keypoint_hrhrnet.py 11.3 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
# 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

from scipy.optimize import linear_sum_assignment
from collections import abc, defaultdict
import numpy as np
import paddle

from ppdet.core.workspace import register, create, serializable
from .meta_arch import BaseArch
from .. import layers as L
from ..keypoint_utils import transpred

Z
zhiboniu 已提交
29
__all__ = ['HigherHRNet']
30 31 32


@register
Z
zhiboniu 已提交
33
class HigherHRNet(BaseArch):
34 35 36
    __category__ = 'architecture'

    def __init__(self,
Z
zhiboniu 已提交
37 38 39
                 backbone='HRNet',
                 hrhrnet_head='HigherHRNetHead',
                 post_process='HrHRNetPostProcess',
40
                 eval_flip=True,
41 42
                 flip_perm=None,
                 max_num_people=30):
43
        """
Z
zhiboniu 已提交
44 45
        HigherHRNet network, see https://arxiv.org/abs/1908.10357;
        HigherHRNet+swahr, see https://arxiv.org/abs/2012.15175
46 47 48 49 50 51

        Args:
            backbone (nn.Layer): backbone instance
            hrhrnet_head (nn.Layer): keypoint_head instance
            bbox_post_process (object): `BBoxPostProcess` instance
        """
Z
zhiboniu 已提交
52
        super(HigherHRNet, self).__init__()
53 54
        self.backbone = backbone
        self.hrhrnet_head = hrhrnet_head
55
        self.post_process = post_process
56 57 58
        self.flip = eval_flip
        self.flip_perm = paddle.to_tensor(flip_perm)
        self.deploy = False
59 60 61
        self.interpolate = L.Upsample(2, mode='bilinear')
        self.pool = L.MaxPool(5, 1, 2)
        self.max_num_people = max_num_people
62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87

    @classmethod
    def from_config(cls, cfg, *args, **kwargs):
        # backbone
        backbone = create(cfg['backbone'])
        # head
        kwargs = {'input_shape': backbone.out_shape}
        hrhrnet_head = create(cfg['hrhrnet_head'], **kwargs)
        post_process = create(cfg['post_process'])

        return {
            'backbone': backbone,
            "hrhrnet_head": hrhrnet_head,
            "post_process": post_process,
        }

    def _forward(self):
        if self.flip and not self.training and not self.deploy:
            self.inputs['image'] = paddle.concat(
                (self.inputs['image'], paddle.flip(self.inputs['image'], [3])))
        body_feats = self.backbone(self.inputs)

        if self.training:
            return self.hrhrnet_head(body_feats, self.inputs)
        else:
            outputs = self.hrhrnet_head(body_feats)
88

89
            if self.flip and not self.deploy:
90 91 92 93 94 95 96 97 98
                outputs = [paddle.split(o, 2) for o in outputs]
                output_rflip = [
                    paddle.flip(paddle.gather(o[1], self.flip_perm, 1), [3])
                    for o in outputs
                ]
                output1 = [o[0] for o in outputs]
                heatmap = (output1[0] + output_rflip[0]) / 2.
                tagmaps = [output1[1], output_rflip[1]]
                outputs = [heatmap] + tagmaps
99
            outputs = self.get_topk(outputs)
100

101 102
            if self.deploy:
                return outputs
103

104 105 106 107 108 109
            res_lst = []
            h = self.inputs['im_shape'][0, 0].numpy().item()
            w = self.inputs['im_shape'][0, 1].numpy().item()
            kpts, scores = self.post_process(*outputs, h, w)
            res_lst.append([kpts, scores])
            return res_lst
110 111 112 113 114 115

    def get_loss(self):
        return self._forward()

    def get_pred(self):
        outputs = {}
116
        res_lst = self._forward()
117
        outputs['keypoint'] = res_lst
118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137
        return outputs

    def get_topk(self, outputs):
        # resize to image size
        outputs = [self.interpolate(x) for x in outputs]
        if len(outputs) == 3:
            tagmap = paddle.concat(
                (outputs[1].unsqueeze(4), outputs[2].unsqueeze(4)), axis=4)
        else:
            tagmap = outputs[1].unsqueeze(4)

        heatmap = outputs[0]
        N, J = 1, self.hrhrnet_head.num_joints
        heatmap_maxpool = self.pool(heatmap)
        # topk
        maxmap = heatmap * (heatmap == heatmap_maxpool)
        maxmap = maxmap.reshape([N, J, -1])
        heat_k, inds_k = maxmap.topk(self.max_num_people, axis=2)

        outputs = [heatmap, tagmap, heat_k, inds_k]
138 139 140 141 142
        return outputs


@register
@serializable
Z
zhiboniu 已提交
143
class HrHRNetPostProcess(object):
144
    '''
Z
zhiboniu 已提交
145
    HrHRNet postprocess contain:
146 147 148 149 150 151 152 153 154 155 156 157 158 159
        1) get topk keypoints in the output heatmap
        2) sample the tagmap's value corresponding to each of the topk coordinate
        3) match different joints to combine to some people with Hungary algorithm
        4) adjust the coordinate by +-0.25 to decrease error std
        5) salvage missing joints by check positivity of heatmap - tagdiff_norm
    Args:
        max_num_people (int): max number of people support in postprocess
        heat_thresh (float): value of topk below this threshhold will be ignored
        tag_thresh (float): coord's value sampled in tagmap below this threshold belong to same people for init

        inputs(list[heatmap]): the output list of modle, [heatmap, heatmap_maxpool, tagmap], heatmap_maxpool used to get topk
        original_height, original_width (float): the original image size
    '''

