added utility script

.gitignore

@@ -177,3 +177,6 @@ ipython_config.py
 
 # Remove previous ipynb_checkpoints
 #   git rm -r .ipynb_checkpoints/
+
+# Logging data
+runs/*

.pre-commit-config.yaml

@@ -38,7 +38,6 @@ repos:
     rev: v1.8.0
     hooks:
     -   id: mypy
-        additional_dependencies: [types-all]
         exclude: ^testing/resources/
 -   repo: https://github.com/psf/black
     rev: 24.4.2

model/src/configs/resnet18.json

@@ -0,0 +1,14 @@
+{
+    "datasets": {
+        "train": {
+            "filelist": "datasets/train.csv"
+        },
+        "val": {
+            "filelist": "datasets/val.csv"
+        }
+    },
+    "backbone": {
+        "class": "model.src.models.VisualCommunicationModel",
+        "model_name": "pretrained"
+    }
+}

model/src/utils/__init__.py

@@ -1,2 +1,3 @@
+from .get_class import get_class
 from .load_model import DEVICE, load_model
 from .vcda_dataset import VCDADataset

model/src/utils/get_class.py

@@ -0,0 +1,10 @@
+from importlib import import_module
+from types import ModuleType
+
+
+def get_class(path: str) -> ModuleType:
+    parts = path.split('.')
+    module_path = '.'.join(parts[:-1])
+    class_name = parts[-1]
+    module = import_module(module_path)
+    return getattr(module, class_name)

model/src/utils/load_model.py

@@ -5,7 +5,7 @@ from pathlib import Path
 
 import torch
 from minio import Minio
-from models import VisualCommunicationModel
+from src.models import VisualCommunicationModel
 
