def detect(cfgfile, learnet, weightfile, imgfile):
m = Darknet(cfgfile, learnet)
m.print_network()
m.load_weights(weightfile)
print('Loading weights from %s... Done!' % (weightfile))
if m.num_classes == 20:
namesfile = 'data/voc.names'
elif m.num_classes == 80:
namesfile = 'data/coco.names'
else:
namesfile = 'data/names'
use_cuda = 1
if use_cuda:
m.cuda()
img = Image.open(imgfile).convert('RGB')
sized = img.resize((m.width, m.height))
for i in range(2):
start = time.time()
boxes = do_detect(m, sized, 0.5, 0.4, use_cuda)
finish = time.time()
if i == 1:
print('%s: Predicted in %f seconds.' % (imgfile, (finish - start)))
class_names = load_class_names(namesfile)
plot_boxes(img, boxes, 'predictions.jpg', class_names)
def detect_skimage(cfgfile, weightfile, imgfile):
from skimage import io
from skimage.transform import resize
m = Darknet(cfgfile)
m.print_network()
m.load_weights(weightfile)
print('Loading weights from %s... Done!' % (weightfile))
if m.num_classes == 20:
namesfile = 'data/voc.names'
elif m.num_classes == 80:
namesfile = 'data/coco.names'
else:
namesfile = 'data/names'
use_cuda = 1
if use_cuda:
m.cuda()
img = io.imread(imgfile)
sized = resize(img, (m.width, m.height)) * 255
for i in range(2):
start = time.time()
boxes = do_detect(m, sized, 0.5, 0.4, use_cuda)
finish = time.time()
if i == 1:
print('%s: Predicted in %f seconds.' % (imgfile, (finish - start)))
class_names = load_class_names(namesfile)
plot_boxes_cv2(img,
boxes,
savename='predictions.jpg',
class_names=class_names)
def valid(datacfg,
darknetcfg,
learnetcfg,
weightfile,
outfile,
traindict,
use_baserw=False):
options = read_data_cfg(datacfg)
valid_images = options['valid']
metadict = traindict #options['meta']
# name_list = options['names']
# backup = cfg.backup
ckpt = weightfile.split('/')[-1].split('.')[0]
backup = weightfile.split('/')[-2]
ckpt_pre = '/ene_' if use_baserw else '/ene'
prefix = 'results/' + backup.split('/')[-1] + ckpt_pre + ckpt
print('saving to: ' + prefix)
# prefix = 'results/' + weightfile.split('/')[1]
# names = load_class_names(name_list)
with open(valid_images) as fp:
tmp_files = fp.readlines()
valid_files = [item.rstrip() for item in tmp_files]
m = Darknet(darknetcfg, learnetcfg)
m.print_network()
m.load_weights(weightfile)
m.cuda()
m.eval()
valid_dataset = dataset.listDataset(valid_images,
shape=(m.width, m.height),
shuffle=False,
transform=transforms.Compose([
transforms.ToTensor(),
]))
valid_batchsize = 2
assert (valid_batchsize > 1)
kwargs = {'num_workers': 4, 'pin_memory': True}
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=valid_batchsize,
shuffle=False,
**kwargs)
if False:
metaset = dataset.MetaDataset(metafiles=metadict,
train=False,
ensemble=True)
metaloader = torch.utils.data.DataLoader(metaset,
batch_size=len(metaset),
shuffle=False,
**kwargs)
metaloader = iter(metaloader)
n_cls = len(metaset.classes)
print('===> Generating dynamic weights...')
