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更换文档检测模型

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liuyebo
2024-08-27 14:42:45 +08:00
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commit 1514e09c40
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paddle_detection/ppdet/modeling/heads/retina_head.py Normal file
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import ParamAttr
from paddle.nn.initializer import Normal, Constant
from ppdet.modeling.bbox_utils import bbox2delta, delta2bbox
from ppdet.modeling.heads.fcos_head import FCOSFeat
from ppdet.core.workspace import register
__all__ = ['RetinaHead']
@register
class RetinaFeat(FCOSFeat):
"""We use FCOSFeat to construct conv layers in RetinaNet.
We rename FCOSFeat to RetinaFeat to avoid confusion.
"""
pass
@register
class RetinaHead(nn.Layer):
"""Used in RetinaNet proposed in paper https://arxiv.org/pdf/1708.02002.pdf
"""
__shared__ = ['num_classes']
__inject__ = [
'conv_feat', 'anchor_generator', 'bbox_assigner', 'loss_class',
'loss_bbox', 'nms'
]
def __init__(self,
num_classes=80,
conv_feat='RetinaFeat',
anchor_generator='RetinaAnchorGenerator',
bbox_assigner='MaxIoUAssigner',
loss_class='FocalLoss',
loss_bbox='SmoothL1Loss',
nms='MultiClassNMS',
prior_prob=0.01,
nms_pre=1000,
weights=[1., 1., 1., 1.]):
super(RetinaHead, self).__init__()
self.num_classes = num_classes
self.conv_feat = conv_feat
self.anchor_generator = anchor_generator
self.bbox_assigner = bbox_assigner
self.loss_class = loss_class
self.loss_bbox = loss_bbox
self.nms = nms
self.nms_pre = nms_pre
self.weights = weights
bias_init_value = -math.log((1 - prior_prob) / prior_prob)
num_anchors = self.anchor_generator.num_anchors
self.retina_cls = nn.Conv2D(
in_channels=self.conv_feat.feat_out,
out_channels=self.num_classes * num_anchors,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(initializer=Normal(
mean=0.0, std=0.01)),
bias_attr=ParamAttr(initializer=Constant(value=bias_init_value)))
self.retina_reg = nn.Conv2D(
in_channels=self.conv_feat.feat_out,
out_channels=4 * num_anchors,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(initializer=Normal(
mean=0.0, std=0.01)),
bias_attr=ParamAttr(initializer=Constant(value=0)))
def forward(self, neck_feats, targets=None):
cls_logits_list = []
bboxes_reg_list = []
for neck_feat in neck_feats:
conv_cls_feat, conv_reg_feat = self.conv_feat(neck_feat)
cls_logits = self.retina_cls(conv_cls_feat)
bbox_reg = self.retina_reg(conv_reg_feat)
cls_logits_list.append(cls_logits)
bboxes_reg_list.append(bbox_reg)
if self.training:
return self.get_loss([cls_logits_list, bboxes_reg_list], targets)
else:
return [cls_logits_list, bboxes_reg_list]
def get_loss(self, head_outputs, targets):
"""Here we calculate loss for a batch of images.
We assign anchors to gts in each image and gather all the assigned
postive and negative samples. Then loss is calculated on the gathered
samples.
"""
cls_logits_list, bboxes_reg_list = head_outputs
anchors = self.anchor_generator(cls_logits_list)
anchors = paddle.concat(anchors)
# matches: contain gt_inds
# match_labels: -1(ignore), 0(neg) or 1(pos)
matches_list, match_labels_list = [], []
# assign anchors to gts, no sampling is involved
for gt_bbox in targets['gt_bbox']:
matches, match_labels = self.bbox_assigner(anchors, gt_bbox)
matches_list.append(matches)
match_labels_list.append(match_labels)
# reshape network outputs
cls_logits = [
_.transpose([0, 2, 3, 1]).reshape([0, -1, self.num_classes])
for _ in cls_logits_list
]
bboxes_reg = [
_.transpose([0, 2, 3, 1]).reshape([0, -1, 4])
for _ in bboxes_reg_list
]
cls_logits = paddle.concat(cls_logits, axis=1)
bboxes_reg = paddle.concat(bboxes_reg, axis=1)
cls_pred_list, cls_tar_list = [], []
reg_pred_list, reg_tar_list = [], []
# find and gather preds and targets in each image
for matches, match_labels, cls_logit, bbox_reg, gt_bbox, gt_class in \
zip(matches_list, match_labels_list, cls_logits, bboxes_reg,
targets['gt_bbox'], targets['gt_class']):
pos_mask = (match_labels == 1)
neg_mask = (match_labels == 0)
chosen_mask = paddle.logical_or(pos_mask, neg_mask)
gt_class = gt_class.reshape([-1])
bg_class = paddle.to_tensor(
[self.num_classes], dtype=gt_class.dtype)
