def main():
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
# disable npu in config by default
if 'use_npu' not in cfg:
cfg.use_npu = False
# disable xpu in config by default
if 'use_xpu' not in cfg:
cfg.use_xpu = False
if cfg.use_gpu:
place = paddle.set_device('gpu')
elif cfg.use_npu:
place = paddle.set_device('npu')
elif cfg.use_xpu:
place = paddle.set_device('xpu')
else:
place = paddle.set_device('cpu')
if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn' and not cfg.use_gpu:
cfg['norm_type'] = 'bn'
check_config(cfg)
check_gpu(cfg.use_gpu)
check_npu(cfg.use_npu)
check_xpu(cfg.use_xpu)
check_version()
run(FLAGS, cfg)
def main():
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
# TODO: bias should be unified
cfg['bias'] = 1 if FLAGS.bias else 0
cfg['classwise'] = True if FLAGS.classwise else False
cfg['output_eval'] = FLAGS.output_eval
cfg['save_prediction_only'] = FLAGS.save_prediction_only
merge_config(FLAGS.opt)
# disable npu in config by default
if 'use_npu' not in cfg:
cfg.use_npu = False
if cfg.use_gpu:
place = paddle.set_device('gpu')
elif cfg.use_npu:
place = paddle.set_device('npu')
else:
place = paddle.set_device('cpu')
if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn' and not cfg.use_gpu:
cfg['norm_type'] = 'bn'
if FLAGS.slim_config:
cfg = build_slim_model(cfg, FLAGS.slim_config, mode='eval')
check_config(cfg)
check_gpu(cfg.use_gpu)
check_npu(cfg.use_npu)
check_version()
run(FLAGS, cfg)
def main():
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
cfg['use_vdl'] = FLAGS.use_vdl
cfg['vdl_log_dir'] = FLAGS.vdl_log_dir
merge_config(FLAGS.opt)
# disable npu in config by default
if 'use_npu' not in cfg:
cfg.use_npu = False
if cfg.use_gpu:
place = paddle.set_device('gpu')
elif cfg.use_npu:
place = paddle.set_device('npu')
else:
place = paddle.set_device('cpu')
if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn' and not cfg.use_gpu:
cfg['norm_type'] = 'bn'
if FLAGS.slim_config:
cfg = build_slim_model(cfg, FLAGS.slim_config, mode='test')
check_config(cfg)
check_gpu(cfg.use_gpu)
check_npu(cfg.use_npu)
check_version()
run(FLAGS, cfg)
def main():
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
cfg['amp'] = FLAGS.amp
cfg['fleet'] = FLAGS.fleet
cfg['use_vdl'] = FLAGS.use_vdl
cfg['vdl_log_dir'] = FLAGS.vdl_log_dir
cfg['save_prediction_only'] = FLAGS.save_prediction_only
cfg['profiler_options'] = FLAGS.profiler_options
cfg['save_proposals'] = FLAGS.save_proposals
cfg['proposals_path'] = FLAGS.proposals_path
merge_config(FLAGS.opt)
# disable npu in config by default
if 'use_npu' not in cfg:
cfg.use_npu = False
# disable xpu in config by default
if 'use_xpu' not in cfg:
cfg.use_xpu = False
if cfg.use_gpu:
place = paddle.set_device('gpu')
elif cfg.use_npu:
place = paddle.set_device('npu')
elif cfg.use_xpu:
place = paddle.set_device('xpu')
else:
place = paddle.set_device('cpu')
if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn' and not cfg.use_gpu:
cfg['norm_type'] = 'bn'
if FLAGS.slim_config:
cfg = build_slim_model(cfg, FLAGS.slim_config)
# FIXME: Temporarily solve the priority problem of FLAGS.opt
merge_config(FLAGS.opt)
check.check_config(cfg)
check.check_gpu(cfg.use_gpu)
check.check_npu(cfg.use_npu)
check.check_version()
run(FLAGS, cfg)
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)
# disable npu in config by default and check use_npu
if 'use_npu' not in cfg:
cfg.use_npu = False
check_npu(cfg.use_npu)
# disable xpu in config by default and check use_xpu
if 'use_xpu' not in cfg:
