pose bottomup higherhrnet: model (#2638)

ppdet/modeling/architectures/__init__.py

@@ -15,6 +15,7 @@ from . import fcos
 from . import solov2
 from . import ttfnet
 from . import s2anet
+from . import keypoint_hrhrnet
 
 from .meta_arch import *
 from .faster_rcnn import *
@@ -26,3 +27,4 @@ from .fcos import *
 from .solov2 import *
 from .ttfnet import *
 from .s2anet import *
+from .keypoint_hrhrnet import *

ppdet/modeling/architectures/keypoint_hrhrnet.py

+# 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
+
+__all__ = ['HigherHrnet']
+
+
+@register
+class HigherHrnet(BaseArch):
+    __category__ = 'architecture'
+
+    def __init__(self,
+                 backbone='Hrnet',
+                 hrhrnet_head='HigherHrnetHead',
+                 post_process='HrHrnetPostProcess',
+                 eval_flip=True,
+                 flip_perm=None):
+        """
+        HigherHrnet network, see https://arxiv.org/abs/
+
+        Args:
+            backbone (nn.Layer): backbone instance
+            hrhrnet_head (nn.Layer): keypoint_head instance
+            bbox_post_process (object): `BBoxPostProcess` instance
+        """
+        super(HigherHrnet, self).__init__()
+        self.backbone = backbone
+        self.hrhrnet_head = hrhrnet_head
+        self.post_process = HrHrnetPostProcess()
+        self.flip = eval_flip
+        self.flip_perm = paddle.to_tensor(flip_perm)
+        self.deploy = False
+
+    @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):
+        batchsize = self.inputs['image'].shape[0]
+        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)
+            if self.deploy:
+                return outputs, [1]
+            if self.flip:
+                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
+
+            res_lst = []
+            bboxnums = []
+            for idx in range(batchsize):
+                item = [o[idx:(idx + 1)] for o in outputs]
+
+                h = self.inputs['im_shape'][idx, 0].numpy().item()
+                w = self.inputs['im_shape'][idx, 1].numpy().item()
+                kpts, scores = self.post_process(item, h, w)
+                res_lst.append([kpts, scores])
+                bboxnums.append(1)
+
+            return res_lst, bboxnums
+
+    def get_loss(self):
+        return self._forward()
+
+    def get_pred(self):
+        outputs = {}
+        res_lst, bboxnums = self._forward()
+        outputs['keypoint'] = res_lst
+        outputs['bbox_num'] = bboxnums
+        return outputs
+
+
+@register
+@serializable
+class HrHrnetPostProcess(object):
+    def __init__(self, max_num_people=30, heat_thresh=0.2, tag_thresh=1.):
+        self.interpolate = L.Upsample(2, mode='bilinear')
+        self.pool = L.MaxPool(5, 1, 2)
+        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
+
+    def __call__(self, inputs, original_height, original_width):
+
+        # resize to image size
+        inputs = [self.interpolate(x) for x in inputs]
+        # aggregate
+        heatmap = inputs[0]
+        if len(inputs) == 3:
+            tagmap = paddle.concat(
+                (inputs[1].unsqueeze(4), inputs[2].unsqueeze(4)), axis=4)
+        else:
+            tagmap = inputs[1].unsqueeze(4)
+
+        N, J, H, W = heatmap.shape
+        assert N == 1, "only support batch size 1"
+        # topk
+        maximum = self.pool(heatmap)
+        maxmap = heatmap * (heatmap == maximum)
+        maxmap = maxmap.reshape([N, J, -1])
+        heat_k, inds_k = maxmap.topk(self.max_num_people, axis=2)
+        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]
+
+        # TODO can we remove the outermost loop altogether
+        # salvage missing joints
+
+        if True:
+            for pid, coords in enumerate(pose_coords):
+                # vj = np.nonzero(valid[pid])[0]
+                # vyx = coords[valid[pid]].astype(np.int32)
+                # tag_mean = tagmap[vj, vyx[:, 0], vyx[:, 1]].mean(axis=0)
+
+                tag_mean = np.array(pose_tags[pid]).mean(
+                    axis=0)  #TODO: replace tagmap sample by history record
+
+                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

ppdet/modeling/backbones/hrnet.py

@@ -688,6 +688,7 @@ class HRNet(nn.Layer):
             has_se=self.has_se,
             norm_decay=norm_decay,
             freeze_norm=freeze_norm,
+            multi_scale_output=len(return_idx) > 1,
             name="st4")
 
     def forward(self, inputs):

