convert pt model to onnx

.dvc/config

@@ -2,3 +2,4 @@
     remote = webdav
 ['remote "webdav"']
     url = webdavs://file.guineapig.love//home/dvc
+    ssl_verify = false

.dvcignore

@@ -1,3 +1,5 @@
 # Add patterns of files dvc should ignore, which could improve
 # the performance. Learn more at
 # https://dvc.org/doc/user-guide/dvcignore
+
+quickdraw_bot/data/processed

.gitignore

@@ -12,5 +12,16 @@ wheels/
 # Dataset
 quickdraw_bot/data
 
+# Temp files
+quickdraw_bot/tmp
+
 # DVC
-dvc/config.local
+dvc/config.local
+
+# .DS_Store
+.DS_Store
+
+# Models
+*.pth
+*.onnx
+*.onnx.data

.pre-commit-config.yaml

@@ -0,0 +1,33 @@
+default_language_version:
+    python: python3.13
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: check-added-large-files
+        args: ["--maxkb=512"]
+      - id: check-yaml
+      - id: check-toml
+      - id: check-docstring-first
+
+  - repo: local
+    hooks:
+      - id: ruff-format
+        name: ruff format
+        entry: uv run ruff format .
+        language: system
+        types: [python]
+
+      - id: ruff-check
+        name: ruff check
+        entry: uv run ruff check --fix .
+        language: system
+        types: [python]
+
+      - id: pytest
+        name: pytest
+        entry: uv run pytest tests/ -vs
+        language: system
+        pass_filenames: false
+        stages: [pre-push]

README.md

@@ -18,4 +18,6 @@
 ## Installation
 ```bash
 uv sync --all-groups
+uv run pre-commit install
+uv run pre-commit install --hook-type pre-push
 ```

pyproject.toml

@@ -13,6 +13,7 @@ dependencies = [
     "dvc~=3.67.1",
     "dvclive~=3.49.1",
     "dvc-webdav~=3.0.1",
+    "matplotlib~=3.11.0",
 ]
 
 [dependency-groups]
@@ -24,7 +25,8 @@ dev = [
 deploy = [
     "onnx~=1.22.0",
     "onnxruntime~=1.27.0",
-    "openvino~=2026.2.0"
+    "onnxscript~=0.7.0",
+    "openvino~=2026.2.0",
 ]
 
 [tool.ruff]
@@ -35,4 +37,15 @@ target-version = "py313"
 select = ["E", "F", "I"]
 
 [tool.ruff.format]
-quote-style = "single"
+quote-style = 'single'
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+pythonpath = ["."]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["quickdraw_bot"]

quickdraw_bot/assets/.gitignore

@@ -0,0 +1 @@
+/model.pth

quickdraw_bot/assets/config.yaml

@@ -0,0 +1,29 @@
+prepare:
+  data_dir: ./data
+  num_of_img_per_class: 10000
+  data_split:
+    train: 0.8
+    valid: 0.1
+    test: 0.1
+  random_seed: 1
+train:
+  device_type: mps
+  train_data_dir: ./data/processed/train
+  valid_data_dir: ./data/processed/valid
+  batch_size: 256
+  num_of_class: 20
+  optimizer_name: sgd  # sgd, adam
+  learning_rate: 0.001
+  warmup_epochs: 5
+  num_of_epochs: 30
+  file_lazy_load: false
+  random_seed: 1
+  exp_msg: init train
+eval:
+  test_data_dir: ./data/processed/test
+  model_path: ./assets/model.pth
+to_onnx:
+  model_path: ./assets/model.pth
+  image_size: [28, 28]
+  cls_map_path: ./data/processed/cate_id_cate_name_map.json
+  onnx_path: ./assets/model.onnx

quickdraw_bot/assets/dvc.yaml

@@ -0,0 +1,9 @@
+metrics:
+- ../doc/exp/train/metrics.json
+- ../doc/exp/eval/metrics.json
+plots:
+- ../doc/exp/train/plots/metrics:
+    x: step
+- ../doc/exp/eval/plots/metrics:
+    x: step
+- ../doc/exp/eval/plots/images

quickdraw_bot/assets/model.onnx.dvc

@@ -0,0 +1,5 @@
+outs:
+- md5: 5a641ce9dc5db7b16a4868773eb968dc
+  size: 1755967
+  hash: md5
+  path: model.onnx

quickdraw_bot/assets/model.pth.dvc

@@ -0,0 +1,5 @@
+outs:
+- md5: 263f47ef298fee74aed6acc3a316e7ad
+  size: 1701245
+  hash: md5
+  path: model.pth

quickdraw_bot/data.dvc

@@ -1,6 +1,6 @@
 outs:
-- md5: f46c877020a059df22fc383cd2f0a0bb.dir
+- md5: dd93e82604be92816a10bfb1e709edf2.dir
-  size: 3
+  size: 2306020784
-  nfiles: 1
+  nfiles: 20
   hash: md5
   path: data

quickdraw_bot/doc/exp/eval/metrics.json

@@ -0,0 +1,8 @@
+{
+    "test": {
+        "accuracy": 0.7296500205993652,
+        "precision": 0.7283352017402649,
+        "recall": 0.7292018532752991,
+        "f1": 0.723344087600708
+    }
+}