cfg.use_xpu = False
check_xpu(cfg.use_xpu)
# 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)
if cfg.use_gpu:
place = fluid.CUDAPlace(0)
elif cfg.use_npu:
place = fluid.NPUPlace(0)
elif cfg.use_xpu:
place = fluid.XPUPlace(0)
else:
place = 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, segm2out, 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, lmk2out, 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 VisualDL to log image
if FLAGS.use_vdl:
assert six.PY3, "VisualDL requires Python >= 3.5"
from visualdl import LogWriter
vdl_writer = LogWriter(FLAGS.vdl_log_dir)
vdl_image_step = 0
vdl_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))
if 'TTFNet' in cfg.architecture:
res['bbox'][1].append([len(res['bbox'][0])])
if 'CornerNet' in cfg.architecture:
from ppdet.utils.post_process import corner_post_process
post_config = getattr(cfg, 'PostProcess', None)
corner_post_process(res, post_config, cfg.num_classes)
bbox_results = None
mask_results = None
segm_results = None
lmk_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)
if 'segm' in res:
segm_results = segm2out([res], clsid2catid)
if 'landmark' in res:
lmk_results = lmk2out([res], is_bbox_normalized)
# 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')
image = ImageOps.exif_transpose(image)
# use VisualDL to log original image
if FLAGS.use_vdl:
original_image_np = np.array(image)
vdl_writer.add_image(
"original/frame_{}".format(vdl_image_frame),
original_image_np, vdl_image_step)
image = visualize_results(image, int(im_id), catid2name,
FLAGS.draw_threshold, bbox_results,
mask_results, segm_results, lmk_results)
# use VisualDL to log image with bbox
if FLAGS.use_vdl:
infer_image_np = np.array(image)
vdl_writer.add_image("bbox/frame_{}".format(vdl_image_frame),
infer_image_np, vdl_image_step)
vdl_image_step += 1
if vdl_image_step % 10 == 0:
vdl_image_step = 0
vdl_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():
"""
Main evaluate function
"""
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)
# disable npu in config by default and check use_npu
if 'use_npu' not in cfg:
cfg.use_npu = False
check_npu(cfg.use_npu)
use_xpu = False
if hasattr(cfg, 'use_xpu'):
check_xpu(cfg.use_xpu)
use_xpu = cfg.use_xpu
# check if paddlepaddle version is satisfied
check_version()
assert not (use_xpu and cfg.use_gpu), \
'Can not run on both XPU and GPU'
assert not (cfg.use_npu and cfg.use_gpu), \
'Can not run on both NPU and GPU'
main_arch = cfg.architecture
multi_scale_test = getattr(cfg, 'MultiScaleTEST', None)
# define executor
if cfg.use_gpu:
place = fluid.CUDAPlace(0)
elif cfg.use_npu:
place = fluid.NPUPlace(0)
elif use_xpu:
place = fluid.XPUPlace(0)
else:
place = fluid.CPUPlace()
exe = fluid.Executor(place)
# build program
model = create(main_arch)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['EvalReader']['inputs_def']
feed_vars, loader = model.build_inputs(**inputs_def)
if multi_scale_test is None:
fetches = model.eval(feed_vars)
else:
fetches = model.eval(feed_vars, multi_scale_test)
eval_prog = eval_prog.clone(True)
reader = create_reader(cfg.EvalReader, devices_num=1)
# When iterable mode, set set_sample_list_generator(reader, place)
loader.set_sample_list_generator(reader)
dataset = cfg['EvalReader']['dataset']
# eval already exists json file
if FLAGS.json_eval:
logger.info(
"In json_eval mode, PaddleDetection will evaluate json files in "
"output_eval directly. And proposal.json, bbox.json and mask.json "
"will be detected by default.")
