未验证 提交 a1446709 编写于 作者: G Guanghua Yu 提交者: GitHub

[Dygraph] update dygraph export model (#1857)

* update dygraph export model

* delete get_export

* adapt faster and cascade
上级 9b279ee3
......@@ -133,7 +133,7 @@ class Detector(object):
boxes_tensor = self.predictor.get_output_handle(output_names[0])
np_boxes = boxes_tensor.copy_to_cpu()
if self.pred_config.mask_resolution is not None:
masks_tensor = self.predictor.get_output_handle(output_names[1])
masks_tensor = self.predictor.get_output_handle(output_names[2])
np_masks = masks_tensor.copy_to_cpu()
t2 = time.time()
ms = (t2 - t1) * 1000.0 / repeats
......
......@@ -79,7 +79,7 @@ class ResizeOp(object):
im_info['scale_factor'] = np.array(
[im_scale_y, im_scale_x]).astype('float32')
# padding im when image_shape fixed by infer_cfg.yml
if self.keep_ratio:
if self.keep_ratio and im_info['input_shape'][1] is not None:
max_size = im_info['input_shape'][1]
padding_im = np.zeros(
(max_size, max_size, im_channel), dtype=np.float32)
......
......@@ -158,17 +158,12 @@ class CascadeRCNN(BaseArch):
loss.update({'loss': total_loss})
return loss
def get_pred(self, return_numpy=True):
def get_pred(self):
bbox, bbox_num = self.bboxes
output = {
'bbox': bbox.numpy(),
'bbox_num': bbox_num.numpy(),
'im_id': self.inputs['im_id'].numpy(),
'bbox': bbox,
'bbox_num': bbox_num,
}
if self.with_mask:
mask = self.mask_post_process(self.bboxes, self.mask_head_out,
self.inputs['im_shape'],
self.inputs['scale_factor'])
output.update(mask)
output.update(self.mask_head_out)
return output
......@@ -92,12 +92,10 @@ class FasterRCNN(BaseArch):
loss.update({'loss': total_loss})
return loss
def get_pred(self, return_numpy=True):
def get_pred(self):
bbox, bbox_num = self.bboxes
output = {
'bbox': bbox.numpy(),
'bbox_num': bbox_num.numpy(),
'im_id': self.inputs['im_id'].numpy()
'bbox': bbox,
'bbox_num': bbox_num,
}
return output
......@@ -133,15 +133,11 @@ class MaskRCNN(BaseArch):
loss.update({'loss': total_loss})
return loss
def get_pred(self, return_numpy=True):
mask = self.mask_post_process(self.bboxes, self.mask_head_out,
self.inputs['im_shape'],
self.inputs['scale_factor'])
def get_pred(self):
bbox, bbox_num = self.bboxes
output = {
'bbox': bbox.numpy(),
'bbox_num': bbox_num.numpy(),
'im_id': self.inputs['im_id'].numpy()
'bbox': bbox,
'bbox_num': bbox_num,
'mask': self.mask_head_out
}
output.update(mask)
return output
......@@ -16,18 +16,24 @@ class BaseArch(nn.Layer):
def __init__(self):
super(BaseArch, self).__init__()
def forward(self, data, input_def, mode, input_tensor=None):
def forward(self,
input_tensor=None,
data=None,
input_def=None,
mode='infer'):
if input_tensor is None:
assert data is not None and input_def is not None
self.inputs = self.build_inputs(data, input_def)
else:
self.inputs = input_tensor
self.inputs['mode'] = mode
self.model_arch()
if mode == 'train':
out = self.get_loss()
elif mode == 'infer':
out = self.get_pred(input_tensor is None)
out = self.get_pred()
else:
out = None
raise "Now, only support train and infer mode!"
