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sam_eval_utils.py
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sam_eval_utils.py
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# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
# International Conference on Computer Vision (ICCV), 2023
import copy
import cv2
import numpy as np
from lvis import LVISEval, LVISResults
from pycocotools.cocoeval import COCOeval
def iou(mask_a: np.ndarray, mask_b: np.ndarray) -> float:
intersection = np.count_nonzero(mask_a & mask_b)
union = np.count_nonzero(mask_a | mask_b)
return float(intersection / (union + 1e-7)) * 100
def filter_results_by_area(results: list[dict], min=None, max=None) -> list[dict]:
filtered = []
for r in results:
if min is not None and r["area"] < min:
continue
if max is not None and r["area"] > max:
continue
filtered.append(r)
return filtered
def get_iou_metric(results: list[dict]) -> dict[str, float]:
small_results = filter_results_by_area(results, None, 32**2)
medium_results = filter_results_by_area(results, 32**2, 96**2)
large_results = filter_results_by_area(results, 96**2, None)
return {
"all": sum(r["iou"] for r in results) / len(results),
"large": sum(r["iou"] for r in large_results) / len(large_results),
"medium": sum(r["iou"] for r in medium_results) / len(medium_results),
"small": sum(r["iou"] for r in small_results) / len(small_results),
}
def evaluate_predictions_on_coco(
coco_gt,
coco_results,
iou_type,
cocoeval_fn=COCOeval,
img_ids=None,
max_dets_per_image=None,
):
"""
Modified from https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/coco_evaluation.py.
Evaluate the coco results using COCOEval API.
"""
assert len(coco_results) > 0
if iou_type == "segm":
coco_results = copy.deepcopy(coco_results)
for c in coco_results:
c.pop("bbox", None)
coco_dt = coco_gt.loadRes(coco_results)
coco_eval = cocoeval_fn(coco_gt, coco_dt, iou_type)
if max_dets_per_image is None:
max_dets_per_image = [1, 10, 100] # Default from COCOEval
else:
assert (
len(max_dets_per_image) >= 3
), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3"
if max_dets_per_image[2] != 100:
raise NotImplementedError
if iou_type != "keypoints":
coco_eval.params.maxDets = max_dets_per_image
if img_ids is not None:
coco_eval.params.imgIds = img_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
return coco_eval
def evaluate_predictions_on_lvis(lvis_gt, lvis_results, iou_type, max_dets_per_image=None, class_names=None):
"""
Modified from https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/lvis_evaluation.py.
Args:
iou_type (str):
max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP
This limit, by default of the LVIS dataset, is 300.
class_names (None or list[str]): if provided, will use it to predict
per-category AP.
Returns:
a dict of {metric name: score}
"""
metrics = {
"bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"],
"segm": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"],
}[iou_type]
if len(lvis_results) == 0:
return {metric: float("nan") for metric in metrics}
if iou_type == "segm":
lvis_results = copy.deepcopy(lvis_results)
for c in lvis_results:
c.pop("bbox", None)
if max_dets_per_image is None:
max_dets_per_image = 300 # Default for LVIS dataset
lvis_results = LVISResults(lvis_gt, lvis_results, max_dets=max_dets_per_image)
lvis_eval = LVISEval(lvis_gt, lvis_results, iou_type)
lvis_eval.run()
lvis_eval.print_results()
results = lvis_eval.get_results()
results = {metric: float(results[metric] * 100) for metric in metrics}
print(results)
return results
class Clicker(object):
"""
Modified from https://github.com/SamsungLabs/ritm_interactive_segmentation/blob/b9b44603672e15aa0be878b54fd26e7e1c5d2311/isegm/inference/clicker.py#L7.
"""
def __init__(self, gt_mask=None, init_clicks=None, ignore_label=-1, click_indx_offset=0):
self.click_indx_offset = click_indx_offset
if gt_mask is not None:
self.gt_mask = gt_mask == 1
self.not_ignore_mask = gt_mask != ignore_label
else:
self.gt_mask = None
self.reset_clicks()
if init_clicks is not None:
for click in init_clicks:
self.add_click(click)
def make_next_click(self, pred_mask):
assert self.gt_mask is not None
click = self._get_next_click(pred_mask)
self.add_click(click)
def get_clicks(self, clicks_limit=None):
return self.clicks_list[:clicks_limit]
def _get_next_click(self, pred_mask, padding=True):
fn_mask = np.logical_and(np.logical_and(self.gt_mask, np.logical_not(pred_mask)), self.not_ignore_mask)
fp_mask = np.logical_and(np.logical_and(np.logical_not(self.gt_mask), pred_mask), self.not_ignore_mask)
if padding:
fn_mask = np.pad(fn_mask, ((1, 1), (1, 1)), "constant")
fp_mask = np.pad(fp_mask, ((1, 1), (1, 1)), "constant")
fn_mask_dt = cv2.distanceTransform(fn_mask.astype(np.uint8), cv2.DIST_L2, 0)
fp_mask_dt = cv2.distanceTransform(fp_mask.astype(np.uint8), cv2.DIST_L2, 0)
if padding:
fn_mask_dt = fn_mask_dt[1:-1, 1:-1]
fp_mask_dt = fp_mask_dt[1:-1, 1:-1]
fn_mask_dt = fn_mask_dt * self.not_clicked_map
fp_mask_dt = fp_mask_dt * self.not_clicked_map
fn_max_dist = np.max(fn_mask_dt)
fp_max_dist = np.max(fp_mask_dt)
is_positive = fn_max_dist > fp_max_dist
if is_positive:
coords_y, coords_x = np.where(fn_mask_dt == fn_max_dist) # coords is [y, x]
else:
coords_y, coords_x = np.where(fp_mask_dt == fp_max_dist) # coords is [y, x]
return Click(is_positive=is_positive, coords=(coords_y[0], coords_x[0]))
def add_click(self, click):
coords = click.coords
click.indx = self.click_indx_offset + self.num_pos_clicks + self.num_neg_clicks
if click.is_positive:
self.num_pos_clicks += 1
else:
self.num_neg_clicks += 1
self.clicks_list.append(click)
if self.gt_mask is not None:
self.not_clicked_map[coords[0], coords[1]] = False
def _remove_last_click(self):
click = self.clicks_list.pop()
coords = click.coords
if click.is_positive:
self.num_pos_clicks -= 1
else:
self.num_neg_clicks -= 1
if self.gt_mask is not None:
self.not_clicked_map[coords[0], coords[1]] = True
def reset_clicks(self):
if self.gt_mask is not None:
# self.not_clicked_map = np.ones_like(self.gt_mask, dtype=np.bool)
self.not_clicked_map = np.ones_like(self.gt_mask, dtype=bool)
self.num_pos_clicks = 0
self.num_neg_clicks = 0
self.clicks_list = []
def get_state(self):
return copy.deepcopy(self.clicks_list)
def set_state(self, state):
self.reset_clicks()
for click in state:
self.add_click(click)
def __len__(self):
return len(self.clicks_list)
class Click:
"""
Modified from https://github.com/SamsungLabs/ritm_interactive_segmentation/blob/b9b44603672e15aa0be878b54fd26e7e1c5d2311/isegm/inference/clicker.py#L7.
"""
def __init__(self, is_positive, coords, indx=None):
self.is_positive = is_positive
self.coords = coords
self.indx = indx
@property
def coords_and_indx(self):
return (*self.coords, self.indx)
def copy(self, **kwargs):
self_copy = copy.deepcopy(self)
for k, v in kwargs.items():
setattr(self_copy, k, v)
return self_copy