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sycod
/
frugal_cviz

Model card Files Community
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d20ece8
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1 Parent(s): 17c2241

init repo

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Files changed (17) hide show
  1. .gitignore +19 -0
  2. Dockerfile +16 -0
  3. Makefile +20 -0
  4. app.py +27 -0
  5. config.yaml +23 -0
  6. notebooks/template-audio.ipynb +1351 -0
  7. notebooks/template-image.ipynb +475 -0
  8. notebooks/template-text.ipynb +1642 -0
  9. requirements.txt +30 -0
  10. tasks/__init__.py +0 -0
  11. tasks/audio.py +88 -0
  12. tasks/image.py +176 -0
  13. tasks/text.py +92 -0
  14. tasks/utils/__init__.py +0 -0
  15. tasks/utils/emissions.py +29 -0
  16. tasks/utils/evaluation.py +19 -0
  17. tasks/utils/load_data.py +59 -0
.gitignore ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ .ipynb_checkpoints/sandbox-checkpoint.ipynb
2
+ .DS_Store
3
+ auto_evals/
4
+ venv/
5
+ __pycache__/
6
+ .env
7
+ .ipynb_checkpoints
8
+ .vscode/
9
+ .venv
10
+
11
+ eval-queue/
12
+ eval-results/
13
+ eval-queue-bk/
14
+ eval-results-bk/
15
+ logs/
16
+
17
+ emissions.csv
18
+ data/
19
+ pyro-sdis/
Dockerfile ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
2
+ # you will also find guides on how best to write your Dockerfile
3
+
4
+ FROM python:3.9
5
+
6
+ RUN useradd -m -u 1000 user
7
+ USER user
8
+ ENV PATH="/home/user/.local/bin:$PATH"
9
+
10
+ WORKDIR /app
11
+
12
+ COPY --chown=user ./requirements.txt requirements.txt
13
+ RUN pip install --no-cache-dir --upgrade -r requirements.txt
14
+
15
+ COPY --chown=user . /app
16
+ CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
Makefile ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ install:
2
+ @pip install --upgrade pip && pip install -r requirements.txt
3
+
4
+ lint:
5
+ pylint --disable=R,C,logging-fstring-interpolation,f-string-without-interpolation --fail-under=7.0 src/*.py *.py *.ipynb
6
+
7
+ test:
8
+ # --cov=my_function test_*.py for details on a function
9
+ # test_*.py → python tests files are prefixed as is
10
+ # --disable-warnings, if needed
11
+ # -vvv for verbose
12
+ # -s for disabling capturing
13
+ # (allows print statements to be shown in the console even if the test passes)
14
+ @python -m pytest -vvv -s
15
+
16
+ format:
17
+ # --force-exclude '<FILE_OR_FOLDER>' if needed (env, imported, models...)
18
+ @black src/*.py *.py *.ipynb
19
+
20
+ all: install lint test format
app.py ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from fastapi import FastAPI
2
+ from dotenv import load_dotenv
3
+ from tasks import text, image, audio
4
+
5
+ # Load environment variables
6
+ load_dotenv()
7
+
8
+ app = FastAPI(
9
+ title="Frugal AI Challenge API",
10
+ description="API for the Frugal AI Challenge evaluation endpoints"
11
+ )
12
+
13
+ # Include all routers
14
+ app.include_router(text.router)
15
+ app.include_router(image.router)
16
+ app.include_router(audio.router)
17
+
18
+ @app.get("/")
19
+ async def root():
20
+ return {
21
+ "message": "Welcome to the Frugal AI Challenge API",
22
+ "endpoints": {
23
+ "text": "/text - Text classification task",
24
+ "image": "/image - Image classification task (coming soon)",
25
+ "audio": "/audio - Audio classification task (coming soon)"
26
+ }
27
+ }
config.yaml ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ local_path: "data"
3
+ img_dir: "Images"
4
+ annot_dir: "Annotation"
5
+ img_db_uri: "img_db.csv"
6
+ train_dir: "train"
7
+ test_dir: "test"
8
+ checkpoint_dir : "model_chkpts"
9
+ app_dir: "app"
10
+
11
+ log:
12
+ log_dir: "logs"
13
+
14
+ models:
15
+ classes_3: ["pug", "Siberian_husky", "borzoi"]
16
+ classes_10: ["Leonberg", "basenji", "malamute", "papillon", "chow", "dhole", "dingo", "Cardigan", "Brabancon_griffon", "boxer"]
17
+
18
+ app_data:
19
+ local_path: "app_data"
20
+ model: "EfficientNetB0_app.keras"
21
+ onnx: "EfficientNetB0_app.onnx"
22
+ # breeds are not in the same order as original classes
23
+ breeds: ['Brabancon_griffon', 'Cardigan', 'Leonberg', 'basenji', 'boxer', 'chow', 'dhole', 'dingo', 'malamute', 'papillon']
notebooks/template-audio.ipynb ADDED
@@ -0,0 +1,1351 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "metadata": {},
6
+ "source": [
7
+ "# Text task notebook template\n",
8
+ "## Loading the necessary libraries"
9
+ ]
10
+ },
11
+ {
12
+ "cell_type": "code",
13
+ "execution_count": 3,
14
+ "metadata": {},
15
+ "outputs": [
16
+ {
17
+ "name": "stderr",
18
+ "output_type": "stream",
19
+ "text": [
20
+ "[codecarbon WARNING @ 19:48:07] Multiple instances of codecarbon are allowed to run at the same time.\n",
21
+ "[codecarbon INFO @ 19:48:07] [setup] RAM Tracking...\n",
22
+ "[codecarbon INFO @ 19:48:07] [setup] CPU Tracking...\n",
23
+ "[codecarbon WARNING @ 19:48:09] We saw that you have a 13th Gen Intel(R) Core(TM) i7-1365U but we don't know it. Please contact us.\n",
24
+ "[codecarbon WARNING @ 19:48:09] No CPU tracking mode found. Falling back on CPU constant mode. \n",
25
+ " Windows OS detected: Please install Intel Power Gadget to measure CPU\n",
26
+ "\n",
27
+ "[codecarbon WARNING @ 19:48:11] We saw that you have a 13th Gen Intel(R) Core(TM) i7-1365U but we don't know it. Please contact us.\n",
28
+ "[codecarbon INFO @ 19:48:11] CPU Model on constant consumption mode: 13th Gen Intel(R) Core(TM) i7-1365U\n",
29
+ "[codecarbon WARNING @ 19:48:11] No CPU tracking mode found. Falling back on CPU constant mode.\n",
30
+ "[codecarbon INFO @ 19:48:11] [setup] GPU Tracking...\n",
31
+ "[codecarbon INFO @ 19:48:11] No GPU found.\n",
32
+ "[codecarbon INFO @ 19:48:11] >>> Tracker's metadata:\n",
33
+ "[codecarbon INFO @ 19:48:11] Platform system: Windows-11-10.0.22631-SP0\n",
34
+ "[codecarbon INFO @ 19:48:11] Python version: 3.12.7\n",
35
+ "[codecarbon INFO @ 19:48:11] CodeCarbon version: 3.0.0_rc0\n",
36
+ "[codecarbon INFO @ 19:48:11] Available RAM : 31.347 GB\n",
37
+ "[codecarbon INFO @ 19:48:11] CPU count: 12\n",
38
+ "[codecarbon INFO @ 19:48:11] CPU model: 13th Gen Intel(R) Core(TM) i7-1365U\n",
39
+ "[codecarbon INFO @ 19:48:11] GPU count: None\n",
40
+ "[codecarbon INFO @ 19:48:11] GPU model: None\n",
41
+ "[codecarbon INFO @ 19:48:11] Saving emissions data to file c:\\git\\submission-template\\notebooks\\emissions.csv\n"
42
+ ]
43
+ }
44
+ ],
45
+ "source": [
46
+ "from fastapi import APIRouter\n",
47
+ "from datetime import datetime\n",
48
+ "from datasets import load_dataset\n",
49
+ "from sklearn.metrics import accuracy_score\n",
50
+ "import random\n",
51
+ "\n",
52
+ "import sys\n",
53
+ "sys.path.append('../tasks')\n",
54
+ "\n",
55
+ "from utils.evaluation import AudioEvaluationRequest\n",
56
+ "from utils.emissions import tracker, clean_emissions_data, get_space_info\n",
57
+ "\n",
58
+ "\n",
59
+ "# Define the label mapping\n",
60
+ "LABEL_MAPPING = {\n",
61
+ " \"chainsaw\": 0,\n",
62
+ " \"environment\": 1\n",
63
+ "}"
64
+ ]
65
+ },
66
+ {
67
+ "cell_type": "markdown",
68
+ "metadata": {},
69
+ "source": [
70
+ "## Loading the datasets and splitting them"
71
+ ]
72
+ },
73
+ {
74
+ "cell_type": "code",
75
+ "execution_count": 4,
76
+ "metadata": {},
77
+ "outputs": [
78
+ {
79
+ "data": {
80
+ "application/vnd.jupyter.widget-view+json": {
81
+ "model_id": "668da7bf85434e098b95c3ec447d78fe",
82
+ "version_major": 2,
83
+ "version_minor": 0
84
+ },
85
+ "text/plain": [
86
+ "README.md: 0%| | 0.00/5.18k [00:00<?, ?B/s]"
87
+ ]
88
+ },
89
+ "metadata": {},
90
+ "output_type": "display_data"
91
+ },
92
+ {
93
+ "name": "stderr",
94
+ "output_type": "stream",
95
+ "text": [
96
+ "c:\\Users\\theo.alvesdacosta\\AppData\\Local\\anaconda3\\Lib\\site-packages\\huggingface_hub\\file_download.py:139: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\theo.alvesdacosta\\.cache\\huggingface\\hub\\datasets--QuotaClimat--frugalaichallenge-text-train. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
97
+ "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
98
+ " warnings.warn(message)\n"
99
+ ]
100
+ },
101
+ {
102
+ "data": {
103
+ "application/vnd.jupyter.widget-view+json": {
104
+ "model_id": "5b68d43359eb429395da8be7d4b15556",
105
+ "version_major": 2,
106
+ "version_minor": 0
107
+ },
108
+ "text/plain": [
109
+ "train.parquet: 0%| | 0.00/1.21M [00:00<?, ?B/s]"
110
+ ]
111
+ },
112
+ "metadata": {},
113
+ "output_type": "display_data"
114
+ },
115
+ {
116
+ "data": {
117
+ "application/vnd.jupyter.widget-view+json": {
118
+ "model_id": "140a304773914e9db8f698eabeb40298",
119
+ "version_major": 2,
120
+ "version_minor": 0
121
+ },
122
+ "text/plain": [
123
+ "Generating train split: 0%| | 0/6091 [00:00<?, ? examples/s]"
124
+ ]
125
+ },
126
+ "metadata": {},
127
+ "output_type": "display_data"
128
+ },
129
+ {
130
+ "data": {
131
+ "application/vnd.jupyter.widget-view+json": {
132
+ "model_id": "6d04e8ab1906400e8e0029949dc523a5",
133
+ "version_major": 2,
134
+ "version_minor": 0
135
+ },
136
+ "text/plain": [
137