ppdet/modeling/heads/__init__.py

@@ -23,6 +23,7 @@ from . import ttf_head
 from . import cascade_head
 from . import face_head
 from . import s2anet_head
+from . import keypoint_hrhrnet_head
 
 from .bbox_head import *
 from .mask_head import *
@@ -35,3 +36,4 @@ from .ttf_head import *
 from .cascade_head import *
 from .face_head import *
 from .s2anet_head import *
+from .keypoint_hrhrnet_head import *

ppdet/modeling/heads/keypoint_hrhrnet_head.py

+# 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.
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from ppdet.core.workspace import register
+from .. import layers as L
+from ..backbones.hrnet import BasicBlock
+
+
+@register
+class HrHrnetHead(nn.Layer):
+    __inject__ = ['loss']
+
+    def __init__(self, num_joints, loss='HrHrnetLoss', swahr=False, width=32):
+        """
+        Head for HigherHrnet network
+
+        Args:
+            num_joints (int): number of keypoints
+            hrloss (object): HrHrnetLoss instance
+            swahr (bool): whether to use swahr
+            width (int): hrnet channel width
+        """
+        super(HrHrnetHead, self).__init__()
+        self.loss = loss
+
+        self.num_joints = num_joints
+        num_featout1 = num_joints * 2
+        num_featout2 = num_joints
+        self.swahr = swahr
+        self.conv1 = L.Conv2d(width, num_featout1, 1, 1, 0, bias=True)
+        self.conv2 = L.Conv2d(width, num_featout2, 1, 1, 0, bias=True)
+        self.deconv = nn.Sequential(
+            L.ConvTranspose2d(
+                num_featout1 + width, width, 4, 2, 1, 0, bias=False),
+            L.BatchNorm2d(width),
+            L.ReLU())
+        self.blocks = nn.Sequential(*(BasicBlock(
+            num_channels=width,
+            num_filters=width,
+            has_se=False,
+            freeze_norm=False,
+            name='HrHrnetHead_{}'.format(i)) for i in range(4)))
+
+        self.interpolate = L.Upsample(2, mode='bilinear')
+        self.concat = L.Concat(dim=1)
+        if swahr:
+            self.scalelayer0 = nn.Sequential(
+                L.Conv2d(
+                    width, num_joints, 1, 1, 0, bias=True),
+                L.BatchNorm2d(num_joints),
+                L.ReLU(),
+                L.Conv2d(
+                    num_joints,
+                    num_joints,
+                    9,
+                    1,
+                    4,
+                    groups=num_joints,
+                    bias=True))
+            self.scalelayer1 = nn.Sequential(
+                L.Conv2d(
+                    width, num_joints, 1, 1, 0, bias=True),
+                L.BatchNorm2d(num_joints),
+                L.ReLU(),
+                L.Conv2d(
+                    num_joints,
+                    num_joints,
+                    9,
+                    1,
+                    4,
+                    groups=num_joints,
+                    bias=True))
+
+    def forward(self, feats, targets=None):
+        x1 = feats[0]
+        xo1 = self.conv1(x1)
+        x2 = self.blocks(self.deconv(self.concat((x1, xo1))))
+        xo2 = self.conv2(x2)
+        num_joints = self.num_joints
+        if self.training:
+            if self.swahr:
+                so1 = self.scalelayer0(x1)
+                so2 = self.scalelayer1(x2)
+                hrhrnet_outputs = ([xo1[:, :num_joints], so1], [xo2, so2],
+                                   xo1[:, num_joints:])
+                return self.loss(hrhrnet_outputs, targets)
+            else:
+                hrhrnet_outputs = (xo1[:, :num_joints], xo2,
+                                   xo1[:, num_joints:])
+                return self.loss(hrhrnet_outputs, targets)
+
+        # averaged heatmap, upsampled tagmap
+        upsampled = self.interpolate(xo1)
+        avg = (upsampled[:, :num_joints] + xo2[:, :num_joints]) / 2
+        return avg, upsampled[:, num_joints:]