......@@ -47,6 +53,3 @@ class BaseArch(nn.Layer):
def get_pred(self, ):
raise NotImplementedError("Should implement get_pred method!")
def get_export_model(self, input_tensor):
return self.forward(None, None, 'infer', input_tensor)
......@@ -43,16 +43,12 @@ class YOLOv3(BaseArch):
loss = self.yolo_head.get_loss(self.yolo_head_outs, self.inputs)
return loss
def get_pred(self, return_numpy=True):
def get_pred(self):
bbox, bbox_num = self.post_process(
self.yolo_head_outs, self.yolo_head.mask_anchors,
self.inputs['im_shape'], self.inputs['scale_factor'])
if return_numpy:
outs = {
"bbox": bbox.numpy(),
"bbox_num": bbox_num.numpy(),
'im_id': self.inputs['im_id'].numpy()
"bbox": bbox,
"bbox_num": bbox_num,
}
else:
outs = [bbox, bbox_num]
return outs
......@@ -160,12 +160,19 @@ class MaskHead(Layer):
bbox, bbox_num = bboxes
if bbox.shape[0] == 0:
mask_head_out = bbox
mask_head_out = paddle.full([1, 6], -1)
return mask_head_out
else:
scale_factor_list = []
# TODO(guanghua): Remove fluid dependency
scale_factor_list = paddle.fluid.layers.create_array('float32')
num_count = 0
for idx, num in enumerate(bbox_num):
for n in range(num):
scale_factor_list.append(scale_factor[idx, 0])
paddle.fluid.layers.array_write(
x=scale_factor[idx, 0],
i=paddle.to_tensor(num_count),
array=scale_factor_list)
num_count += 1
scale_factor_list = paddle.cast(
paddle.concat(scale_factor_list), 'float32')
scale_factor_list = paddle.reshape(scale_factor_list, shape=[-1, 1])
......
......@@ -36,7 +36,6 @@ class RoIAlign(object):
def __call__(self, feats, rois, spatial_scale):
roi, rois_num = rois
if self.start_level == self.end_level:
rois_feat = ops.roi_align(
feats[self.start_level],
......@@ -44,7 +43,7 @@ class RoIAlign(object):
self.resolution,
spatial_scale,
rois_num=rois_num)
return rois_feat
else:
offset = 2
k_min = self.start_level + offset
k_max = self.end_level + offset
......
......@@ -80,7 +80,8 @@ class FPN(Layer):
for lvl in range(self.min_level, self.max_level):
laterals.append(self.lateral_convs[lvl](body_feats[lvl]))
for lvl in range(self.max_level - 1, self.min_level, -1):
for i in range(self.min_level + 1, self.max_level):
lvl = self.max_level + self.min_level - i
upsample = F.interpolate(
laterals[lvl],
scale_factor=2.,
......
......@@ -29,10 +29,19 @@ import numpy as np
from functools import reduce
__all__ = [
'roi_pool', 'roi_align', 'prior_box', 'anchor_generator',
'generate_proposals', 'iou_similarity', 'box_coder', 'yolo_box',
'multiclass_nms', 'distribute_fpn_proposals', 'collect_fpn_proposals',
'matrix_nms', 'batch_norm'
'roi_pool',
'roi_align',
'prior_box',
'anchor_generator',
'generate_proposals',
'iou_similarity',
'box_coder',
'yolo_box',
'multiclass_nms',
'distribute_fpn_proposals',
'collect_fpn_proposals',
'matrix_nms',
'batch_norm',
]
......@@ -51,6 +60,7 @@ def batch_norm(ch, norm_type='bn', name=None):
name=bn_name + '.offset', regularizer=L2Decay(0.)))
@paddle.jit.not_to_static
def roi_pool(input,
rois,
output_size,
......@@ -123,6 +133,7 @@ def roi_pool(input,
"pooled_width", pooled_width, "spatial_scale", spatial_scale)
return pool_out, argmaxes
else:
check_variable_and_dtype(input, 'input', ['float32'], 'roi_pool')
check_variable_and_dtype(rois, 'rois', ['float32'], 'roi_pool')
helper = LayerHelper('roi_pool', **locals())
......@@ -149,6 +160,7 @@ def roi_pool(input,
return pool_out, argmaxes
@paddle.jit.not_to_static
def roi_align(input,
rois,
output_size,
......@@ -228,9 +240,11 @@ def roi_align(input,
"sampling_ratio", sampling_ratio)
return align_out
else:
check_variable_and_dtype(input, 'input', ['float32', 'float64'],
'roi_align')
check_variable_and_dtype(rois, 'rois', ['float32', 'float64'], 'roi_align')
check_variable_and_dtype(rois, 'rois', ['float32', 'float64'],
'roi_align')
helper = LayerHelper('roi_align', **locals())
dtype = helper.input_dtype()
align_out = helper.create_variable_for_type_inference(dtype)
......@@ -253,6 +267,7 @@ def roi_align(input,
return align_out
@paddle.jit.not_to_static
def iou_similarity(x, y, box_normalized=True, name=None):
"""
Computes intersection-over-union (IOU) between two box lists.
