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Running
on
Zero
File size: 10,040 Bytes
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "44d53281",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/kevinx/miniconda3/envs/laser_env/lib/python3.10/site-packages/pydantic/_internal/_config.py:383: UserWarning: Valid config keys have changed in V2:\n",
"* 'schema_extra' has been renamed to 'json_schema_extra'\n",
" warnings.warn(message, UserWarning)\n",
"Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n",
"Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n"
]
}
],
"source": [
"import os\n",
"import sys\n",
"import torch\n",
"from transformers import pipeline, AutoModel\n",
"from transformers.pipelines import PIPELINE_REGISTRY\n",
"\n",
"# Uncomment or set your own\n",
"#os.environ['OPENAI_API_KEY'] = 'dummy-key'\n",
"from vine_hf import VineConfig, VineModel, VinePipeline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "174e479f",
"metadata": {},
"outputs": [],
"source": [
"PIPELINE_REGISTRY.register_pipeline(\n",
" \"vine-video-understanding\",\n",
" pipeline_class=VinePipeline,\n",
" pt_model=VineModel,\n",
" type=\"multimodal\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a9af2770",
"metadata": {},
"outputs": [],
"source": [
"vine_config = VineConfig(\n",
" model_name=\"openai/clip-vit-base-patch32\",\n",
" # Local file example: set use_hf_repo=False and provide local_dir/local_filename\n",
" use_hf_repo=False,\n",
" local_dir=os.path.dirname('/path/to/your/pretrained/model.pt'),\n",
" local_filename=os.path.basename('/path/to/your/pretrained/model.pt'), # Local file path\n",
" segmentation_method=\"grounding_dino_sam2\",\n",
" visualize=True,\n",
" visualization_dir=\"path/to/visualization/dir\",\n",
" debug_visualizations=True,\n",
" device=0, # Change to your desired device\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "274e6515",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded state type: <class 'collections.OrderedDict'>\n"
]
}
],
"source": [
"vine_pipeline = VinePipeline(\n",
" model=VineModel(vine_config), \n",
" tokenizer=None,\n",
" sam_config_path=\"path/to/sam2/configs/sam2_hiera_base_plus.yaml\",\n",
" sam_checkpoint_path=\"path/to/sam2/checkpoints/sam2.1_hiera_base_plus.pt\",\n",
" gd_config_path=\"path/to/GroundingDINO/config/GroundingDINO_SwinT_OGC.py\",\n",
" gd_checkpoint_path=\"path/to/GroundingDINO/checkpoints/groundingdino_swint_ogc.pth\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "123a090d",
"metadata": {},
"outputs": [],
"source": [
"categorical_keywords = ['human', 'dog', 'frisbee']\n",
"unary_keywords = ['running', 'jumping', 'catching', 'throwing']\n",
"binary_keywords = ['behind', 'in front of', 'next to', 'chasing']\n",
"object_pairs = [(0, 1), (0, 2), (1, 2)] # human-dog, dog-frisbee relationships "
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0b42f032",
"metadata": {},
"outputs": [],
"source": [
"demo_video_path = \"/home/kevinx/LASER/LASER/demo/videos/v1.mp4\" # Replace with your video file path"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8202c654",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Segmentation method: grounding_dino_sam2\n",
"Generating Grounding DINO + SAM2 masks...\n",
"<class 'int'>\n",
"✓ SAM2 models initialized successfully\n",
"<class 'int'>\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4314.)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"final text_encoder_type: bert-base-uncased\n",
"✓ GroundingDINO model initialized successfully\n",
"Start detecting objects at time 05:08:58.178592\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Detecting objects: 0%| | 0/3 [00:00<?, ?it/s]FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.\n",
"UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
"UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
"FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
"Detecting objects: 100%|██████████| 3/3 [00:01<00:00, 2.82it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finished detecting objects at time 05:08:59.250419\n",
"Loading inference state at time 05:08:59.544425\n",
"Number of frames: 3\n",
"None\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Processing frames: 100%|██████████| 3/3 [00:00<00:00, 11.77it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Annotated frames: []\n",
"Find the most dense prompt at time 05:09:01.413703\n",
"Most dense frame: 0\n",
"\n",
"\n",
"Start propagating objects at time 05:09:01.416367\n",
"Pass count: 0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"propagate in video: 100%|██████████| 3/3 [00:00<00:00, 20.20it/s]\n",
"propagate in video: 0it [00:00, ?it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Most dense frame: 1\n",
"\n",
"\n",
"Pass count: 1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"propagate in video: 100%|██████████| 3/3 [00:00<00:00, 19.25it/s]\n",
"propagate in video: 0it [00:00, ?it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Most dense frame: 2\n",
"\n",
"\n",
"Pass count: 2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"propagate in video: 100%|██████████| 3/3 [00:00<00:00, 25.92it/s]\n",
"propagate in video: 0it [00:00, ?it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Most dense frame: -1\n",
"\n",
"\n",
"\n",
"Results:\n",
"Summary: {'num_objects_detected': 4, 'num_unary_predictions': 10, 'num_binary_predictions': 3, 'top_categories': [('frisbee', 0.9989640712738037), ('dog', 0.957672655582428), ('dog', 0.957672655582428)], 'top_actions': [('running', 0.8483631610870361), ('running', 0.832377016544342), ('running', 0.8178836107254028)], 'top_relations': [('chasing', 0.9616015553474426), ('chasing', 0.9478002786636353), ('chasing', 0.6380977630615234)]}\n"
]
}
],
"source": [
"try:\n",
" results = vine_pipeline(\n",
" demo_video_path,\n",
" categorical_keywords=categorical_keywords,\n",
" unary_keywords=unary_keywords,\n",
" binary_keywords=binary_keywords,\n",
" object_pairs=object_pairs,\n",
" segmentation_method='grounding_dino_sam2',\n",
" return_top_k=3,\n",
" include_visualizations=False,\n",
" debug_visualizations=False,\n",
" )\n",
" \n",
" print(\"\\nResults:\")\n",
" print(f\"Summary: {results['summary']}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Note: Full execution requires segmentation models to be properly set up.\")\n",
" print(f\"Error: {e}\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "414ede9b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Summary: {'num_objects_detected': 4, 'num_unary_predictions': 10, 'num_binary_predictions': 3, 'top_categories': [('frisbee', 0.9989640712738037), ('dog', 0.957672655582428), ('dog', 0.957672655582428)], 'top_actions': [('running', 0.8483631610870361), ('running', 0.832377016544342), ('running', 0.8178836107254028)], 'top_relations': [('chasing', 0.9616015553474426), ('chasing', 0.9478002786636353), ('chasing', 0.6380977630615234)]}\n"
]
}
],
"source": [
"print(f\"Summary: {results['summary']}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "laser_env",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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