 from shared.data_store import get_model
 

model/train.py

@@ -0,0 +1,166 @@
+import json
+import os
+from argparse import ArgumentParser
+from datetime import datetime, timezone
+from pathlib import Path
+
+import torch
+from dotenv import load_dotenv
+from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
+from ignite.handlers import global_step_from_engine
+from ignite.handlers.checkpoint import Checkpoint
+from ignite.handlers.param_scheduler import create_lr_scheduler_with_warmup
+from ignite.handlers.tensorboard_logger import TensorboardLogger
+from ignite.handlers.tqdm_logger import ProgressBar
+from ignite.metrics import Average, Loss, RunningAverage
+from src.utils import VCDADataset, get_class
+from torch import nn
+from torch.optim.lr_scheduler import ExponentialLR
+from torch.utils.data import DataLoader
+
+from shared.data_store import connect_minio
+
+
+def parse_arguments():
+    parser = ArgumentParser()
+    parser.add_argument('-c', '--config', type=Path, required=True)
+    parser.add_argument('-d', '--device', type=str, default='cpu')
+    parser.add_argument('-b', '--batch-size', type=int, default=32)
+    parser.add_argument('--epochs', type=int, default=50)
+    parser.add_argument('--epoch-length', type=int, default=128)
+    parser.add_argument('--checkpoint', type=Path)
+    parser.add_argument('--lr', type=float, default=1e-5)
+    parser.add_argument('--lr-gamma', type=float, default=0.95)
+    parser.add_argument('--lr-warmup-start', type=float, default=1e-8)
+    parser.add_argument('--lr-warmup-duration', type=int, default=5)
+    parser.add_argument('--train-seed', type=int, default=1312)
+    parser.add_argument('--val-seed', type=int, default=1313)
+    parser.add_argument('--loader-workers', type=int, default=8)
+    return parser.parse_args()
+
+
+args = parse_arguments()
+
+load_dotenv('server.env')
+
+# read model config
+with open(args.config) as fh:
+    config = json.load(fh)
+model_name = args.config.stem
+run_name = datetime.now(timezone.utc).strftime('%Y-%m-%d-%H%M') + '_' + model_name
+device = torch.device(args.device)
+
+# create model
+model_class = get_class(config['backbone']['class'])
+model = model_class().to(device)
+optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
+loss_fn = nn.CrossEntropyLoss()
+
+# create datasets and loaders
+minio_client = connect_minio()
+with open('model/src/dataset/train.csv', encoding='utf-8') as fh:
+    train_data_name_list = fh.read().split('\n')
+train_dataset = VCDADataset(
+    minio_client=minio_client,
+    data_name_list=train_data_name_list,
+)
+train_loader = DataLoader(dataset=train_dataset, num_workers=args.loader_workers)
+
+with open('model/src/dataset/val.csv', encoding='utf-8') as fh:
+    val_data_name_list = fh.read().split('\n')
+val_dataset = VCDADataset(minio_client=minio_client, data_name_list=val_data_name_list)
+val_loader = DataLoader(dataset=val_dataset, num_workers=args.loader_workers)
+
+# create trainer and evaluator
+trainer = create_supervised_trainer(
+    model=model,
+    optimizer=optimizer,
+    loss_fn=loss_fn,
+    device=device,
+)
+Average(output_transform=lambda x: x).attach(trainer, name='loss')
+RunningAverage(output_transform=lambda x: x, alpha=0.5).attach(
+    trainer,
+    'running_avg_loss',
+)
+ProgressBar(desc='Train', ncols=80).attach(trainer, ['running_avg_loss'])
+
+val_metrics = {
+    'loss': Loss(loss_fn=loss_fn, device=device),
+}
+evaluator = create_supervised_evaluator(
+    model=model,
+    metrics=val_metrics,
+    device=device,
+)
+ProgressBar(desc='Val', ncols=80).attach(evaluator)
+
+# create lr scheduler
+lr_scheduler = ExponentialLR(
+    optimizer=optimizer,
+    gamma=args.lr_gamma,
+)
+lr_handler = create_lr_scheduler_with_warmup(
+    lr_scheduler=lr_scheduler,
+    warmup_start_value=args.lr_warmup_start,
+    warmup_duration=args.lr_warmup_duration,
+    warmup_end_value=args.lr,
+)
+trainer.add_event_handler(Events.EPOCH_STARTED, lr_handler)
+
+
+# log metrics to TensorBoard
+@trainer.on(Events.EPOCH_COMPLETED)
+def evaluate():
+    evaluator.run(val_loader, epoch_length=args.val_epoch_length)
+
+
+tb_logger = TensorboardLogger(log_dir=f'runs/logs/{run_name}')
+tb_logger.attach_opt_params_handler(
+    engine=trainer,
+    event_name=Events.EPOCH_COMPLETED,
+    optimizer=optimizer,
+)
+for tag, engine in [('train', trainer), ('val', evaluator)]:
+    tb_logger.attach_output_handler(
+        engine,
+        event_name=Events.EPOCH_COMPLETED,
+        tag=tag,
+        metric_names='all',
+        global_step_transform=global_step_from_engine(trainer),
+    )
+
+# Set up checkpoint saving
+to_save = {
+    'model': model,
+    'optimizer': optimizer,
+    'trainer': trainer,
+    'lr_scheduler': lr_scheduler,
+    'lr_handler': lr_handler,
+}
+checkpoint_handler = Checkpoint(
+    to_save,
+    f"runs/checkpoints/{run_name}",
+    n_saved=3,
+    filename_prefix='best',
+    score_function=lambda engine: -engine.state.metrics['loss'],
+    score_name='neg_val_loss',
+    global_step_transform=global_step_from_engine(trainer),
+)
+evaluator.add_event_handler(Events.COMPLETED, checkpoint_handler)
+
+if args.checkpoint:
+    Checkpoint.load_objects(to_load=to_save, checkpoint=str(args.checkpoint))
+
+# save model config
+os.makedirs('runs/configs/', exist_ok=True)
+with open(f"runs/configs/{run_name}.json", 'w', encoding='utf-8') as fh:
+    json.dump(config, fh)
+
+# start training
+trainer.run(
+    train_loader,
+    max_epochs=args.epochs,
+    epoch_length=args.epoch_length,
+)
+tb_logger.close()

poetry.lock

@@ -1,4 +1,20 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[package]]
+name = "absl-py"
+version = "2.1.0"
+description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "absl-py-2.1.0.tar.gz", hash = "sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff"},
+    {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
+]
+
+[package.source]
+type = "legacy"
+url = "http://192.168.1.2:5001/index"
+reference = "threadripper"
 
 [[package]]
 name = "annotated-types"
@@ -670,6 +686,69 @@ type = "legacy"
 url = "http://192.168.1.2:5001/index"
 reference = "threadripper"
 
