forked from PulseFocusPlatform/PulseFocusPlatform
241 lines
10 KiB
Python
241 lines
10 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle.fluid as fluid
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from paddle.fluid.layer_helper import LayerHelper
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import paddle.fluid.layers as layers
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from paddle.fluid.layers import (tensor, iou_similarity, bipartite_match,
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target_assign, box_coder)
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from ppdet.core.workspace import register, serializable
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__all__ = ['SSDWithLmkLoss']
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@register
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@serializable
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class SSDWithLmkLoss(object):
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"""
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ssd_with_lmk_loss function.
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Args:
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background_label (int): The index of background label, 0 by default.
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overlap_threshold (float): If match_type is `per_prediction`,
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use `overlap_threshold` to determine the extra matching bboxes
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when finding matched boxes. 0.5 by default.
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neg_pos_ratio (float): The ratio of the negative boxes to the positive
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boxes, used only when mining_type is `max_negative`, 3.0 by default.
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neg_overlap (float): The negative overlap upper bound for the unmatched
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predictions. Use only when mining_type is `max_negative`, 0.5 by default.
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loc_loss_weight (float): Weight for localization loss, 1.0 by default.
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conf_loss_weight (float): Weight for confidence loss, 1.0 by default.
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match_type (str): The type of matching method during training, should be
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`bipartite` or `per_prediction`, `per_prediction` by default.
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normalize (bool): Whether to normalize the loss by the total number of
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output locations, True by default.
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"""
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def __init__(self,
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background_label=0,
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overlap_threshold=0.5,
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neg_pos_ratio=3.0,
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neg_overlap=0.5,
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loc_loss_weight=1.0,
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conf_loss_weight=1.0,
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match_type='per_prediction',
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normalize=True):
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super(SSDWithLmkLoss, self).__init__()
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self.background_label = background_label
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self.overlap_threshold = overlap_threshold
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self.neg_pos_ratio = neg_pos_ratio
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self.neg_overlap = neg_overlap
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self.loc_loss_weight = loc_loss_weight
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self.conf_loss_weight = conf_loss_weight
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self.match_type = match_type
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self.normalize = normalize
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def __call__(self,
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location,
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confidence,
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gt_box,
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gt_label,
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landmark_predict,
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lmk_label,
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lmk_ignore_flag,
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prior_box,
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prior_box_var=None):
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def _reshape_to_2d(var):
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return layers.flatten(x=var, axis=2)
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helper = LayerHelper('ssd_loss') #, **locals())
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# Only support mining_type == 'max_negative' now.
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mining_type = 'max_negative'
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# The max `sample_size` of negative box, used only
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# when mining_type is `hard_example`.
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sample_size = None
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num, num_prior, num_class = confidence.shape
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conf_shape = layers.shape(confidence)
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# 1. Find matched boundding box by prior box.
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# 1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
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iou = iou_similarity(x=gt_box, y=prior_box)
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# 1.2 Compute matched boundding box by bipartite matching algorithm.
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matched_indices, matched_dist = bipartite_match(iou, self.match_type,
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self.overlap_threshold)
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# 2. Compute confidence for mining hard examples
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# 2.1. Get the target label based on matched indices
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gt_label = layers.reshape(
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x=gt_label, shape=(len(gt_label.shape) - 1) * (0, ) + (-1, 1))
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gt_label.stop_gradient = True
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target_label, _ = target_assign(
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gt_label, matched_indices, mismatch_value=self.background_label)
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# 2.2. Compute confidence loss.
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# Reshape confidence to 2D tensor.
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confidence = _reshape_to_2d(confidence)
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target_label = tensor.cast(x=target_label, dtype='int64')
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target_label = _reshape_to_2d(target_label)
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target_label.stop_gradient = True
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conf_loss = layers.softmax_with_cross_entropy(confidence, target_label)
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# 3. Mining hard examples
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actual_shape = layers.slice(conf_shape, axes=[0], starts=[0], ends=[2])
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actual_shape.stop_gradient = True
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conf_loss = layers.reshape(
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x=conf_loss, shape=(-1, 0), actual_shape=actual_shape)
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conf_loss.stop_gradient = True
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neg_indices = helper.create_variable_for_type_inference(dtype='int32')
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updated_matched_indices = helper.create_variable_for_type_inference(
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dtype=matched_indices.dtype)
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helper.append_op(
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type='mine_hard_examples',
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inputs={
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'ClsLoss': conf_loss,
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'LocLoss': None,
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'MatchIndices': matched_indices,
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'MatchDist': matched_dist,
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},
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outputs={
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'NegIndices': neg_indices,
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'UpdatedMatchIndices': updated_matched_indices
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},
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attrs={
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'neg_pos_ratio': self.neg_pos_ratio,
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'neg_dist_threshold': self.neg_overlap,
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'mining_type': mining_type,
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'sample_size': sample_size,
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})
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# 4. Assign classification and regression targets
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# 4.1. Encoded bbox according to the prior boxes.
