ppyoloe_head.py 15.6 KB
Newer Older
S
shangliang Xu 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
# Copyright (c) 2022 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.

import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from ppdet.core.workspace import register

from ..bbox_utils import batch_distance2bbox
from ..losses import GIoULoss
from ..initializer import bias_init_with_prob, constant_, normal_
from ..assigners.utils import generate_anchors_for_grid_cell
from ppdet.modeling.backbones.cspresnet import ConvBNLayer
W
wangguanzhong 已提交
25
from ppdet.modeling.ops import get_static_shape, get_act_fn
W
wangxinxin08 已提交
26
from ppdet.modeling.layers import MultiClassNMS
S
shangliang Xu 已提交
27

S
shangliang Xu 已提交
28
__all__ = ['PPYOLOEHead']
S
shangliang Xu 已提交
29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


class ESEAttn(nn.Layer):
    def __init__(self, feat_channels, act='swish'):
        super(ESEAttn, self).__init__()
        self.fc = nn.Conv2D(feat_channels, feat_channels, 1)
        self.conv = ConvBNLayer(feat_channels, feat_channels, 1, act=act)

        self._init_weights()

    def _init_weights(self):
        normal_(self.fc.weight, std=0.001)

    def forward(self, feat, avg_feat):
        weight = F.sigmoid(self.fc(avg_feat))
        return self.conv(feat * weight)


@register
S
shangliang Xu 已提交
48
class PPYOLOEHead(nn.Layer):
49
    __shared__ = ['num_classes', 'eval_size', 'trt', 'exclude_nms']
S
shangliang Xu 已提交
50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
    __inject__ = ['static_assigner', 'assigner', 'nms']

    def __init__(self,
                 in_channels=[1024, 512, 256],
                 num_classes=80,
                 act='swish',
                 fpn_strides=(32, 16, 8),
                 grid_cell_scale=5.0,
                 grid_cell_offset=0.5,
                 reg_max=16,
                 static_assigner_epoch=4,
                 use_varifocal_loss=True,
                 static_assigner='ATSSAssigner',
                 assigner='TaskAlignedAssigner',
                 nms='MultiClassNMS',
65
                 eval_size=None,
S
shangliang Xu 已提交
66 67 68 69 70
                 loss_weight={
                     'class': 1.0,
                     'iou': 2.5,
                     'dfl': 0.5,
                 },
S
shangliang Xu 已提交
71 72 73
                 trt=False,
                 exclude_nms=False):
        super(PPYOLOEHead, self).__init__()
S
shangliang Xu 已提交
74 75 76 77 78 79 80 81 82 83
        assert len(in_channels) > 0, "len(in_channels) should > 0"
        self.in_channels = in_channels
        self.num_classes = num_classes
        self.fpn_strides = fpn_strides
        self.grid_cell_scale = grid_cell_scale
        self.grid_cell_offset = grid_cell_offset
        self.reg_max = reg_max
        self.iou_loss = GIoULoss()
        self.loss_weight = loss_weight
        self.use_varifocal_loss = use_varifocal_loss
84
        self.eval_size = eval_size
S
shangliang Xu 已提交
85 86 87 88 89

        self.static_assigner_epoch = static_assigner_epoch
        self.static_assigner = static_assigner
        self.assigner = assigner
        self.nms = nms
W
wangxinxin08 已提交
90 91
        if isinstance(self.nms, MultiClassNMS) and trt:
            self.nms.trt = trt
S
shangliang Xu 已提交
92
        self.exclude_nms = exclude_nms
S
shangliang Xu 已提交
93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113
        # stem
        self.stem_cls = nn.LayerList()
        self.stem_reg = nn.LayerList()
        act = get_act_fn(
            act, trt=trt) if act is None or isinstance(act,
                                                       (str, dict)) else act
        for in_c in self.in_channels:
            self.stem_cls.append(ESEAttn(in_c, act=act))
            self.stem_reg.append(ESEAttn(in_c, act=act))
        # pred head
        self.pred_cls = nn.LayerList()
        self.pred_reg = nn.LayerList()
        for in_c in self.in_channels:
            self.pred_cls.append(
                nn.Conv2D(
                    in_c, self.num_classes, 3, padding=1))
            self.pred_reg.append(
                nn.Conv2D(
                    in_c, 4 * (self.reg_max + 1), 3, padding=1))
        # projection conv
        self.proj_conv = nn.Conv2D(self.reg_max + 1, 1, 1, bias_attr=False)
114
        self.proj_conv.skip_quant = True
S
shangliang Xu 已提交
115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133
        self._init_weights()