json_eval_results(cfg.metric,
json_directory=FLAGS.output_eval,
dataset=dataset)
return
compile_program = fluid.CompiledProgram(eval_prog).with_data_parallel()
if use_xpu or cfg.use_npu:
compile_program = eval_prog
assert cfg.metric != 'OID', "eval process of OID dataset \
is not supported."
if cfg.metric == "WIDERFACE":
raise ValueError("metric type {} does not support in tools/eval.py, "
"please use tools/face_eval.py".format(cfg.metric))
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 = ['gt_bbox', 'gt_class', 'is_difficult']
keys, values, cls = parse_fetches(fetches, eval_prog, extra_keys)
# 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()
sub_eval_prog = None
sub_keys = None
sub_values = None
# build sub-program
if 'Mask' in main_arch and multi_scale_test:
sub_eval_prog = fluid.Program()
with fluid.program_guard(sub_eval_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['EvalReader']['inputs_def']
inputs_def['mask_branch'] = True
feed_vars, eval_loader = model.build_inputs(**inputs_def)
sub_fetches = model.eval(feed_vars,
multi_scale_test,
mask_branch=True)
assert cfg.metric == 'COCO'
extra_keys = ['im_id', 'im_shape']
sub_keys, sub_values, _ = parse_fetches(sub_fetches, sub_eval_prog,
extra_keys)
sub_eval_prog = sub_eval_prog.clone(True)
# load model
exe.run(startup_prog)
if 'weights' in cfg:
checkpoint.load_params(exe, startup_prog, cfg.weights)
resolution = None
if 'Mask' in cfg.architecture or cfg.architecture == 'HybridTaskCascade':
resolution = model.mask_head.resolution
results = eval_run(exe, compile_program, loader, keys, values, cls, cfg,
sub_eval_prog, sub_keys, sub_values, resolution)
# evaluation
# if map_type not set, use default 11point, only use in VOC eval
map_type = cfg.map_type if 'map_type' in cfg else '11point'
save_only = getattr(cfg, 'save_prediction_only', False)
eval_results(results,
cfg.metric,
cfg.num_classes,
resolution,
is_bbox_normalized,
FLAGS.output_eval,
map_type,
dataset=dataset,
save_only=save_only)
def main():
env = os.environ
FLAGS.dist = 'PADDLE_TRAINER_ID' in env \
and 'PADDLE_TRAINERS_NUM' in env \
and int(env['PADDLE_TRAINERS_NUM']) > 1
num_trainers = int(env.get('PADDLE_TRAINERS_NUM', 1))
if FLAGS.dist:
trainer_id = int(env['PADDLE_TRAINER_ID'])
local_seed = (99 + trainer_id)
random.seed(local_seed)
np.random.seed(local_seed)
if FLAGS.enable_ce:
random.seed(0)
np.random.seed(0)
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)
# disable npu in config by default and check use_npu
if 'use_npu' not in cfg:
cfg.use_npu = False
check_npu(cfg.use_npu)
use_xpu = False
if hasattr(cfg, 'use_xpu'):
check_xpu(cfg.use_xpu)
use_xpu = cfg.use_xpu
# check if paddlepaddle version is satisfied
check_version()
assert not (use_xpu and cfg.use_gpu), \
'Can not run on both XPU and GPU'
assert not (cfg.use_npu and cfg.use_gpu), \
'Can not run on both NPU and GPU'
save_only = getattr(cfg, 'save_prediction_only', False)
if save_only:
raise NotImplementedError('The config file only support prediction,'
' training stage is not implemented now')
main_arch = cfg.architecture
