def VOCTest(hyper_params):
log.debug('Creating network')
model_name = hyper_params.model_name
batch = hyper_params.batch
use_cuda = hyper_params.cuda
weights = hyper_params.weights
conf_thresh = hyper_params.conf_thresh
network_size = hyper_params.network_size
labels = hyper_params.labels
nworkers = hyper_params.nworkers
pin_mem = hyper_params.pin_mem
nms_thresh = hyper_params.nms_thresh
#prefix = hyper_params.prefix
results = hyper_params.results
test_args = {'conf_thresh': conf_thresh, 'network_size': network_size, 'labels': labels}
net = models.__dict__[model_name](hyper_params.classes, weights, train_flag=2, test_args=test_args)
net.eval()
log.info('Net structure\n%s' % net)
#import pdb
#pdb.set_trace()
if use_cuda:
net.cuda()
log.debug('Creating dataset')
loader = torch.utils.data.DataLoader(
CustomDataset(hyper_params),
batch_size = batch,
shuffle = False,
drop_last = False,
num_workers = nworkers if use_cuda else 0,
pin_memory = pin_mem if use_cuda else False,
collate_fn = vn_data.list_collate,
)
log.debug('Running network')
tot_loss = []
coord_loss = []
conf_loss = []
cls_loss = []
anno, det = {}, {}
num_det = 0
for idx, (data, box) in enumerate(loader):
if (idx + 1) % 20 == 0:
log.info('%d/%d' % (idx + 1, len(loader)))
if use_cuda:
data = data.cuda()
with torch.no_grad():
output, loss = net(data, box)
key_val = len(anno)
anno.update({loader.dataset.keys[key_val+k]: v for k,v in enumerate(box)})
det.update({loader.dataset.keys[key_val+k]: v for k,v in enumerate(output)})
netw, neth = network_size
reorg_dets = voc_wrapper.reorgDetection(det, netw, neth) #, prefix)
voc_wrapper.genResults(reorg_dets, results, nms_thresh)
def VOCTest_Single(hyper_params, img_paths):
log.debug('Creating network')
model_name = hyper_params.model_name
batch = hyper_params.batch
use_cuda = hyper_params.cuda
weights = hyper_params.weights
conf_thresh = hyper_params.conf_thresh
network_size = hyper_params.network_size
labels = hyper_params.labels
nworkers = hyper_params.nworkers
pin_mem = hyper_params.pin_mem
nms_thresh = hyper_params.nms_thresh
# prefix = hyper_params.prefix
results = hyper_params.results
test_args = {
'conf_thresh': conf_thresh,
'network_size': network_size,
'labels': labels
}
net = models.__dict__[model_name](hyper_params.classes,
weights,
train_flag=2,
test_args=test_args)
net.eval()
# log.info('Net structure\n%s' % net)
# import pdb
# pdb.set_trace()
if use_cuda:
net.cuda()
ret = []
# loop here
for img_path in img_paths:
print('Reading Data')
data = Image.open(img_path)
lb = vn_data.transform.Letterbox(network_size)
it = tf.ToTensor()
img_tf = vn_data.transform.Compose([lb, it])
data = img_tf(data)
data = torch.unsqueeze(data, 0)
print('Running network')
if use_cuda:
data = data.cuda()
with torch.no_grad():
output, loss = net(data, '')
det = dict()
det[img_path] = output[0]
netw, neth = network_size
reorg_dets = voc_wrapper.reorgDetection(det, netw, neth)
res = voc_wrapper.genResults_Single(reorg_dets, results, nms_thresh)
ret.append(str(res))
return ret
def VOCTest(hyper_params):
log.debug('Creating network')
model_name = hyper_params.model_name
batch = hyper_params.batch
use_cuda = hyper_params.cuda
weights = hyper_params.weights
conf_thresh = hyper_params.conf_thresh
network_size = hyper_params.network_size
labels = hyper_params.labels
nworkers = hyper_params.nworkers
pin_mem = hyper_params.pin_mem
nms_thresh = hyper_params.nms_thresh
#prefix = hyper_params.prefix
results = hyper_params.results
test_args = {
'conf_thresh': conf_thresh,
'network_size': network_size,
'labels': labels
}
net = models.__dict__[model_name](hyper_params.classes,
weights,
train_flag=2,
test_args=test_args)
net.eval()
log.info('Net structure\n%s' % net)
#import pdb
#pdb.set_trace()
if use_cuda:
net.cuda()
log.debug('Creating dataset')
loader = torch.utils.data.DataLoader(
CustomDataset(hyper_params),
batch_size=batch,
shuffle=False,
drop_last=False,
num_workers=nworkers if use_cuda else 0,
pin_memory=pin_mem if use_cuda else False,
collate_fn=vn_data.list_collate,
)
log.debug('Running network')
tot_loss = []
coord_loss = []
conf_loss = []
cls_loss = []
anno, det = {}, {}
num_det = 0
for idx, (data, box) in enumerate(loader):
if (idx + 1) % 20 == 0:
log.info('%d/%d' % (idx + 1, len(loader)))
if use_cuda:
data = data.cuda()
with torch.no_grad():
output, loss = net(data, box)
key_val = len(anno)
anno.update(
{loader.dataset.keys[key_val + k]: v
for k, v in enumerate(box)})
det.update({
loader.dataset.keys[key_val + k]: v
for k, v in enumerate(output)
})
netw, neth = network_size
reorg_dets = voc_wrapper.reorgDetection(det, netw, neth) #, prefix)
voc_wrapper.genResults(reorg_dets, results, nms_thresh)