quickdraw_bot/doc/exp/eval/plots/metrics/test/accuracy.tsv

@@ -0,0 +1,2 @@
+step	accuracy
+0	0.7296500205993652

quickdraw_bot/doc/exp/eval/plots/metrics/test/f1.tsv

@@ -0,0 +1,2 @@
+step	f1
+0	0.723344087600708

quickdraw_bot/doc/exp/eval/plots/metrics/test/precision.tsv

@@ -0,0 +1,2 @@
+step	precision
+0	0.7283352017402649

quickdraw_bot/doc/exp/eval/plots/metrics/test/recall.tsv

@@ -0,0 +1,2 @@
+step	recall
+0	0.7292018532752991

quickdraw_bot/doc/exp/train/metrics.json

@@ -0,0 +1,17 @@
+{
+    "train": {
+        "loss": 1.992799331665039,
+        "accuracy": 0.4883750081062317,
+        "precision": 0.48363304138183594,
+        "recall": 0.4885570704936981,
+        "f1": 0.4849509298801422
+    },
+    "valid": {
+        "loss": 1.4377805100211614,
+        "accuracy": 0.7260000109672546,
+        "precision": 0.723875880241394,
+        "recall": 0.7256932258605957,
+        "f1": 0.7193899154663086
+    },
+    "step": 29
+}

quickdraw_bot/doc/exp/train/plots/metrics/train/accuracy.tsv

@@ -0,0 +1,31 @@
+step	accuracy
+0	0.04570624977350235
+1	0.08645624667406082
+2	0.15463125705718994
+3	0.20809374749660492
+4	0.2542562484741211
+5	0.29279375076293945
+6	0.3235749900341034
+7	0.346756249666214
+8	0.363993763923645
+9	0.3763374984264374
+10	0.3868750035762787
+11	0.39285001158714294
+12	0.400112509727478
+13	0.4029250144958496
+14	0.403425008058548
+15	0.4108937382698059
+16	0.42086875438690186
+17	0.43145623803138733
+18	0.44205623865127563
+19	0.44743749499320984
+20	0.45317500829696655
+21	0.4583125114440918
+22	0.45945000648498535
+23	0.4590874910354614
+24	0.462799996137619
+25	0.46361875534057617
+26	0.47360000014305115
+27	0.47944375872612
+28	0.4831624925136566
+29	0.4883750081062317

quickdraw_bot/doc/exp/train/plots/metrics/train/f1.tsv

@@ -0,0 +1,31 @@
+step	f1
+0	0.03369621932506561
+1	0.08265631645917892
+2	0.14480535686016083
+3	0.1954474151134491
+4	0.24400727450847626
+5	0.2852896749973297
+6	0.3165817856788635
+7	0.34051138162612915
+8	0.3579234480857849
+9	0.3706662654876709
+10	0.3813643753528595
+11	0.38740092515945435
+12	0.3947397768497467
+13	0.3978673219680786
+14	0.3983455300331116
+15	0.4061855971813202
+16	0.41634228825569153
+17	0.4270275831222534
+18	0.4378680884838104
+19	0.4433649480342865
+20	0.44917452335357666
+21	0.45438480377197266
+22	0.45568108558654785
+23	0.45533287525177
+24	0.45924824476242065
+25	0.45992523431777954
+26	0.4698502719402313
+27	0.47596246004104614
+28	0.4798404574394226
+29	0.4849509298801422

quickdraw_bot/doc/exp/train/plots/metrics/train/loss.tsv

@@ -0,0 +1,31 @@
+step	loss
+0	4.0223345439910885
+1	3.1446908485412597
+2	2.845902690887451
+3	2.7091702819824217
+4	2.5876311504364016
+5	2.4898329914093016
+6	2.4078801975250244
+7	2.3469735752105714
+8	2.3044276081085204
+9	2.2692713054656983
+10	2.2433441226959228
+11	2.2251542106628417
+12	2.2075239395141604
+13	2.199652731704712
+14	2.2003354278564453
+15	2.1802532527923586
+16	2.155798588562012
+17	2.127256035041809
+18	2.1059615146636963
+19	2.0936844367980956
+20	2.077068444442749
+21	2.064746246147156
+22	2.0619221328735353
+23	2.058998599433899
+24	2.0547973834991455
+25	2.0490142166137697
+26	2.0298474113464358
+27	2.0157182762145998
+28	2.006533228492737
+29	1.992799331665039

quickdraw_bot/doc/exp/train/plots/metrics/train/precision.tsv

@@ -0,0 +1,31 @@
+step	precision
+0	0.055038902908563614
+1	0.08688722550868988
+2	0.14544154703617096
+3	0.19598175585269928
+4	0.24326808750629425
+5	0.28354209661483765
+6	0.3143269121646881
+7	0.33853644132614136
+8	0.3557613492012024
+9	0.3688707649707794
+10	0.37962812185287476
+11	0.3855380415916443
+12	0.3929717540740967
+13	0.3963885009288788
+14	0.39665019512176514
+15	0.40454378724098206
+16	0.41489875316619873
+17	0.42552798986434937
+18	0.43634524941444397
+19	0.44186848402023315
+20	0.4476196765899658
+21	0.4529317021369934
+22	0.4541561007499695
+23	0.45390117168426514
+24	0.45794767141342163
+25	0.45853471755981445
+26	0.46830785274505615
+27	0.4746767580509186
+28	0.47852134704589844
+29	0.48363304138183594

