Nlq fixes and fp16 support #53
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Adds a quickstart notebook that sets up the conda env, pre-processes annotations/clips/frames, runs an eval on a subset of VQ, and also does a small e2e training run. Note: Meta google accounts don't have an access to Colab, use a regular google account to see the notebook instead.
adding camera pose results for the val set
* edge-case handling for clip_uid as None * checking for corrupt clips + recomputing * updated TQDM description
* downscale clips to 700p before saving
* edge-case handling for clip_uid as None
* pre-extract detections for each query * can facilite faster evaluation
* replace test-challenge inference with general inference * use "WorkerWithDevice" strategy to avoid GPU overload in MP * add cached evaluation to utilize pre-computed BBoxes * add "torch.cuda.set_device(device)" in KYSTracker to assign GPU * add "visualize" flag in perform_retrieval* to save RAM
* Use deepspeed library to measure average detector flops
Add VQ2D Quickstart Colab Notebook
NLQ fixes and support FP16 features
* add annot_key * use separate dir per sample for visualization * set data version based on annot file
* read frames from video_reader lazily * sets a similarity threshold to remove noisy peaks * option to set lost_thresh for KYS
* remove end-to-end evaluate_vq2d.py * add evaluate_vq.py to use precomputed results
* account for errors where extracted clip is shorter than expected * add missing reader.close() * add option to extract specific clips
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