ppdet/modeling/keypoint_utils.py

+# 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.
+
+import cv2
+import numpy as np
+
+
+def get_affine_mat_kernel(h, w, s, inv=False):
+    if w < h:
+        w_ = s
+        h_ = int(np.ceil((s / w * h) / 64.) * 64)
+        scale_w = w
+        scale_h = h_ / w_ * w
+
+    else:
+        h_ = s
+        w_ = int(np.ceil((s / h * w) / 64.) * 64)
+        scale_h = h
+        scale_w = w_ / h_ * h
+
+    center = np.array([np.round(w / 2.), np.round(h / 2.)])
+
+    size_resized = (w_, h_)
+    trans = get_affine_transform(
+        center, np.array([scale_w, scale_h]), 0, size_resized, inv=inv)
+
+    return trans, size_resized
+
+
+def get_affine_transform(center,
+                         input_size,
+                         rot,
+                         output_size,
+                         shift=(0., 0.),
+                         inv=False):
+    """Get the affine transform matrix, given the center/scale/rot/output_size.
+
+    Args:
+        center (np.ndarray[2, ]): Center of the bounding box (x, y).
+        scale (np.ndarray[2, ]): Scale of the bounding box
+            wrt [width, height].
+        rot (float): Rotation angle (degree).
+        output_size (np.ndarray[2, ]): Size of the destination heatmaps.
+        shift (0-100%): Shift translation ratio wrt the width/height.
+            Default (0., 0.).
+        inv (bool): Option to inverse the affine transform direction.
+            (inv=False: src->dst or inv=True: dst->src)
+
+    Returns:
+        np.ndarray: The transform matrix.
+    """
+    assert len(center) == 2
+    assert len(input_size) == 2
+    assert len(output_size) == 2
+    assert len(shift) == 2
+
+    scale_tmp = input_size
+
+    shift = np.array(shift)
+    src_w = scale_tmp[0]
+    dst_w = output_size[0]
+    dst_h = output_size[1]
+
+    rot_rad = np.pi * rot / 180
+    src_dir = rotate_point([0., src_w * -0.5], rot_rad)
+    dst_dir = np.array([0., dst_w * -0.5])
+
+    src = np.zeros((3, 2), dtype=np.float32)
+    src[0, :] = center + scale_tmp * shift
+    src[1, :] = center + src_dir + scale_tmp * shift
+    src[2, :] = _get_3rd_point(src[0, :], src[1, :])
+
+    dst = np.zeros((3, 2), dtype=np.float32)
+    dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
+    dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
+    dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])
+
+    if inv:
+        trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
+    else:
+        trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
+
+    return trans
+
+
+def _get_3rd_point(a, b):
+    """To calculate the affine matrix, three pairs of points are required. This
+    function is used to get the 3rd point, given 2D points a & b.
+
+    The 3rd point is defined by rotating vector `a - b` by 90 degrees
+    anticlockwise, using b as the rotation center.
+
+    Args:
+        a (np.ndarray): point(x,y)
+        b (np.ndarray): point(x,y)
+
+    Returns:
+        np.ndarray: The 3rd point.
+    """
+    assert len(a) == 2
+    assert len(b) == 2
+    direction = a - b
+    third_pt = b + np.array([-direction[1], direction[0]], dtype=np.float32)
+
+    return third_pt
+
+
+def rotate_point(pt, angle_rad):
+    """Rotate a point by an angle.
+
+    Args:
+        pt (list[float]): 2 dimensional point to be rotated
+        angle_rad (float): rotation angle by radian
+
+    Returns:
+        list[float]: Rotated point.
+    """
+    assert len(pt) == 2
+    sn, cs = np.sin(angle_rad), np.cos(angle_rad)
+    new_x = pt[0] * cs - pt[1] * sn
+    new_y = pt[0] * sn + pt[1] * cs
+    rotated_pt = [new_x, new_y]
+
+    return rotated_pt
+
+
+def transpred(kpts, h, w, s):
+    trans, _ = get_affine_mat_kernel(h, w, s, inv=True)
+
+    return warp_affine_joints(kpts[..., :2].copy(), trans)
+
+
+def warp_affine_joints(joints, mat):
+    """Apply affine transformation defined by the transform matrix on the
+    joints.
+
+    Args:
+        joints (np.ndarray[..., 2]): Origin coordinate of joints.
+        mat (np.ndarray[3, 2]): The affine matrix.
+
+    Returns:
+        matrix (np.ndarray[..., 2]): Result coordinate of joints.
+    """
+    joints = np.array(joints)
+    shape = joints.shape
+    joints = joints.reshape(-1, 2)
+    return np.dot(np.concatenate(
+        (joints, joints[:, 0:1] * 0 + 1), axis=1),
+                  mat.T).reshape(shape)