+ "Map: 0%| | 0/6091 [00:00<?, ? examples/s]"
138
+ ]
139
+ },
140
+ "metadata": {},
141
+ "output_type": "display_data"
142
+ }
143
+ ],
144
+ "source": [
145
+ "request = AudioEvaluationRequest()\n",
146
+ "\n",
147
+ "# Load and prepare the dataset\n",
148
+ "dataset = load_dataset(request.dataset_name)\n",
149
+ "\n",
150
+ "# Split dataset\n",
151
+ "train_test = dataset[\"train\"].train_test_split(test_size=request.test_size, seed=request.test_seed)\n",
152
+ "test_dataset = train_test[\"test\"]"
153
+ ]
154
+ },
155
+ {
156
+ "cell_type": "markdown",
157
+ "metadata": {},
158
+ "source": [
159
+ "## Random Baseline"
160
+ ]
161
+ },
162
+ {
163
+ "cell_type": "code",
164
+ "execution_count": 5,
165
+ "metadata": {},
166
+ "outputs": [],
167
+ "source": [
168
+ "# Start tracking emissions\n",
169
+ "tracker.start()\n",
170
+ "tracker.start_task(\"inference\")"
171
+ ]
172
+ },
173
+ {
174
+ "cell_type": "code",
175
+ "execution_count": 6,
176
+ "metadata": {},
177
+ "outputs": [
178
+ {
179
+ "data": {
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+ "text/plain": [
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+ " 7,\n",
1083
+ " 0,\n",
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+ " 5,\n",
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+ " 7,\n",
1087
+ " 0,\n",
1088
+ " 3,\n",
1089
+ " 1,\n",
1090
+ " 7,\n",
1091
+ " 0,\n",
1092
+ " 1,\n",
1093
+ " 4,\n",
1094
+ " 7,\n",
1095
+ " 5,\n",
1096
+ " 0,\n",
1097
+ " 4,\n",
1098
+ " 0,\n",
1099
+ " 0,\n",
1100
+ " 1,\n",
1101
+ " 0,\n",
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+ " 6,\n",
1103
+ " 4,\n",
1104
+ " 0,\n",
1105
+ " 5,\n",
1106
+ " 4,\n",
1107
+ " 6,\n",
1108
+ " 6,\n",
1109
+ " 7,\n",
1110
+ " 2,\n",
1111
+ " 6,\n",
1112
+ " 2,\n",
1113
+ " 6,\n",
1114
+ " 0,\n",
1115
+ " 3,\n",
1116
+ " 2,\n",
1117
+ " 2,\n",
1118
+ " 1,\n",
1119
+ " 5,\n",
1120
+ " 4,\n",
1121
+ " 7,\n",
1122
+ " 6,\n",
1123
+ " 6,\n",
1124
+ " 2,\n",
1125
+ " 5,\n",
1126
+ " 5,\n",
1127
+ " 5,\n",
1128
+ " 0,\n",
1129
+ " 3,\n",
1130
+ " 5,\n",
1131
+ " 4,\n",
1132
+ " 5,\n",
1133
+ " 7,\n",
1134
+ " 5,\n",
1135
+ " 0,\n",
1136
+ " 5,\n",
1137
+ " 0,\n",
1138
+ " 0,\n",
1139
+ " 2,\n",
1140
+ " 0,\n",
1141
+ " 2,\n",
1142
+ " 1,\n",
1143
+ " 0,\n",
1144
+ " 2,\n",
1145
+ " 4,\n",
1146
+ " 3,\n",
1147
+ " 4,\n",
1148
+ " 1,\n",
1149
+ " 7,\n",
1150
+ " 2,\n",
1151
+ " 1,\n",
1152
+ " 0,\n",
1153
+ " 3,\n",
1154
+ " 0,\n",
1155
+ " 3,\n",
1156
+ " 1,\n",
1157
+ " 1,\n",
1158
+ " 0,\n",
1159
+ " 5,\n",
1160
+ " 3,\n",
1161
+ " 1,\n",
1162
+ " 2,\n",
1163
+ " 5,\n",
1164
+ " 6,\n",
1165
+ " 7,\n",
1166
+ " 6,\n",
1167
+ " 7,\n",
1168
+ " 0,\n",
1169
+ " 2,\n",
1170
+ " 6,\n",
1171
+ " 3,\n",
1172
+ " 1,\n",
1173
+ " 5,\n",
1174
+ " 4,\n",
1175
+ " 2,\n",
1176
+ " 4,\n",
1177
+ " 6,\n",
1178
+ " 5,\n",
1179
+ " 2,\n",
1180
+ " 7,\n",
1181
+ " ...]"
1182
+ ]
1183
+ },
1184
+ "execution_count": 6,
1185
+ "metadata": {},
1186
+ "output_type": "execute_result"
1187
+ }
1188
+ ],
1189
+ "source": [
1190
+ "\n",
1191
+ "#--------------------------------------------------------------------------------------------\n",
1192
+ "# YOUR MODEL INFERENCE CODE HERE\n",
1193
+ "# Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.\n",
1194
+ "#-------------------------------------------------------------------------------------------- \n",
1195
+ "\n",
1196
+ "# Make random predictions (placeholder for actual model inference)\n",
1197
+ "true_labels = test_dataset[\"label\"]\n",
1198
+ "predictions = [random.randint(0, 1) for _ in range(len(true_labels))]\n",
1199
+ "\n",
1200
+ "predictions\n",
1201
+ "\n",
1202
+ "#--------------------------------------------------------------------------------------------\n",
1203
+ "# YOUR MODEL INFERENCE STOPS HERE\n",
1204
+ "#-------------------------------------------------------------------------------------------- "
1205
+ ]
1206
+ },
1207
+ {
1208
+ "cell_type": "code",
1209
+ "execution_count": 8,
1210
+ "metadata": {},
1211
+ "outputs": [
1212
+ {
1213
+ "name": "stderr",
1214
+ "output_type": "stream",
1215
+ "text": [
1216
+ "[codecarbon WARNING @ 19:53:32] Background scheduler didn't run for a long period (47s), results might be inaccurate\n",
1217
+ "[codecarbon INFO @ 19:53:32] Energy consumed for RAM : 0.000156 kWh. RAM Power : 11.755242347717285 W\n",
1218
+ "[codecarbon INFO @ 19:53:32] Delta energy consumed for CPU with constant : 0.000564 kWh, power : 42.5 W\n",
1219
+ "[codecarbon INFO @ 19:53:32] Energy consumed for All CPU : 0.000564 kWh\n",
1220
+ "[codecarbon INFO @ 19:53:32] 0.000720 kWh of electricity used since the beginning.\n"
1221
+ ]
1222
+ },
1223
+ {
1224
+ "data": {
1225
+ "text/plain": [
1226
+ "EmissionsData(timestamp='2025-01-21T19:53:32', project_name='codecarbon', run_id='908f2e7e-4bb2-4991-a0f6-56bf8d7eda21', experiment_id='5b0fa12a-3dd7-45bb-9766-cc326314d9f1', duration=47.736408500000834, emissions=4.032368007471064e-05, emissions_rate=8.444466886328872e-07, cpu_power=42.5, gpu_power=0.0, ram_power=11.755242347717285, cpu_energy=0.0005636615353475565, gpu_energy=0, ram_energy=0.00015590305493261682, energy_consumed=0.0007195645902801733, country_name='France', country_iso_code='FRA', region='île-de-france', cloud_provider='', cloud_region='', os='Windows-11-10.0.22631-SP0', python_version='3.12.7', codecarbon_version='3.0.0_rc0', cpu_count=12, cpu_model='13th Gen Intel(R) Core(TM) i7-1365U', gpu_count=None, gpu_model=None, longitude=2.3494, latitude=48.8558, ram_total_size=31.347312927246094, tracking_mode='machine', on_cloud='N', pue=1.0)"
1227
+ ]
1228
+ },
1229
+ "execution_count": 8,
1230
+ "metadata": {},
1231
+ "output_type": "execute_result"
1232
+ }
1233
+ ],
1234
+ "source": [
1235
+ "# Stop tracking emissions\n",
1236
+ "emissions_data = tracker.stop_task()\n",
1237
+ "emissions_data"
1238
+ ]
1239
+ },
1240
+ {
1241
+ "cell_type": "code",
1242
+ "execution_count": 9,
1243
+ "metadata": {},
1244
+ "outputs": [
1245
+ {
1246
+ "data": {
1247
+ "text/plain": [
1248
+ "0.10090237899917966"
1249
+ ]
1250
+ },
1251
+ "execution_count": 9,
1252
+ "metadata": {},
1253
+ "output_type": "execute_result"
1254
+ }
1255
+ ],
1256
+ "source": [
1257
+ "# Calculate accuracy\n",
1258
+ "accuracy = accuracy_score(true_labels, predictions)\n",
1259
+ "accuracy"
1260
+ ]
1261
+ },
1262
+ {
1263
+ "cell_type": "code",
1264
+ "execution_count": 10,
1265
+ "metadata": {},
1266
+ "outputs": [
1267
+ {
1268
+ "data": {
1269
+ "text/plain": [
1270
+ "{'submission_timestamp': '2025-01-21T19:53:46.639165',\n",
1271
+ " 'accuracy': 0.10090237899917966,\n",
1272
+ " 'energy_consumed_wh': 0.7195645902801733,\n",
1273
+ " 'emissions_gco2eq': 0.040323680074710634,\n",
1274
+ " 'emissions_data': {'run_id': '908f2e7e-4bb2-4991-a0f6-56bf8d7eda21',\n",
1275
+ " 'duration': 47.736408500000834,\n",
1276
+ " 'emissions': 4.032368007471064e-05,\n",
1277
+ " 'emissions_rate': 8.444466886328872e-07,\n",
1278
+ " 'cpu_power': 42.5,\n",
1279
+ " 'gpu_power': 0.0,\n",
1280
+ " 'ram_power': 11.755242347717285,\n",
1281
+ " 'cpu_energy': 0.0005636615353475565,\n",
1282
+ " 'gpu_energy': 0,\n",
1283
+ " 'ram_energy': 0.00015590305493261682,\n",
1284
+ " 'energy_consumed': 0.0007195645902801733,\n",
1285
+ " 'country_name': 'France',\n",
1286
+ " 'country_iso_code': 'FRA',\n",
1287
+ " 'region': 'île-de-france',\n",
1288
+ " 'cloud_provider': '',\n",
1289
+ " 'cloud_region': '',\n",
1290
+ " 'os': 'Windows-11-10.0.22631-SP0',\n",
1291
+ " 'python_version': '3.12.7',\n",
1292
+ " 'codecarbon_version': '3.0.0_rc0',\n",
1293
+ " 'cpu_count': 12,\n",
1294
+ " 'cpu_model': '13th Gen Intel(R) Core(TM) i7-1365U',\n",
1295
+ " 'gpu_count': None,\n",
1296
+ " 'gpu_model': None,\n",
1297
+ " 'ram_total_size': 31.347312927246094,\n",
1298
+ " 'tracking_mode': 'machine',\n",
1299
+ " 'on_cloud': 'N',\n",
1300
+ " 'pue': 1.0},\n",
1301
+ " 'dataset_config': {'dataset_name': 'QuotaClimat/frugalaichallenge-text-train',\n",
1302
+ " 'test_size': 0.2,\n",
1303
+ " 'test_seed': 42}}"
1304
+ ]
1305
+ },
1306
+ "execution_count": 10,
1307
+ "metadata": {},
1308
+ "output_type": "execute_result"
1309
+ }
1310
+ ],
1311
+ "source": [
1312
+ "# Prepare results dictionary\n",
1313
+ "results = {\n",
1314
+ " \"submission_timestamp\": datetime.now().isoformat(),\n",
1315
+ " \"accuracy\": float(accuracy),\n",
1316
+ " \"energy_consumed_wh\": emissions_data.energy_consumed * 1000,\n",
1317
+ " \"emissions_gco2eq\": emissions_data.emissions * 1000,\n",
1318
+ " \"emissions_data\": clean_emissions_data(emissions_data),\n",
1319
+ " \"dataset_config\": {\n",
1320
+ " \"dataset_name\": request.dataset_name,\n",
1321
+ " \"test_size\": request.test_size,\n",
1322
+ " \"test_seed\": request.test_seed\n",
1323
+ " }\n",
1324
+ "}\n",
1325
+ "\n",
1326
+ "results"
1327
+ ]
1328
+ }
1329
+ ],
1330
+ "metadata": {
1331
+ "kernelspec": {
1332
+ "display_name": "base",
1333
+ "language": "python",
1334
+ "name": "python3"
1335
+ },
1336
+ "language_info": {
1337