quickdraw_bot/doc/exp/train/plots/metrics/train/recall.tsv

@@ -0,0 +1,31 @@
+step	recall
+0	0.04566790908575058
+1	0.08645831048488617
+2	0.1547394096851349
+3	0.2082621306180954
+4	0.2544386386871338
+5	0.29297617077827454
+6	0.3237552046775818
+7	0.346926212310791
+8	0.364177942276001
+9	0.3765082359313965
+10	0.3870483636856079
+11	0.39303654432296753
+12	0.4002862572669983
+13	0.403104305267334
+14	0.4036043882369995
+15	0.4110710322856903
+16	0.42104363441467285
+17	0.4316273629665375
+18	0.44224199652671814
+19	0.44761422276496887
+20	0.4533519148826599
+21	0.4584938883781433
+22	0.45962250232696533
+23	0.45926111936569214
+24	0.46298152208328247
+25	0.4637938141822815
+26	0.47378331422805786
+27	0.4796329736709595
+28	0.4833483397960663
+29	0.4885570704936981

quickdraw_bot/doc/exp/train/plots/metrics/valid/accuracy.tsv

@@ -0,0 +1,31 @@
+step	accuracy
+0	0.04039999842643738
+1	0.1996999979019165
+2	0.3260500133037567
+3	0.4186500012874603
+4	0.49950000643730164
+5	0.5444499850273132
+6	0.5720000267028809
+7	0.5917500257492065
+8	0.6093500256538391
+9	0.6218000054359436
+10	0.6304000020027161
+11	0.6385499835014343
+12	0.6404500007629395
+13	0.6446499824523926
+14	0.6463500261306763
+15	0.6567999720573425
+16	0.6672000288963318
+17	0.6779500246047974
+18	0.6841999888420105
+19	0.6896499991416931
+20	0.6960499882698059
+21	0.6970999836921692
+22	0.7006000280380249
+23	0.70169997215271
+24	0.7010999917984009
+25	0.7071499824523926
+26	0.7135000228881836
+27	0.7184500098228455
+28	0.7211499810218811
+29	0.7260000109672546

quickdraw_bot/doc/exp/train/plots/metrics/valid/f1.tsv

@@ -0,0 +1,31 @@
+step	f1
+0	0.026379385963082314
+1	0.1792948693037033
+2	0.2925271987915039
+3	0.3919405937194824
+4	0.47940874099731445
+5	0.5274306535720825
+6	0.556631863117218
+7	0.576585054397583
+8	0.5947144031524658
+9	0.6082352995872498
+10	0.6178301572799683
+11	0.6262512803077698
+12	0.6278425455093384
+13	0.6331325769424438
+14	0.6344878673553467
+15	0.6453059315681458
+16	0.6570700407028198
+17	0.6685804128646851
+18	0.6745381355285645
+19	0.6804145574569702
+20	0.6874032020568848
+21	0.6882768273353577
+22	0.6920892596244812
+23	0.693403959274292
+24	0.6925287246704102
+25	0.6985740661621094
+26	0.7053770422935486
+27	0.7106728553771973
+28	0.7137280106544495
+29	0.7193899154663086

quickdraw_bot/doc/exp/train/plots/metrics/valid/loss.tsv

@@ -0,0 +1,31 @@
+step	loss
+0	3.590050941781153
+1	2.7546746489367906
+2	2.5154529040372826
+3	2.2843858411040485
+4	2.0942421291447895
+5	1.9563061029096194
+6	1.8598378127134298
+7	1.7950012200995336
+8	1.7452865039245993
+9	1.7100401875338977
+10	1.6846234753162046
+11	1.6670298757432382
+12	1.6579805748372138
+13	1.6494467530069472
+14	1.6449626789817327
+15	1.6121938424774362
+16	1.5864867349214191
+17	1.5629751878448679
+18	1.5430825224405602
+19	1.5310050943229772
+20	1.5175003311302089
+21	1.5117049654827843
+22	1.5065134658089168
+23	1.5018350715878643
+24	1.5016868627524074
+25	1.4859640613386902
+26	1.4698024339313749
+27	1.4584274548518508
+28	1.4490519852577886
+29	1.4377805100211614

quickdraw_bot/doc/exp/train/plots/metrics/valid/precision.tsv

@@ -0,0 +1,31 @@
+step	precision
+0	0.07338898628950119
+1	0.18911437690258026
+2	0.32449209690093994
+3	0.4227263331413269
+4	0.49964380264282227
+5	0.5427083373069763
+6	0.5689945220947266
+7	0.5907210111618042
+8	0.6088747978210449
+9	0.620964527130127
+10	0.6287857294082642
+11	0.6372801065444946
+12	0.6391931772232056
+13	0.643118143081665
+14	0.6445475816726685
+15	0.655581533908844
+16	0.6666973829269409
+17	0.6765724420547485
+18	0.6822240352630615
+19	0.6881765127182007
+20	0.6948975920677185
+21	0.695229172706604
+22	0.6998213529586792
+23	0.7005149126052856
+24	0.6999821662902832
+25	0.7058628797531128
+26	0.7112910151481628
+27	0.7165747880935669
+28	0.7200020551681519
+29	0.723875880241394