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encoded_bbox = box_coder(
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prior_box=prior_box,
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prior_box_var=prior_box_var,
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target_box=gt_box,
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code_type='encode_center_size')
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# 4.2. Assign regression targets
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target_bbox, target_loc_weight = target_assign(
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encoded_bbox,
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updated_matched_indices,
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mismatch_value=self.background_label)
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# 4.3. Assign classification targets
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target_label, target_conf_weight = target_assign(
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gt_label,
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updated_matched_indices,
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negative_indices=neg_indices,
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mismatch_value=self.background_label)
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target_loc_weight = target_loc_weight * target_label
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encoded_lmk_label = self.decode_lmk(lmk_label, prior_box, prior_box_var)
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target_lmk, target_lmk_weight = target_assign(
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encoded_lmk_label,
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updated_matched_indices,
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mismatch_value=self.background_label)
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lmk_ignore_flag = layers.reshape(
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x=lmk_ignore_flag,
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shape=(len(lmk_ignore_flag.shape) - 1) * (0, ) + (-1, 1))
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target_ignore, nouse = target_assign(
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lmk_ignore_flag,
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updated_matched_indices,
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mismatch_value=self.background_label)
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target_lmk_weight = target_lmk_weight * target_ignore
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landmark_predict = _reshape_to_2d(landmark_predict)
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target_lmk = _reshape_to_2d(target_lmk)
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target_lmk_weight = _reshape_to_2d(target_lmk_weight)
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lmk_loss = layers.smooth_l1(landmark_predict, target_lmk)
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lmk_loss = lmk_loss * target_lmk_weight
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target_lmk.stop_gradient = True
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target_lmk_weight.stop_gradient = True
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target_ignore.stop_gradient = True
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nouse.stop_gradient = True
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# 5. Compute loss.
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# 5.1 Compute confidence loss.
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target_label = _reshape_to_2d(target_label)
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target_label = tensor.cast(x=target_label, dtype='int64')
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conf_loss = layers.softmax_with_cross_entropy(confidence, target_label)
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target_conf_weight = _reshape_to_2d(target_conf_weight)
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conf_loss = conf_loss * target_conf_weight
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# the target_label and target_conf_weight do not have gradient.
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target_label.stop_gradient = True
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target_conf_weight.stop_gradient = True
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# 5.2 Compute regression loss.
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location = _reshape_to_2d(location)
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target_bbox = _reshape_to_2d(target_bbox)
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loc_loss = layers.smooth_l1(location, target_bbox)
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target_loc_weight = _reshape_to_2d(target_loc_weight)
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loc_loss = loc_loss * target_loc_weight
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# the target_bbox and target_loc_weight do not have gradient.
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target_bbox.stop_gradient = True
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target_loc_weight.stop_gradient = True
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# 5.3 Compute overall weighted loss.
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loss = self.conf_loss_weight * conf_loss + self.loc_loss_weight * loc_loss + 0.4 * lmk_loss
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# reshape to [N, Np], N is the batch size and Np is the prior box number.
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loss = layers.reshape(x=loss, shape=(-1, 0), actual_shape=actual_shape)
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loss = layers.reduce_sum(loss, dim=1, keep_dim=True)
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if self.normalize:
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normalizer = layers.reduce_sum(target_loc_weight) + 1
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loss = loss / normalizer
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return loss
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def decode_lmk(self, lmk_label, prior_box, prior_box_var):
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label0, label1, label2, label3, label4 = fluid.layers.split(
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lmk_label, num_or_sections=5, dim=1)
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lmk_labels_list = [label0, label1, label2, label3, label4]
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encoded_lmk_list = []
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for label in lmk_labels_list:
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concat_label = fluid.layers.concat([label, label], axis=1)
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encoded_label = box_coder(
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prior_box=prior_box,
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prior_box_var=prior_box_var,
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target_box=concat_label,
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code_type='encode_center_size')
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encoded_lmk_label, _ = fluid.layers.split(
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encoded_label, num_or_sections=2, dim=2)
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encoded_lmk_list.append(encoded_lmk_label)
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encoded_lmk_concat = fluid.layers.concat(
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[
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encoded_lmk_list[0], encoded_lmk_list[1], encoded_lmk_list[2],
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encoded_lmk_list[3], encoded_lmk_list[4]
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],
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axis=2)
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return encoded_lmk_concat
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