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start working on a quick encoder test, but it's far from finished

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Starbeamrainbowlabs 2023-01-06 19:55:52 +00:00
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59
aimodel/src/encoderonly_test_rainfall.py Normal file
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from loguru import logger
import os
import tensorflow as tf
from lib.ai.components.convnext import make_convnext
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aimodel/src/lib/dataset/dataset_encoderonly.py Normal file
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import os
import math
import json
from loguru import logger
import tensorflow as tf
from lib.dataset.read_metadata import read_metadata
from ..io.readfile import readfile
from .shuffle import shuffle
from .parse_heightmap import parse_heightmap
# TO PARSE:
def parse_item(metadata, output_size=100, input_size="same", water_threshold=0.1, water_bins=2, heightmap=None, rainfall_scale_up=1):
if input_size == "same":
input_size = output_size # This is almost always the case with e.g. the DeepLabV3+ model
water_height_source, water_width_source = metadata["waterdepth"]
water_offset_x = math.ceil((water_width_source - output_size) / 2)
water_offset_y = math.ceil((water_height_source - output_size) / 2)
rainfall_channels, rainfall_height_source, rainfall_width_source = metadata["rainfallradar"]
rainfall_height_source *= rainfall_scale_up
rainfall_width_source *= rainfall_scale_up
rainfall_offset_x = math.ceil((rainfall_width_source - input_size) / 2)
rainfall_offset_y = math.ceil((rainfall_height_source - input_size) / 2)
print("DEBUG DATASET:rainfall shape", metadata["rainfallradar"], "/", f"w {rainfall_width_source} h {rainfall_height_source}")
print("DEBUG DATASET:water shape", metadata["waterdepth"])
print("DEBUG DATASET:water_threshold", water_threshold)
print("DEBUG DATASET:water_bins", water_bins)
print("DEBUG DATASET:output_size", output_size)
print("DEBUG DATASET:input_size", input_size)
print("DEBUG DATASET:water_offset x", water_offset_x, "y", water_offset_y)
print("DEBUG DATASET:rainfall_offset x", rainfall_offset_x, "y", rainfall_offset_y)
if heightmap is not None:
heightmap = tf.expand_dims(heightmap, axis=-1)
# NORMALLY, this wouldn't work 'cause you'd need to know the max of ALL frames, but here we only have a single frame.
heightmap_max = tf.math.reduce_max(heightmap)
heightmap_min = tf.math.reduce_min(tf.where(tf.math.less(heightmap, -500), heightmap, tf.fill(heightmap.shape, 0.0)))
heightmap = (heightmap - heightmap_min) / heightmap_max
heightmap = tf.transpose(heightmap, [1, 0, 2]) # [width, height] → [height, width]
rainfall_channels += 1
def parse_item_inner(item):
parsed = tf.io.parse_single_example(item, features={
"rainfallradar": tf.io.FixedLenFeature([], tf.string),
"waterdepth": tf.io.FixedLenFeature([], tf.string)
})
rainfall = tf.io.parse_tensor(parsed["rainfallradar"], out_type=tf.float32)
water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32)
rainfall = tf.reshape(rainfall, tf.constant(metadata["rainfallradar"], dtype=tf.int32))
water = tf.reshape(water, tf.constant(metadata["waterdepth"], dtype=tf.int32))
