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Merged
merged 11 commits into from
Dec 20, 2023
Merged

Rework enum #8

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a476ef0
Rework Facial parts as an enum
Hartorn Dec 12, 2023
5de5f92
Rebase
Hartorn Dec 13, 2023
0fb078e
Add tests
Hartorn Dec 18, 2023
a996511
Fix notebooks
Hartorn Dec 18, 2023
39360af
fixed conflicts and forced updated examples from main
rabah-khalek Dec 20, 2023
e429119
updated examples
rabah-khalek Dec 20, 2023
7da702b
fixing examples
rabah-khalek Dec 20, 2023
4571d21
fixed tests
rabah-khalek Dec 20, 2023
0dbf619
Merge branch 'main' into rework-enum
rabah-khalek Dec 20, 2023
e5267f6
Merge branch 'rework-enum' of https://github.com/Giskard-AI/loreal-po…
rabah-khalek Dec 20, 2023
9a458c9
Update loreal_poc/marks/facial_parts.py
rabah-khalek Dec 20, 2023
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fixed conflicts and forced updated examples from main
  • Loading branch information
@rabah-khalek
rabah-khalek committed Dec 20, 2023
commit 39360af9e55f02a92f7f2313f25a1c046ddf32b5
223 changes: 20 additions & 203 deletions examples/criteria1_partial_faces.ipynb
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115 changes: 12 additions & 103 deletions examples/ex1_draw_landmarks.ipynb
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56 changes: 3 additions & 53 deletions examples/ex2_draw_partial_landmarks.ipynb
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85 changes: 6 additions & 79 deletions examples/ex4_tests_and_metrics.ipynb
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Expand Up @@ -53,18 +53,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'cpu'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"import torch\n",
"\n",
Expand Down Expand Up @@ -100,90 +89,28 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
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}
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"outputs": [
{
"data": {
"text/html": [
"\n",
" <h4><span style=\"color:green;\">✓</span> Test \"NME_mean\" succeeded</h4>\n",
" <p>Description: Mean of normalised mean Euclidean distances across images</p>\n",
" <p>Metric: <b>0.0623</b> (threshold = 1)</p>\n",
" \n",
" <p>Prediction time: 7.9 s.</p>\n",
" "
],
"text/plain": [
"\n",
" Test \"NME_mean\" succeeded\n",
" Description: Mean of normalised mean Euclidean distances across images\n",
" Metric: 0.0623 (threshold = 1)\n",
" \n",
" Prediction time: 7.9 s.\n",
" "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
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"outputs": [],
"source": [
"Test(metric=NMEMean, threshold=1).run(model, ds)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/bazire/Work/loreal-poc/.venv/lib/python3.11/site-packages/face_alignment/api.py:147: UserWarning: No faces were detected.\n",
" warnings.warn(\"No faces were detected.\")\n",
"/Users/bazire/Work/loreal-poc/loreal_poc/tests/performance.py:47: RuntimeWarning: Mean of empty slice\n",
" mes = np.nanmean(es, axis=1)\n"
]
},
{
"data": {
"text/html": [
"\n",
" <h4><span style=\"color:green;\">✓</span> Test \"NME_mean\" succeeded</h4>\n",
" <p>Description: Mean of normalised mean Euclidean distances across images</p>\n",
" <p>Metric: <b>0.3962</b> (threshold = 1)</p>\n",
" Prediction fail rate: 0.4\n",
" <p>Prediction time: 9.8 s.</p>\n",
" "
],
"text/plain": [
"\n",
" Test \"NME_mean\" succeeded\n",
" Description: Mean of normalised mean Euclidean distances across images\n",
" Metric: 0.3962 (threshold = 1)\n",
" Prediction fail rate: 0.4\n",
" Prediction time: 9.8 s.\n",
" "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"from loreal_poc.marks.facial_parts import FacialParts\n",
"from loreal_poc.dataloaders.wrappers import CroppedDataLoader, CachedDataLoader\n",
"\n",
"facial_part = FacialParts.LEFT_HALF.value\n",
"facial_part = FacialParts.left_half\n",
"\n",
"TestDiff(metric=NMEMean, threshold=1).run(model, ds, CroppedDataLoader(ds, part=facial_part), facial_part=facial_part)"
]
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114 changes: 12 additions & 102 deletions examples/ex5_models_comparison.ipynb
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Expand Up @@ -46,118 +46,23 @@
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'cpu'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torch\n",
"\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"device"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"facealignment_model = FaceAlignmentWrapper(model=FaceAlignment(LandmarksType.TWO_D, device=\"cpu\", flip_input=False))\n",
"opencv_model = OpenCVWrapper()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'test_name': 'Test',\n",
" 'metric_name': 'NME_mean',\n",
" 'metric_value': 0.04136279942306024,\n",
" 'threshold': 1,\n",
" 'passed': True,\n",
" 'facial_part': 'entire face',\n",
" 'model_name': 'OpenCV',\n",
" 'dataloader_name': '300W'}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test = Test(metric=NMEMean, threshold=1).run(model=opencv_model, dataloader=dl)\n",
"\n",
"test.to_dict()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/bazire/Work/loreal-poc/loreal_poc/tests/performance.py:47: RuntimeWarning: Mean of empty slice\n",
" mes = np.nanmean(es, axis=1)\n"
"loading data from : lbfmodel.yaml\n"
]
},
{
"data": {
"text/plain": [
"{'test_name': 'TestDiff',\n",
" 'metric_name': 'NME_mean',\n",
" 'metric_value': 0.39174846002334746,\n",
" 'threshold': 1,\n",
" 'passed': True,\n",
" 'facial_part': 'left half',\n",
" 'model_name': 'OpenCV',\n",
" 'dataloader_name': '300W'}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from loreal_poc.marks.facial_parts import FacialParts\n",
"\n",
"facial_part = FacialParts.LEFT_HALF.value\n",
"\n",
"test_diff = TestDiff(metric=NMEMean, threshold=1).run(\n",
" model=opencv_model, dataloader=dl, dataloader_ref=CroppedDataLoader(dl, part=facial_part), facial_part=facial_part\n",
")\n",
"test_diff.to_dict()"
"facealignment_model = FaceAlignmentWrapper(model=FaceAlignment(LandmarksType.TWO_D, device=\"cpu\", flip_input=False))\n",
"opencv_model = OpenCVWrapper()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/bazire/Work/loreal-poc/loreal_poc/tests/performance.py:85: RuntimeWarning: Mean of empty slice\n",
" return np.nanmean(NMEs.get(prediction_result, marks))\n"
]
}
],
"outputs": [],
"source": [
"# from dataclasses import fields\n",
"def create_report(models, dataloader, tests, facial_parts):\n",
Expand All @@ -177,7 +82,12 @@
" return results\n",
"\n",
"\n",
"report = report([opencv_model], dl, [TestDiff], [FacialParts.BOTTOM_HALF.value, FacialParts.UPPER_HALF.value])"
"report = create_report(\n",
" [opencv_model, facealignment_model],\n",
" dl,\n",
" [TestDiff],\n",
" [FacialParts.bottom_half, FacialParts.upper_half, FacialParts.left_half, FacialParts.right_half],\n",
")"
]
},
{
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1,286 changes: 42 additions & 1,244 deletions examples/ex6_cropping.ipynb
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