if cfg.use_gpu:
devices_num = fluid.core.get_cuda_device_count()
elif cfg.use_npu:
devices_num = fluid.core.get_npu_device_count()
elif use_xpu:
# ToDo(qingshu): XPU only support single card now
devices_num = 1
else:
devices_num = int(os.environ.get('CPU_NUM', 1))
if cfg.use_gpu and 'FLAGS_selected_gpus' in env:
device_id = int(env['FLAGS_selected_gpus'])
elif cfg.use_npu and 'FLAGS_selected_npus' in env:
device_id = int(env['FLAGS_selected_npus'])
elif use_xpu and 'FLAGS_selected_xpus' in env:
device_id = int(env['FLAGS_selected_xpus'])
else:
device_id = 0
if cfg.use_gpu:
place = fluid.CUDAPlace(device_id)
elif cfg.use_npu:
place = fluid.NPUPlace(device_id)
elif use_xpu:
place = fluid.XPUPlace(device_id)
else:
place = fluid.CPUPlace()
exe = fluid.Executor(place)
lr_builder = create('LearningRate')
optim_builder = create('OptimizerBuilder')
# build program
startup_prog = fluid.Program()
train_prog = fluid.Program()
if FLAGS.enable_ce:
startup_prog.random_seed = 1000
train_prog.random_seed = 1000
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
if FLAGS.fp16:
assert (getattr(model.backbone, 'norm_type', None)
!= 'affine_channel'), \
'--fp16 currently does not support affine channel, ' \
' please modify backbone settings to use batch norm'
with mixed_precision_context(FLAGS.loss_scale, FLAGS.fp16) as ctx:
inputs_def = cfg['TrainReader']['inputs_def']
feed_vars, train_loader = model.build_inputs(**inputs_def)
train_fetches = model.train(feed_vars)
loss = train_fetches['loss']
if FLAGS.fp16:
loss *= ctx.get_loss_scale_var()
lr = lr_builder()
optimizer = optim_builder(lr)
optimizer.minimize(loss)
if FLAGS.fp16:
loss /= ctx.get_loss_scale_var()
if 'use_ema' in cfg and cfg['use_ema']:
global_steps = _decay_step_counter()
ema = ExponentialMovingAverage(
cfg['ema_decay'], thres_steps=global_steps)
ema.update()
# parse train fetches
train_keys, train_values, _ = parse_fetches(train_fetches)
train_values.append(lr)
if FLAGS.eval:
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
inputs_def = cfg['EvalReader']['inputs_def']
feed_vars, eval_loader = model.build_inputs(**inputs_def)
fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True)
eval_reader = create_reader(cfg.EvalReader, devices_num=1)
# When iterable mode, set set_sample_list_generator(eval_reader, place)
eval_loader.set_sample_list_generator(eval_reader)
# parse eval fetches
extra_keys = []
if cfg.metric == 'COCO':
extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg.metric == 'VOC':
extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
if cfg.metric == 'WIDERFACE':
extra_keys = ['im_id', 'im_shape', 'gt_bbox']
eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
extra_keys)
# compile program for multi-devices
build_strategy = fluid.BuildStrategy()
build_strategy.fuse_all_optimizer_ops = False
# only enable sync_bn in multi GPU devices
sync_bn = getattr(model.backbone, 'norm_type', None) == 'sync_bn'
build_strategy.sync_batch_norm = sync_bn and devices_num > 1 \
and cfg.use_gpu
exec_strategy = fluid.ExecutionStrategy()