......@@ -303,7 +318,7 @@ def iou_similarity(x, y, box_normalized=True, name=None):
if in_dygraph_mode():
out = core.ops.iou_similarity(x, y, 'box_normalized', box_normalized)
return out
else:
helper = LayerHelper("iou_similarity", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
......@@ -316,6 +331,7 @@ def iou_similarity(x, y, box_normalized=True, name=None):
return out
@paddle.jit.not_to_static
def collect_fpn_proposals(multi_rois,
multi_scores,
min_level,
......@@ -398,7 +414,9 @@ def collect_fpn_proposals(multi_rois,
attrs = ('post_nms_topN', post_nms_top_n)
output_rois, rois_num = core.ops.collect_fpn_proposals(
input_rois, input_scores, rois_num_per_level, *attrs)
return output_rois, rois_num
else:
helper = LayerHelper('collect_fpn_proposals', **locals())
dtype = helper.input_dtype('multi_rois')
check_dtype(dtype, 'multi_rois', ['float32', 'float64'],
......@@ -421,11 +439,10 @@ def collect_fpn_proposals(multi_rois,
inputs=inputs,
outputs=outputs,
attrs={'post_nms_topN': post_nms_top_n})
if rois_num_per_level is not None:
return output_rois, rois_num
return output_rois
@paddle.jit.not_to_static
def distribute_fpn_proposals(fpn_rois,
min_level,
max_level,
......@@ -510,12 +527,14 @@ def distribute_fpn_proposals(fpn_rois,
fpn_rois, rois_num, num_lvl, num_lvl, *attrs)
return multi_rois, restore_ind, rois_num_per_level
else:
check_variable_and_dtype(fpn_rois, 'fpn_rois', ['float32', 'float64'],
'distribute_fpn_proposals')
helper = LayerHelper('distribute_fpn_proposals', **locals())
dtype = helper.input_dtype('fpn_rois')
multi_rois = [
helper.create_variable_for_type_inference(dtype) for i in range(num_lvl)
helper.create_variable_for_type_inference(dtype)
for i in range(num_lvl)
]
restore_ind = helper.create_variable_for_type_inference(dtype='int32')
......@@ -544,11 +563,10 @@ def distribute_fpn_proposals(fpn_rois,
'refer_level': refer_level,
'refer_scale': refer_scale
})
if rois_num is not None:
return multi_rois, restore_ind, rois_num_per_level
return multi_rois, restore_ind
@paddle.jit.not_to_static
def yolo_box(
x,
origin_shape,
......@@ -685,6 +703,7 @@ def yolo_box(
return boxes, scores
@paddle.jit.not_to_static
def prior_box(input,
image,
min_sizes,
......@@ -798,7 +817,7 @@ def prior_box(input,
attrs = tuple(attrs)
box, var = core.ops.prior_box(input, image, *attrs)
return box, var
else:
attrs = {
'min_sizes': min_sizes,
'aspect_ratios': aspect_ratios,
......@@ -828,6 +847,7 @@ def prior_box(input,
return box, var
@paddle.jit.not_to_static
def anchor_generator(input,
anchor_sizes=None,
aspect_ratios=None,
......@@ -916,6 +936,7 @@ def anchor_generator(input,
anchor, var = core.ops.anchor_generator(input, *attrs)
return anchor, var
else:
attrs = {
'anchor_sizes': anchor_sizes,
'aspect_ratios': aspect_ratios,
......@@ -937,6 +958,7 @@ def anchor_generator(input,
return anchor, var
@paddle.jit.not_to_static
def multiclass_nms(bboxes,
scores,
score_threshold,
......@@ -1091,6 +1113,7 @@ def multiclass_nms(bboxes,
return output, nms_rois_num, index
@paddle.jit.not_to_static
def matrix_nms(bboxes,
scores,
score_threshold,
......@@ -1196,7 +1219,7 @@ def matrix_nms(bboxes,
if return_rois_num:
return out, rois_num
return out
