start implementing driver for train_predict, but not finished yet

This commit is contained in:
Starbeamrainbowlabs 2022-10-18 19:37:55 +01:00
parent 4ceec73e5b
commit fe43ddfbf9
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
3 changed files with 146 additions and 2 deletions

View file

@ -52,7 +52,7 @@ def make_dataset(filepaths, metadata, shape_watch_desired=[100,100], compression
dataset = tf.data.TFRecordDataset(filepaths, dataset = tf.data.TFRecordDataset(filepaths,
compression_type=compression_type, compression_type=compression_type,
num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier) num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier) if parallel_reads_multiplier > 0 else None
) )
if shuffle: if shuffle:
dataset = dataset.shuffle(shuffle_buffer_size) dataset = dataset.shuffle(shuffle_buffer_size)
@ -88,6 +88,17 @@ def dataset(dirpath_input, batch_size=64, train_percentage=0.8, parallel_reads_m
return dataset_train, dataset_validate #, filepaths return dataset_train, dataset_validate #, filepaths
def dataset_predict(dirpath_input, parallel_reads_multiplier=1.5, prefetch=True): def dataset_predict(dirpath_input, parallel_reads_multiplier=1.5, prefetch=True):
"""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) if os.path.isdir(dirpath_input) else [ dirpath_input ] filepaths = get_filepaths(dirpath_input) if os.path.isdir(dirpath_input) else [ dirpath_input ]
return make_dataset( return make_dataset(

View file

@ -49,7 +49,7 @@ def make_dataset(filepaths, metadata, shape_water_desired=[100,100], water_thres
dataset = tf.data.TFRecordDataset(filepaths, dataset = tf.data.TFRecordDataset(filepaths,
compression_type=compression_type, compression_type=compression_type,
num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier) num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier) if parallel_reads_multiplier > 0 else None
) )
if shuffle: if shuffle:
dataset = dataset.shuffle(shuffle_buffer_size) dataset = dataset.shuffle(shuffle_buffer_size)
@ -85,6 +85,18 @@ def dataset_segmenter(dirpath_input, batch_size=64, train_percentage=0.8, parall
return dataset_train, dataset_validate #, filepaths return dataset_train, dataset_validate #, filepaths
def dataset_predict(dirpath_input, parallel_reads_multiplier=1.5, prefetch=True, water_threshold=0.1): def dataset_predict(dirpath_input, parallel_reads_multiplier=1.5, prefetch=True, water_threshold=0.1):
"""Creates a tf.data.Dataset() for prediction using the image segmentation head 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.
water_threshold (float, optional): The water depth threshold to consider cells to contain water, in metres. Defaults to 0.1.
Returns:
tf.data.Dataset: A tensorflow Dataset for the given input files.
"""
filepaths = get_filepaths(dirpath_input) if os.path.isdir(dirpath_input) else [ dirpath_input ] filepaths = get_filepaths(dirpath_input) if os.path.isdir(dirpath_input) else [ dirpath_input ]
return make_dataset( return make_dataset(

View file

@ -0,0 +1,121 @@
import io
import json
import os
import sys
import argparse
import re
from loguru import logger
import tensorflow as tf
import numpy as np
from lib.io.writefile import writefile
from lib.io.handle_open import handle_open
from lib.ai.RainfallWaterContraster import RainfallWaterContraster
from lib.dataset.dataset import dataset_predict
from lib.io.find_paramsjson import find_paramsjson
from lib.io.readfile import readfile
from lib.vis.embeddings import vis_embeddings
MODE_JSONL = 1
MODE_PNG = 2
def parse_args():
parser = argparse.ArgumentParser(description="Output water depth image segmentation maps using a given pretrained image segmentation 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 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.")
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 (not hasattr(args, "read_multiplier")) or args.read_multiplier == None:
args.read_multiplier = 1.5
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 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)
ai = RainfallWaterContraster.from_checkpoint(args.checkpoint, **json.loads(readfile(args.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)
logger.info("Output mode is "+("PNG" if output_mode == MODE_PNG else "JSONL"))
logger.info(f"Records per file: {args.records_per_file}")
do_jsonl(args, ai, dataset, write_mode)
sys.stderr.write(">>> Complete\n")
def do_jsonl(args, ai, dataset):
output_mode = MODE_PNG if args.output.endswith(".png") else MODE_JSONL
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 step_rainfall, step_water in ai.embed(dataset):
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, handle_mode=output_mode)
handle.write(json.dumps(step_rainfall.numpy().tolist(), 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 += 1
i_file += 1
handle.close()