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import numpy as np
import torch
from hotr.metrics.utils import _compute_ap, compute_overlap
class APRole(object):
def __init__(self, act_name, scenario_flag=True, iou_threshold=0.5):
self.act_name = act_name
self.iou_threshold = iou_threshold
self.scenario_flag = scenario_flag
# scenario_1 : True
# scenario_2 : False
self.fp = [np.zeros((0,))] * len(act_name)
self.tp = [np.zeros((0,))] * len(act_name)
self.score = [np.zeros((0,))] * len(act_name)
self.num_ann = [0] * len(act_name)
def add_data(self, h_box, o_box, score, i_box, i_act, p_box, p_act):
# i_box, i_act : to check if only in COCO
for label in range(len(self.act_name)):
p_inds = (p_act[:, label] == 1)
self.num_ann[label] += p_inds.sum()
if h_box.shape[0] == 0 : return # if no prediction, just return
# COCO (O), V-COCO (X) __or__ collater, no ann in image => ignore
valid_i_inds = (i_act[:, 0] != -1) # (n_i, )
overlaps = compute_overlap(h_box, i_box) # (n_h, n_i)
assigned_input = np.argmax(overlaps, axis=1) # (n_h, )
v_inds = valid_i_inds[assigned_input] # (n_h, )
h_box = h_box[v_inds]
score = score[:, v_inds, :]
if h_box.shape[0] == 0 : return
n_h = h_box.shape[0]
valid_p_inds = (p_act[:, 0] != -1) | (p_box[:, 0] != -1)
p_act = p_act[valid_p_inds]
p_box = p_box[valid_p_inds]
n_o = o_box.shape[0]
if n_o == 0:
# no prediction for object
score = score.squeeze(axis=2) # (#act, n_h)
for label in range(len(self.act_name)):
h_inds = np.argsort(score[label])[::-1] # (n_h, )
self.score[label] = np.append(self.score[label], score[label, h_inds])
p_inds = (p_act[:, label] == 1)
if p_inds.sum() == 0:
self.tp[label] = np.append(self.tp[label], np.array([0]*n_h))
self.fp[label] = np.append(self.fp[label], np.array([1]*n_h))
continue
h_overlaps = compute_overlap(h_box[h_inds], p_box[p_inds, :4]) # (n_h, n_p)
assigned_p = np.argmax(h_overlaps, axis=1) # (n_h, )
h_max_overlap = h_overlaps[range(n_h), assigned_p] # (n_h, )
o_overlaps = compute_overlap(np.zeros((n_h, 4)), p_box[p_inds][assigned_p, 4:8])
o_overlaps = np.diag(o_overlaps) # (n_h, )
no_role_inds = (p_box[p_inds][assigned_p, 4] == -1) # (n_h, )
# human (o), action (o), no object in actual image
h_iou_inds = (h_max_overlap > self.iou_threshold) # (n_h, )
o_iou_inds = (o_overlaps > self.iou_threshold) # (n_h, )
# scenario1 is not considered (already no object)
o_iou_inds[no_role_inds] = 1
iou_inds = (h_iou_inds & o_iou_inds)
p_nonzero = iou_inds.nonzero()[0]
p_inds = assigned_p[p_nonzero]
p_iou = np.unique(p_inds, return_index=True)[1]
p_tp = p_nonzero[p_iou]
t = np.zeros(n_h, dtype=np.uint8)
t[p_tp] = 1
f = 1-t
self.tp[label] = np.append(self.tp[label], t)
self.fp[label] = np.append(self.fp[label], f)
else:
s_obj_argmax = np.argmax(score.reshape(-1, n_o), axis=1).reshape(-1, n_h) # (#act, n_h)
s_obj_max = np.max(score.reshape(-1, n_o), axis=1).reshape(-1, n_h) # (#act, n_h)
h_overlaps = compute_overlap(h_box, p_box[:, :4]) # (n_h, n_p)
for label in range(len(self.act_name)):
h_inds = np.argsort(s_obj_max[label])[::-1] # (n_h, )
self.score[label] = np.append(self.score[label], s_obj_max[label, h_inds])
p_inds = (p_act[:, label] == 1) # (n_p, )
if p_inds.sum() == 0:
self.tp[label] = np.append(self.tp[label], np.array([0]*n_h))
self.fp[label] = np.append(self.fp[label], np.array([1]*n_h))
continue
h_overlaps = compute_overlap(h_box[h_inds], p_box[:, :4]) # (n_h, n_p) # match for all hboxes
h_max_overlap = np.max(h_overlaps, axis=1) # (n_h, ) # get the max overlap for hbox