160
    def __init__(self, max_num_people=30, heat_thresh=0.1, tag_thresh=1.):
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
        self.max_num_people = max_num_people
        self.heat_thresh = heat_thresh
        self.tag_thresh = tag_thresh

    def lerp(self, j, y, x, heatmap):
        H, W = heatmap.shape[-2:]
        left = np.clip(x - 1, 0, W - 1)
        right = np.clip(x + 1, 0, W - 1)
        up = np.clip(y - 1, 0, H - 1)
        down = np.clip(y + 1, 0, H - 1)
        offset_y = np.where(heatmap[j, down, x] > heatmap[j, up, x], 0.25,
                            -0.25)
        offset_x = np.where(heatmap[j, y, right] > heatmap[j, y, left], 0.25,
                            -0.25)
        return offset_y + 0.5, offset_x + 0.5

177 178
    def __call__(self, heatmap, tagmap, heat_k, inds_k, original_height,
                 original_width):
179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265

        N, J, H, W = heatmap.shape
        assert N == 1, "only support batch size 1"
        heatmap = heatmap[0].cpu().detach().numpy()
        tagmap = tagmap[0].cpu().detach().numpy()
        heats = heat_k[0].cpu().detach().numpy()
        inds_np = inds_k[0].cpu().detach().numpy()
        y = inds_np // W
        x = inds_np % W
        tags = tagmap[np.arange(J)[None, :].repeat(self.max_num_people),
                      y.flatten(), x.flatten()].reshape(J, -1, tagmap.shape[-1])
        coords = np.stack((y, x), axis=2)
        # threshold
        mask = heats > self.heat_thresh
        # cluster
        cluster = defaultdict(lambda: {
            'coords': np.zeros((J, 2), dtype=np.float32),
            'scores': np.zeros(J, dtype=np.float32),
            'tags': []
        })
        for jid, m in enumerate(mask):
            num_valid = m.sum()
            if num_valid == 0:
                continue
            valid_inds = np.where(m)[0]
            valid_tags = tags[jid, m, :]
            if len(cluster) == 0:  # initialize
                for i in valid_inds:
                    tag = tags[jid, i]
                    key = tag[0]
                    cluster[key]['tags'].append(tag)
                    cluster[key]['scores'][jid] = heats[jid, i]
                    cluster[key]['coords'][jid] = coords[jid, i]
                continue
            candidates = list(cluster.keys())[:self.max_num_people]
            centroids = [
                np.mean(
                    cluster[k]['tags'], axis=0) for k in candidates
            ]
            num_clusters = len(centroids)
            # shape is (num_valid, num_clusters, tag_dim)
            dist = valid_tags[:, None, :] - np.array(centroids)[None, ...]
            l2_dist = np.linalg.norm(dist, ord=2, axis=2)
            # modulate dist with heat value, see `use_detection_val`
            cost = np.round(l2_dist) * 100 - heats[jid, m, None]
            # pad the cost matrix, otherwise new pose are ignored
            if num_valid > num_clusters:
                cost = np.pad(cost, ((0, 0), (0, num_valid - num_clusters)),
                              constant_values=((0, 0), (0, 1e-10)))
            rows, cols = linear_sum_assignment(cost)
            for y, x in zip(rows, cols):
                tag = tags[jid, y]
                if y < num_valid and x < num_clusters and \
                   l2_dist[y, x] < self.tag_thresh:
                    key = candidates[x]  # merge to cluster
                else:
                    key = tag[0]  # initialize new cluster
                cluster[key]['tags'].append(tag)
                cluster[key]['scores'][jid] = heats[jid, y]
                cluster[key]['coords'][jid] = coords[jid, y]

        # shape is [k, J, 2] and [k, J]
        pose_tags = np.array([cluster[k]['tags'] for k in cluster])
        pose_coords = np.array([cluster[k]['coords'] for k in cluster])
        pose_scores = np.array([cluster[k]['scores'] for k in cluster])
        valid = pose_scores > 0

        pose_kpts = np.zeros((pose_scores.shape[0], J, 3), dtype=np.float32)
        if valid.sum() == 0:
            return pose_kpts, pose_kpts

        # refine coords
        valid_coords = pose_coords[valid].astype(np.int32)
        y = valid_coords[..., 0].flatten()
        x = valid_coords[..., 1].flatten()
        _, j = np.nonzero(valid)
        offsets = self.lerp(j, y, x, heatmap)
        pose_coords[valid, 0] += offsets[0]
        pose_coords[valid, 1] += offsets[1]

        # mean score before salvage
        mean_score = pose_scores.mean(axis=1)
        pose_kpts[valid, 2] = pose_scores[valid]

        # salvage missing joints
        if True:
            for pid, coords in enumerate(pose_coords):
266
                tag_mean = np.array(pose_tags[pid]).mean(axis=0)
267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286
                norm = np.sum((tagmap - tag_mean)**2, axis=3)**0.5
                score = heatmap - np.round(norm)  # (J, H, W)
                flat_score = score.reshape(J, -1)
                max_inds = np.argmax(flat_score, axis=1)
                max_scores = np.max(flat_score, axis=1)
                salvage_joints = (pose_scores[pid] == 0) & (max_scores > 0)
                if salvage_joints.sum() == 0:
                    continue
                y = max_inds[salvage_joints] // W
                x = max_inds[salvage_joints] % W
                offsets = self.lerp(salvage_joints.nonzero()[0], y, x, heatmap)
                y = y.astype(np.float32) + offsets[0]
                x = x.astype(np.float32) + offsets[1]
                pose_coords[pid][salvage_joints, 0] = y
                pose_coords[pid][salvage_joints, 1] = x
                pose_kpts[pid][salvage_joints, 2] = max_scores[salvage_joints]
        pose_kpts[..., :2] = transpred(pose_coords[..., :2][..., ::-1],
                                       original_height, original_width,
                                       min(H, W))
        return pose_kpts, mean_score