+ "codemirror_mode": {
1338
+ "name": "ipython",
1339
+ "version": 3
1340
+ },
1341
+ "file_extension": ".py",
1342
+ "mimetype": "text/x-python",
1343
+ "name": "python",
1344
+ "nbconvert_exporter": "python",
1345
+ "pygments_lexer": "ipython3",
1346
+ "version": "3.12.7"
1347
+ }
1348
+ },
1349
+ "nbformat": 4,
1350
+ "nbformat_minor": 2
1351
+ }
notebooks/template-image.ipynb ADDED
@@ -0,0 +1,475 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "metadata": {},
6
+ "source": [
7
+ "# 🚧 Info\n",
8
+ "\n",
9
+ "https://huggingface.co/datasets/pyronear/pyro-sdis\n",
10
+ "\n",
11
+ "https://frugalaichallenge.org/participate/"
12
+ ]
13
+ },
14
+ {
15
+ "cell_type": "markdown",
16
+ "metadata": {},
17
+ "source": [
18
+ "# Image task notebook template\n",
19
+ "## Loading the necessary libraries"
20
+ ]
21
+ },
22
+ {
23
+ "cell_type": "code",
24
+ "execution_count": 1,
25
+ "metadata": {},
26
+ "outputs": [
27
+ {
28
+ "ename": "ModuleNotFoundError",
29
+ "evalue": "No module named 'tasks'",
30
+ "output_type": "error",
31
+ "traceback": [
32
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
33
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
34
+ "Cell \u001b[0;32mIn[1], line 9\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mrandom\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# import sys\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# sys.path.append('../')\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtasks\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mevaluation\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ImageEvaluationRequest\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtasks\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01memissions\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m tracker, clean_emissions_data, get_space_info\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtasks\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mload_data\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m load_data\n",
35
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tasks'"
36
+ ]
37
+ }
38
+ ],
39
+ "source": [
40
+ "from fastapi import APIRouter\n",
41
+ "from datetime import datetime\n",
42
+ "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
43
+ "\n",
44
+ "import random\n",
45
+ "# import sys\n",
46
+ "# sys.path.append('../')\n",
47
+ "\n",
48
+ "from tasks.utils.evaluation import ImageEvaluationRequest\n",
49
+ "from tasks.utils.emissions import tracker, clean_emissions_data, get_space_info\n",
50
+ "from tasks.utils.load_data import load_data\n",
51
+ "from tasks.image import parse_boxes,compute_iou,compute_max_iou"
52
+ ]
53
+ },
54
+ {
55
+ "cell_type": "markdown",
56
+ "metadata": {},
57
+ "source": [
58
+ "## Loading the datasets and splitting them"
59
+ ]
60
+ },
61
+ {
62
+ "cell_type": "code",
63
+ "execution_count": 2,
64
+ "metadata": {},
65
+ "outputs": [],
66
+ "source": [
67
+ "request = ImageEvaluationRequest()\n",
68
+ "# Define paths\n",
69
+ "REPO_ID = request.dataset_name\n",
70
+ "OUTPUT_DIR = \"../pyro-sdis\""
71
+ ]
72
+ },
73
+ {
74
+ "cell_type": "markdown",
75
+ "metadata": {},
76
+ "source": [
77
+ "## 🚧 Code JL"
78
+ ]
79
+ },
80
+ {
81
+ "cell_type": "markdown",
82
+ "metadata": {},
83
+ "source": [
84
+ "**Export Dataset**: Use the following function to save the dataset in Ultralytics format:"
85
+ ]
86
+ },
87
+ {
88
+ "cell_type": "code",
89
+ "execution_count": null,
90
+ "metadata": {},
91
+ "outputs": [],
92
+ "source": [
93
+ "# Load and prepare dataset\n",
94
+ "ds = load_data(REPO_ID, OUTPUT_DIR)"
95
+ ]
96
+ },
97
+ {
98
+ "cell_type": "code",
99
+ "execution_count": null,
100
+ "metadata": {},
101
+ "outputs": [
102
+ {
103
+ "name": "stderr",
104
+ "output_type": "stream",
105
+ "text": [
106
+ "Generating train split: 100%|██████████| 29537/29537 [00:03<00:00, 7616.82 examples/s]\n",
107
+ "Generating val split: 100%|██████████| 4099/4099 [00:00<00:00, 10697.80 examples/s]\n"
108
+ ]
109
+ }
110
+ ],
111
+ "source": [
112
+ "# # Create the directory structure\n",
113
+ "# for split in [\"train\", \"val\"]:\n",
114
+ "# os.makedirs(os.path.join(IMAGE_DIR, split), exist_ok=True)\n",
115
+ "# os.makedirs(os.path.join(LABEL_DIR, split), exist_ok=True)\n",
116
+ "\n",
117
+ "# # Load the dataset from the Hugging Face Hub\n",
118
+ "# dataset = load_dataset(REPO_ID)\n",
119
+ "\n",
120
+ "# # Save in Ultralytics format\n",
121
+ "# def save_ultralytics_format(dataset_split, split):\n",
122
+ "# \"\"\"\n",
123
+ "# Save a dataset split into the Ultralytics format.\n",
124
+ "# Args:\n",
125
+ "# dataset_split: The dataset split (e.g., dataset[\"train\"])\n",
126
+ "# split: \"train\" or \"val\"\n",
127
+ "# \"\"\"\n",
128
+ "# for example in dataset_split:\n",
129
+ "# # Save the image to the appropriate folder\n",
130
+ "# image = example[\"image\"] # PIL.Image.Image\n",
131
+ "# image_name = example[\"image_name\"] # Original file name\n",
132
+ "# output_image_path = os.path.join(IMAGE_DIR, split, image_name)\n",
133
+ "\n",
134
+ "# # Save the image object to disk\n",
135
+ "# image.save(output_image_path)\n",
136
+ "\n",
137
+ "# # Save label\n",
138
+ "# annotations = example[\"annotations\"]\n",
139
+ "# label_name = image_name.replace(\".jpg\", \".txt\").replace(\".png\", \".txt\")\n",
140
+ "# output_label_path = os.path.join(LABEL_DIR, split, label_name)\n",
141
+ " \n",
142
+ "# with open(output_label_path, \"w\") as label_file:\n",
143
+ "# label_file.write(annotations)\n",
144
+ "\n",
145
+ "# # Save train and validation splits\n",
146
+ "# save_ultralytics_format(dataset[\"train\"], \"train\")\n",
147
+ "# save_ultralytics_format(dataset[\"val\"], \"val\")\n",
148
+ "\n",
149
+ "# print(\"Dataset exported to Ultralytics format.\")"
150
+ ]
151
+ },
152
+ {
153
+ "cell_type": "markdown",
154
+ "metadata": {},
155
+ "source": [
156
+ "**Training** with Ultralytics YOLO"
157
+ ]
158
+ },
159
+ {
160
+ "cell_type": "code",
161
+ "execution_count": null,
162
+ "metadata": {},
163
+ "outputs": [],
164
+ "source": [
165
+ "# from huggingface_hub import hf_hub_download\n",
166
+ "\n",
167
+ "# # Correctly set repo_id and repo_type\n",
168
+ "# repo_id = \"pyronear/pyro-sdis\"\n",
169
+ "# filename = \"data.yaml\"\n",
170
+ "\n",
171
+ "# # Download data.yaml to the current directory\n",
172
+ "# yaml_path = hf_hub_download(repo_id=repo_id, filename=filename, repo_type=\"dataset\", local_dir=\".\")\n",
173
+ "# print(f\"data.yaml downloaded to: {yaml_path}\")"
174
+ ]
175
+ },
176
+ {
177
+ "cell_type": "markdown",
178
+ "metadata": {},
179
+ "source": [
180
+ "Train with Yolo (command line)"
181
+ ]
182
+ },
183
+ {
184
+ "cell_type": "code",
185
+ "execution_count": null,
186
+ "metadata": {},
187
+ "outputs": [],
188
+ "source": [
189
+ "# yolo task=detect mode=train data=data.yaml model=yolov8n.pt epochs=50 imgsz=640 single_cls=True"
190
+ ]
191
+ },
192
+ {
193
+ "cell_type": "markdown",
194
+ "metadata": {},
195
+ "source": [
196
+ "## 🚧 fin Code JL"
197
+ ]
198
+ },
199
+ {
200
+ "cell_type": "code",
201
+ "execution_count": null,
202
+ "metadata": {},
203
+ "outputs": [],
204
+ "source": [
205
+ "# Split dataset\n",
206
+ "train_test = dataset[\"train\"].train_test_split(test_size=request.test_size, seed=request.test_seed)\n",
207
+ "test_dataset = train_test[\"test\"]"
208
+ ]
209
+ },
210
+ {
211
+ "cell_type": "code",
212
+ "execution_count": 7,
213
+ "metadata": {},
214
+ "outputs": [
215
+ {
216
+ "data": {
217
+ "text/plain": [
218
+ "Dataset({\n",
219
+ " features: ['image', 'annotations', 'image_name', 'partner', 'camera', 'date'],\n",
220
+ " num_rows: 29537\n",
221
+ "})"
222
+ ]
223
+ },
224
+ "execution_count": 7,
225
+ "metadata": {},
226
+ "output_type": "execute_result"
227
+ }
228
+ ],
229
+ "source": [
230
+ "dataset[\"train\"]"
231
+ ]
232
+ },
233
+ {
234
+ "cell_type": "code",
235
+ "execution_count": 14,
236
+ "metadata": {},
237
+ "outputs": [
238
+ {
239
+ "data": {
240
+ "text/plain": [
241
+ "datasets.dataset_dict.DatasetDict"
242
+ ]
243
+ },
244
+ "execution_count": 14,
245
+ "metadata": {},
246
+ "output_type": "execute_result"
247
+ }
248
+ ],
249
+ "source": [
250
+ "type(dataset)"
251
+ ]
252
+ },
253
+ {
254
+ "cell_type": "markdown",
255
+ "metadata": {},
256
+ "source": [
257
+ "## Random Baseline"
258
+ ]
259
+ },
260
+ {
261
+ "cell_type": "code",
262
+ "execution_count": 8,
263
+ "metadata": {},
264
+ "outputs": [
265
+ {
266
+ "name": "stderr",
267
+ "output_type": "stream",
268
+ "text": [
269
+ "[codecarbon WARNING @ 17:11:39] Already started tracking\n",
270
+ "[codecarbon INFO @ 17:11:39] A task is already under measure\n"
271
+ ]
272
+ }
273
+ ],
274
+ "source": [
275
+ "# Start tracking emissions\n",
276
+ "tracker.start()\n",
277
+ "tracker.start_task(\"inference\")"
278
+ ]
279
+ },
280
+ {
281
+ "cell_type": "code",
282
+ "execution_count": 11,
283
+ "metadata": {},
284
+ "outputs": [],
285
+ "source": [
286
+ "\n",
287