metax, mask = metaloader.next()
metax, mask = metax.cuda(), mask.cuda()
metax, mask = Variable(metax, volatile=True), Variable(mask,
volatile=True)
dynamic_weights = m.meta_forward(metax, mask)
for i in range(len(dynamic_weights)):
assert dynamic_weights[i].size(0) == sum(metaset.meta_cnts)
inds = np.cumsum([0] + metaset.meta_cnts)
new_weight = []
for j in range(len(metaset.meta_cnts)):
new_weight.append(
torch.mean(dynamic_weights[i][inds[j]:inds[j + 1]], dim=0))
dynamic_weights[i] = torch.stack(new_weight)
print(dynamic_weights[i].shape)
else:
metaset = dataset.MetaDataset(metafiles=metadict,
train=False,
ensemble=True,
with_ids=True)
metaloader = torch.utils.data.DataLoader(metaset,
batch_size=64,
shuffle=False,
**kwargs)
# metaloader = iter(metaloader)
n_cls = len(metaset.classes)
print(n_cls)
enews = [0.0] * n_cls
cnt = [0.0] * n_cls
print('===> Generating dynamic weights...')
kkk = 0
# import pdb; pdb.set_trace
for metax, mask, clsids in metaloader:
print('===> {}/{}'.format(kkk, len(metaset) // 64))
kkk += 1
metax, mask = metax.cuda(), mask.cuda()
metax, mask = Variable(metax,
volatile=True), Variable(mask,
volatile=True)
dws = m.meta_forward(metax, mask)
dw = dws[0]
for ci, c in enumerate(clsids):
# print(ci, c, enews[c], cnt[c], dw[ci])
enews[c] = enews[c] * cnt[c] / (cnt[c] +
1) + dw[ci] / (cnt[c] + 1)
cnt[c] += 1
dynamic_weights = [torch.stack(enews)]
# import pdb; pdb.set_trace()
# import pickle
# with open('dynamic_weights.pkl', 'wb') as f:
# tmp = [x.data.cpu().numpy() for x in dynamic_weights]
# pickle.dump(tmp, f)
# import pdb; pdb.set_trace()
if use_baserw:
import pickle
# f = 'data/rws/voc_novel{}_.pkl'.format(cfg.novelid)
f = 'dynamic_weights.pkl'.format(0)
print('===> Loading from {}...'.format(f))
with open(f, 'rb') as f:
# with open('data/rws/voc_novel0_.pkl', 'rb') as f:
rws = pickle.load(f)
dynamic_weights = [
Variable(torch.from_numpy(rw)).cuda() for rw in rws
]
# tki = cfg._real_base_ids
# for i in range(len(rws)):
# dynamic_weights[i][tki] = rws[i][tki]
# dynamic_weights[i] = rws[i]
if not os.path.exists(prefix):
# os.mkdir(prefix)
os.makedirs(prefix)
fps = [0] * n_cls
for i, cls_name in enumerate(metaset.classes):
buf = '%s/%s%s.txt' % (prefix, outfile, cls_name)
fps[i] = open(buf, 'w')
lineId = -1
conf_thresh = 0.005
nms_thresh = 0.45
for batch_idx, (data, target) in enumerate(valid_loader):
# import pdb; pdb.set_trace()
data = data.cuda()
data = Variable(data, volatile=True)
output = m.detect_forward(data, dynamic_weights)
if isinstance(output, tuple):
output = (output[0].data, output[1].data)
else:
output = output.data
# import pdb; pdb.set_trace()
batch_boxes = get_region_boxes_v2(output, n_cls, conf_thresh,
m.num_classes, m.anchors,
m.num_anchors, 0, 1)
if isinstance(output, tuple):
bs = output[0].size(0)
else:
assert output.size(0) % n_cls == 0
bs = output.size(0) // n_cls
# import pdb; pdb.set_trace()
for b in range(bs):
lineId = lineId + 1
imgpath = valid_dataset.lines[lineId].rstrip()
print(imgpath)
imgid = os.path.basename(imgpath).split('.')[0]
width, height = get_image_size(imgpath)
for i in range(n_cls):