# Apparently the water depth data is also in HW instead of WH.... sighs
# * YES IT IS, BUT TENSORFLOW *wants* NHWC NOT NWHC....!
# water = tf.transpose(water, [1, 0])
# [channels, height, weight] → [height, width, channels] - ref ConvNeXt does not support data_format=channels_first
# BUG: For some reasons we have data that's not transposed correctly still!! O.o
# I can't believe in this entire project I have yet to get the rotation of the rainfall radar data correct....!
# %TRANSPOSE%
rainfall = tf.transpose(rainfall, [1, 2, 0])
if heightmap is not None:
rainfall = tf.concat([rainfall, heightmap], axis=-1)
if rainfall_scale_up > 1:
rainfall = tf.repeat(tf.repeat(rainfall, rainfall_scale_up, axis=0), rainfall_scale_up, axis=1)
if input_size is not None:
rainfall = tf.image.crop_to_bounding_box(rainfall,
offset_width=rainfall_offset_x,
offset_height=rainfall_offset_y,
target_width=input_size,
target_height=input_size,
)
# rainfall = tf.image.resize(rainfall, tf.cast(tf.constant(metadata["rainfallradar"]) / 2, dtype=tf.int32))
water = tf.expand_dims(water, axis=-1) # [height, width] → [height, width, channels=1]
water = tf.image.crop_to_bounding_box(water,
offset_width=water_offset_x,
offset_height=water_offset_y,
target_width=output_size,
target_height=output_size
)
print("DEBUG:dataset BEFORE_SQUEEZE water", water.shape)
water = tf.squeeze(water)
print("DEBUG:dataset AFTER_SQUEEZE water", water.shape)
# LOSS cross entropy
# water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.int32)
# water = tf.one_hot(water, water_bins, axis=-1, dtype=tf.int32)
# LOSS dice
water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.float32)
rainfall = tf.image.extract_patches(tf.expand_dims(rainfall, axis=0),
sizes=[1,windowsize,windowsize],
strides=[1,1,1,1],
rates=[1,1,1,1],
padding="VALID"
)
rainfall = tf.reshape(rainfall, [-1, windowsize, windowsize, rainfall_channels])
# TODO: extract single water values here to match the above rainfall patches
print("DEBUG DATASET_OUT:rainfall shape", rainfall.shape)
print("DEBUG DATASET_OUT:water shape", water.shape)
return rainfall, water
return tf.function(parse_item_inner)
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):
if "NO_PREFETCH" in os.environ:
logger.info("disabling data prefetching.")
heightmap = None
if filepath_heightmap is not None:
logger.info(f"Using heightmap from '{filepath_heightmap}'.")
heightmap = parse_heightmap(filepath_heightmap)
dataset = tf.data.TFRecordDataset(filepaths,
compression_type=compression_type,
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)
dataset = dataset.map(parse_item(heightmap=heightmap, **kwargs), num_parallel_calls=tf.data.AUTOTUNE) \
.unbatch()
if batch_size != None:
dataset = dataset.batch(batch_size, drop_remainder=True)
if prefetch:
dataset = dataset.prefetch(0 if "NO_PREFETCH" in os.environ else tf.data.AUTOTUNE)
return dataset
def get_filepaths(dirpath_input, do_shuffle=True):
result = 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
))
if do_shuffle:
result = shuffle(result)
else:
result = sorted(result, key=lambda filepath: int(os.path.basename(filepath).split(".", 1)[0]))
return result
def dataset_mono(dirpath_input, train_percentage=0.8, **kwargs):
filepaths = get_filepaths(dirpath_input)
filepaths_count = len(filepaths)
dataset_splitpoint = math.floor(filepaths_count * train_percentage)
filepaths_train = filepaths[:dataset_splitpoint]
filepaths_validate = filepaths[dataset_splitpoint:]
metadata = read_metadata(dirpath_input)
dataset_train = make_dataset(filepaths_train, metadata=metadata, **kwargs)
dataset_validate = make_dataset(filepaths_validate, metadata=metadata, **kwargs)
return dataset_train, dataset_validate #, filepaths
def dataset_mono_predict(dirpath_input, **kwargs):
"""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, do_shuffle=False) if os.path.isdir(dirpath_input) else [ dirpath_input ]
return make_dataset(
filepaths=filepaths,
metadata=read_metadata(dirpath_input),
batch_size=None,
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__":
ds_train, ds_validate = dataset_mono("/mnt/research-data/main/rainfallwater_records-viperfinal/")
for thing in ds_validate():
as_str = str(thing)
print(thing[:200])