def predict(self,
images,
draw_threshold=0.5,
output_dir='output',
save_txt=False):
self.dataset.set_images(images)
loader = create('TestReader')(self.dataset, 0)
imid2path = self.dataset.get_imid2path()
anno_file = self.dataset.get_anno()
clsid2catid, catid2name = get_categories(self.cfg.metric, anno_file)
# Run Infer
self.status['mode'] = 'test'
self.model.eval()
for step_id, data in enumerate(loader):
self.status['step_id'] = step_id
# forward
outs = self.model(data)
for key in ['im_shape', 'scale_factor', 'im_id']:
outs[key] = data[key]
for key, value in outs.items():
outs[key] = value.numpy()
batch_res = get_infer_results(outs, clsid2catid)
bbox_num = outs['bbox_num']
start = 0
for i, im_id in enumerate(outs['im_id']):
image_path = imid2path[int(im_id)]
image = Image.open(image_path).convert('RGB')
self.status['original_image'] = np.array(image.copy())
end = start + bbox_num[i]
bbox_res = batch_res['bbox'][start:end] \
if 'bbox' in batch_res else None
mask_res = batch_res['mask'][start:end] \
if 'mask' in batch_res else None
segm_res = batch_res['segm'][start:end] \
if 'segm' in batch_res else None
image = visualize_results(image, bbox_res, mask_res, segm_res,
int(outs['im_id']), catid2name,
draw_threshold)
self.status['result_image'] = np.array(image.copy())
if self._compose_callback:
self._compose_callback.on_step_end(self.status)
# save image with detection
save_name = self._get_save_image_name(output_dir, image_path)
logger.info(
"Detection bbox results save in {}".format(save_name))
image.save(save_name, quality=95)
if save_txt:
save_path = os.path.splitext(save_name)[0] + '.txt'
save_result(save_path, bbox_res, catid2name,
draw_threshold)
start = end
def predict(self, images, draw_threshold=0.5, output_dir='output'):
self.dataset.set_images(images)
loader = create('TestReader')(self.dataset, 0)
imid2path = self.dataset.get_imid2path()
anno_file = self.dataset.get_anno()
with_background = self.cfg.with_background
clsid2catid, catid2name = get_categories(self.cfg.metric, anno_file,
with_background)
# Run Infer
for step_id, data in enumerate(loader):
self.status['step_id'] = step_id
# forward
self.model.eval()
outs = self.model(data)
for key in ['im_shape', 'scale_factor', 'im_id']:
outs[key] = data[key]
for key, value in outs.items():
outs[key] = value.numpy()
# FIXME: for more elegent coding
if 'mask' in outs and 'bbox' in outs:
mask_resolution = self.model.mask_post_process.mask_resolution
from ppdet.py_op.post_process import mask_post_process
outs['mask'] = mask_post_process(outs, outs['im_shape'],
outs['scale_factor'],
mask_resolution)
batch_res = get_infer_results(outs, clsid2catid)
bbox_num = outs['bbox_num']
start = 0
for i, im_id in enumerate(outs['im_id']):
image_path = imid2path[int(im_id)]
image = Image.open(image_path).convert('RGB')
end = start + bbox_num[i]
bbox_res = batch_res['bbox'][start:end] \
if 'bbox' in batch_res else None
mask_res = batch_res['mask'][start:end] \
if 'mask' in batch_res else None
segm_res = batch_res['segm'][start:end] \
if 'segm' in batch_res else None
image = visualize_results(image, bbox_res, mask_res, segm_res,
int(outs['im_id']), catid2name,
draw_threshold)
# save image with detection
save_name = self._get_save_image_name(output_dir, image_path)
logger.info("Detection bbox results save in {}".format(
save_name))
image.save(save_name, quality=95)
start = end
def main():
# 数据源
catid2name = get_cid2cname()
test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img,
FLAGS.random_seed)
test_images = test_images[:FLAGS.num_img]
bbox_out = get_bbox_out(test_images)
for im_id, im_path in enumerate(test_images):
image = Image.open(im_path).convert('RGB')
image = visualize_results(image, int(im_id), catid2name,
FLAGS.draw_threshold, bbox_out)
save_name = get_save_image_name(FLAGS.output_dir, im_path)
image.save(save_name, quality=95)
logger.info("Detection bbox results save in {}".format(save_name))