    @classmethod
    def from_config(cls, cfg, input_shape):
        return {'in_channels': [i.channels for i in input_shape], }

    def _init_weights(self):
        bias_cls = bias_init_with_prob(0.01)
        for cls_, reg_ in zip(self.pred_cls, self.pred_reg):
            constant_(cls_.weight)
            constant_(cls_.bias, bias_cls)
            constant_(reg_.weight)
            constant_(reg_.bias, 1.0)

        self.proj = paddle.linspace(0, self.reg_max, self.reg_max + 1)
        self.proj_conv.weight.set_value(
            self.proj.reshape([1, self.reg_max + 1, 1, 1]))
        self.proj_conv.weight.stop_gradient = True

134
        if self.eval_size:
S
shangliang Xu 已提交
135
            anchor_points, stride_tensor = self._generate_anchors()
W
wangxinxin08 已提交
136 137
            self.anchor_points = anchor_points
            self.stride_tensor = stride_tensor
S
shangliang Xu 已提交
138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162

    def forward_train(self, feats, targets):
        anchors, anchor_points, num_anchors_list, stride_tensor = \
            generate_anchors_for_grid_cell(
                feats, self.fpn_strides, self.grid_cell_scale,
                self.grid_cell_offset)

        cls_score_list, reg_distri_list = [], []
        for i, feat in enumerate(feats):
            avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
            cls_logit = self.pred_cls[i](self.stem_cls[i](feat, avg_feat) +
                                         feat)
            reg_distri = self.pred_reg[i](self.stem_reg[i](feat, avg_feat))
            # cls and reg
            cls_score = F.sigmoid(cls_logit)
            cls_score_list.append(cls_score.flatten(2).transpose([0, 2, 1]))
            reg_distri_list.append(reg_distri.flatten(2).transpose([0, 2, 1]))
        cls_score_list = paddle.concat(cls_score_list, axis=1)
        reg_distri_list = paddle.concat(reg_distri_list, axis=1)

        return self.get_loss([
            cls_score_list, reg_distri_list, anchors, anchor_points,
            num_anchors_list, stride_tensor
        ], targets)

S
shangliang Xu 已提交
163
    def _generate_anchors(self, feats=None, dtype='float32'):
S
shangliang Xu 已提交
164 165 166 167 168 169 170
        # just use in eval time
        anchor_points = []
        stride_tensor = []
        for i, stride in enumerate(self.fpn_strides):
            if feats is not None:
                _, _, h, w = feats[i].shape
            else:
171 172
                h = int(self.eval_size[0] / stride)
                w = int(self.eval_size[1] / stride)
S
shangliang Xu 已提交
173 174 175 176 177
            shift_x = paddle.arange(end=w) + self.grid_cell_offset
            shift_y = paddle.arange(end=h) + self.grid_cell_offset
            shift_y, shift_x = paddle.meshgrid(shift_y, shift_x)
            anchor_point = paddle.cast(
                paddle.stack(
S
shangliang Xu 已提交
178
                    [shift_x, shift_y], axis=-1), dtype=dtype)
S
shangliang Xu 已提交
179
            anchor_points.append(anchor_point.reshape([-1, 2]))
S
shangliang Xu 已提交
180
            stride_tensor.append(paddle.full([h * w, 1], stride, dtype=dtype))
S
shangliang Xu 已提交
181 182 183 184 185
        anchor_points = paddle.concat(anchor_points)
        stride_tensor = paddle.concat(stride_tensor)
        return anchor_points, stride_tensor

    def forward_eval(self, feats):
186
        if self.eval_size:
S
shangliang Xu 已提交
187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294
            anchor_points, stride_tensor = self.anchor_points, self.stride_tensor
        else:
            anchor_points, stride_tensor = self._generate_anchors(feats)
        cls_score_list, reg_dist_list = [], []
        for i, feat in enumerate(feats):
            b, _, h, w = feat.shape
            l = h * w
            avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
            cls_logit = self.pred_cls[i](self.stem_cls[i](feat, avg_feat) +
                                         feat)
            reg_dist = self.pred_reg[i](self.stem_reg[i](feat, avg_feat))
            reg_dist = reg_dist.reshape([-1, 4, self.reg_max + 1, l]).transpose(
                [0, 2, 1, 3])
            reg_dist = self.proj_conv(F.softmax(reg_dist, axis=1))
            # cls and reg
            cls_score = F.sigmoid(cls_logit)
            cls_score_list.append(cls_score.reshape([b, self.num_classes, l]))
            reg_dist_list.append(reg_dist.reshape([b, 4, l]))