# for same human, multiple pairs exist. find the human box that has the same idx with max overlap hbox.
h_max_temp = np.expand_dims(h_max_overlap, axis=1)
h_over_thresh = (h_overlaps == h_max_temp) # (n_h, n_p)
h_over_thresh = h_over_thresh & np.expand_dims(p_inds, axis=0) # (n_h, n_p) # find only for current act
h_valid = h_over_thresh.sum(axis=1)>0 # (n_h, ) # at least one is True
# h_valid -> if all is False, then argmax becomes 0. <- prevent
assigned_p = np.argmax(h_over_thresh, axis=1) # (n_h, ) # p only for current act
o_mapping_box = o_box[s_obj_argmax[label]][h_inds] # (n_h, ) # find where T is.
p_mapping_box = p_box[assigned_p, 4:8] # (n_h, 4)
o_overlaps = compute_overlap(o_mapping_box, p_mapping_box)
o_overlaps = np.diag(o_overlaps) # (n_h, )
o_overlaps.setflags(write=1)
if (~h_valid).sum() > 0:
o_overlaps[~h_valid] = 0 # (n_h, )
no_role_inds = (p_box[assigned_p, 4] == -1) # (n_h, )
nan_box_inds = np.all(o_mapping_box == 0, axis=1) | np.all(np.isnan(o_mapping_box), axis=1)
no_role_inds = no_role_inds & h_valid
nan_box_inds = nan_box_inds & h_valid
h_iou_inds = (h_max_overlap > self.iou_threshold) # (n_h, )
o_iou_inds = (o_overlaps > self.iou_threshold) # (n_h, )
if self.scenario_flag: # scenario_1
o_iou_inds[no_role_inds & nan_box_inds] = 1
o_iou_inds[no_role_inds & ~nan_box_inds] = 0
else: # scenario_2
o_iou_inds[no_role_inds] = 1
iou_inds = (h_iou_inds & o_iou_inds)
p_nonzero = iou_inds.nonzero()[0]
p_inds = assigned_p[p_nonzero]
p_iou = np.unique(p_inds, return_index=True)[1]
p_tp = p_nonzero[p_iou]
t = np.zeros(n_h, dtype=np.uint8)
t[p_tp] = 1
f = 1-t
self.tp[label] = np.append(self.tp[label], t)
self.fp[label] = np.append(self.fp[label], f)
def evaluate(self, print_log=False):
average_precisions = dict()
role_num = 1 if self.scenario_flag else 2
for label in range(len(self.act_name)):
# sort by score
indices = np.argsort(-self.score[label])
self.fp[label] = self.fp[label][indices]
self.tp[label] = self.tp[label][indices]
if self.num_ann[label] == 0:
average_precisions[label] = 0
continue
# compute false positives and true positives
self.fp[label] = np.cumsum(self.fp[label])
self.tp[label] = np.cumsum(self.tp[label])
# compute recall and precision
recall = self.tp[label] / self.num_ann[label]
precision = self.tp[label] / np.maximum(self.tp[label] + self.fp[label], np.finfo(np.float64).eps)
# compute average precision
average_precisions[label] = _compute_ap(recall, precision) * 100
if print_log: print(f'\n============= AP (Role scenario_{role_num}) ==============')
s, n = 0, 0
for label in range(len(self.act_name)):
if 'point' in self.act_name[label]:
continue
label_name = "_".join(self.act_name[label].split("_")[1:])
if print_log: print('{: >23}: AP = {:0.2f} (#pos = {:d})'.format(label_name, average_precisions[label], self.num_ann[label]))
if self.num_ann[label] != 0 :
s += average_precisions[label]
n += 1
mAP = s/n
if print_log:
print('| mAP(role scenario_{:d}): {:0.2f}'.format(role_num, mAP))
print('----------------------------------------------------')
return mAP |