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)
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, 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()
resultBBox = []
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)
# bbox 四个值：左上角坐标 + 宽度 + 高度
# {'image_id': 0, 'category_id': 0, 'bbox': [695.04443359375, 723.8153686523438, 128.288818359375, 61.5987548828125], 'score': 0.9990022778511047}
im_ids = res['im_id'][0]
image_path = imid2path[int(im_ids[0])]
prefix = image_path.split('/')[-1]
imageName = prefix.split('.')[0]
for i, result in enumerate(bbox_results):
score = result["score"]
bbox = result["bbox"]
x1 = str(int(bbox[0]))
y1 = str(int(bbox[1]))
x2 = str(int(bbox[2] + bbox[0]))
y2 = str(int(bbox[3] + bbox[1]))
if (score > 0.01):
resStr = imageName + ' ' + str(round(
score,
3)) + ' ' + x1 + ' ' + y1 + ' ' + x2 + ' ' + y2 + '\n'
resultBBox.append(resStr)
# visualize result
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)
resulttxtPath = "/home/aistudio/work/PaddleDetection-release-2.0-beta/output/test_result.txt"
f = open(resulttxtPath, 'w+', encoding='utf-8')
for i, p in enumerate(resultBBox):
f.write(p)
f.close()
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)
pruned_params = FLAGS.pruned_params
assert (
FLAGS.pruned_params is not None
), "FLAGS.pruned_params is empty!!! Please set it by '--pruned_params' option."
pruned_params = FLAGS.pruned_params.strip().split(",")
logger.info("pruned params: {}".format(pruned_params))
pruned_ratios = [float(n) for n in FLAGS.pruned_ratios.strip().split(",")]
logger.info("pruned ratios: {}".format(pruned_ratios))
assert (len(pruned_params) == len(pruned_ratios)
), "The length of pruned params and pruned ratios should be equal."
assert (pruned_ratios > [0] * len(pruned_ratios)
and pruned_ratios < [1] * len(pruned_ratios)
), "The elements of pruned ratios should be in range (0, 1)."
base_flops = flops(infer_prog)
pruner = Pruner()
infer_prog, _, _ = pruner.prune(infer_prog,
fluid.global_scope(),
params=pruned_params,
ratios=pruned_ratios,
place=place,
only_graph=True)
pruned_flops = flops(infer_prog)
logger.info("pruned FLOPS: {}".format(
float(base_flops - pruned_flops) / base_flops))
reader = create_reader(cfg.TestReader, devices_num=1)
loader.set_sample_list_generator(reader, place)
exe.run(startup_prog)
if cfg.weights:
checkpoint.load_checkpoint(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()
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')
image = visualize_results(image, int(im_id), catid2name,
FLAGS.draw_threshold, bbox_results,
mask_results)
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)
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)
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():
_, feed_vars = create_feed(test_feed, use_pyreader=False)
test_fetches = model.test(feed_vars)
infer_prog = infer_prog.clone(True)
reader = create_reader(test_feed)
feeder = fluid.DataFeeder(place=place, feed_list=feed_vars.values())
exe.run(startup_prog)
if cfg.weights:
checkpoint.load_checkpoint(exe, infer_prog, cfg.weights)
if FLAGS.save_inference_model:
save_infer_model(FLAGS, exe, feed_vars, test_fetches, 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(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 == "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
if hasattr(model, 'is_bbox_normalized') and \
callable(model.is_bbox_normalized):
is_bbox_normalized = model.is_bbox_normalized()
imid2path = reader.imid2path
for iter_id, data in enumerate(reader()):
outs = exe.run(infer_prog,
feed=feeder.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')
image = visualize_results(image, int(im_id), catid2name,
FLAGS.draw_threshold, bbox_results,
mask_results)
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)
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 predict(self,
images,
draw_threshold=0.5,