quickdraw_bot/doc/exp/train/plots/metrics/valid/recall.tsv

@@ -0,0 +1,31 @@
+step	recall
+0	0.04072221741080284
+1	0.19861623644828796
+2	0.32381999492645264
+3	0.41670864820480347
+4	0.4983140230178833
+5	0.5435963273048401
+6	0.5713088512420654
+7	0.5910568237304688
+8	0.608674168586731
+9	0.6212138533592224
+10	0.6297356486320496
+11	0.6379297971725464
+12	0.6398895382881165
+13	0.6441172361373901
+14	0.6458127498626709
+15	0.6563705205917358
+16	0.6666883230209351
+17	0.6775047779083252
+18	0.6838763952255249
+19	0.6894745826721191
+20	0.6957231163978577
+21	0.6967899203300476
+22	0.7002253532409668
+23	0.7013353109359741
+24	0.7007752656936646
+25	0.706775963306427
+26	0.7130882740020752
+27	0.7181775569915771
+28	0.7208318114280701
+29	0.7256932258605957

quickdraw_bot/doc/exp/train/report.html

@@ -0,0 +1,114 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <meta http-equiv="refresh" content="5">
+    <title>DVC Plot</title>
+    
+
+    <script src="https://cdn.jsdelivr.net/npm/vega@5.20.2"></script>
+    <script src="https://cdn.jsdelivr.net/npm/vega-lite@5.2.0"></script>
+    <script src="https://cdn.jsdelivr.net/npm/vega-embed@6.18.2"></script>
+    
+    <style>
+        table {
+            border-spacing: 15px;
+        }
+    </style>
+</head>
+<body>
+    
+        <div id="metrics_json" style="text-align: center; padding: 10x">
+            <p>metrics_json</p>
+            <div style="display: flex;justify-content: center;">
+                <table>
+<thead>
+<tr><th style="text-align: right;">  train.loss</th><th style="text-align: right;">  train.accuracy</th><th style="text-align: right;">  train.precision</th><th style="text-align: right;">  train.recall</th><th style="text-align: right;">  train.f1</th><th style="text-align: right;">  valid.loss</th><th style="text-align: right;">  valid.accuracy</th><th style="text-align: right;">  valid.precision</th><th style="text-align: right;">  valid.recall</th><th style="text-align: right;">  valid.f1</th><th style="text-align: right;">  step</th></tr>
+</thead>
+<tbody>
+<tr><td style="text-align: right;">      1.9928</td><td style="text-align: right;">        0.488375</td><td style="text-align: right;">         0.483633</td><td style="text-align: right;">      0.488557</td><td style="text-align: right;">  0.484951</td><td style="text-align: right;">     1.43778</td><td style="text-align: right;">           0.726</td><td style="text-align: right;">         0.723876</td><td style="text-align: right;">      0.725693</td><td style="text-align: right;">   0.71939</td><td style="text-align: right;">    29</td></tr>
+</tbody>
+</table>
+            </div>
+        </div>
+
+    <div id = "static_valid_f1">
+        <script type = "text/javascript">
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+    <div id = "static_valid_loss">
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+    <div id = "static_valid_accuracy">
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+    <div id = "static_valid_recall">
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+    <div id = "static_valid_precision">
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+            vegaEmbed('#static_valid_precision', spec);
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+</body>
+</html>

quickdraw_bot/eval.py

@@ -0,0 +1,73 @@
+# eval.py
+#
+# author: deng
+# date  : 20260618
+
+import torch
+from dvclive import Live
+from torch.utils.data import DataLoader
+from torchmetrics import MetricCollection
+from torchmetrics.classification import Accuracy, ConfusionMatrix, F1Score, Precision, Recall
+
+from quickdraw_bot.utils.dataset import QuickDrawDataset
+from quickdraw_bot.utils.utils import load_config
+
+
+class Eval:
+    def __init__(self, config_path: str = './assets/config.yaml'):
+        self.config = load_config(config_path)['eval']
+        self._device = torch.device('mps' if torch.mps.is_available() else 'cpu')
+
+    def _get_dataloader(self):
+        test_dataset = QuickDrawDataset(data_dir=self.config['test_data_dir'], return_cate_name=False)
+        test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False)
+        return test_dataloader
+
+    def _get_model(self) -> tuple[torch.nn.Module, int]:
+        model = torch.load(self.config['model_path'], map_location=self._device, weights_only=False)
+        model.eval()
+        num_classes = [m for m in model.modules() if isinstance(m, torch.nn.Linear)][-1].out_features
+        return model, num_classes
+
+    def _get_metrics(self, num_classes: int) -> tuple[MetricCollection, ConfusionMatrix]:
+        metric_collection = MetricCollection(
+            [
+                Accuracy(task='multiclass', num_classes=num_classes, top_k=1),
+                Precision(task='multiclass', num_classes=num_classes, average='macro'),
+                Recall(task='multiclass', num_classes=num_classes, average='macro'),
+                F1Score(task='multiclass', num_classes=num_classes, average='macro'),
+            ]
+        ).to(self._device)
+        confusion_matrix = ConfusionMatrix(
+            task='multiclass',
+            threshold=0.5,
+            num_classes=num_classes,
+        ).to(self._device)
+        return metric_collection, confusion_matrix
+
+    def run(self):
+        test_dataloader = self._get_dataloader()
+        model, num_classes = self._get_model()
+        metrics, confusion_matrix = self._get_metrics(num_classes=num_classes)
+
+        with Live(dir='./doc/exp/eval', dvcyaml='./assets/dvc.yaml') as live:
+            metrics.reset()
+            with torch.no_grad():
+                for inputs, targets in test_dataloader:
+                    inputs = inputs.to(self._device)
+                    targets = targets.to(self._device)
+                    outputs = model(inputs)
+                    metrics.update(outputs, targets)
+                    confusion_matrix.update(outputs, targets)
+            test_metrics = metrics.compute()
+            confusion_matrix_fig, _ = confusion_matrix.plot()
+
+            live.log_metric('test/accuracy', test_metrics['MulticlassAccuracy'].item())
+            live.log_metric('test/precision', test_metrics['MulticlassPrecision'].item())
+            live.log_metric('test/recall', test_metrics['MulticlassRecall'].item())
+            live.log_metric('test/f1', test_metrics['MulticlassF1Score'].item())
+            live.log_image('test_confusion_matrix.png', confusion_matrix_fig)
+
+
+if __name__ == '__main__':
+    Eval().run()

