def val_part(my_model,images_val,targets_val,this_dataset):
my_model.eval()
images_val_2 = list(images_val.to(device) for images_val in images_val)
targets_val_2 = [{k: v.to(device) for k, v in t.items()} for t in targets_val]
targets_val_3 = [{k: v.tolist() for k, v in t.items()} for t in targets_val]
# targets_val_3 = [{k: v.tolist() for k, v in t['image_id']} for t in targets_val_3]
for i in range(len(targets_val_3)):
# print(train_dataset.img_path_list[targets_val_3[i]['image_id'][0]]['image_path'])
# print(targets_val_3[i]['image_id'])
# targets_val_3[i]['image_id'] = train_dataset.img_path_list[targets_val_3[i]['image_id'][0]]['image_path']
targets_val_3[i].update({'image_id': [this_dataset.img_path_list[targets_val_3[i]['image_id'][0]]['image_path']]})
# print(targets_val_3[i]['image_id'])
val_dict = my_model(images_val_2, targets_val_2)
# 1、把loss_dict 转化为predict_all（）return的结果的格式 √
# 2、调用eval_one_batch √
# 3、打印评估结果和save best map
preds = []
for i in range(len(val_dict)):
for j in range(len(val_dict[i]['scores'])):
preds.append([
targets_val_3[i]['image_id'][0],
# x_train[0]['image_path'],
val_dict[i]['scores'][j],
val_dict[i]['boxes'][j][0].cpu().detach().item(),
val_dict[i]['boxes'][j][1].cpu().detach().item(),
val_dict[i]['boxes'][j][2].cpu().detach().item(),
val_dict[i]['boxes'][j][3].cpu().detach().item(),
]) # 这里pred['labels'][j]-1 用于与标签对应 0-没有佩戴，1-有佩戴
# eval_one_batch(eval_labels)
# 开始计算最后得分
sum_ap = 0
all_labels = [i for i in range(2)] # 所有目标类别
for label in all_labels: # 逐个类别计算ap
if len(val_dict) != 0: # 当包含预测框的时候，进行计算ap值
rec, prec, ap = voc_eval(targets_val_3, preds, label)
else:
ap = 0
sum_ap += ap
map = sum_ap / len(all_labels)
# result = dict()
# result['score'] = round(map * 100, 2)
# result['label'] = "The Score is MAP."
# result['info'] = ""
# print(json.dumps(result))
print('mAP is %.2f' % map)
return map
def do_python_eval(use_07=True):
devkit_path = VOCroot + 'VOC2007'
cachedir = os.path.join(devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = use_07
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(labelmap):
filename = 'tmp/det_test_%s.txt' % (cls)
rec, prec, ap = voc_eval(
filename, annopath, imgsetpath.format('test'), cls, cachedir,
ovthresh=0.5, use_07_metric=use_07_metric)
aps += [ap]
# print('AP for {} = {:.4f}'.format(cls, ap))
# with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
# pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
return np.mean(aps)
def valmodel(model, path, dboxes, trans, encoder):
od = torch.load(path)
#print(od.keys())
model.load_state_dict(od["model"])
model.eval()
model.cuda()
lm = od["label_map"]
#print(lm)
img_folder = "../../VOCdevkit/VOC2007/JPEGImages"
ann_folder = "../../VOCdevkit/VOC2007/Annotations"
tgt_folder = "../../VOCdevkit/VOC2007/ImageSets/Main/test.txt"
vd = VOCDetection(img_folder, ann_folder, tgt_folder, label_map=lm, \
transform = trans)
#print(vd.label_map)
#import sys; sys.exit()
if not os.path.exists("pr_data"):
os.mkdir("pr_data")
else:
shutil.rmtree("pr_data")
os.mkdir("pr_data")
img_info = [[] for _ in range(21)]
end = time.time()
for idx, fname in enumerate(vd.images):
print("Parse image: {}/{}".format(idx + 1, len(vd)), end="\r")
img, (h, w), bbox, label = vd[idx]
with torch.no_grad():
ploc, plabel = model(img.unsqueeze(0).cuda())
try:
result = encoder.decode_batch(ploc, plabel, 0.50, 200)[0]
except:
#raise
print("No object detected in idx: {}".format(idx), end="\r")
continue
loc, label, prob = [r.cpu().numpy() for r in result]
for loc_, label_, prob_ in zip(loc, label, prob):
img_info[label_].append((fname[0].split(".")[0], prob_,
loc_[0]*w, loc_[1]*h, \
loc_[2]*w, loc_[3]*h))
print("")
print("Test: total time elapsed: {:.3f}".format(time.time() - end))
for i in range(1, 21):
fn = "pr_data/pred_" + vd.label_map[i] + ".txt"
with open(fn, "w") as fout:
for rec in img_info[i]:
fout.write(
"{} {:.4f} {:.1f} {:.1f} {:.1f} {:.1f}\n".format(*rec))
from eval import voc_eval
import glob
s = 0
files = glob.glob("./pr_data/pred_*.txt")
