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Update tutorial dependencies and conversion notebooks #93

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697f11d
Fix PyTorch conversion notebook execution and update dependencies
Jun 11, 2026
32e5092
Update NVIDIA TAO conversion notebook dependencies
Jun 12, 2026
d7b133b
Fix ONNX conversion notebook execution and update dependencies
Jun 12, 2026
173d0e9
Bump training tutorial dependency pins
Jun 12, 2026
3240b07
Bump depthai-nodes to 0.5.1
Jun 12, 2026
5121955
Fix PyTorch conversion notebook artifact paths
Jun 12, 2026
14e0ed5
Address notebook review comments
Jun 12, 2026
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40 changes: 29 additions & 11 deletions conversion/nvidia_tao_conversion.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -99,7 +99,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -145,9 +145,8 @@
"metadata": {},
"outputs": [],
"source": [
"%pip install -q pillow onnx onnxruntime modelconv==0.4.5 -U\n",
"%pip install -q depthai==3.0.0 -U\n",
"%pip install -q depthai-nodes==0.3.0 -U"
"%pip install -q pillow luxonis-ml==0.8.5 onnx==1.21.0 onnxruntime==1.23.2 modelconv==0.5.5 depthai==3.7.1 depthai-nodes==0.5.1\n",
"%pip install -q numpy==2.0.2"
]
},
{
Expand Down Expand Up @@ -243,10 +242,10 @@
}
],
"source": [
"import os\n",
"import onnxruntime as ort\n",
"import numpy as np\n",
"import cv2\n",
"from scipy.spatial.distance import cosine\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Load ONNX model\n",
Expand Down Expand Up @@ -285,8 +284,15 @@
" embeddings[img_path] = normalize_embedding(output.flatten()) # Flatten to 1D vector\n",
"\n",
"# Compute cosine similarity between pairs\n",
"def cosine_similarity(emb1, emb2):\n",
" return 1 - cosine(emb1, emb2) # Higher value = more similar\n",
"def cosine_similarity(emb1, emb2, eps=1e-12):\n",
" emb1 = np.asarray(emb1, dtype=np.float32).ravel()\n",
" emb2 = np.asarray(emb2, dtype=np.float32).ravel()\n",
"\n",
" denom = np.linalg.norm(emb1) * np.linalg.norm(emb2)\n",
" if denom < eps:\n",
" return 0.0\n",
"\n",
" return float(np.dot(emb1, emb2) / denom)\n",
"\n",
"pairs = [\n",
" (\"person_0_0.png\", \"person_0_1.png\"), # Same person\n",
Expand Down Expand Up @@ -442,7 +448,7 @@
"metadata": {},
"outputs": [],
"source": [
"%pip install -q hubai-sdk"
"%pip install -q -U hubai-sdk"
]
},
{
Expand All @@ -458,7 +464,13 @@
"metadata": {},
"outputs": [],
"source": [
"HUBAI_API_KEY = \"<YOUR_HUBAI_API_KEY>\""
"import os\n",
"import getpass\n",
"\n",
"if not os.environ.get(\"HUBAI_API_KEY\"):\n",
" os.environ[\"HUBAI_API_KEY\"] = getpass.getpass(\"Enter your HubAI API key: \")\n",
"\n",
"print(\"HUBAI_API_KEY is set:\", bool(os.environ.get(\"HUBAI_API_KEY\")))"
]
},
{
Expand All @@ -467,19 +479,25 @@
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"from hubai_sdk import HubAIClient\n",
"\n",
"client = HubAIClient(api_key=HUBAI_API_KEY)\n",
"client = HubAIClient(api_key=os.environ[\"HUBAI_API_KEY\"])\n",
"\n",
"ARTIFACT_DIR = Path(\"reidentification-nvidia-tao-exported-to-rvc4\")\n",
"ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)\n",
"\n",
"response = client.convert.RVC4(\n",
" path=\"resnet50_market1501_aicity156.tar.xz\",\n",
" name=\"ReIdentification NVIDIA TAO\",\n",
" description_short=\"ReIdentificationNet takes cropped images of a person from different perspectives as network input and outputs the embedding features for that person.\",\n",
" license_type=\"MIT\",\n",
" is_public=False,\n",
" output_dir=str(ARTIFACT_DIR),\n",
")\n",
"\n",
"model_path = response.downloaded_path\n",
"MODEL_PATH = Path(response.downloaded_path)\n",
"print(\"Model artifact:\", MODEL_PATH)\n",
"\n",
"# =============================================================================\n",
"# RVC4 conversion\n",
Expand Down
288 changes: 199 additions & 89 deletions conversion/onnx_conversion.ipynb
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@klemen1999 klemen1999 Jun 12, 2026

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Same nitpicks as mentioned in the "pytorch_conversion.ipynb"

Large diffs are not rendered by default.

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