+ "#--------------------------------------------------------------------------------------------\n",
288
+ "# YOUR MODEL INFERENCE CODE HERE\n",
289
+ "# Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.\n",
290
+ "#--------------------------------------------------------------------------------------------\n",
291
+ "\n",
292
+ "# Make random predictions (placeholder for actual model inference)\n",
293
+ "\n",
294
+ "predictions = []\n",
295
+ "true_labels = []\n",
296
+ "pred_boxes = []\n",
297
+ "true_boxes_list = [] # List of lists, each inner list contains boxes for one image\n",
298
+ "\n",
299
+ "for example in test_dataset:\n",
300
+ " # Parse true annotation (YOLO format: class_id x_center y_center width height)\n",
301
+ " annotation = example.get(\"annotations\", \"\").strip()\n",
302
+ " has_smoke = len(annotation) > 0\n",
303
+ " true_labels.append(int(has_smoke))\n",
304
+ " \n",
305
+ " # Make random classification prediction\n",
306
+ " pred_has_smoke = random.random() > 0.5\n",
307
+ " predictions.append(int(pred_has_smoke))\n",
308
+ " \n",
309
+ " # If there's a true box, parse it and make random box prediction\n",
310
+ " if has_smoke:\n",
311
+ " # Parse all true boxes from the annotation\n",
312
+ " image_true_boxes = parse_boxes(annotation)\n",
313
+ " true_boxes_list.append(image_true_boxes)\n",
314
+ " \n",
315
+ " # For baseline, make one random box prediction per image\n",
316
+ " # In a real model, you might want to predict multiple boxes\n",
317
+ " random_box = [\n",
318
+ " random.random(), # x_center\n",
319
+ " random.random(), # y_center\n",
320
+ " random.random() * 0.5, # width (max 0.5)\n",
321
+ " random.random() * 0.5 # height (max 0.5)\n",
322
+ " ]\n",
323
+ " pred_boxes.append(random_box)\n",
324
+ "\n",
325
+ "\n",
326
+ "#--------------------------------------------------------------------------------------------\n",
327
+ "# YOUR MODEL INFERENCE STOPS HERE\n",
328
+ "#-------------------------------------------------------------------------------------------- "
329
+ ]
330
+ },
331
+ {
332
+ "cell_type": "code",
333
+ "execution_count": 9,
334
+ "metadata": {},
335
+ "outputs": [
336
+ {
337
+ "name": "stderr",
338
+ "output_type": "stream",
339
+ "text": [
340
+ "[codecarbon WARNING @ 17:12:24] Background scheduler didn't run for a long period (1885s), results might be inaccurate\n",
341
+ "[codecarbon INFO @ 17:12:24] Energy consumed for RAM : 0.003142 kWh. RAM Power : 6.0 W\n",
342
+ "[codecarbon INFO @ 17:12:24] Energy consumed for all CPUs : 0.002618 kWh. Total CPU Power : 5.0 W\n",
343
+ "[codecarbon INFO @ 17:12:24] 0.005760 kWh of electricity used since the beginning.\n"
344
+ ]
345
+ }
346
+ ],
347
+ "source": [
348
+ "# Stop tracking emissions\n",
349
+ "emissions_data = tracker.stop_task()"
350
+ ]
351
+ },
352
+ {
353
+ "cell_type": "code",
354
+ "execution_count": 12,
355
+ "metadata": {},
356
+ "outputs": [],
357
+ "source": [
358
+ "import numpy as np\n",
359
+ "\n",
360
+ "# Calculate classification metrics\n",
361
+ "classification_accuracy = accuracy_score(true_labels, predictions)\n",
362
+ "classification_precision = precision_score(true_labels, predictions)\n",
363
+ "classification_recall = recall_score(true_labels, predictions)\n",
364
+ "\n",
365
+ "# Calculate mean IoU for object detection (only for images with smoke)\n",
366
+ "# For each image, we compute the max IoU between the predicted box and all true boxes\n",
367
+ "ious = []\n",
368
+ "for true_boxes, pred_box in zip(true_boxes_list, pred_boxes):\n",
369
+ " max_iou = compute_max_iou(true_boxes, pred_box)\n",
370
+ " ious.append(max_iou)\n",
371
+ "\n",
372
+ "mean_iou = float(np.mean(ious)) if ious else 0.0"
373
+ ]
374
+ },
375
+ {
376
+ "cell_type": "code",
377
+ "execution_count": 13,
378
+ "metadata": {},
379
+ "outputs": [
380
+ {
381
+ "data": {
382
+ "text/plain": [
383
+ "{'submission_timestamp': '2025-01-23T17:13:47.158903',\n",
384
+ " 'classification_accuracy': 0.4974610697359513,\n",
385
+ " 'classification_precision': 0.8362892223738063,\n",
386
+ " 'classification_recall': 0.49625581866019025,\n",
387
+ " 'mean_iou': 0.0026954029097350594,\n",
388
+ " 'energy_consumed_wh': 5.759879923426909,\n",
389
+ " 'emissions_gco2eq': 0.2006914961719638,\n",
390
+ " 'emissions_data': {'run_id': 'fbab9dd9-2893-4216-91c4-232be358d4dd',\n",
391
+ " 'duration': 1885.054949500016,\n",
392
+ " 'emissions': 0.0002006914961719638,\n",
393
+ " 'emissions_rate': 1.0646457428260931e-07,\n",
394
+ " 'cpu_power': 5.0,\n",
395
+ " 'gpu_power': 0.0,\n",
396
+ " 'ram_power': 6.0,\n",
397
+ " 'cpu_energy': 0.002618128800231918,\n",
398
+ " 'gpu_energy': 0,\n",
399
+ " 'ram_energy': 0.0031417511231949906,\n",
400
+ " 'energy_consumed': 0.005759879923426909,\n",
401
+ " 'country_name': 'Switzerland',\n",
402
+ " 'country_iso_code': 'CHE',\n",
403
+ " 'region': 'zurich',\n",
404
+ " 'cloud_provider': '',\n",
405
+ " 'cloud_region': '',\n",
406
+ " 'os': 'macOS-15.2-arm64-arm-64bit',\n",
407
+ " 'python_version': '3.12.7',\n",
408
+ " 'codecarbon_version': '2.8.3',\n",
409
+ " 'cpu_count': 8,\n",
410
+ " 'cpu_model': 'Apple M1',\n",
411
+ " 'gpu_count': None,\n",
412
+ " 'gpu_model': None,\n",
413
+ " 'ram_total_size': 16.0,\n",
414
+ " 'tracking_mode': 'machine',\n",
415
+ " 'on_cloud': 'N',\n",
416
+ " 'pue': 1.0},\n",
417
+ " 'dataset_config': {'dataset_name': 'pyronear/pyro-sdis',\n",
418
+ " 'test_size': 0.2,\n",
419
+ " 'test_seed': 42}}"
420
+ ]
421
+ },
422
+ "execution_count": 13,
423
+ "metadata": {},
424
+ "output_type": "execute_result"
425
+ }
426
+ ],
427
+ "source": [
428
+ "\n",
429
+ "# Prepare results dictionary\n",
430
+ "results = {\n",
431
+ " \"submission_timestamp\": datetime.now().isoformat(),\n",
432
+ " \"classification_accuracy\": float(classification_accuracy),\n",
433
+ " \"classification_precision\": float(classification_precision),\n",
434
+ " \"classification_recall\": float(classification_recall),\n",
435
+ " \"mean_iou\": mean_iou,\n",
436
+ " \"energy_consumed_wh\": emissions_data.energy_consumed * 1000,\n",
437
+ " \"emissions_gco2eq\": emissions_data.emissions * 1000,\n",
438
+ " \"emissions_data\": clean_emissions_data(emissions_data),\n",
439
+ " \"dataset_config\": {\n",
440
+ " \"dataset_name\": request.dataset_name,\n",
441
+ " \"test_size\": request.test_size,\n",
442
+ " \"test_seed\": request.test_seed\n",
443
+ " }\n",
444
+ "}\n",
445
+ "results"
446
+ ]
447
+ },
448
+ {
449
+ "cell_type": "markdown",
450
+ "metadata": {},
451
+ "source": []
452
+ }
453
+ ],
454
+ "metadata": {
455
+ "kernelspec": {
456
+ "display_name": ".venv",
457
+ "language": "python",
458
+ "name": "python3"
459
+ },
460
+ "language_info": {
461
+ "codemirror_mode": {
462
+ "name": "ipython",
463
+ "version": 3
464
+ },
465
+ "file_extension": ".py",
466
+ "mimetype": "text/x-python",
467
+ "name": "python",
468
+ "nbconvert_exporter": "python",
469
+ "pygments_lexer": "ipython3",
470
+ "version": "3.12.7"
471
+ }
472
+ },
473
+ "nbformat": 4,
474
+ "nbformat_minor": 2
475
+ }
notebooks/template-text.ipynb ADDED
@@ -0,0 +1,1642 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "metadata": {},
6
+ "source": [
7
+ "# Text task notebook template\n",
8
+ "## Loading the necessary libraries"
9
+ ]
10
+ },
11
+ {
12
+ "cell_type": "code",
13
+ "execution_count": 3,
14
+ "metadata": {},
15
+ "outputs": [
16
+ {
17
+ "name": "stderr",
18
+ "output_type": "stream",
19
+ "text": [
20
+ "[codecarbon WARNING @ 19:48:07] Multiple instances of codecarbon are allowed to run at the same time.\n",
21
+ "[codecarbon INFO @ 19:48:07] [setup] RAM Tracking...\n",
22
+ "[codecarbon INFO @ 19:48:07] [setup] CPU Tracking...\n",
23
+ "[codecarbon WARNING @ 19:48:09] We saw that you have a 13th Gen Intel(R) Core(TM) i7-1365U but we don't know it. Please contact us.\n",
24
+ "[codecarbon WARNING @ 19:48:09] No CPU tracking mode found. Falling back on CPU constant mode. \n",
25
+ " Windows OS detected: Please install Intel Power Gadget to measure CPU\n",
26
+ "\n",
27
+ "[codecarbon WARNING @ 19:48:11] We saw that you have a 13th Gen Intel(R) Core(TM) i7-1365U but we don't know it. Please contact us.\n",
28
+ "[codecarbon INFO @ 19:48:11] CPU Model on constant consumption mode: 13th Gen Intel(R) Core(TM) i7-1365U\n",
29
+ "[codecarbon WARNING @ 19:48:11] No CPU tracking mode found. Falling back on CPU constant mode.\n",
30
+ "[codecarbon INFO @ 19:48:11] [setup] GPU Tracking...\n",
31
+ "[codecarbon INFO @ 19:48:11] No GPU found.\n",
32
+ "[codecarbon INFO @ 19:48:11] >>> Tracker's metadata:\n",
33
+ "[codecarbon INFO @ 19:48:11] Platform system: Windows-11-10.0.22631-SP0\n",
34
+ "[codecarbon INFO @ 19:48:11] Python version: 3.12.7\n",
35
+ "[codecarbon INFO @ 19:48:11] CodeCarbon version: 3.0.0_rc0\n",
36
+ "[codecarbon INFO @ 19:48:11] Available RAM : 31.347 GB\n",
37
+ "[codecarbon INFO @ 19:48:11] CPU count: 12\n",
38
+ "[codecarbon INFO @ 19:48:11] CPU model: 13th Gen Intel(R) Core(TM) i7-1365U\n",
39
+ "[codecarbon INFO @ 19:48:11] GPU count: None\n",