quickdraw_bot/prepare.py

@@ -0,0 +1,92 @@
+# prepare.py
+#
+# author: deng
+# date  : 20260616
+
+import json
+import random
+from pathlib import Path
+from shutil import rmtree
+
+import numpy as np
+
+from quickdraw_bot.utils.utils import load_config
+
+
+class Prepare:
+    def __init__(self, config_path: str = './assets/config.yaml'):
+        self.config = load_config(config_path)['prepare']
+        self._set_random_seed()
+
+    def _set_random_seed(self):
+        random.seed(self.config['random_seed'])
+        np.random.seed(self.config['random_seed'])
+
+    def _load_dataset(self) -> tuple[dict[str, np.ndarray], dict[str, int]]:
+        data: dict[str, list] = {'images': [], 'cate_names': [], 'cate_ids': []}
+        cls_id_map: dict[str, int] = {}
+        raw_data_dir = Path(self.config['data_dir']) / 'raw'
+        for npy_file in sorted(raw_data_dir.glob('*.npy')):
+            class_name = npy_file.stem.removeprefix('full_numpy_bitmap_')
+            if class_name not in cls_id_map:
+                cls_id_map[class_name] = len(cls_id_map)
+            images = np.load(npy_file)  # shape: (N, 784)
+            images = images.reshape(-1, 1, 28, 28)  # shape: (N, 1, 28, 28)
+            images = images.astype(np.int8)
+            if images.shape[0] < self.config['num_of_img_per_class']:
+                print(f'Class {class_name} has less than {self.config["num_of_img_per_class"]} samples, keep all')
+                data['images'].extend(images)
+                data['cate_names'].extend([class_name] * images.shape[0])
+                data['cate_ids'].extend([cls_id_map[class_name]] * images.shape[0])
+            else:
+                random_indice = np.random.choice(images.shape[0], self.config['num_of_img_per_class'], replace=False)
+                data['images'].extend(images[random_indice])
+                data['cate_names'].extend([class_name] * self.config['num_of_img_per_class'])
+                data['cate_ids'].extend([cls_id_map[class_name]] * self.config['num_of_img_per_class'])
+        data['images'] = np.array(data['images']).astype(np.uint8)
+        data['cate_names'] = np.array(data['cate_names']).astype('S30')
+        data['cate_ids'] = np.array(data['cate_ids']).astype(np.uint16)
+        return data, cls_id_map
+
+    def _split_data(self, data: dict[str, np.ndarray]) -> dict[str, dict[str, np.ndarray]]:
+        weights = self.config['data_split']
+        if abs(sum(weights.values()) - 1.0) > 1e-6:
+            raise ValueError('Sum of data_split weights must be 1.0')
+
+        element_count = len(next(iter(data.values())))
+        shuffled_indices = np.random.permutation(element_count)
+
+        sets: dict[str, dict[str, np.ndarray]] = {}
+        start = 0
+        for i, (name, weight) in enumerate(weights.items()):
+            if i == len(weights) - 1:
+                idx = shuffled_indices[start:]
+            else:
+                end = start + round(weight * element_count)
+                idx = shuffled_indices[start:end]
+                start = end
+            sets[name] = {key: value[idx] for key, value in data.items()}
+        return sets
+
+    def _save_data(self, sets: dict[str, dict[str, np.ndarray]], cls_id_map: dict[str, int]) -> None:
+        save_dir = Path(self.config['data_dir']) / 'processed'
+        if save_dir.exists():
+            rmtree(save_dir)
+        save_dir.mkdir()
+        for usage, data in sets.items():
+            usage_dir = save_dir / usage
+            usage_dir.mkdir()
+            for key, value in data.items():
+                np.save(f'{usage_dir}/{key}.npy', value)
+        cate_id_cate_name_map = {v: k for k, v in cls_id_map.items()}
+        with open(f'{save_dir}/cate_id_cate_name_map.json', 'w') as f:
+            json.dump(cate_id_cate_name_map, f, indent=2)
+
+    def run(self):
+        data, cls_id_map = self._load_dataset()
+        sets = self._split_data(data)
+        self._save_data(sets, cls_id_map)
+
+
+if __name__ == '__main__':
+    Prepare().run()