+[[package]]
+name = "grpcio"
+version = "1.66.1"
+description = "HTTP/2-based RPC framework"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "grpcio-1.66.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:4877ba180591acdf127afe21ec1c7ff8a5ecf0fe2600f0d3c50e8c4a1cbc6492"},
+    {file = "grpcio-1.66.1-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:3750c5a00bd644c75f4507f77a804d0189d97a107eb1481945a0cf3af3e7a5ac"},
+    {file = "grpcio-1.66.1-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:a013c5fbb12bfb5f927444b477a26f1080755a931d5d362e6a9a720ca7dbae60"},
+    {file = "grpcio-1.66.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b1b24c23d51a1e8790b25514157d43f0a4dce1ac12b3f0b8e9f66a5e2c4c132f"},
+    {file = "grpcio-1.66.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b7ffb8ea674d68de4cac6f57d2498fef477cef582f1fa849e9f844863af50083"},
+    {file = "grpcio-1.66.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:307b1d538140f19ccbd3aed7a93d8f71103c5d525f3c96f8616111614b14bf2a"},
+    {file = "grpcio-1.66.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:1c17ebcec157cfb8dd445890a03e20caf6209a5bd4ac5b040ae9dbc59eef091d"},
+    {file = "grpcio-1.66.1-cp310-cp310-win32.whl", hash = "sha256:ef82d361ed5849d34cf09105d00b94b6728d289d6b9235513cb2fcc79f7c432c"},
+    {file = "grpcio-1.66.1-cp310-cp310-win_amd64.whl", hash = "sha256:292a846b92cdcd40ecca46e694997dd6b9be6c4c01a94a0dfb3fcb75d20da858"},
+    {file = "grpcio-1.66.1-cp311-cp311-linux_armv7l.whl", hash = "sha256:c30aeceeaff11cd5ddbc348f37c58bcb96da8d5aa93fed78ab329de5f37a0d7a"},
+    {file = "grpcio-1.66.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:8a1e224ce6f740dbb6b24c58f885422deebd7eb724aff0671a847f8951857c26"},
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+    {file = "grpcio-1.66.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:9c9bebc6627873ec27a70fc800f6083a13c70b23a5564788754b9ee52c5aef6c"},
+    {file = "grpcio-1.66.1-cp39-cp39-win32.whl", hash = "sha256:30a1c2cf9390c894c90bbc70147f2372130ad189cffef161f0432d0157973f45"},
+    {file = "grpcio-1.66.1-cp39-cp39-win_amd64.whl", hash = "sha256:17663598aadbedc3cacd7bbde432f541c8e07d2496564e22b214b22c7523dac8"},
+    {file = "grpcio-1.66.1.tar.gz", hash = "sha256:35334f9c9745add3e357e3372756fd32d925bd52c41da97f4dfdafbde0bf0ee2"},
+]
+
+[package.extras]
+protobuf = ["grpcio-tools (>=1.66.1)"]
+
+[package.source]
+type = "legacy"
+url = "http://192.168.1.2:5001/index"
+reference = "threadripper"
+
 [[package]]
 name = "gunicorn"
 version = "21.2.0"
@@ -789,6 +868,26 @@ type = "legacy"
 url = "http://192.168.1.2:5001/index"
 reference = "threadripper"
 
+[[package]]
+name = "markdown"
+version = "3.7"
+description = "Python implementation of John Gruber's Markdown."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "Markdown-3.7-py3-none-any.whl", hash = "sha256:7eb6df5690b81a1d7942992c97fad2938e956e79df20cbc6186e9c3a77b1c803"},
+    {file = "markdown-3.7.tar.gz", hash = "sha256:2ae2471477cfd02dbbf038d5d9bc226d40def84b4fe2986e49b59b6b472bbed2"},
+]
+
+[package.extras]
+docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
+testing = ["coverage", "pyyaml"]
+
+[package.source]
+type = "legacy"
+url = "http://192.168.1.2:5001/index"
+reference = "threadripper"
+
 [[package]]
 name = "markupsafe"
 version = "2.1.5"
@@ -1256,6 +1355,7 @@ description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
+    {file = "nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_aarch64.whl", hash = "sha256:004186d5ea6a57758fd6d57052a123c73a4815adf365eb8dd6a85c9eaa7535ff"},
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 ]
@@ -1521,6 +1621,31 @@ type = "legacy"
 url = "http://192.168.1.2:5001/index"
 reference = "threadripper"
 
+[[package]]
+name = "protobuf"
+version = "5.28.0"
+description = ""
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "protobuf-5.28.0-cp310-abi3-win32.whl", hash = "sha256:66c3edeedb774a3508ae70d87b3a19786445fe9a068dd3585e0cefa8a77b83d0"},
+    {file = "protobuf-5.28.0-cp310-abi3-win_amd64.whl", hash = "sha256:6d7cc9e60f976cf3e873acb9a40fed04afb5d224608ed5c1a105db4a3f09c5b6"},
+    {file = "protobuf-5.28.0-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:532627e8fdd825cf8767a2d2b94d77e874d5ddb0adefb04b237f7cc296748681"},
+    {file = "protobuf-5.28.0-cp38-abi3-manylinux2014_aarch64.whl", hash = "sha256:018db9056b9d75eb93d12a9d35120f97a84d9a919bcab11ed56ad2d399d6e8dd"},
+    {file = "protobuf-5.28.0-cp38-abi3-manylinux2014_x86_64.whl", hash = "sha256:6206afcb2d90181ae8722798dcb56dc76675ab67458ac24c0dd7d75d632ac9bd"},
+    {file = "protobuf-5.28.0-cp38-cp38-win32.whl", hash = "sha256:eef7a8a2f4318e2cb2dee8666d26e58eaf437c14788f3a2911d0c3da40405ae8"},
+    {file = "protobuf-5.28.0-cp38-cp38-win_amd64.whl", hash = "sha256:d001a73c8bc2bf5b5c1360d59dd7573744e163b3607fa92788b7f3d5fefbd9a5"},
+    {file = "protobuf-5.28.0-cp39-cp39-win32.whl", hash = "sha256:dde9fcaa24e7a9654f4baf2a55250b13a5ea701493d904c54069776b99a8216b"},
+    {file = "protobuf-5.28.0-cp39-cp39-win_amd64.whl", hash = "sha256:853db610214e77ee817ecf0514e0d1d052dff7f63a0c157aa6eabae98db8a8de"},
+    {file = "protobuf-5.28.0-py3-none-any.whl", hash = "sha256:510ed78cd0980f6d3218099e874714cdf0d8a95582e7b059b06cabad855ed0a0"},
+    {file = "protobuf-5.28.0.tar.gz", hash = "sha256:dde74af0fa774fa98892209992295adbfb91da3fa98c8f67a88afe8f5a349add"},
+]
+
+[package.source]
+type = "legacy"
+url = "http://192.168.1.2:5001/index"
+reference = "threadripper"
+
 [[package]]
 name = "py"
 version = "1.11.0"
@@ -1892,6 +2017,26 @@ type = "legacy"
 url = "http://192.168.1.2:5001/index"
 reference = "threadripper"
 