else:
helper = LayerHelper('matrix_nms', **locals())
output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
index = helper.create_variable_for_type_inference(dtype='int')
......@@ -1231,6 +1254,7 @@ def matrix_nms(bboxes,
return output
@paddle.jit.not_to_static
def box_coder(prior_box,
prior_box_var,
target_box,
......@@ -1357,7 +1381,7 @@ def box_coder(prior_box,
raise TypeError(
"Input variance of box_coder must be Variable or list")
return output_box
else:
helper = LayerHelper("box_coder", **locals())
output_box = helper.create_variable_for_type_inference(
......@@ -1374,7 +1398,8 @@ def box_coder(prior_box,
elif isinstance(prior_box_var, list):
attrs['variance'] = prior_box_var
else:
raise TypeError("Input variance of box_coder must be Variable or list")
raise TypeError(
"Input variance of box_coder must be Variable or list")
helper.append_op(
type="box_coder",
inputs=inputs,
......@@ -1383,6 +1408,7 @@ def box_coder(prior_box,
return output_box
@paddle.jit.not_to_static
def generate_proposals(scores,
bbox_deltas,
im_shape,
......@@ -1472,6 +1498,7 @@ def generate_proposals(scores,
scores, bbox_deltas, im_shape, anchors, variances, *attrs)
return rpn_rois, rpn_roi_probs, rpn_rois_num
else:
helper = LayerHelper('generate_proposals_v2', **locals())
check_variable_and_dtype(scores, 'scores', ['float32'],
......@@ -1494,7 +1521,8 @@ def generate_proposals(scores,
'RpnRoiProbs': rpn_roi_probs,
}
if return_rois_num:
rpn_rois_num = helper.create_variable_for_type_inference(dtype='int32')
rpn_rois_num = helper.create_variable_for_type_inference(
dtype='int32')
rpn_rois_num.stop_gradient = True
outputs['RpnRoisNum'] = rpn_rois_num
......@@ -1518,10 +1546,7 @@ def generate_proposals(scores,
rpn_rois.stop_gradient = True
rpn_roi_probs.stop_gradient = True
if return_rois_num:
return rpn_rois, rpn_roi_probs, rpn_rois_num
else:
return rpn_rois, rpn_roi_probs
def sigmoid_cross_entropy_with_logits(input,
......
......@@ -73,7 +73,8 @@ def bbox_post_process(bboxes,
@jit
def mask_post_process(bboxes,
def mask_post_process(bbox,
bbox_nums,
masks,
im_shape,
scale_factor,
......@@ -81,7 +82,6 @@ def mask_post_process(bboxes,
binary_thresh=0.5):
if masks.shape[0] == 0:
return masks
bbox, bbox_nums = bboxes
M = resolution
scale = (M + 2.0) / M
boxes = bbox[:, 2:]
......@@ -98,7 +98,6 @@ def mask_post_process(bboxes,
boxes_n = boxes[st_num:end_num]
labels_n = labels[st_num:end_num]
masks_n = masks[st_num:end_num]
im_h = int(round(im_shape[i][0] / scale_factor[i]))
im_w = int(round(im_shape[i][1] / scale_factor[i]))
boxes_n = expand_bbox(boxes_n, scale)
......
......@@ -5,7 +5,7 @@ from __future__ import print_function
import os
import sys
import json
from ppdet.py_op.post_process import get_det_res, get_seg_res
from ppdet.py_op.post_process import get_det_res, get_seg_res, mask_post_process
import logging
logger = logging.getLogger(__name__)
......@@ -33,7 +33,8 @@ def json_eval_results(metric, json_directory=None, dataset=None):
logger.info("{} not exists!".format(v_json))
def get_infer_results(outs_res, eval_type, catid):
def get_infer_results(outs_res, eval_type, catid, im_info,
mask_resolution=None):
"""
Get result at the stage of inference.
