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evaluation_functions.py
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evaluation_functions.py
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import numpy as np
import logging
import torch.cuda
#from eval_config import *
import time
from utils.auxilary_functions import average_precision
from torchvision.ops.boxes import box_iou
import torch.nn.functional as F
from scan_functions import form_kws, generate_maps, intersection_metric, phoc_like
logging.basicConfig(format='[%(asctime)s, %(levelname)s, %(name)s] %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.INFO)
def reduced(istr):
return ''.join([c if (c.isalnum() or c=='_' or c==' ') else '*' for c in istr.lower()])
def seg_free_eval(form_test_set, cnn, classes, args, Ns=None, eval_multiple_thres=False):
device = next(cnn.parameters()).device
stopwords = form_test_set.stopwords
reduced_charset = args.reduced_charset
#classes = args.classes
if reduced_charset:
classes = reduced(classes)
nclasses = ''
for c in classes:
if c in nclasses:
continue
else:
nclasses += c
classes = nclasses
cdict = {c: i for i, c in enumerate(classes)}
N = form_test_set.__len__()
if Ns is not None and Ns < N:
N = Ns
transcrs = []
for i in range(N):
bboxes = form_test_set.__getitem__(i)[-1]
transcrs += [tt[1] for tt in bboxes]
if reduced_charset:
transcrs = [reduced(tt) for tt in transcrs]
uwords = np.unique(transcrs)
udict = {w: i for i, w in enumerate(uwords)}
lbls = np.asarray([udict[w] for w in transcrs])
cnts = np.bincount(lbls)
if reduced_charset:
queries = [w for w in uwords if w not in stopwords and cnts[udict[w]] >= 1 and len(w) > 1 and '*' not in w]
else:
queries = [w for w in uwords if w not in stopwords and cnts[udict[w]] >= 1 and len(w) > 1]
# print(queries)
# threshold for returning most relevant words!
doc_scale = args.doc_scale
cos_thres = args.cos_thres # .5
ctc_thres = args.ctc_thres # None #3.5
if ctc_thres < 0:
ctc_thres = None
ctc_mode = args.ctc_mode # False
K = args.K
levels = args.clevels
iou_mode = args.iou_mode # 0: typical IoU, 1: per-axis IOU, 2: intersection
masked_form = args.masked_form
query_descs = [phoc_like(query, cdict, levels) for query in queries]
retrieval_dict = {query: ([], [], 0) for query in queries}
tsum = 0
for i in range(N):
img, _, bboxes = form_test_set.__getitem__(i)
if doc_scale != 1.0:
img = F.interpolate(img.unsqueeze(0), scale_factor=doc_scale, mode='bilinear')[0]
img = img[0]
tbboxes, ttranscrs = [], []
for bbox in bboxes:
tbbox = doc_scale * bbox[0]
si, ei = tbbox[1], min(img.shape[0], tbbox[1] + tbbox[3])
sj, ej = tbbox[0], min(img.shape[1], tbbox[0] + tbbox[2])
tbboxes += [[int(sj), int(ej), int(si), int(ei)]]
if reduced_charset:
ttranscrs += [reduced(bbox[1].strip())]
else:
ttranscrs += [bbox[1]]
if masked_form:
mask = np.zeros(img.shape)
for bbox in tbboxes:
sj, ej, si, ei = bbox
mask[si:ei, sj:ej] = 1
img *= torch.Tensor(mask)
img = img.to(device)
rmap, cmap, scale_cmap, valid_map, steps = generate_maps(img, cnn, carea_ratio=args.carea_ratio)
# prepare usage of torcvision box iou
tbboxes = torch.Tensor(np.asarray(tbboxes)[:, [0, 2, 1, 3]])
tnow = time.time()
for j, query in enumerate(queries): # [100:400]:
predicted_bboxes, predicted_scores = form_kws(rmap, cmap, scale_cmap, valid_map, steps, query,
query_descs[j], classes,
k=K, clevels=levels, cos_thres=cos_thres, ctc_thres=ctc_thres,
ctc_mode=ctc_mode, prob_thres=args.prob_thres)
qidxs = [ii for ii, tt in enumerate(ttranscrs) if query == tt]
realv = len(qidxs)
if len(predicted_scores) == 0:
retrieval_dict[query] = (
retrieval_dict[query][0],
retrieval_dict[query][1],
retrieval_dict[query][2] + realv
)
continue
bdscale = 8 # * doc_scale
predicted_bboxes = torch.Tensor(np.asarray(predicted_bboxes)) #[:, [0, 2, 1, 3]] / bdscale
matches = np.zeros(len(predicted_scores))
if realv > 0:
if iou_mode == 0 or iou_mode == 1:
iou_scores, iou_args = box_iou(tbboxes[qidxs].view(-1, 4) / bdscale,
predicted_bboxes.view(-1, 4)).max(dim=-1)
elif iou_mode == 2:
iou_scores = intersection_metric(tbboxes.view(-1, 4).numpy() / bdscale,
predicted_bboxes.view(-1, 4).numpy(),
qidxs)
iou_args = iou_scores.argmax(axis=-1)
iou_scores = iou_scores.max(axis=-1)
else:
print('not valid iou mode')
exit(0)
if len(iou_args) >= 1:
_, ainds = np.unique(iou_args, return_index=True)
iou_scores, iou_args = iou_scores[ainds], iou_args[ainds]
if iou_mode == 1:
xthres = .1
if sum(iou_scores > xthres) > 0:
# then y-axis
gb, pb = tbboxes[qidxs].view(-1, 4).clone() / bdscale, predicted_bboxes.view(-1, 4).clone()
gb = gb[torch.LongTensor(ainds)[iou_scores > xthres]]
pb = pb[iou_args[iou_scores > xthres]]
gb[:, 1] = 0
pb[:, 1] = 0
gb[:, 3] = 10
pb[:, 3] = 10
y_iou_scores = torch.diagonal(box_iou(gb, pb))
matches[iou_args[iou_scores > xthres]] = y_iou_scores
else:
matches[iou_args] = iou_scores
retrieval_dict[query] = (
np.concatenate([retrieval_dict[query][0], matches]),
np.concatenate([retrieval_dict[query][1], np.asarray(predicted_scores)]),
retrieval_dict[query][2] + realv
)
tsum += (time.time() - tnow)
if i % 10 == 0:
print('time @ ' + str(i) + ' : ' + str(tsum / ((i + 1) * len(queries))))
print('overall time: ' + str(tsum / (N * len(queries))))
if eval_multiple_thres:
for iou_thres in [0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:
# calculate AP for each query!
aps = []
for key, (matches, scores, real_gt) in retrieval_dict.items():
if real_gt > 0:
if len(matches) > 0:
sorted_inds = np.argsort(scores)
matches = matches[sorted_inds]
ap = average_precision(matches > iou_thres, real_gt)
else:
ap = 0
else:
# print('huh? query: ' + str(key))
continue
aps += [ap]
map_metric = np.mean(aps)
print('MAP @ ' + str(iou_thres) + ' IOU: ' + str(map_metric))
else:
# check only 25% for validation!
iou_thres = 0.25
aps = []
for key, (matches, scores, real_gt) in retrieval_dict.items():
if real_gt > 0:
if len(matches) > 0:
sorted_inds = np.argsort(scores)
matches = matches[sorted_inds]
ap = average_precision(matches > iou_thres, real_gt)
else:
ap = 0
else:
# print('huh? query: ' + str(key))
continue
aps += [ap]
map_metric = np.mean(aps)
print('MAP @ ' + str(iou_thres) + ' IOU: ' + str(map_metric))
return map_metric