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110 lines
4.1 KiB
110 lines
4.1 KiB
# Copyright (c) 2022 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 paddlers.models.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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