quickdraw_bot/to_onnx.py

@@ -0,0 +1,98 @@
+# to_onnx.py
+#
+# author: deng
+# date  : 20260618
+
+import json
+import time
+from pathlib import Path
+
+import cv2
+import numpy as np
+import onnxruntime as ort
+import torch
+
+from quickdraw_bot.utils.utils import load_config
+
+
+class Pipeline(torch.nn.Module):
+    def __init__(self, model: torch.nn.Module, input_size: tuple[int, int]):
+        super().__init__()
+        self._model = model
+        self._input_size = input_size
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        inputs = inputs.float() / 255.0
+        inputs = torch.nn.functional.interpolate(inputs, size=self._input_size, mode='bilinear', align_corners=False)
+        logits = self._model(inputs)
+        probs = torch.nn.functional.softmax(logits, dim=-1)
+        return probs
+
+
+class ToONNX:
+    def __init__(self, config_path: str = './assets/config.yaml'):
+        self.config = load_config(config_path)['to_onnx']
+
+    def _get_cls_map(self) -> dict[int, str]:
+        cls_map_path = Path(self.config['cls_map_path'])
+        if not cls_map_path.exists():
+            return None
+        with open(cls_map_path, 'r') as f:
+            cls_map = json.load(f)
+        return cls_map
+
+    def _get_model(self) -> torch.nn.Module:
+        model = torch.load(self.config['model_path'], map_location='cpu', weights_only=False)
+        model.eval()
+        return model
+
+    def _get_pipeline(self) -> torch.nn.Module:
+        pipeline = Pipeline(
+            model=self._get_model(),
+            input_size=tuple(self.config['image_size']),
+        )
+        return pipeline
+
+    def run(self) -> None:
+        pipeline = self._get_pipeline()
+        dummy_input = torch.randint(0, 256, (1, 1, self.config['image_size'][0], self.config['image_size'][1]), dtype=torch.uint8)
+        torch.onnx.export(
+            pipeline,
+            dummy_input,
+            self.config['onnx_path'],
+            input_names=['inputs'],
+            output_names=['outputs'],
+            dynamic_axes={'inputs': {0: 'batch_size', 2: 'height', 3: 'width'}, 'outputs': {0: 'batch_size'}},
+            opset_version=18,
+            dynamo=False,
+            verbose=True,
+        )
+        print(f'Done! ONNX file saved to {self.config["onnx_path"]}')
+
+    def test_infer(self, image_path: str) -> None:
+
+        # Get cls map
+        cls_map: dict[str, str] = self._get_cls_map()
+        cls_names = list(cls_map.values())
+
+        # Load Image
+        image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+        image = image[np.newaxis, np.newaxis, :, :]
+
+        # Infer
+        onnx_session = ort.InferenceSession(
+            self.config['onnx_path'],
+            providers=['CPUExecutionProvider'],
+        )
+        start_time = time.perf_counter()
+        outputs = onnx_session.run(None, {'inputs': image})[0]
+        elapsed_time_ms = (time.perf_counter() - start_time) * 1000
+        result = [{cls_name: round(float(prob), 3) for cls_name, prob in zip(cls_names, prob_arr)} for prob_arr in outputs]
+        print(f'Elapsed time: {elapsed_time_ms:.2f} ms')
+        print(f'Image: {image_path}')
+        print(f'Result: {result}')
+
+
+if __name__ == '__main__':
+    ToONNX().run()
+    ToONNX().test_infer('./assets/favicon.png')