output_dir='output',
save_txt=False):
self.dataset.set_images(images)
loader = create('TestReader')(self.dataset, 0)
imid2path = self.dataset.get_imid2path()
anno_file = self.dataset.get_anno()
clsid2catid, catid2name = get_categories(self.cfg.metric,
anno_file=anno_file)
# Run Infer
self.status['mode'] = 'test'
self.model.eval()
if self.cfg.get('print_flops', False):
self._flops(loader)
results = []
for step_id, data in enumerate(loader):
self.status['step_id'] = step_id
# forward
outs = self.model(data)
for key in ['im_shape', 'scale_factor', 'im_id']:
outs[key] = data[key]
for key, value in outs.items():
if hasattr(value, 'numpy'):
outs[key] = value.numpy()
results.append(outs)
# sniper
if type(self.dataset) == SniperCOCODataSet:
results = self.dataset.anno_cropper.aggregate_chips_detections(
results)
for outs in results:
batch_res = get_infer_results(outs, clsid2catid)
bbox_num = outs['bbox_num']
start = 0
for i, im_id in enumerate(outs['im_id']):
image_path = imid2path[int(im_id)]
image = Image.open(image_path).convert('RGB')
image = ImageOps.exif_transpose(image)
self.status['original_image'] = np.array(image.copy())
end = start + bbox_num[i]
bbox_res = batch_res['bbox'][start:end] \
if 'bbox' in batch_res else None
mask_res = batch_res['mask'][start:end] \
if 'mask' in batch_res else None
segm_res = batch_res['segm'][start:end] \
if 'segm' in batch_res else None
keypoint_res = batch_res['keypoint'][start:end] \
if 'keypoint' in batch_res else None
image = visualize_results(image, bbox_res, mask_res, segm_res,
keypoint_res, int(im_id), catid2name,
draw_threshold)
self.status['result_image'] = np.array(image.copy())
if self._compose_callback:
self._compose_callback.on_step_end(self.status)
# save image with detection
save_name = self._get_save_image_name(output_dir, image_path)
logger.info(
"Detection bbox results save in {}".format(save_name))
image.save(save_name, quality=95)
if save_txt:
save_path = os.path.splitext(save_name)[0] + '.txt'
results = {}
results["im_id"] = im_id
if bbox_res:
results["bbox_res"] = bbox_res
if keypoint_res:
results["keypoint_res"] = keypoint_res
save_result(save_path, results, catid2name, draw_threshold)
start = end
def run(FLAGS, cfg, place):
# Model
main_arch = cfg.architecture
model = create(cfg.architecture)
# data
dataset = cfg.TestDataset
test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img)
dataset.set_images(test_images)
test_loader = create('TestReader')(dataset, cfg['worker_num'])
extra_key = ['im_shape', 'scale_factor', 'im_id']
# TODO: support other metrics
imid2path = dataset.get_imid2path()
anno_file = dataset.get_anno()
with_background = cfg.with_background
use_default_label = dataset.use_default_label
if cfg.metric == 'COCO':
from ppdet.utils.coco_eval import get_category_info
if cfg.metric == 'VOC':
from ppdet.utils.voc_eval import get_category_info
clsid2catid, catid2name = get_category_info(anno_file, with_background,
use_default_label)
# Init Model
load_weight(model, cfg.weights)
# Run Infer
for iter_id, data in enumerate(test_loader):
# forward
model.eval()
outs = model(data)
for key in extra_key:
outs[key] = data[key]
for key, value in outs.items():
outs[key] = value.numpy()
if 'mask' in outs and 'bbox' in outs:
mask_resolution = model.mask_post_process.mask_resolution
from ppdet.py_op.post_process import mask_post_process
outs['mask'] = mask_post_process(outs, outs['im_shape'],
outs['scale_factor'],
mask_resolution)
eval_type = []
if 'bbox' in outs:
eval_type.append('bbox')
if 'mask' in outs:
eval_type.append('mask')
batch_res = get_infer_results([outs], eval_type, clsid2catid)
logger.info('Infer iter {}'.format(iter_id))
bbox_res = None
mask_res = None
bbox_num = outs['bbox_num']
start = 0
for i, im_id in enumerate(outs['im_id']):
image_path = imid2path[int(im_id)]
image = Image.open(image_path).convert('RGB')
end = start + bbox_num[i]