40
+ "[codecarbon INFO @ 19:48:11] GPU model: None\n",
41
+ "[codecarbon INFO @ 19:48:11] Saving emissions data to file c:\\git\\submission-template\\notebooks\\emissions.csv\n"
42
+ ]
43
+ }
44
+ ],
45
+ "source": [
46
+ "from fastapi import APIRouter\n",
47
+ "from datetime import datetime\n",
48
+ "from datasets import load_dataset\n",
49
+ "from sklearn.metrics import accuracy_score\n",
50
+ "import random\n",
51
+ "\n",
52
+ "import sys\n",
53
+ "sys.path.append('../tasks')\n",
54
+ "\n",
55
+ "from utils.evaluation import TextEvaluationRequest\n",
56
+ "from utils.emissions import tracker, clean_emissions_data, get_space_info\n",
57
+ "\n",
58
+ "\n",
59
+ "# Define the label mapping\n",
60
+ "LABEL_MAPPING = {\n",
61
+ " \"0_not_relevant\": 0,\n",
62
+ " \"1_not_happening\": 1,\n",
63
+ " \"2_not_human\": 2,\n",
64
+ " \"3_not_bad\": 3,\n",
65
+ " \"4_solutions_harmful_unnecessary\": 4,\n",
66
+ " \"5_science_unreliable\": 5,\n",
67
+ " \"6_proponents_biased\": 6,\n",
68
+ " \"7_fossil_fuels_needed\": 7\n",
69
+ "}"
70
+ ]
71
+ },
72
+ {
73
+ "cell_type": "markdown",
74
+ "metadata": {},
75
+ "source": [
76
+ "## Loading the datasets and splitting them"
77
+ ]
78
+ },
79
+ {
80
+ "cell_type": "code",
81
+ "execution_count": 4,
82
+ "metadata": {},
83
+ "outputs": [
84
+ {
85
+ "data": {
86
+ "application/vnd.jupyter.widget-view+json": {
87
+ "model_id": "668da7bf85434e098b95c3ec447d78fe",
88
+ "version_major": 2,
89
+ "version_minor": 0
90
+ },
91
+ "text/plain": [
92
+ "README.md: 0%| | 0.00/5.18k [00:00<?, ?B/s]"
93
+ ]
94
+ },
95
+ "metadata": {},
96
+ "output_type": "display_data"
97
+ },
98
+ {
99
+ "name": "stderr",
100
+ "output_type": "stream",
101
+ "text": [
102
+ "c:\\Users\\theo.alvesdacosta\\AppData\\Local\\anaconda3\\Lib\\site-packages\\huggingface_hub\\file_download.py:139: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\theo.alvesdacosta\\.cache\\huggingface\\hub\\datasets--QuotaClimat--frugalaichallenge-text-train. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
103
+ "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
104
+ " warnings.warn(message)\n"
105
+ ]
106
+ },
107
+ {
108
+ "data": {
109
+ "application/vnd.jupyter.widget-view+json": {
110
+ "model_id": "5b68d43359eb429395da8be7d4b15556",
111
+ "version_major": 2,
112
+ "version_minor": 0
113
+ },
114
+ "text/plain": [
115
+ "train.parquet: 0%| | 0.00/1.21M [00:00<?, ?B/s]"
116
+ ]
117
+ },
118
+ "metadata": {},
119
+ "output_type": "display_data"
120
+ },
121
+ {
122
+ "data": {
123
+ "application/vnd.jupyter.widget-view+json": {
124
+ "model_id": "140a304773914e9db8f698eabeb40298",
125
+ "version_major": 2,
126
+ "version_minor": 0
127
+ },
128
+ "text/plain": [
129
+ "Generating train split: 0%| | 0/6091 [00:00<?, ? examples/s]"
130
+ ]
131
+ },
132
+ "metadata": {},
133
+ "output_type": "display_data"
134
+ },
135
+ {
136
+ "data": {
137
+ "application/vnd.jupyter.widget-view+json": {
138
+ "model_id": "6d04e8ab1906400e8e0029949dc523a5",
139
+ "version_major": 2,
140
+ "version_minor": 0
141
+ },
142
+ "text/plain": [
143
+ "Map: 0%| | 0/6091 [00:00<?, ? examples/s]"
144
+ ]
145
+ },
146
+ "metadata": {},
147
+ "output_type": "display_data"
148
+ }
149
+ ],
150
+ "source": [
151
+ "request = TextEvaluationRequest()\n",
152
+ "\n",
153
+ "# Load and prepare the dataset\n",
154
+ "dataset = load_dataset(request.dataset_name)\n",
155
+ "\n",
156
+ "# Convert string labels to integers\n",
157
+ "dataset = dataset.map(lambda x: {\"label\": LABEL_MAPPING[x[\"label\"]]})\n",
158
+ "\n",
159
+ "# Split dataset\n",
160
+ "train_test = dataset[\"train\"].train_test_split(test_size=request.test_size, seed=request.test_seed)\n",
161
+ "test_dataset = train_test[\"test\"]"
162
+ ]
163
+ },
164
+ {
165
+ "cell_type": "markdown",
166
+ "metadata": {},
167
+ "source": [
168
+ "## Random Baseline"
169
+ ]
170
+ },
171
+ {
172
+ "cell_type": "code",
173
+ "execution_count": 5,
174
+ "metadata": {},
175
+ "outputs": [],
176
+ "source": [
177
+ "# Start tracking emissions\n",
178
+ "tracker.start()\n",
179
+ "tracker.start_task(\"inference\")"
180
+ ]
181
+ },
182
+ {
183
+ "cell_type": "code",
184
+ "execution_count": 6,
185
+ "metadata": {},
186
+ "outputs": [
187
+ {
188
+ "data": {
189
+ "text/plain": [
190
+ "[1,\n",
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+ " 7,\n",
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+ " 6,\n",
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1192
+ },
1193
+ "execution_count": 6,
1194
+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
1198
+ "source": [
1199
+ "\n",
1200
+ "#--------------------------------------------------------------------------------------------\n",
1201
+ "# YOUR MODEL INFERENCE CODE HERE\n",
1202
+ "# Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.\n",
1203
+ "#-------------------------------------------------------------------------------------------- \n",
1204
+ "\n",
1205
+ "# Make random predictions (placeholder for actual model inference)\n",
1206
+ "true_labels = test_dataset[\"label\"]\n",
1207
+ "predictions = [random.randint(0, 7) for _ in range(len(true_labels))]\n",
1208
+ "\n",
1209
+ "predictions\n",
1210
+ "\n",
1211
+ "#--------------------------------------------------------------------------------------------\n",
1212
+ "# YOUR MODEL INFERENCE STOPS HERE\n",
1213
+ "#-------------------------------------------------------------------------------------------- "
1214
+ ]
1215
+ },
1216
+ {
1217
+ "cell_type": "code",
1218
+ "execution_count": 8,
1219
+ "metadata": {},
1220
+ "outputs": [
1221
+ {
1222
+ "name": "stderr",
1223
+ "output_type": "stream",
1224
+ "text": [
1225
+ "[codecarbon WARNING @ 19:53:32] Background scheduler didn't run for a long period (47s), results might be inaccurate\n",
1226
+ "[codecarbon INFO @ 19:53:32] Energy consumed for RAM : 0.000156 kWh. RAM Power : 11.755242347717285 W\n",
1227
+ "[codecarbon INFO @ 19:53:32] Delta energy consumed for CPU with constant : 0.000564 kWh, power : 42.5 W\n",
1228
+ "[codecarbon INFO @ 19:53:32] Energy consumed for All CPU : 0.000564 kWh\n",
1229
+ "[codecarbon INFO @ 19:53:32] 0.000720 kWh of electricity used since the beginning.\n"
1230
+ ]
1231
+ },
1232
+ {
1233
+ "data": {
1234
+ "text/plain": [
1235
+ "EmissionsData(timestamp='2025-01-21T19:53:32', project_name='codecarbon', run_id='908f2e7e-4bb2-4991-a0f6-56bf8d7eda21', experiment_id='5b0fa12a-3dd7-45bb-9766-cc326314d9f1', duration=47.736408500000834, emissions=4.032368007471064e-05, emissions_rate=8.444466886328872e-07, cpu_power=42.5, gpu_power=0.0, ram_power=11.755242347717285, cpu_energy=0.0005636615353475565, gpu_energy=0, ram_energy=0.00015590305493261682, energy_consumed=0.0007195645902801733, country_name='France', country_iso_code='FRA', region='île-de-france', cloud_provider='', cloud_region='', os='Windows-11-10.0.22631-SP0', python_version='3.12.7', codecarbon_version='3.0.0_rc0', cpu_count=12, cpu_model='13th Gen Intel(R) Core(TM) i7-1365U', gpu_count=None, gpu_model=None, longitude=2.3494, latitude=48.8558, ram_total_size=31.347312927246094, tracking_mode='machine', on_cloud='N', pue=1.0)"
1236
+ ]
1237
+ },
1238
+ "execution_count": 8,
1239
+ "metadata": {},
1240
+ "output_type": "execute_result"
1241
+ }
1242
+ ],
1243
+ "source": [
1244
+ "# Stop tracking emissions\n",
1245
+ "emissions_data = tracker.stop_task()\n",
1246
+ "emissions_data"
1247
+ ]
1248
+ },
1249
+ {
1250
+ "cell_type": "code",
1251
+ "execution_count": 9,
1252
+ "metadata": {},
1253
+ "outputs": [
1254
+ {
1255
+ "data": {
1256
+ "text/plain": [
1257
+ "0.10090237899917966"
1258
+ ]
1259
+ },
1260
+ "execution_count": 9,
1261
+ "metadata": {},
1262
+ "output_type": "execute_result"
1263
+ }
1264
+ ],
1265
+ "source": [
1266
+ "# Calculate accuracy\n",
1267
+ "accuracy = accuracy_score(true_labels, predictions)\n",
1268
+ "accuracy"
1269
+ ]
1270
+ },
1271
+ {
1272
+ "cell_type": "code",
1273
+ "execution_count": 10,
1274
+ "metadata": {},
1275
+ "outputs": [
1276
+ {
1277
+ "data": {
1278
+ "text/plain": [
1279
+ "{'submission_timestamp': '2025-01-21T19:53:46.639165',\n",
1280
+ " 'accuracy': 0.10090237899917966,\n",
1281
+ " 'energy_consumed_wh': 0.7195645902801733,\n",
1282
+ " 'emissions_gco2eq': 0.040323680074710634,\n",
1283
+ " 'emissions_data': {'run_id': '908f2e7e-4bb2-4991-a0f6-56bf8d7eda21',\n",
1284
+ " 'duration': 47.736408500000834,\n",
1285
+ " 'emissions': 4.032368007471064e-05,\n",
1286
+ " 'emissions_rate': 8.444466886328872e-07,\n",
1287
+ " 'cpu_power': 42.5,\n",
1288
+ " 'gpu_power': 0.0,\n",
1289
+ " 'ram_power': 11.755242347717285,\n",
1290
+ " 'cpu_energy': 0.0005636615353475565,\n",
1291
+ " 'gpu_energy': 0,\n",
1292
+ " 'ram_energy': 0.00015590305493261682,\n",
1293
+ " 'energy_consumed': 0.0007195645902801733,\n",
1294
+ " 'country_name': 'France',\n",
1295
+ " 'country_iso_code': 'FRA',\n",
1296
+ " 'region': 'île-de-france',\n",
1297
+ " 'cloud_provider': '',\n",
1298
+ " 'cloud_region': '',\n",
1299