# oi = i * bs + b
oi = b * n_cls + i
boxes = batch_boxes[oi]
boxes = nms(boxes, nms_thresh)
for box in boxes:
x1 = (box[0] - box[2] / 2.0) * width
y1 = (box[1] - box[3] / 2.0) * height
x2 = (box[0] + box[2] / 2.0) * width
y2 = (box[1] + box[3] / 2.0) * height
det_conf = box[4]
for j in range((len(box) - 5) / 2):
cls_conf = box[5 + 2 * j]
cls_id = box[6 + 2 * j]
prob = det_conf * cls_conf
fps[i].write('%s %f %f %f %f %f\n' %
(imgid, prob, x1, y1, x2, y2))
for i in range(n_cls):
fps[i].close()
def valid(datacfg, darknetcfg, learnetcfg, weightfile, outfile):
options = read_data_cfg(datacfg)
valid_images = options['valid']
metadict = options['meta']
# name_list = options['names']
# backup = cfg.backup
ckpt = weightfile.split('/')[-1].split('.')[0]
backup = weightfile.split('/')[-2]
prefix = 'results/' + backup.split('/')[-1] + '/e' + ckpt
print('saving to: ' + prefix)
# prefix = 'results/' + weightfile.split('/')[1]
# names = load_class_names(name_list)
with open(valid_images) as fp:
tmp_files = fp.readlines()
valid_files = [item.rstrip() for item in tmp_files]
m = Darknet(darknetcfg, learnetcfg)
m.print_network()
m.load_weights(weightfile)
m.cuda()
m.eval()
torch.set_grad_enabled(False)
valid_dataset = dataset.listDataset(valid_images, shape=(m.width, m.height),
shuffle=False,
transform=transforms.Compose([
transforms.ToTensor(),
]))
valid_batchsize = 2
assert(valid_batchsize > 1)
kwargs = {'num_workers': 4, 'pin_memory': True}
valid_loader = torch.utils.data.DataLoader(
valid_dataset, batch_size=valid_batchsize, shuffle=False, **kwargs)
metaset = dataset.MetaDataset(metafiles=metadict, train=False)
metaloader = torch.utils.data.DataLoader(
metaset,
batch_size=metaset.batch_size,
shuffle=False,
**kwargs
)
metaloader = iter(metaloader)
n_cls = len(metaset.classes)
if not os.path.exists(prefix):
# os.mkdir(prefix)
os.makedirs(prefix)
fps = [0]*n_cls
for i, cls_name in enumerate(metaset.classes):
buf = '%s/%s%s.txt' % (prefix, outfile, cls_name)
fps[i] = open(buf, 'w')
lineId = -1
conf_thresh = 0.005
nms_thresh = 0.45
for batch_idx, (data, target) in enumerate(valid_loader):
metax, mask = metaloader.next()
# print(ids)
data = data.cuda()
mask = mask.cuda()
metax = metax.cuda()
data = Variable(data)
mask = Variable(mask)
metax = Variable(metax)
output = m(data, metax, mask)
if isinstance(output, tuple):
output = (output[0].data, output[1].data)
else:
output = output.data
batch_boxes = get_region_boxes_v2(output, n_cls, conf_thresh, m.num_classes, m.anchors, m.num_anchors, 0, 1)
if isinstance(output, tuple):
bs = output[0].size(0)
else:
assert output.size(0) % n_cls == 0
bs = output.size(0) // n_cls
for b in range(bs):
lineId = lineId + 1
imgpath = valid_dataset.lines[lineId].rstrip()
print(imgpath)
imgid = os.path.basename(imgpath).split('.')[0]
width, height = get_image_size(imgpath)
for i in range(n_cls):
# oi = i * bs + b
oi = b * n_cls + i
boxes = batch_boxes[oi]
boxes = nms(boxes, nms_thresh)
for box in boxes:
x1 = (box[0] - box[2]/2.0) * width
y1 = (box[1] - box[3]/2.0) * height
x2 = (box[0] + box[2]/2.0) * width
y2 = (box[1] + box[3]/2.0) * height
det_conf = box[4]