# 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)
if 'bbox' in batch_res:
bbox_res = batch_res['bbox'][start:end]
if 'mask' in batch_res:
mask_res = batch_res['mask'][start:end]
image = visualize_results(image, bbox_res, mask_res,
int(outs['im_id']), catid2name,
FLAGS.draw_threshold)
# 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 image with detection
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)
start = end
def main():
## 配置
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_gpu(cfg.use_gpu)
check_version()
# 数据源
dataset = cfg.TestReader['dataset']
test_images = get_test_images(FLAGS.infer_img,
FLAGS.infer_dir, FLAGS.random_seed, FLAGS.num_img)
dataset.set_images(test_images)
imid2path = dataset.get_imid2path()
catid2name = get_cid2cname()
imid2name = {k: str(os.path.basename(v).split('.')[0])
for k, v in imid2path.items()}
# 模型
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)
extra_keys = ['im_id']
keys, values, _ = parse_fetches(test_fetches, infer_prog, extra_keys)
# 执行器
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# 数据
reader = create_reader(cfg.TestReader)
loader.set_sample_list_generator(reader, place)
#### 运行 ####
exe.run(startup_prog)
# 加载权重
assert 'weights' in cfg, \
'model can not load weights'
checkpoint.load_params(exe, infer_prog, cfg.weights)
results = []
im_ids = []
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))
results.append(res)
im_ids.extend([int(id) for id in res['im_id'][0]])
## 输出json结果
if FLAGS.json_mode:
save_json_results(results, imid2name, FLAGS.output_dir, FLAGS.json_name)
return
# 输出画图结果
bbox_results = bbox2out(results)
for im_id in im_ids:
image_path = imid2path[im_id]
image = Image.open(image_path).convert('RGB')
image = visualize_results(image,
int(im_id), catid2name,
FLAGS.draw_threshold, bbox_results)
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 run(FLAGS, cfg, place):
# Model
main_arch = cfg.architecture
model = create(cfg.architecture)
# data
dataset = cfg.TestDataset
test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img)
dataset.set_images(test_images)
test_loader, _ = create('TestReader')(dataset, cfg['worker_num'], place)
# TODO: support other metrics
imid2path = dataset.get_imid2path()
from ppdet.utils.coco_eval import get_category_info
anno_file = dataset.get_anno()
with_background = cfg.with_background
use_default_label = dataset.use_default_label
clsid2catid, catid2name = get_category_info(anno_file, with_background,
use_default_label)
# Init Model
load_weight(model, cfg.weights)
# Run Infer
for iter_id, data in enumerate(test_loader):
# forward
model.eval()
outs = model(data, cfg.TestReader['inputs_def']['fields'], 'infer')
batch_res = get_infer_results([outs], outs.keys(), clsid2catid)
logger.info('Infer iter {}'.format(iter_id))
bbox_res = None
mask_res = None
im_ids = outs['im_id']
bbox_num = outs['bbox_num']
start = 0
for i, im_id in enumerate(im_ids):
im_id = im_ids[i]
image_path = imid2path[int(im_id)]
image = Image.open(image_path).convert('RGB')
end = start + bbox_num[i]
# 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)
if 'bbox' in batch_res:
bbox_res = batch_res['bbox'][start:end]
if 'mask' in batch_res:
mask_res = batch_res['mask'][start:end]
image = visualize_results(image, bbox_res, mask_res, int(im_id),
catid2name, FLAGS.draw_threshold)
# 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 image with detection
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)
start = end
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']
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)
# When iterable mode, set set_sample_list_generator(reader, place)
loader.set_sample_list_generator(reader)
not_quant_pattern = []
if FLAGS.not_quant_pattern:
not_quant_pattern = FLAGS.not_quant_pattern
config = {
'weight_quantize_type': 'channel_wise_abs_max',
'activation_quantize_type': 'moving_average_abs_max',