+ " 'os': 'Windows-11-10.0.22631-SP0',\n",
1300
+ " 'python_version': '3.12.7',\n",
1301
+ " 'codecarbon_version': '3.0.0_rc0',\n",
1302
+ " 'cpu_count': 12,\n",
1303
+ " 'cpu_model': '13th Gen Intel(R) Core(TM) i7-1365U',\n",
1304
+ " 'gpu_count': None,\n",
1305
+ " 'gpu_model': None,\n",
1306
+ " 'ram_total_size': 31.347312927246094,\n",
1307
+ " 'tracking_mode': 'machine',\n",
1308
+ " 'on_cloud': 'N',\n",
1309
+ " 'pue': 1.0},\n",
1310
+ " 'dataset_config': {'dataset_name': 'QuotaClimat/frugalaichallenge-text-train',\n",
1311
+ " 'test_size': 0.2,\n",
1312
+ " 'test_seed': 42}}"
1313
+ ]
1314
+ },
1315
+ "execution_count": 10,
1316
+ "metadata": {},
1317
+ "output_type": "execute_result"
1318
+ }
1319
+ ],
1320
+ "source": [
1321
+ "# Prepare results dictionary\n",
1322
+ "results = {\n",
1323
+ " \"submission_timestamp\": datetime.now().isoformat(),\n",
1324
+ " \"accuracy\": float(accuracy),\n",
1325
+ " \"energy_consumed_wh\": emissions_data.energy_consumed * 1000,\n",
1326
+ " \"emissions_gco2eq\": emissions_data.emissions * 1000,\n",
1327
+ " \"emissions_data\": clean_emissions_data(emissions_data),\n",
1328
+ " \"dataset_config\": {\n",
1329
+ " \"dataset_name\": request.dataset_name,\n",
1330
+ " \"test_size\": request.test_size,\n",
1331
+ " \"test_seed\": request.test_seed\n",
1332
+ " }\n",
1333
+ "}\n",
1334
+ "\n",
1335
+ "results"
1336
+ ]
1337
+ },
1338
+ {
1339
+ "cell_type": "markdown",
1340
+ "metadata": {},
1341
+ "source": [
1342
+ "## Development of the model"
1343
+ ]
1344
+ },
1345
+ {
1346
+ "cell_type": "code",
1347
+ "execution_count": 11,
1348
+ "metadata": {},
1349
+ "outputs": [
1350
+ {
1351
+ "data": {
1352
+ "application/vnd.jupyter.widget-view+json": {
1353
+ "model_id": "90f50ab19698484489f36976745efad3",
1354
+ "version_major": 2,
1355
+ "version_minor": 0
1356
+ },
1357
+ "text/plain": [
1358
+ "config.json: 0%| | 0.00/1.15k [00:00<?, ?B/s]"
1359
+ ]
1360
+ },
1361
+ "metadata": {},
1362
+ "output_type": "display_data"
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+ },
1364
+ {
1365
+ "name": "stderr",
1366
+ "output_type": "stream",
1367
+ "text": [
1368
+ "c:\\Users\\theo.alvesdacosta\\AppData\\Local\\anaconda3\\Lib\\site-packages\\huggingface_hub\\file_download.py:139: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\theo.alvesdacosta\\.cache\\huggingface\\hub\\models--facebook--bart-large-mnli. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
1369
+ "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
1370
+ " warnings.warn(message)\n"
1371
+ ]
1372
+ },
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+ {
1374
+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "version_minor": 0
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+ },
1380
+ "text/plain": [
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+ "model.safetensors: 0%| | 0.00/1.63G [00:00<?, ?B/s]"
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+ "output_type": "display_data"
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+ {
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+ "data": {
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+ "model_id": "bc29cb379c644b00b1bdf61d5426d99d",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
1394
+ "text/plain": [
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+ "tokenizer_config.json: 0%| | 0.00/26.0 [00:00<?, ?B/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
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+ "data": {
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
1408
+ "text/plain": [
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+ "vocab.json: 0%| | 0.00/899k [00:00<?, ?B/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ {
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+ "data": {
1417
+ "application/vnd.jupyter.widget-view+json": {
1418
+ "model_id": "3a5f53e451e8483ca7c33f42245abd13",
1419
+ "version_major": 2,
1420
+ "version_minor": 0
1421
+ },
1422
+ "text/plain": [
1423
+ "merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s]"
1424
+ ]
1425
+ },
1426
+ "metadata": {},
1427
+ "output_type": "display_data"
1428
+ },
1429
+ {
1430
+ "data": {
1431
+ "application/vnd.jupyter.widget-view+json": {
1432
+ "model_id": "84f922d1b68a4a0faa5e920d004efca0",
1433
+ "version_major": 2,
1434
+ "version_minor": 0
1435
+ },
1436
+ "text/plain": [
1437
+ "tokenizer.json: 0%| | 0.00/1.36M [00:00<?, ?B/s]"
1438
+ ]
1439
+ },
1440
+ "metadata": {},
1441
+ "output_type": "display_data"
1442
+ },
1443
+ {
1444
+ "name": "stderr",
1445
+ "output_type": "stream",
1446
+ "text": [
1447
+ "Device set to use cpu\n"
1448
+ ]
1449
+ }
1450
+ ],
1451
+ "source": [
1452
+ "from transformers import pipeline\n",
1453
+ "classifier = pipeline(\"zero-shot-classification\",\n",
1454
+ " model=\"facebook/bart-large-mnli\")\n"
1455
+ ]
1456
+ },
1457
+ {
1458
+ "cell_type": "code",
1459
+ "execution_count": 14,
1460
+ "metadata": {},
1461
+ "outputs": [],
1462
+ "source": [
1463
+ "sequence_to_classify = \"one day I will see the world\"\n",
1464
+ "\n",
1465
+ "candidate_labels = [\n",
1466
+ " \"Not related to climate change disinformation\",\n",
1467
+ " \"Climate change is not real and not happening\",\n",
1468
+ " \"Climate change is not human-induced\",\n",
1469
+ " \"Climate change impacts are not that bad\",\n",
1470
+ " \"Climate change solutions are harmful and unnecessary\",\n",
1471
+ " \"Climate change science is unreliable\",\n",
1472
+ " \"Climate change proponents are biased\",\n",
1473
+ " \"Fossil fuels are needed to address climate change\"\n",
1474
+ "]"
1475
+ ]
1476
+ },
1477
+ {
1478
+ "cell_type": "code",
1479
+ "execution_count": 15,
1480
+ "metadata": {},
1481
+ "outputs": [
1482
+ {
1483
+ "data": {
1484
+ "text/plain": [
1485
+ "{'sequence': 'one day I will see the world',\n",
1486
+ " 'labels': ['Fossil fuels are needed to address climate change',\n",
1487
+ " 'Climate change science is unreliable',\n",
1488
+ " 'Not related to climate change disinformation',\n",
1489
+ " 'Climate change proponents are biased',\n",
1490
+ " 'Climate change impacts are not that bad',\n",
1491
+ " 'Climate change solutions are harmful and unnecessary',\n",
1492
+ " 'Climate change is not human-induced',\n",
1493
+ " 'Climate change is not real and not happening'],\n",
1494
+ " 'scores': [0.16242119669914246,\n",
1495
+ " 0.15683825314044952,\n",
1496
+ " 0.1564282774925232,\n",
1497
+ " 0.14603719115257263,\n",
1498
+ " 0.12794046103954315,\n",
1499
+ " 0.10180754214525223,\n",
1500
+ " 0.0936085507273674,\n",
1501
+ " 0.0549185685813427]}"
1502
+ ]
1503
+ },
1504
+ "execution_count": 15,
1505
+ "metadata": {},
1506
+ "output_type": "execute_result"
1507
+ }
1508
+ ],
1509
+ "source": [
1510
+ "classifier(sequence_to_classify, candidate_labels)"
1511
+ ]
1512
+ },
1513
+ {
1514
+ "cell_type": "code",
1515
+ "execution_count": 26,
1516
+ "metadata": {},
1517
+ "outputs": [
1518
+ {
1519
+ "name": "stderr",
1520
+ "output_type": "stream",
1521
+ "text": [
1522
+ "[codecarbon WARNING @ 11:00:07] Already started tracking\n"
1523
+ ]
1524
+ },
1525
+ {
1526
+ "data": {
1527
+ "application/vnd.jupyter.widget-view+json": {
1528
+ "model_id": "5d66a13f76a4411d95b62d4a73012495",
1529
+ "version_major": 2,
1530
+ "version_minor": 0
1531
+ },
1532
+ "text/plain": [
1533
+ "0it [00:00, ?it/s]"
1534
+ ]
1535
+ },
1536
+ "metadata": {},
1537
+ "output_type": "display_data"
1538
+ },
1539
+ {
1540
+ "name": "stderr",
1541
+ "output_type": "stream",
1542
+ "text": [
1543
+ "[codecarbon WARNING @ 11:05:57] Background scheduler didn't run for a long period (349s), results might be inaccurate\n",
1544
+ "[codecarbon INFO @ 11:05:57] Energy consumed for RAM : 0.018069 kWh. RAM Power : 11.755242347717285 W\n",
1545
+ "[codecarbon INFO @ 11:05:57] Delta energy consumed for CPU with constant : 0.004122 kWh, power : 42.5 W\n",
1546
+ "[codecarbon INFO @ 11:05:57] Energy consumed for All CPU : 0.065327 kWh\n",
1547
+ "[codecarbon INFO @ 11:05:57] 0.083395 kWh of electricity used since the beginning.\n"
1548
+ ]
1549
+ },
1550
+ {
1551
+ "data": {
1552
+ "text/plain": [
1553
+ "EmissionsData(timestamp='2025-01-22T11:05:57', project_name='codecarbon', run_id='908f2e7e-4bb2-4991-a0f6-56bf8d7eda21', experiment_id='5b0fa12a-3dd7-45bb-9766-cc326314d9f1', duration=349.19709450000664, emissions=0.0002949120266226386, emissions_rate=8.445461750018632e-07, cpu_power=42.5, gpu_power=0.0, ram_power=11.755242347717285, cpu_energy=0.004122396676597424, gpu_energy=0, ram_energy=0.0011402244733631148, energy_consumed=0.005262621149960539, country_name='France', country_iso_code='FRA', region='île-de-france', cloud_provider='', cloud_region='', os='Windows-11-10.0.22631-SP0', python_version='3.12.7', codecarbon_version='3.0.0_rc0', cpu_count=12, cpu_model='13th Gen Intel(R) Core(TM) i7-1365U', gpu_count=None, gpu_model=None, longitude=2.3494, latitude=48.8558, ram_total_size=31.347312927246094, tracking_mode='machine', on_cloud='N', pue=1.0)"
