11dec2936d
Summary: Formats the covered files with pyfmt. paintitblack Reviewed By: itamaro Differential Revision: D90476315 fbshipit-source-id: ee94c471788b8e7d067813d8b3e0311214d17f3f
93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
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# pyre-unsafe
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import logging
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import numpy as np
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import torch
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from sam3.perflib.masks_ops import mask_iou
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try:
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from torch_generic_nms import generic_nms as generic_nms_cuda
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GENERIC_NMS_AVAILABLE = True
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except ImportError:
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logging.debug(
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"Falling back to triton or CPU mask NMS implementation -- please install `torch_generic_nms` via\n\t"
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'pip uninstall -y torch_generic_nms; TORCH_CUDA_ARCH_LIST="8.0 9.0" pip install git+https://github.com/ronghanghu/torch_generic_nms'
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)
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GENERIC_NMS_AVAILABLE = False
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def nms_masks(
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pred_probs: torch.Tensor,
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pred_masks: torch.Tensor,
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prob_threshold: float,
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iou_threshold: float,
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) -> torch.Tensor:
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"""
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Args:
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- pred_probs: (num_det,) float Tensor, containing the score (probability) of each detection
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- pred_masks: (num_det, H_mask, W_mask) float Tensor, containing the binary segmentation mask of each detection
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- prob_threshold: float, score threshold to prefilter detections (NMS is performed on detections above threshold)
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- iou_threshold: float, mask IoU threshold for NMS
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Returns:
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- keep: (num_det,) bool Tensor, indicating whether each detection is kept after score thresholding + NMS
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"""
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# prefilter the detections with prob_threshold ("valid" are those above prob_threshold)
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is_valid = pred_probs > prob_threshold # (num_det,)
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probs = pred_probs[is_valid] # (num_valid,)
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masks_binary = pred_masks[is_valid] > 0 # (num_valid, H_mask, W_mask)
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if probs.numel() == 0:
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return is_valid # no valid detection, return empty keep mask
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ious = mask_iou(masks_binary, masks_binary) # (num_valid, num_valid)
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kept_inds = generic_nms(ious, probs, iou_threshold)
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# valid_inds are the indices among `probs` of valid detections before NMS (or -1 for invalid)
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valid_inds = torch.where(is_valid, is_valid.cumsum(dim=0) - 1, -1) # (num_det,)
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keep = torch.isin(valid_inds, kept_inds) # (num_det,)
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return keep
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def generic_nms(
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ious: torch.Tensor, scores: torch.Tensor, iou_threshold=0.5
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) -> torch.Tensor:
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"""A generic version of `torchvision.ops.nms` that takes a pairwise IoU matrix."""
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assert ious.dim() == 2 and ious.size(0) == ious.size(1)
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assert scores.dim() == 1 and scores.size(0) == ious.size(0)
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if ious.is_cuda:
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if GENERIC_NMS_AVAILABLE:
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return generic_nms_cuda(ious, scores, iou_threshold, use_iou_matrix=True)
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else:
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from sam3.perflib.triton.nms import nms_triton
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return nms_triton(ious, scores, iou_threshold)
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return generic_nms_cpu(ious, scores, iou_threshold)
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def generic_nms_cpu(
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ious: torch.Tensor, scores: torch.Tensor, iou_threshold=0.5
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) -> torch.Tensor:
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"""
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A generic version of `torchvision.ops.nms` that takes a pairwise IoU matrix. (CPU implementation
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based on https://github.com/jwyang/faster-rcnn.pytorch/blob/master/lib/model/nms/nms_cpu.py)
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"""
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ious_np = ious.float().detach().cpu().numpy()
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scores_np = scores.float().detach().cpu().numpy()
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order = scores_np.argsort()[::-1]
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kept_inds = []
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while order.size > 0:
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i = order.item(0)
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kept_inds.append(i)
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inds = np.where(ious_np[i, order[1:]] <= iou_threshold)[0]
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order = order[inds + 1]
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return torch.tensor(kept_inds, dtype=torch.int64, device=scores.device)
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