for j in range((len(box)-5)/2):
cls_conf = box[5+2*j]
cls_id = box[6+2*j]
prob =det_conf * cls_conf
fps[i].write('%s %f %f %f %f %f\n' % (imgid, prob, x1, y1, x2, y2))
for i in range(n_cls):
fps[i].close()
nms_thresh = 0.4
iou_thresh = 0.5
## --------------------------------------------------------------------------
## MAIN
backupdir = cfg.backup
print('logging to ' + backupdir)
#if not os.path.exists(backupdir):
# os.mkdir(backupdir)
torch.manual_seed(seed)
if use_cuda:
os.environ['CUDA_VISIBLE_DEVICES'] = gpus
torch.cuda.manual_seed(seed)
model = Darknet(darknetcfg, learnetcfg)
region_loss = model.loss
model.load_weights(weightfile)
model.print_network()
###################################################
### Meta-model parameters
region_loss.seen = model.seen
processed_batches = 0 if cfg.tuning else model.seen / batch_size
trainlist = dataset.build_dataset(data_options)
nsamples = len(trainlist)
init_width = model.width
init_height = model.height
init_epoch = 0 if cfg.tuning else model.seen / nsamples
max_epochs = max_batches * batch_size / nsamples + 1
def valid(valid_images, darknetcfg, learnetcfg, weightfile,
dynamic_weight_file, prefix, conf_thresh, nms_thresh):
with open(valid_images) as fp:
tmp_files = fp.readlines()
valid_files = [item.rstrip() for item in tmp_files]
m = Darknet(darknetcfg, learnetcfg)
m.print_network()
m.load_weights(weightfile)
m.cuda()
m.eval()
print(
'===> Loading dynamic weights from {}...'.format(dynamic_weight_file))
with open(dynamic_weight_file, 'rb') as f:
rws = pickle.load(f)
dynamic_weights = [Variable(torch.from_numpy(rw)).cuda() for rw in rws]
valid_dataset = dataset.listDataset(valid_images,
shape=(m.width, m.height),
shuffle=False,
transform=transforms.Compose([
transforms.ToTensor(),
]))
valid_batchsize = 2
kwargs = {'num_workers': 4, 'pin_memory': True}
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=valid_batchsize,
shuffle=False,
**kwargs)
n_cls = 20
lineId = -1
for batch_idx, (data, target) in enumerate(valid_loader):
data = data.cuda()
data = Variable(data, volatile=True)
output = m.detect_forward(data, dynamic_weights)
if isinstance(output, tuple):
output = (output[0].data, output[1].data)
else:
output = output.data
batch_boxes = get_region_boxes_v2(output, n_cls, conf_thresh,
m.num_classes, m.anchors,
m.num_anchors, 0, 1)
if isinstance(output, tuple):
bs = output[0].size(0)
else:
assert output.size(0) % n_cls == 0
bs = output.size(0) // n_cls
for b in range(bs):
lineId = lineId + 1
imgpath = valid_dataset.lines[lineId].rstrip()
print(imgpath)
imgid = os.path.basename(imgpath).split('.')[0]
width, height = get_image_size(imgpath)
i = 2
oi = b * n_cls + i
boxes = batch_boxes[oi]
boxes = nms(boxes, nms_thresh)
boxes_ = []
for box in boxes:
x1 = (box[0] - box[2] / 2.0) * width
y1 = (box[1] - box[3] / 2.0) * height
x2 = (box[0] + box[2] / 2.0) * width
y2 = (box[1] + box[3] / 2.0) * height
det_conf = box[4]
for j in range((len(box) - 5) / 2):
cls_conf = box[5 + 2 * j]
cls_id = box[6 + 2 * j]
prob = det_conf * cls_conf
boxes_ += [[prob, x1, y1, x2, y2]]
plot_boxes_(imgpath, boxes_, '{}/{}.jpg'.format(prefix, imgid))