def test_step(epoch, data, cfg, model, device):
# get raw output; this can be for several layers
outputs = pytorch_model_outputs(model, data, device)
# cls metrics
confmat = get_confmat_from_raw(outputs, data)
print(confmat)
acc = get_acc_from_confmat(confmat)
recall = get_recall_from_confmat(confmat)
precision = get_precision_from_confmat(confmat)
if cfg.smoothing:
sm_confmat = get_smoothed_confmat_from_raw(cfg, outputs, data)
print(sm_confmat)
sm_acc = get_acc_from_confmat(sm_confmat)
sm_recall = get_recall_from_confmat(sm_confmat)
sm_precision = get_precision_from_confmat(sm_confmat)
# ap = get_ap_from_raw(outputs,data)
# mAP = get_map_from_ap(ap,data)
# similarity metric
features = get_features_from_raw(outputs)
print("simmat")
t = Timer()
t.tic()
sim_mat = get_simmat_from_features(features)
print(sim_mat.shape)
t.toc()
print(t)
print("end [simmat]")
# store results
results = edict()
results.acc = acc
results.recall = recall
results.precision = precision
if cfg.smoothing:
results.sm_acc = sm_acc
results.sm_recall = sm_recall
results.sm_precision = sm_precision
# results.ap = ap
# results.mAP = mAP
print(results)
results.features = features
results.sim_mat = sim_mat
return results
def image_upload(track_id,
tpid,
tpe,
seq,
image_type,
content,
task_seq='!!!',
batch='!!!'):
if not task_seq:
task_seq = '!!!'
if not batch:
batch = '!!!'
_timer = Timer()
_timer.stage_begin('image write')
if not image_write(track_id, tpid, tpe, seq, image_type, content, task_seq,
batch):
return False
_timer.stage_begin('set batch')
if not batch_manager.set_batch(track_id, tpid, tpe, seq, image_type,
batch):
return False
_timer.finish()
logger().debug(_timer.dump())
return True