forked from PulseFocusPlatform/PulseFocusPlatform
111 lines
4.1 KiB
Python
111 lines
4.1 KiB
Python
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# Copyright (c) 2021 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
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import paddle.nn as nn
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from ppdet.core.workspace import register
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from ..layers import AnchorGeneratorSSD
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@register
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class FaceHead(nn.Layer):
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"""
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Head block for Face detection network
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Args:
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num_classes (int): Number of output classes.
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in_channels (int): Number of input channels.
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anchor_generator(object): instance of anchor genertor method.
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kernel_size (int): kernel size of Conv2D in FaceHead.
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padding (int): padding of Conv2D in FaceHead.
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conv_decay (float): norm_decay (float): weight decay for conv layer weights.
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loss (object): loss of face detection model.
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"""
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__shared__ = ['num_classes']
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__inject__ = ['anchor_generator', 'loss']
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def __init__(self,
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num_classes=80,
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in_channels=[96, 96],
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anchor_generator=AnchorGeneratorSSD().__dict__,
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kernel_size=3,
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padding=1,
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conv_decay=0.,
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loss='SSDLoss'):
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super(FaceHead, self).__init__()
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# add background class
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self.num_classes = num_classes + 1
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self.in_channels = in_channels
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self.anchor_generator = anchor_generator
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self.loss = loss
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if isinstance(anchor_generator, dict):
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self.anchor_generator = AnchorGeneratorSSD(**anchor_generator)
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self.num_priors = self.anchor_generator.num_priors
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self.box_convs = []
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self.score_convs = []
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for i, num_prior in enumerate(self.num_priors):
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box_conv_name = "boxes{}".format(i)
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box_conv = self.add_sublayer(
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box_conv_name,
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nn.Conv2D(
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in_channels=self.in_channels[i],
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out_channels=num_prior * 4,
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kernel_size=kernel_size,
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padding=padding))
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self.box_convs.append(box_conv)
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score_conv_name = "scores{}".format(i)
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score_conv = self.add_sublayer(
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score_conv_name,
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nn.Conv2D(
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in_channels=self.in_channels[i],
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out_channels=num_prior * self.num_classes,
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kernel_size=kernel_size,
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padding=padding))
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self.score_convs.append(score_conv)
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@classmethod
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def from_config(cls, cfg, input_shape):
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return {'in_channels': [i.channels for i in input_shape], }
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def forward(self, feats, image, gt_bbox=None, gt_class=None):
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box_preds = []
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cls_scores = []
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prior_boxes = []
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for feat, box_conv, score_conv in zip(feats, self.box_convs,
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self.score_convs):
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box_pred = box_conv(feat)
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box_pred = paddle.transpose(box_pred, [0, 2, 3, 1])
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box_pred = paddle.reshape(box_pred, [0, -1, 4])
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box_preds.append(box_pred)
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cls_score = score_conv(feat)
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cls_score = paddle.transpose(cls_score, [0, 2, 3, 1])
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cls_score = paddle.reshape(cls_score, [0, -1, self.num_classes])
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cls_scores.append(cls_score)
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prior_boxes = self.anchor_generator(feats, image)
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if self.training:
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return self.get_loss(box_preds, cls_scores, gt_bbox, gt_class,
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prior_boxes)
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else:
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return (box_preds, cls_scores), prior_boxes
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def get_loss(self, boxes, scores, gt_bbox, gt_class, prior_boxes):
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return self.loss(boxes, scores, gt_bbox, gt_class, prior_boxes)
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