1554
+ ]
1555
+ },
1556
+ "execution_count": 26,
1557
+ "metadata": {},
1558
+ "output_type": "execute_result"
1559
+ }
1560
+ ],
1561
+ "source": [
1562
+ "# Start tracking emissions\n",
1563
+ "tracker.start()\n",
1564
+ "tracker.start_task(\"inference\")\n",
1565
+ "\n",
1566
+ "from tqdm.auto import tqdm\n",
1567
+ "predictions = []\n",
1568
+ "\n",
1569
+ "\n",
1570
+ "\n",
1571
+ "# Option 1: Simple loop approach\n",
1572
+ "\n",
1573
+ "for i, text in tqdm(enumerate(test_dataset[\"quote\"])):\n",
1574
+ "\n",
1575
+ " result = classifier(text, candidate_labels)\n",
1576
+ "\n",
1577
+ " # Get index of highest scoring label\n",
1578
+ "\n",
1579
+ " pred_label = candidate_labels.index(result[\"labels\"][0])\n",
1580
+ "\n",
1581
+ " predictions.append(pred_label)\n",
1582
+ " if i == 100:\n",
1583
+ " break\n",
1584
+ "\n",
1585
+ "\n",
1586
+ "# Stop tracking emissions\n",
1587
+ "emissions_data = tracker.stop_task()\n",
1588
+ "emissions_data\n"
1589
+ ]
1590
+ },
1591
+ {
1592
+ "cell_type": "code",
1593
+ "execution_count": 28,
1594
+ "metadata": {},
1595
+ "outputs": [
1596
+ {
1597
+ "data": {
1598
+ "text/plain": [
1599
+ "0.4"
1600
+ ]
1601
+ },
1602
+ "execution_count": 28,
1603
+ "metadata": {},
1604
+ "output_type": "execute_result"
1605
+ }
1606
+ ],
1607
+ "source": [
1608
+ "# Calculate accuracy\n",
1609
+ "accuracy = accuracy_score(true_labels[:100], predictions[:100])\n",
1610
+ "accuracy"
1611
+ ]
1612
+ },
1613
+ {
1614
+ "cell_type": "code",
1615
+ "execution_count": null,
1616
+ "metadata": {},
1617
+ "outputs": [],
1618
+ "source": []
1619
+ }
1620
+ ],
1621
+ "metadata": {
1622
+ "kernelspec": {
1623
+ "display_name": "base",
1624
+ "language": "python",
1625
+ "name": "python3"
1626
+ },
1627
+ "language_info": {
1628
+ "codemirror_mode": {
1629
+ "name": "ipython",
1630
+ "version": 3
1631
+ },
1632
+ "file_extension": ".py",
1633
+ "mimetype": "text/x-python",
1634
+ "name": "python",
1635
+ "nbconvert_exporter": "python",
1636
+ "pygments_lexer": "ipython3",
1637
+ "version": "3.12.7"
1638
+ }
1639
+ },
1640
+ "nbformat": 4,
1641
+ "nbformat_minor": 2
1642
+ }
requirements.txt ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ black # PEP8 formatter
2
+ black[jupyter] # PEP8 formatter for Jupyter NB
3
+ codecarbon>=2.3.1
4
+ datasets>=2.14.0
5
+ dill # Pickle substitute for big data sets
6
+ fastapi>=0.68.0
7
+ gradio>=4.0.0
8
+ keras # DL library
9
+ librosa==0.10.2.post1
10
+ mlflow # ML tracker and more
11
+ notebook # all Jupyter notebook needs
12
+ plotly # interactive plots (with JS)
13
+ pylint # great linter
14
+ pydantic>=1.10.0
15
+ pytest # unit tests
16
+ pytest-cov # unit tests, cover a specific function
17
+ python-dotenv # Environment variables
18
+ pyyaml # YAML support
19
+ requests>=2.31.0
20
+ seaborn # plots
21
+ scikit-learn # ML must-have
22
+ scikit-image # scikit learn images
23
+ streamlit # cool Python GUI
24
+ tensorboard # DL monitoring
25
+ tensorflow-macos
26
+ ultralytics
27
+ uvicorn>=0.15.0
28
+ # tensorflow # DL, CPU only
29
+ # tensorflow[and-cuda] # DL with GPU (uncomment for MacOS)
30
+ # tensorflow-metal
tasks/__init__.py ADDED
File without changes
tasks/audio.py ADDED
@@ -0,0 +1,88 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from fastapi import APIRouter
2
+ from datetime import datetime
3
+ from datasets import load_dataset
4
+ from sklearn.metrics import accuracy_score
5
+ import random
6
+ import os
7
+
8
+ from .utils.evaluation import AudioEvaluationRequest
9
+ from .utils.emissions import tracker, clean_emissions_data, get_space_info
10
+
11
+ from dotenv import load_dotenv
12
+ load_dotenv()
13
+
14
+ router = APIRouter()
15
+
16
+ DESCRIPTION = "Random Baseline"
17
+ ROUTE = "/audio"
18
+
19
+
20
+
21
+ @router.post(ROUTE, tags=["Audio Task"],
22
+ description=DESCRIPTION)
23
+ async def evaluate_audio(request: AudioEvaluationRequest):
24
+ """
25
+ Evaluate audio classification for rainforest sound detection.
26
+
27
+ Current Model: Random Baseline
28
+ - Makes random predictions from the label space (0-1)
29
+ - Used as a baseline for comparison
30
+ """
31
+ # Get space info
32
+ username, space_url = get_space_info()
33
+
34
+ # Define the label mapping
35
+ LABEL_MAPPING = {
36
+ "chainsaw": 0,
37
+ "environment": 1
38
+ }
39
+ # Load and prepare the dataset
40
+ # Because the dataset is gated, we need to use the HF_TOKEN environment variable to authenticate
41
+ dataset = load_dataset(request.dataset_name,token=os.getenv("HF_TOKEN"))
42
+
43
+ # Split dataset
44
+ train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
45
+ test_dataset = train_test["test"]
46
+
47
+ # Start tracking emissions
48
+ tracker.start()
49
+ tracker.start_task("inference")
50
+
51
+ #--------------------------------------------------------------------------------------------
52
+ # YOUR MODEL INFERENCE CODE HERE
53
+ # Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
54
+ #--------------------------------------------------------------------------------------------
55
+
56
+ # Make random predictions (placeholder for actual model inference)
57
+ true_labels = test_dataset["label"]
58
+ predictions = [random.randint(0, 1) for _ in range(len(true_labels))]
59
+
60
+ #--------------------------------------------------------------------------------------------
61
+ # YOUR MODEL INFERENCE STOPS HERE
62
+ #--------------------------------------------------------------------------------------------
63
+
64
+ # Stop tracking emissions
65
+ emissions_data = tracker.stop_task()
66
+
67
+ # Calculate accuracy
68
+ accuracy = accuracy_score(true_labels, predictions)
69
+
70
+ # Prepare results dictionary
71
+ results = {
72
+ "username": username,
73
+ "space_url": space_url,
74
+ "submission_timestamp": datetime.now().isoformat(),
75
+ "model_description": DESCRIPTION,
76
+ "accuracy": float(accuracy),
77
+ "energy_consumed_wh": emissions_data.energy_consumed * 1000,
78
+ "emissions_gco2eq": emissions_data.emissions * 1000,
79
+ "emissions_data": clean_emissions_data(emissions_data),
80
+ "api_route": ROUTE,
81
+ "dataset_config": {
82
+ "dataset_name": request.dataset_name,
83
+ "test_size": request.test_size,
84
+ "test_seed": request.test_seed
85
+ }
86
+ }
87
+
88
+ return results
tasks/image.py ADDED
@@ -0,0 +1,176 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from fastapi import APIRouter
2
+ from datetime import datetime
3
+ from datasets import load_dataset
4
+ import numpy as np
5
+ from sklearn.metrics import accuracy_score, precision_score, recall_score
6
+ import random
7
+ import os
8
+
9
+ from .utils.evaluation import ImageEvaluationRequest
10
+ from .utils.emissions import tracker, clean_emissions_data, get_space_info
11
+
12
+ from dotenv import load_dotenv
13
+ load_dotenv()
14
+
15
+ router = APIRouter()
16
+
17
+ DESCRIPTION = "Random Baseline"
18
+ ROUTE = "/image"
19
+
20
+ def parse_boxes(annotation_string):
21
+ """Parse multiple boxes from a single annotation string.
22
+ Each box has 5 values: class_id, x_center, y_center, width, height"""
23
+ values = [float(x) for x in annotation_string.strip().split()]
24
+ boxes = []
25
+ # Each box has 5 values
26
+ for i in range(0, len(values), 5):
27
+ if i + 5 <= len(values):
28
+ # Skip class_id (first value) and take the next 4 values
29
+ box = values[i+1:i+5]
30
+ boxes.append(box)
31
+ return boxes
32
+
33
+ def compute_iou(box1, box2):
34
+ """Compute Intersection over Union (IoU) between two YOLO format boxes."""
35
+ # Convert YOLO format (x_center, y_center, width, height) to corners
36
+ def yolo_to_corners(box):
37
+ x_center, y_center, width, height = box
38
+ x1 = x_center - width/2
39
+ y1 = y_center - height/2
40
+ x2 = x_center + width/2
41
+ y2 = y_center + height/2
42
+ return np.array([x1, y1, x2, y2])
43
+
44
+ box1_corners = yolo_to_corners(box1)
45
+ box2_corners = yolo_to_corners(box2)
46
+
47
+ # Calculate intersection
48
+ x1 = max(box1_corners[0], box2_corners[0])
49
+ y1 = max(box1_corners[1], box2_corners[1])
50
+ x2 = min(box1_corners[2], box2_corners[2])
51
+ y2 = min(box1_corners[3], box2_corners[3])
52
+
53
+ intersection = max(0, x2 - x1) * max(0, y2 - y1)
54
+
55
+ # Calculate union
56
+ box1_area = (box1_corners[2] - box1_corners[0]) * (box1_corners[3] - box1_corners[1])
57
+ box2_area = (box2_corners[2] - box2_corners[0]) * (box2_corners[3] - box2_corners[1])
58
+ union = box1_area + box2_area - intersection
59
+
60
+ return intersection / (union + 1e-6)
61
+
62
+ def compute_max_iou(true_boxes, pred_box):
63
+ """Compute maximum IoU between a predicted box and all true boxes"""
64
+ max_iou = 0
65
+ for true_box in true_boxes:
66
+ iou = compute_iou(true_box, pred_box)
67
+ max_iou = max(max_iou, iou)
68
+ return max_iou
69
+
70
+ @router.post(ROUTE, tags=["Image Task"],
71
+ description=DESCRIPTION)
72
+ async def evaluate_image(request: ImageEvaluationRequest):
73
+ """
74
+ Evaluate image classification and object detection for forest fire smoke.