        cls_score_list = paddle.concat(cls_score_list, axis=-1)
        reg_dist_list = paddle.concat(reg_dist_list, axis=-1)

        return cls_score_list, reg_dist_list, anchor_points, stride_tensor

    def forward(self, feats, targets=None):
        assert len(feats) == len(self.fpn_strides), \
            "The size of feats is not equal to size of fpn_strides"

        if self.training:
            return self.forward_train(feats, targets)
        else:
            return self.forward_eval(feats)

    @staticmethod
    def _focal_loss(score, label, alpha=0.25, gamma=2.0):
        weight = (score - label).pow(gamma)
        if alpha > 0:
            alpha_t = alpha * label + (1 - alpha) * (1 - label)
            weight *= alpha_t
        loss = F.binary_cross_entropy(
            score, label, weight=weight, reduction='sum')
        return loss

    @staticmethod
    def _varifocal_loss(pred_score, gt_score, label, alpha=0.75, gamma=2.0):
        weight = alpha * pred_score.pow(gamma) * (1 - label) + gt_score * label
        loss = F.binary_cross_entropy(
            pred_score, gt_score, weight=weight, reduction='sum')
        return loss

    def _bbox_decode(self, anchor_points, pred_dist):
        b, l, _ = get_static_shape(pred_dist)
        pred_dist = F.softmax(pred_dist.reshape([b, l, 4, self.reg_max + 1
                                                 ])).matmul(self.proj)
        return batch_distance2bbox(anchor_points, pred_dist)

    def _bbox2distance(self, points, bbox):
        x1y1, x2y2 = paddle.split(bbox, 2, -1)
        lt = points - x1y1
        rb = x2y2 - points
        return paddle.concat([lt, rb], -1).clip(0, self.reg_max - 0.01)

    def _df_loss(self, pred_dist, target):
        target_left = paddle.cast(target, 'int64')
        target_right = target_left + 1
        weight_left = target_right.astype('float32') - target
        weight_right = 1 - weight_left
        loss_left = F.cross_entropy(
            pred_dist, target_left, reduction='none') * weight_left
        loss_right = F.cross_entropy(
            pred_dist, target_right, reduction='none') * weight_right
        return (loss_left + loss_right).mean(-1, keepdim=True)

    def _bbox_loss(self, pred_dist, pred_bboxes, anchor_points, assigned_labels,
                   assigned_bboxes, assigned_scores, assigned_scores_sum):
        # select positive samples mask
        mask_positive = (assigned_labels != self.num_classes)
        num_pos = mask_positive.sum()
        # pos/neg loss
        if num_pos > 0:
            # l1 + iou
            bbox_mask = mask_positive.unsqueeze(-1).tile([1, 1, 4])
            pred_bboxes_pos = paddle.masked_select(pred_bboxes,
                                                   bbox_mask).reshape([-1, 4])
            assigned_bboxes_pos = paddle.masked_select(
                assigned_bboxes, bbox_mask).reshape([-1, 4])
            bbox_weight = paddle.masked_select(
                assigned_scores.sum(-1), mask_positive).unsqueeze(-1)

            loss_l1 = F.l1_loss(pred_bboxes_pos, assigned_bboxes_pos)

            loss_iou = self.iou_loss(pred_bboxes_pos,
                                     assigned_bboxes_pos) * bbox_weight
            loss_iou = loss_iou.sum() / assigned_scores_sum

            dist_mask = mask_positive.unsqueeze(-1).tile(
                [1, 1, (self.reg_max + 1) * 4])
            pred_dist_pos = paddle.masked_select(
                pred_dist, dist_mask).reshape([-1, 4, self.reg_max + 1])
            assigned_ltrb = self._bbox2distance(anchor_points, assigned_bboxes)
            assigned_ltrb_pos = paddle.masked_select(
                assigned_ltrb, bbox_mask).reshape([-1, 4])
            loss_dfl = self._df_loss(pred_dist_pos,
                                     assigned_ltrb_pos) * bbox_weight
            loss_dfl = loss_dfl.sum() / assigned_scores_sum
        else:
            loss_l1 = paddle.zeros([1])
            loss_iou = paddle.zeros([1])
295
            loss_dfl = pred_dist.sum() * 0.
S
shangliang Xu 已提交
296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335
        return loss_l1, loss_iou, loss_dfl