The output format is dictionary containing bbox or mask result.
......@@ -49,16 +50,25 @@ def get_infer_results(outs_res, eval_type, catid):
if 'bbox' in eval_type:
box_res = []
for outs in outs_res:
box_res += get_det_res(outs['bbox'], outs['bbox_num'],
outs['im_id'], catid)
for i, outs in enumerate(outs_res):
im_ids = im_info[i][2]
box_res += get_det_res(outs['bbox'].numpy(),
outs['bbox_num'].numpy(), im_ids, catid)
infer_res['bbox'] = box_res
if 'mask' in eval_type:
seg_res = []
for outs in outs_res:
seg_res += get_seg_res(outs['mask'], outs['bbox_num'],
outs['im_id'], catid)
# mask post process
for i, outs in enumerate(outs_res):
im_shape = im_info[i][0]
scale_factor = im_info[i][1]
im_ids = im_info[i][2]
mask = mask_post_process(outs['bbox'].numpy(),
outs['bbox_num'].numpy(),
outs['mask'].numpy(), im_shape,
scale_factor[0], mask_resolution)
seg_res += get_seg_res(mask, outs['bbox_num'].numpy(), im_ids,
catid)
infer_res['mask'] = seg_res
return infer_res
......
......@@ -75,12 +75,18 @@ def run(FLAGS, cfg, place):
outs_res = []
start_time = time.time()
sample_num = 0
im_info = []
for iter_id, data in enumerate(eval_loader):
# forward
fields = cfg['EvalReader']['inputs_def']['fields']
model.eval()
outs = model(data, cfg['EvalReader']['inputs_def']['fields'], 'infer')
outs = model(data=data, input_def=fields, mode='infer')
outs_res.append(outs)
im_info.append([
data[fields.index('im_shape')].numpy(),
data[fields.index('scale_factor')].numpy(),
data[fields.index('im_id')].numpy()
])
# log
sample_num += len(data)
if iter_id % 100 == 0:
......@@ -102,7 +108,15 @@ def run(FLAGS, cfg, place):
clsid2catid, catid2name = get_category_info(anno_file, with_background,
use_default_label)
infer_res = get_infer_results(outs_res, eval_type, clsid2catid)
mask_resolution = None
if cfg['MaskPostProcess']['mask_resolution'] is not None:
mask_resolution = int(cfg['MaskPostProcess']['mask_resolution'])
infer_res = get_infer_results(
outs_res,
eval_type,
clsid2catid,
im_info,
mask_resolution=mask_resolution)
eval_results(infer_res, cfg.metric, anno_file)
......
......@@ -53,63 +53,43 @@ def parse_args():
return args
def dygraph_to_static(model, save_dir, cfg):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
inputs_def = cfg['TestReader']['inputs_def']
image_shape = inputs_def.get('image_shape')
if image_shape is None:
image_shape = [3, None, None]
# Save infer cfg
dump_infer_config(cfg, os.path.join(save_dir, 'infer_cfg.yml'), image_shape)
input_spec = [{
"image": InputSpec(
shape=[None] + image_shape, name='image'),
"im_shape": InputSpec(
shape=[None, 2], name='im_shape'),
"scale_factor": InputSpec(
shape=[None, 2], name='scale_factor')
}]
export_model = to_static(model, input_spec=input_spec)
# save Model
paddle.jit.save(export_model, os.path.join(save_dir, 'model'))
def run(FLAGS, cfg):
# Model
main_arch = cfg.architecture
model = create(cfg.architecture)
inputs_def = cfg['TestReader']['inputs_def']
assert 'image_shape' in inputs_def, 'image_shape must be specified.'
