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https://github.com/sbrl/research-rainfallradar
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encoderonly model: getting there
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2 changed files with 102 additions and 29 deletions
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@ -4,8 +4,9 @@ import os
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import tensorflow as tf
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from lib.dataset.dataset_encoderonly import dataset_encoderonly
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from lib.ai.components.convnext import make_convnext
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from lib.ai.helpers.summarywriter import summarywriter
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# ███████ ███ ██ ██ ██
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# ██ ████ ██ ██ ██
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@ -14,8 +15,27 @@ from lib.ai.components.convnext import make_convnext
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# ███████ ██ ████ ████
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# TODO: env vars & settings here
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DIRPATH_INPUT = os.environ["DIRPATH_INPUT"]
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DIRPATH_OUTPUT = os.environ["DIRPATH_OUTPUT"]
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PATH_HEIGHTMAP = os.environ["PATH_HEIGHTMAP"] if "PATH_HEIGHTMAP" in os.environ else None
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CHANNELS = os.environ["CHANNELS"] if "CHANNELS" in os.environ else 8
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BATCH_SIZE = int(os.environ["BATCH_SIZE"]) if "BATCH_SIZE" in os.environ else 64
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WINDOW_SIZE = int(os.environ["WINDOW_SIZE"]) if "WINDOW_SIZE" in os.environ else 33
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STEPS_PER_EPOCH = int(os.environ["STEPS_PER_EPOCH"]) if "STEPS_PER_EPOCH" in os.environ else None
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logger.info("Encoder-only rainfall radar TEST")
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logger.info(f"> DIRPATH_INPUT {DIRPATH_INPUT}")
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logger.info(f"> DIRPATH_OUTPUT {DIRPATH_OUTPUT}")
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logger.info(f"> PATH_HEIGHTMAP {PATH_HEIGHTMAP}")
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logger.info(f"> CHANNELS {CHANNELS}")
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logger.info(f"> BATCH_SIZE {BATCH_SIZE}")
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logger.info(f"> WINDOW_SIZE {WINDOW_SIZE}")
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logger.info(f"> STEPS_PER_EPOCH {STEPS_PER_EPOCH}")
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if not os.path.exists(DIRPATH_OUTPUT):
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os.makedirs(os.path.join(DIRPATH_OUTPUT, "checkpoints"))
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# ██████ █████ ████████ █████ ███████ ███████ ████████
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@ -24,7 +44,12 @@ from lib.ai.components.convnext import make_convnext
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# ██ ██ ██ ██ ██ ██ ██ ██ ██ ██
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# ██████ ██ ██ ██ ██ ██ ███████ ███████ ██
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dataset_train, dataset_validate = dataset_encoderonly(
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dirpath_input=DIRPATH_INPUT,
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filepath_heightmap=PATH_HEIGHTMAP,
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batch_size=BATCH_SIZE,
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windowsize=WINDOW_SIZE
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)
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# ███ ███ ██████ ██████ ███████ ██
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@ -33,9 +58,42 @@ from lib.ai.components.convnext import make_convnext
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# ██ ██ ██ ██ ██ ██ ██ ██ ██
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# ██ ██ ██████ ██████ ███████ ███████
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def make_encoderonly(windowsize, channels, encoder="convnext", water_bins=2):
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if encoder == "convnext":
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model = make_convnext(input_shape=(windowsize, windowsize, channels), num_classes=water_bins, **kwargs)
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elif encoder == "resnet":
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layer_in = tf.keras.Input(shape=(windowsize, windowsize, channels))
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layer_next = tf.keras.applications.resnet50.ResNet50(
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weights=None,
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include_top=True,
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classes=water_bins,
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input_tensor=layer_in,
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pooling="max",
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)
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# TODO: Have an
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model = tf.keras.Model(
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inputs = layer_in,
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outputs = layer_next
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)
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else:
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raise Exception(f"Error: Unknown encoder '{encoder}' (known encoders: convnext, resnet).")