# iteration number when CompiledProgram tries to drop local execution scopes.
# Set it to be 1 to save memory usages, so that unused variables in
# local execution scopes can be deleted after each iteration.
exec_strategy.num_iteration_per_drop_scope = 1
if FLAGS.dist:
dist_utils.prepare_for_multi_process(exe, build_strategy, startup_prog,
train_prog)
exec_strategy.num_threads = 1
exe.run(startup_prog)
compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(
loss_name=loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
if use_xpu or cfg.use_npu:
compiled_train_prog = train_prog
if FLAGS.eval:
compiled_eval_prog = fluid.CompiledProgram(eval_prog)
if use_xpu or cfg.use_npu:
compiled_eval_prog = eval_prog
fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
ignore_params = cfg.finetune_exclude_pretrained_params \
if 'finetune_exclude_pretrained_params' in cfg else []
start_iter = 0
if FLAGS.resume_checkpoint:
checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint)
start_iter = checkpoint.global_step()
elif cfg.pretrain_weights and fuse_bn and not ignore_params:
checkpoint.load_and_fusebn(exe, train_prog, cfg.pretrain_weights)
elif cfg.pretrain_weights:
checkpoint.load_params(
exe, train_prog, cfg.pretrain_weights, ignore_params=ignore_params)
train_reader = create_reader(
cfg.TrainReader, (cfg.max_iters - start_iter) * devices_num,
cfg,
devices_num=devices_num,
num_trainers=num_trainers)
# When iterable mode, set set_sample_list_generator(train_reader, place)
train_loader.set_sample_list_generator(train_reader)
# 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()
# if map_type not set, use default 11point, only use in VOC eval
map_type = cfg.map_type if 'map_type' in cfg else '11point'
train_stats = TrainingStats(cfg.log_iter, train_keys)
train_loader.start()
start_time = time.time()
end_time = time.time()
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(cfg.save_dir, cfg_name)
time_stat = deque(maxlen=cfg.log_iter)
best_box_ap_list = [0.0, 0] #[map, iter]
# use VisualDL to log data
if FLAGS.use_vdl:
assert six.PY3, "VisualDL requires Python >= 3.5"
from visualdl import LogWriter
vdl_writer = LogWriter(FLAGS.vdl_log_dir)
vdl_loss_step = 0
vdl_mAP_step = 0
for it in range(start_iter, cfg.max_iters):
start_time = end_time
end_time = time.time()
time_stat.append(end_time - start_time)
time_cost = np.mean(time_stat)
eta_sec = (cfg.max_iters - it) * time_cost
eta = str(datetime.timedelta(seconds=int(eta_sec)))
outs = exe.run(compiled_train_prog, fetch_list=train_values)
stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
# use vdl-paddle to log loss
if FLAGS.use_vdl:
if it % cfg.log_iter == 0:
for loss_name, loss_value in stats.items():
vdl_writer.add_scalar(loss_name, loss_value, vdl_loss_step)
vdl_loss_step += 1
train_stats.update(stats)
logs = train_stats.log()
if it % cfg.log_iter == 0 and (not FLAGS.dist or trainer_id == 0):
ips = float(cfg['TrainReader']['batch_size']) / time_cost
strs = 'iter: {}, lr: {:.6f}, {}, eta: {}, batch_cost: {:.5f} sec, ips: {:.5f} images/sec'.format(
it, np.mean(outs[-1]), logs, eta, time_cost, ips)
logger.info(strs)
# NOTE : profiler tools, used for benchmark
if FLAGS.is_profiler and it == 5:
profiler.start_profiler("All")
elif FLAGS.is_profiler and it == 10:
profiler.stop_profiler("total", FLAGS.profiler_path)
return
if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1) \
and (not FLAGS.dist or trainer_id == 0):
save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
if 'use_ema' in cfg and cfg['use_ema']:
exe.run(ema.apply_program)
checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name))
if FLAGS.eval:
# evaluation
resolution = None
if 'Mask' in cfg.architecture:
resolution = model.mask_head.resolution
results = eval_run(
exe,
compiled_eval_prog,
eval_loader,
eval_keys,
eval_values,
eval_cls,
cfg,
resolution=resolution)
box_ap_stats = eval_results(
results, cfg.metric, cfg.num_classes, resolution,
is_bbox_normalized, FLAGS.output_eval, map_type,
cfg['EvalReader']['dataset'])
# use vdl_paddle to log mAP
if FLAGS.use_vdl:
vdl_writer.add_scalar("mAP", box_ap_stats[0], vdl_mAP_step)
vdl_mAP_step += 1
if box_ap_stats[0] > best_box_ap_list[0]:
best_box_ap_list[0] = box_ap_stats[0]
best_box_ap_list[1] = it
checkpoint.save(exe, train_prog,
os.path.join(save_dir, "best_model"))
logger.info("Best test box ap: {}, in iter: {}".format(
best_box_ap_list[0], best_box_ap_list[1]))
if 'use_ema' in cfg and cfg['use_ema']:
exe.run(ema.restore_program)
train_loader.reset()