image_shape = inputs_def.get('image_shape')
assert not None in image_shape, 'image_shape should not contain None'
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(FLAGS.output_dir, cfg_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
image_shape = dump_infer_config(cfg,
os.path.join(save_dir, 'infer_cfg.yml'),
image_shape)
class ExportModel(nn.Layer):
def __init__(self, model):
super(ExportModel, self).__init__()
self.model = model
@to_static(input_spec=[
{
'image': InputSpec(
shape=[None] + image_shape, name='image')
},
{
'im_shape': InputSpec(
shape=[None, 2], name='im_shape')
},
{
'scale_factor': InputSpec(
shape=[None, 2], name='scale_factor')
},
])
def forward(self, image, im_shape, scale_factor):
inputs = {}
inputs_tensor = [image, im_shape, scale_factor]
for t in inputs_tensor:
inputs.update(t)
outs = self.model.get_export_model(inputs)
return outs
export_model = ExportModel(model)
# debug for dy2static, remove later
#paddle.jit.set_code_level()
# Init Model
load_weight(export_model.model, cfg.weights)
export_model.eval()
load_weight(model, cfg.weights)
# export config and model
paddle.jit.save(export_model, os.path.join(save_dir, 'model'))
dygraph_to_static(model, save_dir, cfg)
logger.info('Export model to {}'.format(save_dir))
......
......@@ -109,7 +109,8 @@ def dump_infer_config(config, path, image_shape):
os._exit(0)
if 'Mask' in config['architecture']:
infer_cfg['mask_resolution'] = config['Mask']['mask_resolution']
infer_cfg['mask_resolution'] = config['MaskPostProcess'][
'mask_resolution']
infer_cfg['with_background'], infer_cfg['Preprocess'], infer_cfg[
'label_list'], image_shape = parse_reader(
config['TestReader'], config['TestDataset'], config['metric'],
......
......@@ -147,15 +147,32 @@ def run(FLAGS, cfg, place):
# Run Infer
for iter_id, data in enumerate(test_loader):
# forward
fields = cfg.TestReader['inputs_def']['fields']
model.eval()
outs = model(data, cfg.TestReader['inputs_def']['fields'], 'infer')
batch_res = get_infer_results([outs], outs.keys(), clsid2catid)
outs = model(
data=data,
input_def=cfg.TestReader['inputs_def']['fields'],
mode='infer')
im_info = [[
data[fields.index('im_shape')].numpy(),
data[fields.index('scale_factor')].numpy(),
data[fields.index('im_id')].numpy()
]]
im_ids = data[fields.index('im_id')].numpy()
mask_resolution = None
if cfg['MaskPostProcess']['mask_resolution'] is not None:
mask_resolution = int(cfg['MaskPostProcess']['mask_resolution'])
batch_res = get_infer_results(
[outs],
outs.keys(),
clsid2catid,
im_info,
mask_resolution=mask_resolution)
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):
......
......@@ -35,6 +35,7 @@ from ppdet.utils.stats import TrainingStats
from ppdet.utils.check import check_gpu, check_version, check_config
from ppdet.utils.cli import ArgsParser
from ppdet.utils.checkpoint import load_weight, load_pretrain_weight, save_model
from export_model import dygraph_to_static
from paddle.distributed import ParallelEnv
import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
......@@ -149,6 +150,8 @@ def run(FLAGS, cfg, place):
model = paddle.DataParallel(model)
fields = train_loader.collate_fn.output_fields
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(cfg.save_dir, cfg_name)
# Run Train
time_stat = deque(maxlen=cfg.log_iter)
start_time = time.time()
......@@ -167,7 +170,7 @@ def run(FLAGS, cfg, place):
# Model Forward
model.train()
outputs = model(data, fields, 'train')
outputs = model(data=data, input_def=fields, mode='train')
# Model Backward
loss = outputs['loss']
......@@ -193,11 +196,12 @@ def run(FLAGS, cfg, place):
if ParallelEnv().local_rank == 0 and (
cur_eid % cfg.snapshot_epoch == 0 or
(cur_eid + 1) == int(cfg.epoch)):
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_name = str(cur_eid) if cur_eid + 1 != int(
cfg.epoch) else "model_final"
save_dir = os.path.join(cfg.save_dir, cfg_name)
save_model(model, optimizer, save_dir, save_name, cur_eid + 1)
# TODO(guanghua): dygraph model to static model
# if ParallelEnv().local_rank == 0 and (cur_eid + 1) == int(cfg.epoch)):
# dygraph_to_static(model, os.path.join(save_dir, 'static_model_final'), cfg)
def main():
......
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