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model.compile(
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optimizer="Adam",
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loss = tf.keras.losses.SparseCategoricalCrossentropy(),
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metrics = [
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tf.keras.metrics.SparseCategoricalAccuracy()
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]
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)
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return model
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model = make_encoderonly(
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windowsize=WINDOW_SIZE,
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channels=CHANNELS
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)
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summarywriter(model, os.path.join(DIRPATH_OUTPUT, "summary.txt"))
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# ████████ ██████ █████ ██ ███ ██ ██ ███ ██ ██████
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# ██ ██ ██ ██ ██ ██ ████ ██ ██ ████ ██ ██
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@ -43,6 +101,28 @@ from lib.ai.components.convnext import make_convnext
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# ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██
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# ██ ██ ██ ██ ██ ██ ██ ████ ██ ██ ████ ██████
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model.fit(dataset_train,
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validation_data=dataset_validate,
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epochs=25,
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callbacks=[
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tf.keras.callbacks.CSVLogger(
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filename=os.path.join(DIRPATH_OUTPUT, "metrics.tsv"),
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separator="\t"
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),
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CallbackCustomModelCheckpoint(
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model_to_checkpoint=model,
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filepath=os.path.join(
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DIRPATH_OUTPUT,
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"checkpoints"
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"checkpoint_e{epoch:d}_loss{loss:.3f}.hdf5"
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),
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monitor="loss"
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),
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],
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steps_per_epoch=STEPS_PER_EPOCH,
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)
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logger.info(">>> Training complete")
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# ██████ ██████ ███████ ██████ ██ ██████ ████████ ██ ██████ ███ ██
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@ -52,7 +132,7 @@ from lib.ai.components.convnext import make_convnext
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# ██ ██ ██ ███████ ██████ ██ ██████ ██ ██ ██████ ██ ████
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# TODO FILL THIS IN
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@ -15,19 +15,15 @@ from .parse_heightmap import parse_heightmap
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# TO PARSE:
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def parse_item(metadata, output_size=100, input_size="same", water_threshold=0.1, water_bins=2, heightmap=None, rainfall_scale_up=1):
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def parse_item(metadata, water_threshold=0.1, water_bins=2, heightmap=None, rainfall_scale_up=1, windowsize=33):
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if input_size == "same":
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input_size = output_size # This is almost always the case with e.g. the DeepLabV3+ model
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water_height_source, water_width_source = metadata["waterdepth"]
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water_offset_x = math.ceil((water_width_source - output_size) / 2)
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water_offset_y = math.ceil((water_height_source - output_size) / 2)
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rainfall_channels, rainfall_height_source, rainfall_width_source = metadata["rainfallradar"]
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rainfall_height_source *= rainfall_scale_up
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rainfall_width_source *= rainfall_scale_up
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rainfall_offset_x = math.ceil((rainfall_width_source - input_size) / 2)
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rainfall_offset_y = math.ceil((rainfall_height_source - input_size) / 2)
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print("DEBUG DATASET:rainfall shape", metadata["rainfallradar"], "/", f"w {rainfall_width_source} h {rainfall_height_source}")
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print("DEBUG DATASET:water shape", metadata["waterdepth"])
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@ -35,8 +31,6 @@ def parse_item(metadata, output_size=100, input_size="same", water_threshold=0.1
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print("DEBUG DATASET:water_bins", water_bins)
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print("DEBUG DATASET:output_size", output_size)
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print("DEBUG DATASET:input_size", input_size)
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print("DEBUG DATASET:water_offset x", water_offset_x, "y", water_offset_y)
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print("DEBUG DATASET:rainfall_offset x", rainfall_offset_x, "y", rainfall_offset_y)
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if heightmap is not None:
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heightmap = tf.expand_dims(heightmap, axis=-1)
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@ -72,21 +66,14 @@ def parse_item(metadata, output_size=100, input_size="same", water_threshold=0.1
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rainfall = tf.concat([rainfall, heightmap], axis=-1)
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if rainfall_scale_up > 1:
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rainfall = tf.repeat(tf.repeat(rainfall, rainfall_scale_up, axis=0), rainfall_scale_up, axis=1)
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if input_size is not None:
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rainfall = tf.image.crop_to_bounding_box(rainfall,
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offset_width=rainfall_offset_x,
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offset_height=rainfall_offset_y,
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target_width=input_size,