+[[package]]
+name = "pytorch-ignite"
+version = "0.5.1"
+description = "A lightweight library to help with training neural networks in PyTorch."
+optional = false
+python-versions = "*"
+files = [
+    {file = "pytorch_ignite-0.5.1-py3-none-any.whl", hash = "sha256:08c289f59c4185448cf0d97c0ef35f409f640fe1e7355f8fa13e504dcbe47b6d"},
+    {file = "pytorch_ignite-0.5.1.tar.gz", hash = "sha256:f41050bb0f85da6a4e5483cbc584ddf300382f537cd2f31cbb05ad62b8a6682e"},
+]
+
+[package.dependencies]
+packaging = "*"
+torch = ">=1.3,<3"
+
+[package.source]
+type = "legacy"
+url = "http://192.168.1.2:5001/index"
+reference = "threadripper"
+
 [[package]]
 name = "pytz"
 version = "2024.1"
@@ -2103,6 +2248,50 @@ type = "legacy"
 url = "http://192.168.1.2:5001/index"
 reference = "threadripper"
 
+[[package]]
+name = "tensorboard"
+version = "2.17.1"
+description = "TensorBoard lets you watch Tensors Flow"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "tensorboard-2.17.1-py3-none-any.whl", hash = "sha256:253701a224000eeca01eee6f7e978aea7b408f60b91eb0babdb04e78947b773e"},
+]
+
+[package.dependencies]
+absl-py = ">=0.4"
+grpcio = ">=1.48.2"
+markdown = ">=2.6.8"
+numpy = ">=1.12.0"
+packaging = "*"
+protobuf = ">=3.19.6,<4.24.0 || >4.24.0"
+setuptools = ">=41.0.0"
+six = ">1.9"
+tensorboard-data-server = ">=0.7.0,<0.8.0"
+werkzeug = ">=1.0.1"
+
+[package.source]
+type = "legacy"
+url = "http://192.168.1.2:5001/index"
+reference = "threadripper"
+
+[[package]]
+name = "tensorboard-data-server"
+version = "0.7.2"
+description = "Fast data loading for TensorBoard"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"},
+    {file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"},
+    {file = "tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530"},
+]
+
+[package.source]
+type = "legacy"
+url = "http://192.168.1.2:5001/index"
+reference = "threadripper"
+
 [[package]]
 name = "torch"
 version = "2.2.2"
@@ -2506,4 +2695,4 @@ reference = "threadripper"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.12"
-content-hash = "8caa4d24aec4c8cd5b1e726f2e00bc0cf8d865350aa5dc4282fc88d53d1b944c"
+content-hash = "64b30e34cd397481936704022e9e7e8a256a85215b180199c56ca8770c0bdc0e"

pyproject.toml

@@ -35,6 +35,8 @@ torchvision = "^0.17.1"
 torchinfo = "^1.8.0"
 minio = "^7.2.7"
 tqdm = "^4.66.4"
+pytorch-ignite = "^0.5.1"
+tensorboard = "^2.17.1"
 
 
 [tool.poetry.group.shared.dependencies]

shared/data_store/get_image.py

@@ -15,6 +15,7 @@ def get_image(
     """Get image from image subfolder in bucket in Minio."""
     assert isinstance(client, Minio)
     assert isinstance(object_name, str)
+    assert len(object_name) > 0
     # prepare arguments
     bucket_name = os.getenv('MINIO_BUCKET_NAME')
     subfolder = 'images'