'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'],
'not_quant_pattern': not_quant_pattern
}
infer_prog = quant_aware(infer_prog, place, config, for_test=True)
exe.run(startup_prog)
if cfg.weights:
checkpoint.load_params(exe, infer_prog, cfg.weights)
infer_prog = convert(infer_prog, place, config, save_int8=False)
# 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()
imid2path = dataset.get_imid2path()
iter_id = 0
try:
loader.start()
while True:
outs = exe.run(infer_prog, 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))
iter_id += 1
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')
image = visualize_results(image, int(im_id), catid2name,
FLAGS.draw_threshold, bbox_results,
mask_results)
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)
except (StopIteration, fluid.core.EOFException):
loader.reset()
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)
def predict(self,
images,
draw_threshold=0.5,
output_dir='output',
save_results=False):
self.dataset.set_images(images)
loader = create('TestReader')(self.dataset, 0)
def setup_metrics_for_loader():
# mem
metrics = copy.deepcopy(self._metrics)
mode = self.mode
save_prediction_only = self.cfg[
'save_prediction_only'] if 'save_prediction_only' in self.cfg else None
output_eval = self.cfg[
'output_eval'] if 'output_eval' in self.cfg else None
# modify
self.mode = '_test'
self.cfg['save_prediction_only'] = True
self.cfg['output_eval'] = output_dir
self._init_metrics()
# restore
self.mode = mode
self.cfg.pop('save_prediction_only')
if save_prediction_only is not None:
self.cfg['save_prediction_only'] = save_prediction_only
self.cfg.pop('output_eval')
if output_eval is not None:
self.cfg['output_eval'] = output_eval
_metrics = copy.deepcopy(self._metrics)
self._metrics = metrics
return _metrics
if save_results:
metrics = setup_metrics_for_loader()
else:
metrics = []
imid2path = self.dataset.get_imid2path()
anno_file = self.dataset.get_anno()
clsid2catid, catid2name = get_categories(
self.cfg.metric, anno_file=anno_file)
# Run Infer
self.status['mode'] = 'test'
self.model.eval()
if self.cfg.get('print_flops', False):
flops_loader = create('TestReader')(self.dataset, 0)
self._flops(flops_loader)
results = []
for step_id, data in enumerate(tqdm(loader)):
self.status['step_id'] = step_id
# forward
outs = self.model(data)
for _m in metrics:
_m.update(data, outs)
for key in ['im_shape', 'scale_factor', 'im_id']:
if isinstance(data, typing.Sequence):
outs[key] = data[0][key]
else:
outs[key] = data[key]
for key, value in outs.items():
if hasattr(value, 'numpy'):
outs[key] = value.numpy()
results.append(outs)
# sniper
if type(self.dataset) == SniperCOCODataSet:
results = self.dataset.anno_cropper.aggregate_chips_detections(
results)
for _m in metrics:
_m.accumulate()
_m.reset()
for outs in results:
batch_res = get_infer_results(outs, clsid2catid)
bbox_num = outs['bbox_num']
start = 0
for i, im_id in enumerate(outs['im_id']):
image_path = imid2path[int(im_id)]
image = Image.open(image_path).convert('RGB')
image = ImageOps.exif_transpose(image)
self.status['original_image'] = np.array(image.copy())
end = start + bbox_num[i]
bbox_res = batch_res['bbox'][start:end] \
if 'bbox' in batch_res else None
mask_res = batch_res['mask'][start:end] \
if 'mask' in batch_res else None
segm_res = batch_res['segm'][start:end] \
if 'segm' in batch_res else None
keypoint_res = batch_res['keypoint'][start:end] \
if 'keypoint' in batch_res else None
image = visualize_results(
image, bbox_res, mask_res, segm_res, keypoint_res,
int(im_id), catid2name, draw_threshold)
self.status['result_image'] = np.array(image.copy())
if self._compose_callback:
self._compose_callback.on_step_end(self.status)
# save image with detection
save_name = self._get_save_image_name(output_dir, image_path)
logger.info("Detection bbox results save in {}".format(
save_name))
image.save(save_name, quality=95)
start = end