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target_height=input_size,
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)
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# rainfall = tf.image.resize(rainfall, tf.cast(tf.constant(metadata["rainfallradar"]) / 2, dtype=tf.int32))
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water = tf.expand_dims(water, axis=-1) # [height, width] → [height, width, channels=1]
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water = tf.image.crop_to_bounding_box(water,
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offset_width=water_offset_x,
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offset_height=water_offset_y,
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target_width=output_size,
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target_height=output_size
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water = tf.image.crop_to_bounding_box(water, # for path generation; we only want the water bit where the patches convolve
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offset_width=math.floor(windowsize/2),
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offset_height=math.floor(windowsize/2),
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target_width=water_width_source - (math.floor(windowsize/2)*2),
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target_height=water_height_source - (math.floor(windowsize/2)*2)
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)
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print("DEBUG:dataset BEFORE_SQUEEZE water", water.shape)
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@ -96,7 +83,7 @@ def parse_item(metadata, output_size=100, input_size="same", water_threshold=0.1
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# water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.int32)
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# water = tf.one_hot(water, water_bins, axis=-1, dtype=tf.int32)
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# LOSS dice
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water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.float32)
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water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.int32)
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rainfall = tf.image.extract_patches(tf.expand_dims(rainfall, axis=0),
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sizes=[1,windowsize,windowsize],
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@ -106,7 +93,7 @@ def parse_item(metadata, output_size=100, input_size="same", water_threshold=0.1
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)
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rainfall = tf.reshape(rainfall, [-1, windowsize, windowsize, rainfall_channels])
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# TODO: extract single water values here to match the above rainfall patches
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water = tf.reshape([-1]) # we flatten because we cropped to the right shape above
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print("DEBUG DATASET_OUT:rainfall shape", rainfall.shape)
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print("DEBUG DATASET_OUT:water shape", water.shape)
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@ -114,7 +101,7 @@ def parse_item(metadata, output_size=100, input_size="same", water_threshold=0.1
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return tf.function(parse_item_inner)
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def make_dataset(filepaths, compression_type="GZIP", parallel_reads_multiplier=1.5, shuffle_buffer_size=128, batch_size=64, prefetch=True, shuffle=True, filepath_heightmap=None, **kwargs):
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def make_dataset(filepaths, compression_type="GZIP", parallel_reads_multiplier=3, shuffle_buffer_size=2**16, batch_size=64, prefetch=True, shuffle=True, filepath_heightmap=None, **kwargs):
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if "NO_PREFETCH" in os.environ:
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logger.info("disabling data prefetching.")
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@ -128,10 +115,16 @@ def make_dataset(filepaths, compression_type="GZIP", parallel_reads_multiplier=1
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num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier) if parallel_reads_multiplier > 0 else None
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)
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if shuffle:
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dataset = dataset.shuffle(shuffle_buffer_size)
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dataset = dataset.shuffle(128) # additional shuffle buffer to mix things up
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dataset = dataset.map(parse_item(heightmap=heightmap, **kwargs), num_parallel_calls=tf.data.AUTOTUNE) \
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.unbatch()
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if shuffle:
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# memory used = (windowsize*windowsize + 1) * buffersize * channels
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# defaults = (33*33 + 1) * 2**16 * 8 = about 2.219GiB
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dataset = dataset.shuffle(shuffle_buffer_size)
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if batch_size != None:
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dataset = dataset.batch(batch_size, drop_remainder=True)
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if prefetch:
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@ -152,7 +145,7 @@ def get_filepaths(dirpath_input, do_shuffle=True):
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return result
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def dataset_mono(dirpath_input, train_percentage=0.8, **kwargs):
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def dataset_encoderonly(dirpath_input, train_percentage=0.8, **kwargs):
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filepaths = get_filepaths(dirpath_input)
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filepaths_count = len(filepaths)
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dataset_splitpoint = math.floor(filepaths_count * train_percentage)
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@ -167,7 +160,7 @@ def dataset_mono(dirpath_input, train_percentage=0.8, **kwargs):
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return dataset_train, dataset_validate #, filepaths
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def dataset_mono_predict(dirpath_input, **kwargs):
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def dataset_encoderonly_predict(dirpath_input, **kwargs):
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"""Creates a tf.data.Dataset() for prediction using the contrastive learning model.
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Note that this WILL MANGLE THE ORDERING if you set parallel_reads_multiplier to anything other than 0!!
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