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Merged
merged 11 commits into from
Jan 4, 2024
Merged

[GSK-2424] Improve a bit FFHQ structure #15

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23de9b5
Improve a bit FFHQ structure
Hartorn Jan 2, 2024
b18ec1b
Re-organize the batching a bit
Hartorn Jan 3, 2024
62907d1
Fix indexing for batching
Hartorn Jan 3, 2024
1a4c7a0
Output for notebook
Hartorn Jan 3, 2024
382315f
Fix batching for prediction
Hartorn Jan 3, 2024
c78a235
fixed predict, reproducing main
rabah-khalek Jan 3, 2024
566d1fb
Update loreal_poc/dataloaders/base.py
rabah-khalek Jan 4, 2024
a9cac74
Update loreal_poc/dataloaders/base.py
rabah-khalek Jan 4, 2024
9480fa9
Update loreal_poc/dataloaders/base.py
rabah-khalek Jan 4, 2024
6df14bd
Merge pull request #16 from Giskard-AI/fix-batching
rabah-khalek Jan 4, 2024
8836c95
pdm format
rabah-khalek Jan 4, 2024
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30 changes: 12 additions & 18 deletions examples/criteria1_partial_faces.ipynb
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Expand Up @@ -34,15 +34,7 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loading data from : lbfmodel.yaml\n"
]
}
],
"outputs": [],
"source": [
"# model = FaceAlignmentWrapper(model=FaceAlignment(LandmarksType.TWO_D, device=device, flip_input=False))\n",
"model = OpenCVWrapper()\n",
Expand All @@ -69,7 +61,7 @@
" <p>Description: Mean of normalised mean Euclidean distances across images</p>\n",
" <p>Metric: <b>0.0383</b> (threshold = 1)</p>\n",
" \n",
" <p>Prediction time: 0.71 s.</p>\n",
" <p>Prediction time: 0.34 s.</p>\n",
" "
],
"text/plain": [
Expand All @@ -78,7 +70,7 @@
" Description: Mean of normalised mean Euclidean distances across images\n",
" Metric: 0.0383 (threshold = 1)\n",
" \n",
" Prediction time: 0.71 s.\n",
" Prediction time: 0.34 s.\n",
" "
]
},
Expand Down Expand Up @@ -117,7 +109,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/rak/Documents/loreal-poc/loreal_poc/tests/performance.py:47: RuntimeWarning: Mean of empty slice\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"
]
}
Expand Down Expand Up @@ -159,8 +151,10 @@
"squares = []\n",
"chosen_idx = 1\n",
"image, _, _ = ds[chosen_idx]\n",
"image = image[0]\n",
"for rel_margin in rel_margins:\n",
" img, marks, meta = ds[chosen_idx]\n",
" img, marks, meta = img[0], marks[0], meta[0]\n",
" h, w, _ = img.shape\n",
" margins = np.array([w, h]) * rel_margin\n",
" cropped_mark = crop_mark(marks, facial_part)\n",
Expand Down Expand Up @@ -211,7 +205,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/rak/Documents/loreal-poc/loreal_poc/tests/performance.py:47: RuntimeWarning: Mean of empty slice\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"
]
},
Expand All @@ -223,7 +217,7 @@
" <p>Description: Mean of normalised mean Euclidean distances across images</p>\n",
" <p>Metric: <b>0.5774</b> (threshold = 1)</p>\n",
" Prediction fail rate: 0.6\n",
" <p>Prediction time: 0.82 s.</p>\n",
" <p>Prediction time: 0.41 s.</p>\n",
" "
],
"text/plain": [
Expand All @@ -232,7 +226,7 @@
" Description: Mean of normalised mean Euclidean distances across images\n",
" Metric: 0.5774 (threshold = 1)\n",
" Prediction fail rate: 0.6\n",
" Prediction time: 0.82 s.\n",
" Prediction time: 0.41 s.\n",
" "
]
},
Expand Down Expand Up @@ -261,7 +255,7 @@
" <p>Description: Standard deviation of normalised Mean Euclidean distances across images</p>\n",
" <p>Metric: <b>0.0532</b> (threshold = 1)</p>\n",
" Prediction fail rate: 0.6\n",
" <p>Prediction time: 0.65 s.</p>\n",
" <p>Prediction time: 0.43 s.</p>\n",
" "
],
"text/plain": [
Expand All @@ -270,7 +264,7 @@
" Description: Standard deviation of normalised Mean Euclidean distances across images\n",
" Metric: 0.0532 (threshold = 1)\n",
" Prediction fail rate: 0.6\n",
" Prediction time: 0.65 s.\n",
" Prediction time: 0.43 s.\n",
" "
]
},
Expand Down Expand Up @@ -307,7 +301,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
"version": "3.11.7"
}
},
"nbformat": 4,
Expand Down
100 changes: 94 additions & 6 deletions examples/ex1_draw_landmarks.ipynb
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26 changes: 13 additions & 13 deletions examples/ex2_draw_partial_landmarks.ipynb
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49 changes: 41 additions & 8 deletions examples/ex3_calculate_euc_dists.ipynb
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Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {
"collapsed": false,
"jupyter": {
Expand All @@ -19,28 +19,61 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"array([4. , 1. , 2.23606798, 2.23606798])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a_min_b = A - B\n",
"np.sqrt(np.einsum(\"ij,ij->i\", a_min_b, a_min_b))"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"array([4. , 1. , 2.23606798, 2.23606798])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.linalg.norm(A - B, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"array([4. , 1. , 2.23606798, 2.23606798])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d_all = np.sqrt(np.einsum(\"ij->i\", (A - B) ** 2))\n",
"d_all"
Expand All @@ -63,7 +96,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
"version": "3.11.7"
}
},
"nbformat": 4,
Expand Down
93 changes: 83 additions & 10 deletions examples/ex4_tests_and_metrics.ipynb
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Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {
"collapsed": false,
"jupyter": {
Expand Down Expand Up @@ -37,7 +37,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {
"collapsed": false,
"jupyter": {
Expand All @@ -51,9 +51,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"'cpu'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torch\n",
"\n",
Expand All @@ -75,7 +86,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {
"collapsed": false,
"jupyter": {
Expand All @@ -89,23 +100,85 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
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"outputs": [],
"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: 13.1 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: 13.1 s.\n",
" "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Test(metric=NMEMean, threshold=1).run(model, ds)"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"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: 18.74 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: 18.74 s.\n",
" "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from loreal_poc.marks.facial_parts import FacialParts\n",
"from loreal_poc.dataloaders.wrappers import CroppedDataLoader, CachedDataLoader\n",
Expand All @@ -132,7 +205,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
"version": "3.11.7"
}
},
"nbformat": 4,
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