -0,0 +1,15 @@
+from loguru import logger
+import tensorflow as tf
+
+class CallbackCustomModelCheckpoint(tf.keras.callbacks.Callback):
+	def __init__(self, model_to_checkpoint, **kwargs) -> None:
+		super().__init__()
+		
+		self.model_to_checkpoint = model_to_checkpoint
+		self.checkpointer = tf.keras.callbacks.ModelCheckpoint(**kwargs)
+	
+	def on_epoch_end(self, epoch, logs=None):
+		logger.info("Saving checkpoint")
+		self.checkpointer.set_model(self.model_to_checkpoint)
+		self.checkpointer.on_epoch_end(epoch=epoch, logs=logs)
+		logger.info("Checkpoint saved successfully")

aimodel/src/lib/ai/components/LossContrastive.py

@@ -8,10 +8,10 @@ class LossContrastive(tf.keras.losses.Loss):
 	
 	def call(self, y_true, y_pred):
 		rainfall, water = tf.unstack(y_pred, axis=-2)
-		print("LOSS:call y_true", y_true.shape)
+		# print("LOSS:call y_true", y_true.shape)
-		print("LOSS:call y_pred", y_pred.shape)
+		# print("LOSS:call y_pred", y_pred.shape)
-		print("BEFORE_RESHAPE rainfall", rainfall)
+		# print("BEFORE_RESHAPE rainfall", rainfall)
-		print("BEFORE_RESHAPE water", water)
+		# print("BEFORE_RESHAPE water", water)
 		
 		# # Ensure the shapes are defined
 		# rainfall = tf.reshape(rainfall, [self.batch_size, rainfall.shape[1]])
@@ -20,7 +20,7 @@ class LossContrastive(tf.keras.losses.Loss):
 		
 		logits = tf.linalg.matmul(rainfall, tf.transpose(water)) * tf.clip_by_value(tf.math.exp(self.weight_temperature), 0, 100)
 		
-		print("LOGITS", logits)
+		# print("LOGITS", logits)
 		
 		labels			= tf.eye(self.batch_size, dtype=tf.int32)
 		loss_rainfall	= tf.keras.metrics.binary_crossentropy(labels, logits, from_logits=True, axis=0)
@@ -29,9 +29,9 @@ class LossContrastive(tf.keras.losses.Loss):
 		loss = (loss_rainfall + loss_water) / 2
 		
 		# cosine_similarity results in tensor of range -1 - 1, but tf.sparse.eye has range 0 - 1
-		print("LABELS", labels)
+		# print("LABELS", labels)
-		print("LOSS_rainfall", loss_rainfall)
+		# print("LOSS_rainfall", loss_rainfall)
-		print("LOSS_water", loss_water)
+		# print("LOSS_water", loss_water)
-		print("LOSS", loss)
+		# print("LOSS", loss)
 		return loss
 	

aimodel/src/lib/ai/helpers/make_callbacks.py

@@ -2,7 +2,9 @@ import os
 
 import tensorflow as tf
 
-def make_callbacks(dirpath):
+from ..components.CallbackCustomModelCheckpoint import CallbackCustomModelCheckpoint
+
+def make_callbacks(dirpath, model_predict):
 	dirpath_checkpoints = os.path.join(dirpath, "checkpoints")
 	filepath_metrics = os.path.join(dirpath, "metrics.tsv")
 	
@@ -10,7 +12,8 @@ def make_callbacks(dirpath):
 		os.mkdir(dirpath_checkpoints)
 	
 	return [
-		tf.keras.callbacks.ModelCheckpoint(
+		CallbackCustomModelCheckpoint(
+			model_to_checkpoint=model_predict,
 			filepath=os.path.join(
 				dirpath_checkpoints,
 				"checkpoint_weights_e{epoch:d}_loss{loss:.3f}.hdf5"

aimodel/src/lib/ai/model_rainfallwater_contrastive.py

@@ -63,4 +63,9 @@ def model_rainfallwater_contrastive(metadata, shape_water, batch_size=64, featur
 		loss=LossContrastive(batch_size=batch_size, weight_temperature=weight_temperature)
 	)
 	
-	return model
+	model_predict = tf.keras.Model(
+		inputs = input_rainfall,
+		outputs = rainfall
+	)
+	
+	return model, model_predict