def eval(net, dataset):
for i, (inputs, box_targets, label_targets) in enumerate(dataloader):
print('%d/%d' % (i, len(dataloader)))
gt_boxes.append(box_targets.squeeze(0))
gt_labels.append(label_targets.squeeze(0))
loc_preds, cls_preds = net(Variable(inputs.cuda(), volatile=True))
box_preds, label_preds, score_preds = box_coder.decode(
loc_preds.cuda().data.squeeze(),
F.softmax(cls_preds.squeeze(), dim=1).cuda().data,
score_thresh=0.01)
pred_boxes.append(box_preds)
pred_labels.append(label_preds)
pred_scores.append(score_preds)
print(
voc_eval(pred_boxes,
pred_labels,
pred_scores,
gt_boxes,
gt_labels,
gt_difficults,
iou_thresh=0.5,
use_07_metric=True))
def eval(net):
net.eval()
def transform(img, boxes, labels):
img, boxes = resize(img, boxes, size=(img_size, img_size))
img = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])(img)
return img, boxes, labels
dataset = ListDataset(root=args.data_root, \
list_file=args.voc07_test,
transform=transform)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=8)
box_coder = SSDBoxCoder(net)
pred_boxes = []
pred_labels = []
pred_scores = []
gt_boxes = []
gt_labels = []
with open('torchcv/datasets/voc/voc07_test_difficult.txt') as f:
gt_difficults = []
for line in f.readlines():
line = line.strip().split()
d = np.array([int(x) for x in line[1:]])
gt_difficults.append(d)
for i, (inputs, box_targets, label_targets) in enumerate(dataloader):
print('%d/%d' % (i, len(dataloader)))
gt_boxes.append(box_targets.squeeze(0))
gt_labels.append(label_targets.squeeze(0))
loc_preds, cls_preds = net(Variable(inputs.cuda(), volatile=True))
box_preds, label_preds, score_preds = box_coder.decode(
loc_preds.cpu().data.squeeze(),
F.softmax(cls_preds.squeeze(), dim=1).cpu().data,
score_thresh=0.01)
pred_boxes.append(box_preds)
pred_labels.append(label_preds)
pred_scores.append(score_preds)
aps = (voc_eval(pred_boxes,
pred_labels,
pred_scores,
gt_boxes,
gt_labels,
gt_difficults,
iou_thresh=0.5,
use_07_metric=True))
net.train()
return aps
def eval(net, test_num=10000):
net.eval()
def transform(img, boxes, labels):
img, boxes = resize(img, boxes, size=(opt.img_size, opt.img_size))
img = transforms.Compose([transforms.ToTensor(), caffe_normalize])(img)
return img, boxes, labels
dataset = ListDataset(root=opt.eval_img_root,
list_file=opt.eval_img_list,
transform=transform)
box_coder = SSDBoxCoder(net.module)
pred_boxes = []
pred_labels = []
pred_scores = []
gt_boxes = []
gt_labels = []
# with open('torchcv/datasets/voc/voc07_test_difficult.txt') as f:
# gt_difficults = []
# for line in f.readlines():
# line = line.strip().split()
# d = np.array([int(x) for x in line[1:]])
# gt_difficults.append(d)
nums_img = dataset.__len__()
for i in tqdm(range(nums_img)):
inputs, box_targets, label_targets = dataset.__getitem__(i)
gt_boxes.append(box_targets)
gt_labels.append(label_targets)
inputs = inputs.unsqueeze(0)
with torch.no_grad():
loc_preds, cls_preds = net(Variable(inputs.cuda()))
box_preds, label_preds, score_preds = box_coder.decode(
loc_preds.cpu().data.squeeze(),
F.softmax(cls_preds.squeeze(), dim=1).cpu().data,
score_thresh=0.1)
pred_boxes.append(box_preds)
pred_labels.append(label_preds)
pred_scores.append(score_preds)
aps = (voc_eval(pred_boxes,
pred_labels,
pred_scores,
gt_boxes,
gt_labels,
gt_difficults=None,
iou_thresh=0.5,
use_07_metric=False))
net.train()
return aps
def evaluate(net, img_dir, list_file, img_size, test_code):
net.cuda()
net.eval()
def transform(img, boxes, labels):
img, boxes = resize(img, boxes, size=(img_size, img_size))
img = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])(img)
return img, boxes, labels
print('Loading dataset..')
dataset = ListDataset(root=img_dir, list_file=list_file,
transform=transform)
if test_code:
dataset.num_imgs = 1
dl = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False,
num_workers=2)
box_coder = SSDBoxCoder(net)
pred_boxes = []
pred_labels = []
pred_scores = []
gt_boxes = []
gt_labels = []
tqdm_dl = tqdm(dl, desc="Evaluate", ncols=0)
for i, (inputs, box_targets, label_targets) in enumerate(tqdm_dl):
gt_boxes.append(box_targets.squeeze(0))
gt_labels.append(label_targets.squeeze(0))
loc_preds, cls_preds = net(Variable(inputs.cuda(), volatile=True))
box_preds, label_preds, score_preds = box_coder.decode(
loc_preds.cpu().data.squeeze(),
F.softmax(cls_preds.squeeze(), dim=1).cpu().data,
score_thresh=0.01)
pred_boxes.append(box_preds)
pred_labels.append(label_preds)
pred_scores.append(score_preds)
ap_map_dict = voc_eval(pred_boxes, pred_labels, pred_scores, gt_boxes,
gt_labels, iou_thresh=0.5, use_07_metric=False)
return ap_map_dict
nums_img = dataset.__len__()
for i in tqdm(range(nums_img)):
inputs, box_targets, label_targets = dataset.__getitem__(i)
gt_boxes.append(box_targets)
gt_labels.append(label_targets)
inputs = inputs.unsqueeze(0)
with torch.no_grad():
loc_preds, cls_preds = net(Variable(inputs.cuda()))
box_preds, label_preds, score_preds = box_coder.decode(
loc_preds.cpu().data.squeeze(),
F.softmax(cls_preds.squeeze(), dim=1).cpu().data,
score_thresh=0.1)
pred_boxes.append(box_preds)
pred_labels.append(label_preds)
pred_scores.append(score_preds)
print('Caculating AP..')
aps = voc_eval(pred_boxes,
pred_labels,
pred_scores,
gt_boxes,
gt_labels,
gt_difficults=None,
iou_thresh=0.5,
use_07_metric=False)
print('ap = ', aps['ap'])
print('map = ', aps['map'])