def main():
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
check_config(cfg)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
# check if paddlepaddle version is satisfied
check_version()
main_arch = cfg.architecture
dataset = cfg.TestReader['dataset']
test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img)
dataset.set_images(test_images)
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
model = create(main_arch)
startup_prog = fluid.Program()
infer_prog = fluid.Program()
with fluid.program_guard(infer_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['TestReader']['inputs_def']
inputs_def['iterable'] = True
feed_vars, loader = model.build_inputs(**inputs_def)
test_fetches = model.test(feed_vars)
infer_prog = infer_prog.clone(True)
reader = create_reader(cfg.TestReader, devices_num=1)
loader.set_sample_list_generator(reader, place)
exe.run(startup_prog)
if cfg.weights:
checkpoint.load_params(exe, infer_prog, cfg.weights)
# parse infer fetches
assert cfg.metric in ['COCO', 'VOC', 'OID', 'WIDERFACE'], \
"unknown metric type {}".format(cfg.metric)
extra_keys = []
if cfg['metric'] in ['COCO', 'OID']:
extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg['metric'] == 'VOC' or cfg['metric'] == 'WIDERFACE':
extra_keys = ['im_id', 'im_shape']
keys, values, _ = parse_fetches(test_fetches, infer_prog, extra_keys)
# parse dataset category
if cfg.metric == 'COCO':
from ppdet.utils.coco_eval import bbox2out, mask2out, get_category_info
if cfg.metric == 'OID':
from ppdet.utils.oid_eval import bbox2out, get_category_info
if cfg.metric == "VOC":
from ppdet.utils.voc_eval import bbox2out, get_category_info
if cfg.metric == "WIDERFACE":
from ppdet.utils.widerface_eval_utils import bbox2out, get_category_info
anno_file = dataset.get_anno()
with_background = dataset.with_background
use_default_label = dataset.use_default_label
clsid2catid, catid2name = get_category_info(anno_file, with_background,
use_default_label)
# whether output bbox is normalized in model output layer
is_bbox_normalized = False
if hasattr(model, 'is_bbox_normalized') and \
callable(model.is_bbox_normalized):
is_bbox_normalized = model.is_bbox_normalized()
# use tb-paddle to log image
if FLAGS.use_tb:
from tb_paddle import SummaryWriter
tb_writer = SummaryWriter(FLAGS.tb_log_dir)
tb_image_step = 0
tb_image_frame = 0 # each frame can display ten pictures at most.
imid2path = dataset.get_imid2path()
for iter_id, data in enumerate(loader()):
outs = exe.run(infer_prog,
feed=data,
fetch_list=values,
return_numpy=False)
res = {
k: (np.array(v), v.recursive_sequence_lengths())
for k, v in zip(keys, outs)
}
logger.info('Infer iter {}'.format(iter_id))
bbox_results = None
mask_results = None
if 'bbox' in res:
bbox_results = bbox2out([res], clsid2catid, is_bbox_normalized)
if 'mask' in res:
mask_results = mask2out([res], clsid2catid,
model.mask_head.resolution)
# visualize result
im_ids = res['im_id'][0]
for im_id in im_ids:
image_path = imid2path[int(im_id)]
image = Image.open(image_path).convert('RGB')
# use tb-paddle to log original image
if FLAGS.use_tb:
original_image_np = np.array(image)
tb_writer.add_image("original/frame_{}".format(tb_image_frame),
original_image_np,
tb_image_step,
dataformats='HWC')
image = visualize_results(image, int(im_id), catid2name,
FLAGS.draw_threshold, bbox_results,
mask_results)
# use tb-paddle to log image with bbox
if FLAGS.use_tb:
infer_image_np = np.array(image)
tb_writer.add_image("bbox/frame_{}".format(tb_image_frame),
infer_image_np,
tb_image_step,
dataformats='HWC')
tb_image_step += 1
if tb_image_step % 10 == 0:
tb_image_step = 0
tb_image_frame += 1
save_name = get_save_image_name(FLAGS.output_dir, image_path)
logger.info("Detection bbox results save in {}".format(save_name))
image.save(save_name, quality=95)
def main():
cfg = load_config(FLAGS.config)
if 'architecture' in cfg:
main_arch = cfg.architecture
else:
raise ValueError("'architecture' not specified in config file.")