ppdet/modeling/layers.py

@@ -950,3 +950,101 @@ class MaskMatrixNMS(object):
         cate_scores = paddle.gather(cate_scores, index=sort_inds)
         cate_labels = paddle.gather(cate_labels, index=sort_inds)
         return seg_preds, cate_scores, cate_labels
+
+
+def Conv2d(in_channels,
+           out_channels,
+           kernel_size,
+           stride=1,
+           padding=0,
+           dilation=1,
+           groups=1,
+           bias=True,
+           weight_init=Normal(std=0.001),
+           bias_init=Constant(0.)):
+    weight_attr = paddle.framework.ParamAttr(initializer=weight_init)
+    if bias:
+        bias_attr = paddle.framework.ParamAttr(initializer=bias_init)
+    else:
+        bias_attr = False
+    conv = nn.Conv2D(
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        groups,
+        weight_attr=weight_attr,
+        bias_attr=bias_attr)
+    return conv
+
+
+def ConvTranspose2d(in_channels,
+                    out_channels,
+                    kernel_size,
+                    stride=1,
+                    padding=0,
+                    output_padding=0,
+                    groups=1,
+                    bias=True,
+                    dilation=1,
+                    weight_init=Normal(std=0.001),
+                    bias_init=Constant(0.)):
+    weight_attr = paddle.framework.ParamAttr(initializer=weight_init)
+    if bias:
+        bias_attr = paddle.framework.ParamAttr(initializer=bias_init)
+    else:
+        bias_attr = False
+    conv = nn.Conv2DTranspose(
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        output_padding,
+        dilation,
+        groups,
+        weight_attr=weight_attr,
+        bias_attr=bias_attr)
+    return conv
+
+
+def BatchNorm2d(num_features, eps=1e-05, momentum=0.9, affine=True):
+    if not affine:
+        weight_attr = False
+        bias_attr = False
+    else:
+        weight_attr = None
+        bias_attr = None
+    batchnorm = nn.BatchNorm2D(
+        num_features,
+        momentum,
+        eps,
+        weight_attr=weight_attr,
+        bias_attr=bias_attr)
+    return batchnorm
+
+
+def ReLU():
+    return nn.ReLU()
+
+
+def Upsample(scale_factor=None, mode='nearest', align_corners=False):
+    return nn.Upsample(None, scale_factor, mode, align_corners)
+
+
+def MaxPool(kernel_size, stride, padding, ceil_mode=False):
+    return nn.MaxPool2D(kernel_size, stride, padding, ceil_mode=ceil_mode)
+
+
+class Concat(nn.Layer):
+    def __init__(self, dim=0):
+        super(Concat, self).__init__()
+        self.dim = dim
+
+    def forward(self, inputs):
+        return paddle.concat(inputs, axis=self.dim)
+
+    def extra_repr(self):
+        return 'dim={}'.format(self.dim)

ppdet/modeling/losses/__init__.py

@@ -19,6 +19,7 @@ from . import ssd_loss
 from . import fcos_loss
 from . import solov2_loss
 from . import ctfocal_loss
+from . import keypoint_loss
 
 from .yolo_loss import *
 from .iou_aware_loss import *
@@ -27,3 +28,4 @@ from .ssd_loss import *
 from .fcos_loss import *
 from .solov2_loss import *
 from .ctfocal_loss import *
+from .keypoint_loss import *