files.sort()
for f in files:
name = (f.split("_")[-1]).split(".")[0]
r = voc_eval(f,
annopath=os.path.join(ann_folder, "%s.xml"),
imagesetfile=vd.file_filter,
classname=name,
cachedir="./cache",
ovthresh=0.45)
s += r[-1]
s /= 20
print('mAP {:.3f}'.format(s))
return s
dets = all_boxes[0+1][0]
if dets != []:
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index[1], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
anno_path = 'vis/Annotations/%s.xml'
image_set_file = 'vis/Annotations/images'
classname = 'dog'
cachedir = 'vis/Annotations/'
_, _, ap = voc_eval(det_text, anno_path, image_set_file.format('test'), classname, cachedir)
if iteration % 10 == 0:
print('loss' + str(ap))
ap.backward()
optimizer.step()
'''
# backprop
optimizer.zero_grad()
def test(
cfg,
weights,
batch_size=16,
img_size=416,
iou_thres=0.5,
conf_thres=0.001,
nms_thres=0.5,
model=None
):
if model is None:
device = torch_utils.select_device()
# Initialize model
model = Darknet(cfg, img_size).to(device)
# Load weights
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location=device)['model'], strict=False)
else: # darknet format
_ = load_darknet_weights(model, weights)
else:
device = next(model.parameters()).device # get model device
# Get dataloader
vocset = VOCDetection(root=os.path.join('~', 'data', 'VOCdevkit'), splits=((2007, 'test'),),
img_size=img_size, mode='test')
dataloader = torch.utils.data.DataLoader(vocset,
batch_size=batch_size,
num_workers=8,
collate_fn=vocset.collate_fn)
nC = vocset.num_class #num class
classes = vocset.classes
det_results_path = os.path.join('eval', 'results', 'VOC2007', 'Main')
if os.path.exists(det_results_path):
shutil.rmtree(det_results_path)
os.makedirs(det_results_path)
model.eval()
seen = 0
pbar = tqdm(total=len(dataloader) * batch_size, desc='Computing mAP')
for batch_i, (imgs, targets, shapes, img_paths) in enumerate(dataloader):
output, _ = model(imgs.to(device))
# nms
output = nms(output, conf_thres, nms_thres, method='nms')
for si, detections in enumerate(output):
seen += 1
if len(detections) == 0:
continue
# Rescale boxes from 416 to true image size
scale_coords(img_size, detections[:, :4], shapes[si]).round()
image_ind = os.path.split(img_paths[si])[-1][:-4]
for bbox in detections:
coor = bbox[:4]
score = bbox[4]
class_ind = int(bbox[5])
class_name = classes[class_ind]
score = score = '%.4f' % score
xmin, ymin, xmax, ymax = map(str, coor)
bbox_mess = ' '.join([image_ind, score, xmin, ymin, xmax, ymax]) + '\n'
with open(os.path.join(det_results_path, 'comp3_det_test_' + class_name + '.txt'), 'a') as f:
f.write(bbox_mess)
pbar.update(batch_size)
pbar.close()
filename = os.path.join('eval', 'results', 'VOC2007', 'Main', 'comp3_det_test_{:s}.txt')
cachedir = os.path.join('eval', 'cache')
annopath = os.path.join(vocset._root, 'VOC2007', 'Annotations', '{:s}.xml')
imagesetfile = os.path.join(vocset._root, 'VOC2007', 'ImageSets', 'Main', 'test.txt')
APs = {}
for i, cls in enumerate(classes):
rec, prec, ap = voc_eval(filename, annopath, imagesetfile, cls, cachedir, iou_thres, False)
APs[cls] = ap
# if os.path.exists(cachedir):
# shutil.rmtree(cachedir)
mAP = np.mean([APs[cls] for cls in APs])
return APs, mAP
val_dict[i]['boxes'][j][3].cpu().detach().item(),
]) # 这里pred['labels'][j]-1 用于与标签对应 0-没有佩戴，1-有佩戴
# eval_one_batch(eval_labels)
# 开始计算最后得分
sum_ap = 0
all_labels = [i for i in range(2)] # 所有目标类别
for label in all_labels: # 逐个类别计算ap
# prediction1 = [] # 在计算 ap 的时候，需要把prediction按照最后预测的类别进行筛选
# for pred in eval_labels:
# if pred[3] == label:
# prediction1.append([pred[0], pred[1], pred[2][0], pred[2][1], pred[2][2], pred[2][3]])
if len(val_dict) != 0: # 当包含预测框的时候，进行计算ap值
rec, prec, ap = voc_eval(targets_val_3, preds, label)
else:
ap = 0
sum_ap += ap
map = sum_ap / len(all_labels)
result = dict()
result['score'] = round(map * 100, 2)
result['label'] = "The Score is MAP."
result['info'] = ""
print(json.dumps(result))
# 等等
# losses = sum(loss for loss in loss_dict.values())
#
# optimizer.zero_grad()