merge_config(FLAGS.opt)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
# print_total_cfg(cfg)
if 'test_feed' not in cfg:
test_feed = create(main_arch + 'TestFeed')
else:
test_feed = create(cfg.test_feed)
test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img)
test_feed.dataset.add_images(test_images)
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
infer_prog, feed_var_names, fetch_list = fluid.io.load_inference_model(
dirname=FLAGS.model_path,
model_filename=FLAGS.model_name,
params_filename=FLAGS.params_name,
executor=exe)
reader = create_reader(test_feed)
feeder = fluid.DataFeeder(
place=place, feed_list=feed_var_names, program=infer_prog)
# parse infer fetches
assert cfg.metric in ['COCO', 'VOC'], \
"unknown metric type {}".format(cfg.metric)
extra_keys = []
if cfg['metric'] == 'COCO':
extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg['metric'] == 'VOC':
extra_keys = ['im_id', 'im_shape']
keys, values, _ = parse_fetches({
'bbox': fetch_list
}, infer_prog, extra_keys)
# parse dataset category
if cfg.metric == 'COCO':
from ppdet.utils.coco_eval import bbox2out, mask2out, get_category_info
if cfg.metric == "VOC":
from ppdet.utils.voc_eval import bbox2out, get_category_info
anno_file = getattr(test_feed.dataset, 'annotation', None)
with_background = getattr(test_feed, 'with_background', True)
use_default_label = getattr(test_feed, 'use_default_label', False)
clsid2catid, catid2name = get_category_info(anno_file, with_background,
use_default_label)
# whether output bbox is normalized in model output layer
is_bbox_normalized = False
# use tb-paddle to log image
if FLAGS.use_tb:
from tb_paddle import SummaryWriter
tb_writer = SummaryWriter(FLAGS.tb_log_dir)
tb_image_step = 0
tb_image_frame = 0 # each frame can display ten pictures at most.
imid2path = reader.imid2path
keys = ['bbox']
infer_time = True
compile_prog = fluid.compiler.CompiledProgram(infer_prog)
for iter_id, data in enumerate(reader()):
feed_data = [[d[0], d[1]] for d in data]
# for infer time
if infer_time:
warmup_times = 10
repeats_time = 100
feed_data_dict = feeder.feed(feed_data)
for i in range(warmup_times):
exe.run(compile_prog,
feed=feed_data_dict,
fetch_list=fetch_list,
return_numpy=False)
start_time = time.time()
for i in range(repeats_time):
exe.run(compile_prog,
feed=feed_data_dict,
fetch_list=fetch_list,
return_numpy=False)
print("infer time: {} ms/sample".format((time.time() - start_time) *
1000 / repeats_time))
infer_time = False
outs = exe.run(compile_prog,
feed=feeder.feed(feed_data),
fetch_list=fetch_list,
return_numpy=False)
res = {
k: (np.array(v), v.recursive_sequence_lengths())
for k, v in zip(keys, outs)
}
res['im_id'] = [[d[2] for d in data]]
logger.info('Infer iter {}'.format(iter_id))
bbox_results = None
mask_results = None
if 'bbox' in res:
bbox_results = bbox2out([res], clsid2catid, is_bbox_normalized)
if 'mask' in res:
mask_results = mask2out([res], clsid2catid,
model.mask_head.resolution)
# visualize result
im_ids = res['im_id'][0]
for im_id in im_ids:
image_path = imid2path[int(im_id)]
image = Image.open(image_path).convert('RGB')
# use tb-paddle to log original image
if FLAGS.use_tb:
original_image_np = np.array(image)
tb_writer.add_image(
"original/frame_{}".format(tb_image_frame),
original_image_np,
tb_image_step,
dataformats='HWC')
image = visualize_results(image,
int(im_id), catid2name,
FLAGS.draw_threshold, bbox_results,
mask_results)
# use tb-paddle to log image with bbox
if FLAGS.use_tb:
infer_image_np = np.array(image)
tb_writer.add_image(
"bbox/frame_{}".format(tb_image_frame),
infer_image_np,
tb_image_step,
dataformats='HWC')
tb_image_step += 1
if tb_image_step % 10 == 0:
tb_image_step = 0
tb_image_frame += 1
save_name = get_save_image_name(FLAGS.output_dir, image_path)
logger.info("Detection bbox results save in {}".format(save_name))
image.save(save_name, quality=95)