75
+
76
+ Current Model: Random Baseline
77
+ - Makes random predictions for both classification and bounding boxes
78
+ - Used as a baseline for comparison
79
+
80
+ Metrics:
81
+ - Classification accuracy: Whether an image contains smoke or not
82
+ - Object Detection accuracy: IoU (Intersection over Union) for smoke bounding boxes
83
+ """
84
+ # Get space info
85
+ username, space_url = get_space_info()
86
+
87
+ # Load and prepare the dataset
88
+ dataset = load_dataset(request.dataset_name, token=os.getenv("HF_TOKEN"))
89
+
90
+ # Split dataset
91
+ train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
92
+ test_dataset = train_test["test"]
93
+
94
+ # Start tracking emissions
95
+ tracker.start()
96
+ tracker.start_task("inference")
97
+
98
+ #--------------------------------------------------------------------------------------------
99
+ # YOUR MODEL INFERENCE CODE HERE
100
+ # Update the code below to replace the random baseline with your model inference
101
+ #--------------------------------------------------------------------------------------------
102
+
103
+ predictions = []
104
+ true_labels = []
105
+ pred_boxes = []
106
+ true_boxes_list = [] # List of lists, each inner list contains boxes for one image
107
+
108
+ for example in test_dataset:
109
+ # Parse true annotation (YOLO format: class_id x_center y_center width height)
110
+ annotation = example.get("annotations", "").strip()
111
+ has_smoke = len(annotation) > 0
112
+ true_labels.append(int(has_smoke))
113
+
114
+ # Make random classification prediction
115
+ pred_has_smoke = random.random() > 0.5
116
+ predictions.append(int(pred_has_smoke))
117
+
118
+ # If there's a true box, parse it and make random box prediction
119
+ if has_smoke:
120
+ # Parse all true boxes from the annotation
121
+ image_true_boxes = parse_boxes(annotation)
122
+ true_boxes_list.append(image_true_boxes)
123
+
124
+ # For baseline, make one random box prediction per image
125
+ # In a real model, you might want to predict multiple boxes
126
+ random_box = [
127
+ random.random(), # x_center
128
+ random.random(), # y_center
129
+ random.random() * 0.5, # width (max 0.5)
130
+ random.random() * 0.5 # height (max 0.5)
131
+ ]
132
+ pred_boxes.append(random_box)
133
+
134
+ #--------------------------------------------------------------------------------------------
135
+ # YOUR MODEL INFERENCE STOPS HERE
136
+ #--------------------------------------------------------------------------------------------
137
+
138
+ # Stop tracking emissions
139
+ emissions_data = tracker.stop_task()
140
+
141
+ # Calculate classification metrics
142
+ classification_accuracy = accuracy_score(true_labels, predictions)
143
+ classification_precision = precision_score(true_labels, predictions)
144
+ classification_recall = recall_score(true_labels, predictions)
145
+
146
+ # Calculate mean IoU for object detection (only for images with smoke)
147
+ # For each image, we compute the max IoU between the predicted box and all true boxes
148
+ ious = []
149
+ for true_boxes, pred_box in zip(true_boxes_list, pred_boxes):
150
+ max_iou = compute_max_iou(true_boxes, pred_box)
151
+ ious.append(max_iou)
152
+
153
+ mean_iou = float(np.mean(ious)) if ious else 0.0
154
+
155
+ # Prepare results dictionary
156
+ results = {
157
+ "username": username,
158
+ "space_url": space_url,
159
+ "submission_timestamp": datetime.now().isoformat(),
160
+ "model_description": DESCRIPTION,
161
+ "classification_accuracy": float(classification_accuracy),
162
+ "classification_precision": float(classification_precision),
163
+ "classification_recall": float(classification_recall),
164
+ "mean_iou": mean_iou,
165
+ "energy_consumed_wh": emissions_data.energy_consumed * 1000,
166
+ "emissions_gco2eq": emissions_data.emissions * 1000,
167
+ "emissions_data": clean_emissions_data(emissions_data),
168
+ "api_route": ROUTE,
169
+ "dataset_config": {
170
+ "dataset_name": request.dataset_name,
171
+ "test_size": request.test_size,
172
+ "test_seed": request.test_seed
173
+ }
174
+ }
175
+
176
+ return results
tasks/text.py ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from fastapi import APIRouter
2
+ from datetime import datetime
3
+ from datasets import load_dataset
4
+ from sklearn.metrics import accuracy_score
5
+ import random
6
+
7
+ from .utils.evaluation import TextEvaluationRequest
8
+ from .utils.emissions import tracker, clean_emissions_data, get_space_info
9
+
10
+ router = APIRouter()
11
+
12
+ DESCRIPTION = "Random Baseline"
13
+ ROUTE = "/text"
14
+
15
+ @router.post(ROUTE, tags=["Text Task"],
16
+ description=DESCRIPTION)
17
+ async def evaluate_text(request: TextEvaluationRequest):
18
+ """
19
+ Evaluate text classification for climate disinformation detection.
20
+
21
+ Current Model: Random Baseline
22
+ - Makes random predictions from the label space (0-7)
23
+ - Used as a baseline for comparison
24
+ """
25
+ # Get space info
26
+ username, space_url = get_space_info()
27
+
28
+ # Define the label mapping
29
+ LABEL_MAPPING = {
30
+ "0_not_relevant": 0,
31
+ "1_not_happening": 1,
32
+ "2_not_human": 2,
33
+ "3_not_bad": 3,
34
+ "4_solutions_harmful_unnecessary": 4,
35
+ "5_science_unreliable": 5,
36
+ "6_proponents_biased": 6,
37
+ "7_fossil_fuels_needed": 7
38
+ }
39
+
40
+ # Load and prepare the dataset
41
+ dataset = load_dataset(request.dataset_name)
42
+
43
+ # Convert string labels to integers
44
+ dataset = dataset.map(lambda x: {"label": LABEL_MAPPING[x["label"]]})
45
+
46
+ # Split dataset
47
+ train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
48
+ test_dataset = train_test["test"]
49
+
50
+ # Start tracking emissions
51
+ tracker.start()
52
+ tracker.start_task("inference")
53
+
54
+ #--------------------------------------------------------------------------------------------
55
+ # YOUR MODEL INFERENCE CODE HERE
56
+ # Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
57
+ #--------------------------------------------------------------------------------------------
58
+
59
+ # Make random predictions (placeholder for actual model inference)
60
+ true_labels = test_dataset["label"]
61
+ predictions = [random.randint(0, 7) for _ in range(len(true_labels))]
62
+
63
+ #--------------------------------------------------------------------------------------------
64
+ # YOUR MODEL INFERENCE STOPS HERE
65
+ #--------------------------------------------------------------------------------------------
66
+
67
+
68
+ # Stop tracking emissions
69
+ emissions_data = tracker.stop_task()
70
+
71
+ # Calculate accuracy
72
+ accuracy = accuracy_score(true_labels, predictions)
73
+
74
+ # Prepare results dictionary
75
+ results = {
76
+ "username": username,
77
+ "space_url": space_url,
78
+ "submission_timestamp": datetime.now().isoformat(),
79
+ "model_description": DESCRIPTION,
80
+ "accuracy": float(accuracy),
81
+ "energy_consumed_wh": emissions_data.energy_consumed * 1000,
82
+ "emissions_gco2eq": emissions_data.emissions * 1000,
83
+ "emissions_data": clean_emissions_data(emissions_data),
84
+ "api_route": ROUTE,
85
+ "dataset_config": {
86
+ "dataset_name": request.dataset_name,
87
+ "test_size": request.test_size,
88
+ "test_seed": request.test_seed
89
+ }
90
+ }
91
+
92
+ return results
tasks/utils/__init__.py ADDED
File without changes
tasks/utils/emissions.py ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from codecarbon import EmissionsTracker
2
+ import os
3
+
4
+
5
+ # Initialize tracker
6
+ tracker = EmissionsTracker(allow_multiple_runs=True)
7
+
8
+ class EmissionsData:
9
+ def __init__(self, energy_consumed: float, emissions: float):
10
+ self.energy_consumed = energy_consumed
11
+ self.emissions = emissions
12
+
13
+ def clean_emissions_data(emissions_data):
14
+ """Remove unwanted fields from emissions data"""
15
+ data_dict = emissions_data.__dict__
16
+ fields_to_remove = ['timestamp', 'project_name', 'experiment_id', 'latitude', 'longitude']
17
+ return {k: v for k, v in data_dict.items() if k not in fields_to_remove}
18
+
19
+ def get_space_info():
20
+ """Get the space username and URL from environment variables"""
21
+ space_name = os.getenv("SPACE_ID", "")
22
+ if space_name:
23
+ try:
24
+ username = space_name.split("/")[0]
25
+ space_url = f"https://huggingface.co/spaces/{space_name}"
26
+ return username, space_url
27
+ except Exception as e:
28
+ print(f"Error getting space info: {e}")
29
+ return "local-user", "local-development"
tasks/utils/evaluation.py ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import Optional
2
+ from pydantic import BaseModel, Field
3
+
4
+
5
+ class BaseEvaluationRequest(BaseModel):
6
+ test_size: float = Field(0.2, ge=0.0, le=1.0, description="Size of the test split (between 0 and 1)")
7
+ test_seed: int = Field(42, ge=0, description="Random seed for reproducibility")
8
+
9
+ class TextEvaluationRequest(BaseEvaluationRequest):
10
+ dataset_name: str = Field("QuotaClimat/frugalaichallenge-text-train",
11
+ description="The name of the dataset on HuggingFace Hub")
12
+
13
+ class ImageEvaluationRequest(BaseEvaluationRequest):
14
+ dataset_name: str = Field("pyronear/pyro-sdis",
15
+ description="The name of the dataset on HuggingFace Hub")
16
+
17
+ class AudioEvaluationRequest(BaseEvaluationRequest):
18
+ dataset_name: str = Field("rfcx/frugalai",
19
+ description="The name of the dataset on HuggingFace Hub")
tasks/utils/load_data.py ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Load dataset and save locally in Ultralytics format"""
2
+
3
+ from datasets import load_dataset
4
+ import os
5
+
6
+
7
+ def load_data(REPO_ID, OUTPUT_DIR):
8
+ """Load data and save to local directory"""
9
+
10
+ IMAGE_DIR = os.path.join(OUTPUT_DIR, "images")
11
+ LABEL_DIR = os.path.join(OUTPUT_DIR, "labels")
12
+
13
+ # 🚧 CHECK IF FOLDER EXISTS
14
+ # 🚧 CHECK IF FOLDER EXISTS
15
+ # 🚧 CHECK IF FOLDER EXISTS
16
+ # 🚧 CHECK IF FOLDER EXISTS
17
+
18
+ # Create the directory structure
19
+ for split in ["train", "val"]:
20
+ os.makedirs(os.path.join(IMAGE_DIR, split), exist_ok=True)
21
+ os.makedirs(os.path.join(LABEL_DIR, split), exist_ok=True)
22
+
23
+ # Load the dataset from the Hugging Face Hub
24
+ dataset = load_dataset(REPO_ID)
25
+
26
+ # Save in Ultralytics format
27
+ def save_ultralytics_format(dataset_split, split):
28
+ """
29
+ Save a dataset split into the Ultralytics format.
30
+ Args:
31
+ dataset_split: The dataset split (e.g., dataset["train"])
32
+ split: "train" or "val"
33
+ """
34
+ for example in dataset_split:
35
+ # Save the image to the appropriate folder
36
+ image = example["image"] # PIL.Image.Image
37
+ image_name = example["image_name"] # Original file name
38
+ output_image_path = os.path.join(IMAGE_DIR, split, image_name)
39
+
40
+ # Save the image object to disk
41
+ image.save(output_image_path)
42
+
43
+ # Save label
44
+ annotations = example["annotations"]
45
+ label_name = image_name.replace(".jpg", ".txt").replace(".png", ".txt")
46
+ output_label_path = os.path.join(LABEL_DIR, split, label_name)
47
+
48
+ with open(output_label_path, "w") as label_file:
49
+ label_file.write(annotations)
50
+
51
+ # Save train and validation splits
52
+ save_ultralytics_format(dataset["train"], "train")
53
+ save_ultralytics_format(dataset["val"], "val")
54
+
55
+ print("Dataset exported to Ultralytics format.")
56
+
57
+
58
+ if __name__ == "__main__":
59
+ help()