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import numpy as np
from hotr.metrics.utils import _compute_ap, compute_overlap
import pdb
class APAgent(object):
def __init__(self, act_name, iou_threshold=0.5):
self.act_name = act_name
self.iou_threshold = iou_threshold
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, box, act, cat, i_box, i_act):
for label in range(len(self.act_name)):
i_inds = (i_act[:, label] == 1)
self.num_ann[label] += i_inds.sum()
n_pred = box.shape[0]
if n_pred == 0 : return
######################
valid_i_inds = (i_act[:, 0] != -1) # (n_i, ) # both in COCO & V-COCO
overlaps = compute_overlap(box, i_box) # (n_pred, n_i)
assigned_input = np.argmax(overlaps, axis=1) # (n_pred, )
v_inds = valid_i_inds[assigned_input] # (n_pred, )
n_valid = v_inds.sum()
if n_valid == 0 : return
valid_box = box[v_inds]
valid_act = act[v_inds]
valid_cat = cat[v_inds]
######################
s = valid_act * np.expand_dims(valid_cat, axis=1) # (n_v, #act)
for label in range(len(self.act_name)):
inds = np.argsort(s[:, label])[::-1] # (n_v, )
self.score[label] = np.append(self.score[label], s[inds, label])
correct_i_inds = (i_act[:, label] == 1)
if correct_i_inds.sum() == 0:
self.tp[label] = np.append(self.tp[label], np.array([0]*n_valid))
self.fp[label] = np.append(self.fp[label], np.array([1]*n_valid))
continue
overlaps = compute_overlap(valid_box[inds], i_box) # (n_v, n_i)
assigned_input = np.argmax(overlaps, axis=1) # (n_v, )
max_overlap = overlaps[range(n_valid), assigned_input] # (n_v, )
iou_inds = (max_overlap > self.iou_threshold) & correct_i_inds[assigned_input] # (n_v, )
i_nonzero = iou_inds.nonzero()[0]
i_inds = assigned_input[i_nonzero]
i_iou = np.unique(i_inds, return_index=True)[1]
i_tp = i_nonzero[i_iou]
t = np.zeros(n_valid, dtype=np.uint8)
t[i_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):
average_precisions = dict()
for label in range(len(self.act_name)):
if self.num_ann[label] == 0:
average_precisions[label] = 0
continue
# sort by score
indices = np.argsort(-self.score[label])
self.fp[label] = self.fp[label][indices]
self.tp[label] = self.tp[label][indices]
# 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
print('\n================== AP (Agent) ===================')
s, n = 0, 0
for label in range(len(self.act_name)):
label_name = "_".join(self.act_name[label].split("_")[1:])
print('{: >23}: AP = {:0.2f} (#pos = {:d})'.format(label_name, average_precisions[label], self.num_ann[label]))
s += average_precisions[label]
n += 1
mAP = s/n
print('| mAP(agent): {:0.2f}'.format(mAP))
print('----------------------------------------------------')
return mAP |