quickdraw_bot/train.py

@@ -0,0 +1,168 @@
+# train.py
+#
+# author: deng
+# date  : 20260617
+
+import random
+
+import numpy as np
+import torch
+from dvclive import Live
+from torch.utils.data import DataLoader
+from torchmetrics import MetricCollection
+from torchmetrics.classification import Accuracy, ConfusionMatrix, F1Score, Precision, Recall
+from tqdm import tqdm
+
+from quickdraw_bot.utils.dataset import QuickDrawDataset
+from quickdraw_bot.utils.model import BabyCNN
+from quickdraw_bot.utils.utils import load_config
+
+
+class Train:
+    def __init__(self, config_path: str = './assets/config.yaml'):
+        self.config = load_config(config_path)['train']
+        self._device = torch.device(self.config['device_type'])
+
+        self._ensure_reproducibility()
+
+    def _ensure_reproducibility(self) -> None:
+        torch.use_deterministic_algorithms(mode=True, warn_only=True)
+        random.seed(self.config['random_seed'])
+        np.random.seed(self.config['random_seed'])
+        torch.manual_seed(self.config['random_seed'])
+
+    def _get_dataloader(self):
+        train_dataset = QuickDrawDataset(
+            data_dir=self.config['train_data_dir'],
+            enable_data_aug=True,
+            file_lazy_load=self.config['file_lazy_load'],
+            return_cate_name=False,
+            # vis_dir='./tmp'
+        )
+        valid_dataset = QuickDrawDataset(
+            data_dir=self.config['valid_data_dir'],
+            enable_data_aug=False,
+            file_lazy_load=self.config['file_lazy_load'],
+            return_cate_name=False,
+        )
+
+        train_dataloader = DataLoader(
+            train_dataset,
+            batch_size=self.config['batch_size'],
+            shuffle=True,
+            num_workers=4,
+            pin_memory=False,  # not support for mps
+            persistent_workers=True,
+        )
+        valid_dataloader = DataLoader(
+            valid_dataset,
+            batch_size=self.config['batch_size'],
+            shuffle=False,
+            num_workers=1,
+            pin_memory=False,  # not support for mps
+            persistent_workers=True,
+        )
+        return train_dataloader, valid_dataloader
+
+    def _get_model(self) -> torch.nn.Module:
+        model = BabyCNN(num_classes=self.config['num_of_class'], dropout_p=0.3).to(self._device)
+        model.train()
+        return model
+
+    def _get_optimizer(self, model: torch.nn.Module) -> torch.optim.Optimizer:
+        if self.config['optimizer_name'] == 'adam':
+            optimizer = torch.optim.Adam(model.parameters(), lr=self.config['learning_rate'])
+        elif self.config['optimizer_name'] == 'sgd':
+            optimizer = torch.optim.SGD(model.parameters(), lr=self.config['learning_rate'])
+        else:
+            raise ValueError(f'Unknown optimizer name: {self.config["optimizer_name"]}')
+        return optimizer
+
+    def _get_scheduler(self, optimizer: torch.optim.Optimizer) -> torch.optim.lr_scheduler._LRScheduler:
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=1, eta_min=0.0001)
+        if self.config['warmup_epochs'] > 0:
+            warmup = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01, end_factor=1.0, total_iters=self.config['warmup_epochs'])
+            scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[warmup, scheduler], milestones=[self.config['warmup_epochs']])
+        return scheduler
+
+    def _get_loss(self) -> torch.nn.modules.loss._Loss:
+        loss = torch.nn.CrossEntropyLoss(label_smoothing=0.1).to(self._device)
+        return loss
+
+    def _get_metrics(self) -> tuple[MetricCollection, ConfusionMatrix]:
+        metric_collection = MetricCollection(
+            [
+                Accuracy(task='multiclass', num_classes=self.config['num_of_class'], top_k=1),
+                Precision(task='multiclass', num_classes=self.config['num_of_class'], average='macro'),
+                Recall(task='multiclass', num_classes=self.config['num_of_class'], average='macro'),
+                F1Score(task='multiclass', num_classes=self.config['num_of_class'], average='macro'),
+            ]
+        ).to(self._device)
+        confusion_matrix = ConfusionMatrix(
+            task='multiclass',
+            threshold=0.5,
+            num_classes=self.config['num_of_class'],
+        ).to(self._device)
+        return metric_collection, confusion_matrix
+
+    def run(self):
+        train_dataloader, valid_dataloader = self._get_dataloader()
+        model = self._get_model()
+        optimizer = self._get_optimizer(model)
+        scheduler = self._get_scheduler(optimizer)
+        loss = self._get_loss()
+        metrics, _ = self._get_metrics()
+
+        with Live(dir='./doc/exp/train', report='html', dvcyaml='./assets/dvc.yaml', exp_message=self.config['exp_msg']) as live:
+            for epoch in tqdm(range(self.config['num_of_epochs']), desc='Training Epoch'):
+                metrics.reset()
+                model.train()
+                total_train_loss = 0.0
+                for inputs, targets in train_dataloader:
+                    inputs = inputs.to(self._device)
+                    targets = targets.to(self._device)
+                    optimizer.zero_grad()
+                    outputs = model(inputs)
+                    train_loss = loss(outputs, targets)
+                    total_train_loss += train_loss.item()
+                    train_loss.backward()
+                    optimizer.step()
+                    metrics.update(outputs, targets)
+                train_metrics = metrics.compute()
+                avg_train_loss = total_train_loss / len(train_dataloader)
+                train_learning_rate = round(optimizer.param_groups[0]['lr'], 6)
+
+                metrics.reset()
+                model.eval()
+                total_valid_loss = 0.0
+                with torch.no_grad():
+                    for inputs, targets in valid_dataloader:
+                        inputs = inputs.to(self._device)
+                        targets = targets.to(self._device)
+                        outputs = model(inputs)
+                        valid_loss = loss(outputs, targets)
+                        total_valid_loss += valid_loss.item()
+                        metrics.update(outputs, targets)
+                valid_metrics = metrics.compute()
+                avg_valid_loss = total_valid_loss / len(valid_dataloader)
+
+                live.log_metric('train/loss', avg_train_loss)
+                live.log_metric('train/learning_rate', train_learning_rate)
+                live.log_metric('train/accuracy', train_metrics['MulticlassAccuracy'].item())
+                live.log_metric('train/precision', train_metrics['MulticlassPrecision'].item())
+                live.log_metric('train/recall', train_metrics['MulticlassRecall'].item())
+                live.log_metric('train/f1', train_metrics['MulticlassF1Score'].item())
+                live.log_metric('valid/loss', avg_valid_loss)
+                live.log_metric('valid/accuracy', valid_metrics['MulticlassAccuracy'].item())
+                live.log_metric('valid/precision', valid_metrics['MulticlassPrecision'].item())
+                live.log_metric('valid/recall', valid_metrics['MulticlassRecall'].item())
+                live.log_metric('valid/f1', valid_metrics['MulticlassF1Score'].item())
+
+                scheduler.step()
+                live.next_step()
+
+            torch.save(model, './assets/model.pth')
+
+
+if __name__ == '__main__':
+    Train().run()