    def get_loss(self, head_outs, gt_meta):
        pred_scores, pred_distri, anchors,\
        anchor_points, num_anchors_list, stride_tensor = head_outs

        anchor_points_s = anchor_points / stride_tensor
        pred_bboxes = self._bbox_decode(anchor_points_s, pred_distri)

        gt_labels = gt_meta['gt_class']
        gt_bboxes = gt_meta['gt_bbox']
        pad_gt_mask = gt_meta['pad_gt_mask']
        # label assignment
        if gt_meta['epoch_id'] < self.static_assigner_epoch:
            assigned_labels, assigned_bboxes, assigned_scores = \
                self.static_assigner(
                    anchors,
                    num_anchors_list,
                    gt_labels,
                    gt_bboxes,
                    pad_gt_mask,
                    bg_index=self.num_classes,
                    pred_bboxes=pred_bboxes.detach() * stride_tensor)
            alpha_l = 0.25
        else:
            assigned_labels, assigned_bboxes, assigned_scores = \
                self.assigner(
                pred_scores.detach(),
                pred_bboxes.detach() * stride_tensor,
                anchor_points,
                num_anchors_list,
                gt_labels,
                gt_bboxes,
                pad_gt_mask,
                bg_index=self.num_classes)
            alpha_l = -1
        # rescale bbox
        assigned_bboxes /= stride_tensor
        # cls loss
        if self.use_varifocal_loss:
S
shangliang Xu 已提交
336 337
            one_hot_label = F.one_hot(assigned_labels,
                                      self.num_classes + 1)[..., :-1]
S
shangliang Xu 已提交
338 339 340
            loss_cls = self._varifocal_loss(pred_scores, assigned_scores,
                                            one_hot_label)
        else:
S
shangliang Xu 已提交
341
            loss_cls = self._focal_loss(pred_scores, assigned_scores, alpha_l)
S
shangliang Xu 已提交
342 343

        assigned_scores_sum = assigned_scores.sum()
W
wangguanzhong 已提交
344
        if paddle.distributed.get_world_size() > 1:
S
shangliang Xu 已提交
345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366
            paddle.distributed.all_reduce(assigned_scores_sum)
            assigned_scores_sum = paddle.clip(
                assigned_scores_sum / paddle.distributed.get_world_size(),
                min=1)
        loss_cls /= assigned_scores_sum

        loss_l1, loss_iou, loss_dfl = \
            self._bbox_loss(pred_distri, pred_bboxes, anchor_points_s,
                            assigned_labels, assigned_bboxes, assigned_scores,
                            assigned_scores_sum)
        loss = self.loss_weight['class'] * loss_cls + \
               self.loss_weight['iou'] * loss_iou + \
               self.loss_weight['dfl'] * loss_dfl
        out_dict = {
            'loss': loss,
            'loss_cls': loss_cls,
            'loss_iou': loss_iou,
            'loss_dfl': loss_dfl,
            'loss_l1': loss_l1,
        }
        return out_dict

S
shangliang Xu 已提交
367
    def post_process(self, head_outs, scale_factor):
S
shangliang Xu 已提交
368 369 370 371 372 373 374 375 376
        pred_scores, pred_dist, anchor_points, stride_tensor = head_outs
        pred_bboxes = batch_distance2bbox(anchor_points,
                                          pred_dist.transpose([0, 2, 1]))
        pred_bboxes *= stride_tensor
        # scale bbox to origin
        scale_y, scale_x = paddle.split(scale_factor, 2, axis=-1)
        scale_factor = paddle.concat(
            [scale_x, scale_y, scale_x, scale_y], axis=-1).reshape([-1, 1, 4])
        pred_bboxes /= scale_factor
S
shangliang Xu 已提交
377 378 379 380 381 382
        if self.exclude_nms:
            # `exclude_nms=True` just use in benchmark
            return pred_bboxes.sum(), pred_scores.sum()
        else:
            bbox_pred, bbox_num, _ = self.nms(pred_bboxes, pred_scores)
            return bbox_pred, bbox_num