def next_action():
inp, out = options()
with open(inp, 'r') as istream:
payload = json.loads(istream.read())
print "Query with pid=%s is completed, fetch results" % payload['workflow']['pid']
def poll_request():
inp, out = options()
with open(inp, 'r') as istream:
payload = json.loads(istream.read())
# Emulate polling for the PID by waiting a random amount of time since
# making the request
# You would obviously poll DAS with the PID in question to see if it had
# finished, instead
host = payload['workflow']['das_server']
query = payload['workflow']['query']
print "poll_request, host=%s, query=%s" % (host, query)
pid = payload['workflow']['pid']
timestamp = payload['workflow']['timestamp']
now = int(time.time())
if ( now - timestamp > random.randint(10, 30) ):
print "poll_request: pid=",payload['workflow']['pid']," Complete!"
else: #Push the event for this script back on the stack, so it loops...
print "poll_request: pid=",payload['workflow']['pid']," Still waiting..."
payload['workflow']['Events'].insert(0,'das_poll_request')
with open(out, 'w') as ostream:
ostream.write(json.dumps(payload))
def make_request():
"Make request action"
inp, out = options()
with open(inp, "r") as istream:
payload = json.loads(istream.read())
host = payload["workflow"]["das_server"]
query = payload["workflow"]["query"]
# emulate fake request which returns uid and we assign it
payload["workflow"]["pid"] = generate_uid(32, query)
payload["workflow"]["timestamp"] = int(time.time())
print "make_request: pid=%s" % payload["workflow"]["pid"]
with open(out, "w") as ostream:
ostream.write(json.dumps(payload))
def spawn_workflow():
inp, out = options()
with open(inp, 'r') as istream:
payload = json.loads(istream.read())
# This just does a repeated clone of the worklow and modifies a parameter
# (which isn't used, it's just for show). You could adapt this to fetch a
# bunch of dataset names, for example, and create a new payload for each one
host = payload['workflow'].get('das_server', 'https://cmsweb.cern.ch')
payloads = []
for idx in range(2):
pld = copy.deepcopy(payload)
query = 'das-query-%s' % idx
pld['workflow']['query'] = query
pld['workflow']['das_server'] = host
print "spawn-workflows: query=%s" % query
payloads.append(pld)
with open(out, 'w') as ostream:
ostream.write(json.dumps(payloads))
gt_bboxes,
gt_labels,
iou_thresh=iou_thresh)
bbox_align_stats = eval_detection_voc(refn_bboxes,
pred_labels,
pred_scores,
gt_bboxes,
gt_labels,
iou_thresh=iou_thresh)
straddling_stats = eval_detection_voc(mtsm_bboxes,
pred_labels,
pred_scores,
gt_bboxes,
gt_labels,
iou_thresh=iou_thresh)
sstr = self.pprint(detector_stats, bbox_align_stats,
straddling_stats, iou_thresh)
with open(
join([logsdir, 'metrics_{:.2f}.table'.format(iou_thresh)]),
'w') as f:
f.write(sstr)
if __name__ == '__main__':
opts = options().parse(train_mode=False)
evaluater = Evaulate(opts)
logsdir = opts['logs_root']
evaluater.evaulate_single_model(logsdir)