quickdraw_bot/utils/dataset.py

@@ -0,0 +1,87 @@
+# dataset.py
+#
+# author: deng
+# date  : 20260617
+
+from pathlib import Path
+
+import numpy as np
+import torch
+from torchvision.transforms import v2
+
+
+class QuickDrawDataset(torch.utils.data.Dataset):
+    def __init__(
+        self,
+        data_dir: str,
+        image_shape: tuple[int, int, int] = (1, 28, 28),
+        enable_data_aug: bool = False,
+        file_lazy_load: bool = False,
+        return_cate_name: bool = False,
+        vis_dir: str = None,
+    ) -> None:
+        super().__init__()
+        self._images: torch.Tensor | np.ndarray = None
+        self._cate_names: list[str] = []
+        self._cate_ids: torch.Tensor = []
+        self._transform: callable = None
+
+        self._enable_data_aug = enable_data_aug
+        self._data_dir = Path(data_dir)
+        self._image_shape = image_shape
+        self._file_lazy_load = file_lazy_load
+        self._return_cate_name = return_cate_name
+        self._vis_dir = Path(vis_dir) if vis_dir is not None else None
+
+        self._set_data_transform()
+        self._collect_data()
+
+    def _set_data_transform(self) -> None:
+        aug_pipeline = []
+        if self._enable_data_aug:
+            aug_pipeline = [
+                v2.RandomHorizontalFlip(p=0.2),
+                v2.RandomApply([v2.RandomAffine(degrees=(-30, 30), translate=(0.2, 0.2), scale=(0.8, 1.2), shear=(-10, 10))], p=0.5),
+                v2.RandomPerspective(distortion_scale=0.15, p=0.2),
+                v2.RandomApply([v2.ElasticTransform(alpha=15.0, sigma=3.0)], p=0.2),
+                v2.RandomErasing(p=0.2, scale=(0.02, 0.2)),
+            ]
+        self._transform = v2.Compose(
+            [
+                *aug_pipeline,
+                v2.Resize(self._image_shape[1:]),
+                v2.ToDtype(torch.float32, scale=True),
+            ]
+        )
+
+    def _collect_data(self) -> None:
+        self._cate_names = [cate_name.decode() for cate_name in np.load(self._data_dir / 'cate_names.npy')]
+        self._cate_ids = torch.from_numpy(np.load(self._data_dir / 'cate_ids.npy')).long()
+        if self._file_lazy_load:
+            self._images = np.load(self._data_dir / 'images.npy', mmap_mode='r')
+        else:
+            self._images = torch.from_numpy(np.load(self._data_dir / 'images.npy'))
+
+    def __len__(self) -> int:
+        return len(self._cate_ids)
+
+    def __getitem__(self, index: int) -> tuple[torch.Tensor, torch.Tensor]:
+        if self._file_lazy_load:
+            x = torch.from_numpy(self._images[index])
+        else:
+            x = self._images[index]
+        x = self._transform(x)
+        y = self._cate_ids[index]
+
+        if self._vis_dir is not None:
+            vis_path = self._vis_dir / f'{index:05d}_{self._cate_names[index]}.png'
+            if not vis_path.exists():
+                v2.ToPILImage()(x).save(vis_path)
+
+        if self._return_cate_name:
+            return x, y, self._cate_names[index]
+        return x, y
+
+    def set_data_aug(self, enable_data_aug: bool) -> None:
+        self._enable_data_aug = enable_data_aug
+        self._set_data_transform()

quickdraw_bot/utils/model.py

@@ -0,0 +1,51 @@
+# model.py
+#
+# author: deng
+# date  : 20260617
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class BabyCNN(nn.Module):
+    def __init__(self, num_classes: int = 10, dropout_p: float = 0.5) -> None:
+        super().__init__()
+
+        # Conv Block 1: 28x28 -> 14x14
+        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1)
+        self.bn1 = nn.BatchNorm2d(num_features=32)
+
+        # Conv Block 2: 14x14 -> 7x7
+        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
+        self.bn2 = nn.BatchNorm2d(num_features=64)
+
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.dropout = nn.Dropout(p=dropout_p)
+
+        # FC Layers
+        self.fc1 = nn.Linear(in_features=64 * 7 * 7, out_features=128)
+        self.fc2 = nn.Linear(in_features=128, out_features=num_classes)
+
+        self._init_weights()
+
+    def _init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                if m.bias is not None:
+                    nn.init.zeros_(m.bias)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.ones_(m.weight)
+                nn.init.zeros_(m.bias)
+            elif isinstance(m, nn.Linear):
+                nn.init.kaiming_normal_(m.weight, nonlinearity='relu')
+                nn.init.zeros_(m.bias)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.pool(F.relu(self.bn1(self.conv1(x))))
+        x = self.pool(F.relu(self.bn2(self.conv2(x))))
+        x = x.view(x.size(0), -1)
+        x = self.dropout(F.relu(self.fc1(x)))
+        x = self.fc2(x)
+        return x

quickdraw_bot/utils/utils.py

@@ -0,0 +1,8 @@
+# utils.py
+
+import yaml
+
+
+def load_config(config_path: str) -> dict:
+    with open(config_path, 'r') as f:
+        return yaml.safe_load(f)

tests/test_utils.py

@@ -0,0 +1,23 @@
+# test_utils.py
+
+import pytest
+
+from quickdraw_bot.utils.utils import load_config
+
+
+class TestUtils:
+    def test_load_config_load_valid_yaml(self, tmp_path):
+        config_file = tmp_path / 'config.yaml'
+        config_file.write_text('key: value\nnested:\n  a: 1\n')
+        result = load_config(str(config_file))
+        assert result == {'key': 'value', 'nested': {'a': 1}}
+
+    def test_load_config_returns_dict(self, tmp_path):
+        config_file = tmp_path / 'config.yaml'
+        config_file.write_text('foo: bar\n')
+        result = load_config(str(config_file))
+        assert isinstance(result, dict)
+
+    def test_load_config_file_not_found(self):
+        with pytest.raises(FileNotFoundError):
+            load_config('/nonexistent/path/config.yaml')

uv.lock

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 ]
 
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
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     { name = "dvclive" },
+    { name = "matplotlib" },
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     { name = "torch" },
@@ -2234,6 +2479,7 @@ dependencies = [
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