# a trick to assign num_classes to negative targets
gt_class = paddle.concat([gt_class, bg_class], axis=-1)
matches = paddle.where(neg_mask,
paddle.full_like(matches, gt_class.size - 1),
matches)
cls_pred = cls_logit[chosen_mask]
cls_tar = gt_class[matches[chosen_mask]]
reg_pred = bbox_reg[pos_mask].reshape([-1, 4])
reg_tar = gt_bbox[matches[pos_mask]].reshape([-1, 4])
reg_tar = bbox2delta(anchors[pos_mask], reg_tar, self.weights)
cls_pred_list.append(cls_pred)
cls_tar_list.append(cls_tar)
reg_pred_list.append(reg_pred)
reg_tar_list.append(reg_tar)
cls_pred = paddle.concat(cls_pred_list)
cls_tar = paddle.concat(cls_tar_list)
reg_pred = paddle.concat(reg_pred_list)
reg_tar = paddle.concat(reg_tar_list)
avg_factor = max(1.0, reg_pred.shape[0])
cls_loss = self.loss_class(
cls_pred, cls_tar, reduction='sum') / avg_factor
if reg_pred.shape[0] == 0:
reg_loss = paddle.zeros([1])
reg_loss.stop_gradient = False
else:
reg_loss = self.loss_bbox(
reg_pred, reg_tar, reduction='sum') / avg_factor
loss = cls_loss + reg_loss
out_dict = {
'loss_cls': cls_loss,
'loss_reg': reg_loss,
'loss': loss,
}
return out_dict
def get_bboxes_single(self,
anchors,
cls_scores_list,
bbox_preds_list,
im_shape,
scale_factor,
rescale=True):
assert len(cls_scores_list) == len(bbox_preds_list)
mlvl_bboxes = []
mlvl_scores = []
for anchor, cls_score, bbox_pred in zip(anchors, cls_scores_list,
bbox_preds_list):
cls_score = cls_score.reshape([-1, self.num_classes])
bbox_pred = bbox_pred.reshape([-1, 4])
if self.nms_pre is not None and cls_score.shape[0] > self.nms_pre:
max_score = cls_score.max(axis=1)
_, topk_inds = max_score.topk(self.nms_pre)
bbox_pred = bbox_pred.gather(topk_inds)
anchor = anchor.gather(topk_inds)
cls_score = cls_score.gather(topk_inds)
bbox_pred = delta2bbox(bbox_pred, anchor, self.weights).squeeze()
mlvl_bboxes.append(bbox_pred)
mlvl_scores.append(F.sigmoid(cls_score))
mlvl_bboxes = paddle.concat(mlvl_bboxes)
mlvl_bboxes = paddle.squeeze(mlvl_bboxes)
if rescale:
mlvl_bboxes = mlvl_bboxes / paddle.concat(
[scale_factor[::-1], scale_factor[::-1]])
mlvl_scores = paddle.concat(mlvl_scores)
mlvl_scores = mlvl_scores.transpose([1, 0])
return mlvl_bboxes, mlvl_scores
def decode(self, anchors, cls_logits, bboxes_reg, im_shape, scale_factor):
batch_bboxes = []
batch_scores = []
for img_id in range(cls_logits[0].shape[0]):
num_lvls = len(cls_logits)
cls_scores_list = [cls_logits[i][img_id] for i in range(num_lvls)]
bbox_preds_list = [bboxes_reg[i][img_id] for i in range(num_lvls)]
bboxes, scores = self.get_bboxes_single(
anchors, cls_scores_list, bbox_preds_list, im_shape[img_id],
scale_factor[img_id])
batch_bboxes.append(bboxes)
batch_scores.append(scores)
batch_bboxes = paddle.stack(batch_bboxes, axis=0)
batch_scores = paddle.stack(batch_scores, axis=0)
return batch_bboxes, batch_scores
def post_process(self, head_outputs, im_shape, scale_factor):
cls_logits_list, bboxes_reg_list = head_outputs
anchors = self.anchor_generator(cls_logits_list)
cls_logits = [_.transpose([0, 2, 3, 1]) for _ in cls_logits_list]
bboxes_reg = [_.transpose([0, 2, 3, 1]) for _ in bboxes_reg_list]
bboxes, scores = self.decode(anchors, cls_logits, bboxes_reg, im_shape,
scale_factor)
bbox_pred, bbox_num, nms_keep_idx = self.nms(bboxes, scores)
return bbox_pred, bbox_num, nms_keep_idx
def get_scores_single(self, cls_scores_list):
mlvl_logits = []
for cls_score in cls_scores_list:
cls_score = cls_score.reshape([-1, self.num_classes])
if self.nms_pre is not None and cls_score.shape[0] > self.nms_pre:
max_score = cls_score.max(axis=1)
_, topk_inds = max_score.topk(self.nms_pre)
cls_score = cls_score.gather(topk_inds)
mlvl_logits.append(cls_score)
mlvl_logits = paddle.concat(mlvl_logits)
mlvl_logits = mlvl_logits.transpose([1, 0])
return mlvl_logits
def decode_cls_logits(self, cls_logits_list):
cls_logits = [_.transpose([0, 2, 3, 1]) for _ in cls_logits_list]
batch_logits = []
for img_id in range(cls_logits[0].shape[0]):
num_lvls = len(cls_logits)
cls_scores_list = [cls_logits[i][img_id] for i in range(num_lvls)]
logits = self.get_scores_single(cls_scores_list)
batch_logits.append(logits)
batch_logits = paddle.stack(batch_logits, axis=0)
return batch_logits