def test_proposal_target():
bbox = torch.tensor(
[[115, 131, 613, 513], [230, 53, 460, 506], [643, 133, 697, 272],
[706, 158, 800, 257], [300, 400, 500, 600]],
device=device)
bbox = bbox.t()
img_size = (600, 800)
feat_size = (37, 50)
anchor_creator = region.AnchorCreator(device=torch.device('cuda:1'))
anchors = anchor_creator(img_size, feat_size).view(4, -1)
in_index = region.find_inside_index(anchors, img_size)
rpn_cls_out = torch.rand(1, 18, 37, 50, device=device)
rpn_reg_out = torch.rand(1, 36, 37, 50, device=device)
props_creator = region.ProposalCreator(6000, 2000, 0.7, 16)
rois, scores = props_creator(rpn_cls_out, rpn_reg_out, anchors, img_size)
print('number of rois:', rois.shape)
print('scores', scores.shape)
gt_label = torch.tensor([2, 2, 5, 6, 8], device=device)
props_tar_creator = region.ProposalTargetCreator()
roi_bbox, roi_label, roi_param = props_tar_creator(rois, bbox, gt_label)
print('roi_bbox.shape', roi_bbox.shape)
print('roi_label.shape', roi_label.shape)
print('roi_param.shape', roi_param.shape)
print('roi_label==1:', (roi_label == 1).sum())
print('roi_label==0:', (roi_label == 0).sum())
start = time.time()
for i in range(5000):
roi_bbox, roi_label, roi_param = props_tar_creator(
rois, bbox, gt_label)
end = time.time()
print('time used: {} secs'.format(end - start))
def test_iou_performance():
# "for VOC 5000 images, we only need to do calc_iou 5000 * 16 * 2"
anchor_creator = region.AnchorCreator(device=torch.device('cuda:0'))
anchors = anchor_creator((600, 800), (37, 50)).view(4, -1)
bbox = anchors[:, :5]
print(anchors.shape)
print(bbox.shape)
start = time.time()
for i in range(80000*2):
iou_tab = utils.calc_iou(anchors, bbox)
print(time.time() - start)
def test_anchor():
img_size = (600,800)
grid=(37,50)
print('Test AnchorCreator'.center(50, '-'))
anchor_creator = region.AnchorCreator(device=torch.device('cuda:0'))
print('start to create anchors using new method:')
anchors = anchor_creator(img_size=(600, 800), grid=(37, 50))
print(anchors.shape)
print('first 10 anchors')
print(anchors.view(4,9,-1)[:,:,0])
ws = anchors[2] - anchors[0]
hs = anchors[3] - anchors[1]
old_gen = region.AnchorGenerator(scales=[128, 256, 512],
aspect_ratios=[0.5, 1.0, 2.0], allow_cross=True)
print('start to create anchors using old method')
old_anchors = list(old_gen.anchors(img_size=img_size, grid=grid))
print('number of old anchors:', len(old_anchors))
print('anchor 10:\n', old_anchors[0])
for i in range(10):
print('old anchor', i)
print_old(old_anchors[i])
print('Next test performance')
idx = 0
start = time.time()
for h in range(500, 700):
for w in range(800, 1000):
grid = (int(h/16), int(w/16))
idx += 1
anchors = anchor_creator((h,w), grid)
secs = time.time() - start
print('create {} set of anchors, used {} seconds or {} mins'\
.format(idx, secs, secs/60))
print('Next test performance(old)')
idx = 0
start = time.time()
for h in range(500, 700):
for w in range(800, 1000):
grid = (int(h/16), int(w/16))
idx += 1
anchors = old_gen.anchors((h, w), grid)
anchors = list(anchors)
secs = time.time() - start
print('create {} set of anchors, used {} seconds or {} mins'\
.format(idx, secs, secs/60))
def test_find_inside_index():
img_size = (600, 800)
grid = (37, 50)
anchor_creator = region.AnchorCreator(device=torch.device('cuda:0'))
anchors = anchor_creator(img_size=(600, 800), grid=(37, 50))
anchors = anchors.view(4, -1)
inside_idx = region.find_inside_index(anchors, img_size)
labels = torch.full((16650, ), -1)
print('inside_idx type:', type(inside_idx))
print('inside_idx:\n', inside_idx)
labels[inside_idx] = 1
labels[8000:8050] = 0
print('1 labels:', (labels == 1).sum())
print('test random sample')
region.random_sample_label(labels, 128, 256)
print('1 labels:', (labels == 1).sum())
print('0 labels:', (labels == 0).sum())
print('-1 labels:', (labels == -1).sum())
def test_anchor_target_creator():
bbox = torch.tensor([[115, 131, 613, 513], [230, 53, 460, 506],
[643, 133, 697, 272], [706, 158, 800, 257]],
device=torch.device('cuda:0'))
bbox = bbox.t()
img_size = (600, 800)
feat_size = (37, 50)
anchor_creator = region.AnchorCreator(device=torch.device('cuda:0'))
anchors = anchor_creator(img_size, feat_size).view(4, -1)
in_index = region.find_inside_index(anchors, img_size)
in_anchors = anchors[:, in_index]
anchor_tar_creator = region.AnchorTargetCreator()
print('in_anchors.shape', in_anchors.shape)
labels, param, bbox_labels = anchor_tar_creator(img_size, feat_size,
in_anchors, bbox)
print('1 labels:', (labels == 1).sum())
print('0 labels:', (labels == 0).sum())
print('-1 labels:', (labels == -1).sum())
pos_index = utils.index_of(labels == 1)
pos_anchors = in_anchors[:, pos_index[0]]
pos_bbox_labels = bbox_labels[:, pos_index[0]]
print('pos_anchors:', pos_anchors, pos_anchors.shape)
print('pos_bbox_labels:', pos_bbox_labels, pos_bbox_labels.shape)
ious = utils.calc_iou(pos_anchors, pos_bbox_labels)
print('check ious:', ious.tolist())
print('check ious diag:', ious.diag())
print('Next test performance')
img_size2 = (605, 805)
start = time.time()
for i in range(5000):
if i % 2 == 0:
labels, param, bbox_labels = anchor_tar_creator(
img_size, feat_size, in_anchors, bbox)
else:
labels, param, bbox_labels = anchor_tar_creator(
img_size2, feat_size, in_anchors, bbox)
used = time.time() - start
print('used {} secs'.format(used))
import sys, os, time
import os.path as osp
cur_dir = osp.dirname(osp.realpath(__file__))
sys.path.append(osp.join(cur_dir, '..'))
from lib import data
from lib import config
from lib import utils
from lib import region
import torch
img_size = (600, 800)
grid = (37, 50)
anchor_creator = region.AnchorCreator(device=torch.device('cuda:0'))
anchors = anchor_creator(img_size=(600, 800), grid=(37, 50))
print(anchors.shape)
anchors = anchors.view(4, -1)
device = torch.device('cuda:0')
def test_proposal_creator_data():
rpn_cls_out = torch.rand(1, 18, 37, 50, device=device)
rpn_reg_out = torch.rand(1, 36, 37, 50, device=device)
props_creator = region.ProposalCreator(6000, 50, 0.7, 16)
rois, scores = props_creator(rpn_cls_out, rpn_reg_out, anchors, img_size)
print(rois.shape)
print(rois)
print(scores)