ppdet/modeling/losses/keypoint_loss.py

+# 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 itertools import cycle, islice
+from collections import abc
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from ppdet.core.workspace import register, serializable
+
+__all__ = ['HrHrnetLoss']
+
+
+@register
+@serializable
+class HrHrnetLoss(nn.Layer):
+    def __init__(self, num_joints, swahr):
+        """
+        HrHrnetLoss layer
+
+        Args:
+            num_joints (int): number of keypoints
+        """
+        super(HrHrnetLoss, self).__init__()
+        if swahr:
+            self.heatmaploss = HeatMapSWAHRLoss(num_joints)
+        else:
+            self.heatmaploss = HeatMapLoss()
+        self.aeloss = AELoss()
+        self.ziploss = ZipLoss(
+            [self.heatmaploss, self.heatmaploss, self.aeloss])
+
+    def forward(self, inputs, records):
+        targets = []
+        targets.append([records['heatmap_gt1x'], records['mask_1x']])
+        targets.append([records['heatmap_gt2x'], records['mask_2x']])
+        targets.append(records['tagmap'])
+        keypoint_losses = dict()
+        loss = self.ziploss(inputs, targets)
+        keypoint_losses['heatmap_loss'] = loss[0] + loss[1]
+        keypoint_losses['pull_loss'] = loss[2][0]
+        keypoint_losses['push_loss'] = loss[2][1]
+        keypoint_losses['loss'] = recursive_sum(loss)
+        return keypoint_losses
+
+
+class HeatMapLoss(object):
+    def __init__(self, loss_factor=1.0):
+        super(HeatMapLoss, self).__init__()
+        self.loss_factor = loss_factor
+
+    def __call__(self, preds, targets):
+        heatmap, mask = targets
+        loss = ((preds - heatmap)**2 * mask.cast('float').unsqueeze(1))
+        loss = paddle.clip(loss, min=0, max=2).mean()
+        loss *= self.loss_factor
+        return loss
+
+
+class HeatMapSWAHRLoss(object):
+    def __init__(self, num_joints, loss_factor=1.0):
+        super(HeatMapSWAHRLoss, self).__init__()
+        self.loss_factor = loss_factor
+        self.num_joints = num_joints
+
+    def __call__(self, preds, targets):
+        heatmaps_gt, mask = targets
+        heatmaps_pred = preds[0]
+        scalemaps_pred = preds[1]
+
+        heatmaps_scaled_gt = paddle.where(heatmaps_gt > 0, 0.5 * heatmaps_gt * (
+            1 + (1 +
+                 (scalemaps_pred - 1.) * paddle.log(heatmaps_gt + 1e-10))**2),
+                                          heatmaps_gt)
+
+        regularizer_loss = paddle.mean(
+            paddle.pow((scalemaps_pred - 1.) * (heatmaps_gt > 0).astype(float),
+                       2))
+        omiga = 0.01
+        # thres = 2**(-1/omiga), threshold for positive weight
+        hm_weight = heatmaps_scaled_gt**(
+            omiga
+        ) * paddle.abs(1 - heatmaps_pred) + paddle.abs(heatmaps_pred) * (
+            1 - heatmaps_scaled_gt**(omiga))
+
+        loss = (((heatmaps_pred - heatmaps_scaled_gt)**2) *
+                mask.cast('float').unsqueeze(1)) * hm_weight
+        loss = loss.mean()
+        loss = self.loss_factor * (loss + 1.0 * regularizer_loss)
+        return loss
+
+
+class AELoss(object):
+    def __init__(self, pull_factor=0.001, push_factor=0.001):
+        super(AELoss, self).__init__()
+        self.pull_factor = pull_factor
+        self.push_factor = push_factor
+
+    def apply_single(self, pred, tagmap):
+        if tagmap.numpy()[:, :, 3].sum() == 0:
+            return (paddle.zeros([1]), paddle.zeros([1]))
+        nonzero = paddle.nonzero(tagmap[:, :, 3] > 0)
+        if nonzero.shape[0] == 0:
+            return (paddle.zeros([1]), paddle.zeros([1]))
+        p_inds = paddle.unique(nonzero[:, 0])
+        num_person = p_inds.shape[0]
+        if num_person == 0:
+            return (paddle.zeros([1]), paddle.zeros([1]))
+
+        pull = 0
+        tagpull_num = 0
+        embs_all = []
+        person_unvalid = 0
+        for person_idx in p_inds.numpy():
+            valid_single = tagmap[person_idx.item()]
+            validkpts = paddle.nonzero(valid_single[:, 3] > 0)
+            valid_single = paddle.index_select(valid_single, validkpts)
+            emb = paddle.gather_nd(pred, valid_single[:, :3])
+            if emb.shape[0] == 1:
+                person_unvalid += 1
+            mean = paddle.mean(emb, axis=0)
+            embs_all.append(mean)
+            pull += paddle.mean(paddle.pow(emb - mean, 2), axis=0)
+            tagpull_num += emb.shape[0]
+        pull /= max(num_person - person_unvalid, 1)
+        if num_person < 2:
+            return pull, paddle.zeros([1])
+
+        embs_all = paddle.stack(embs_all)
+        A = embs_all.expand([num_person, num_person])
+        B = A.transpose([1, 0])
+        diff = A - B
+
+        diff = paddle.pow(diff, 2)
+        push = paddle.exp(-diff)
+        push = paddle.sum(push) - num_person
+
+        push /= 2 * num_person * (num_person - 1)
+        return pull, push
+
+    def __call__(self, preds, tagmaps):
+        bs = preds.shape[0]
+        losses = [self.apply_single(preds[i], tagmaps[i]) for i in range(bs)]
+        pull = self.pull_factor * sum(loss[0] for loss in losses) / len(losses)
+        push = self.push_factor * sum(loss[1] for loss in losses) / len(losses)
+        return pull, push
+
+
+class ZipLoss(object):
+    def __init__(self, loss_funcs):
+        super(ZipLoss, self).__init__()
+        self.loss_funcs = loss_funcs
+
+    def __call__(self, inputs, targets):
+        assert len(self.loss_funcs) == len(targets) >= len(inputs)
+
+        def zip_repeat(*args):
+            longest = max(map(len, args))
+            filled = [islice(cycle(x), longest) for x in args]
+            return zip(*filled)
+
+        return tuple(
+            fn(x, y)
+            for x, y, fn in zip_repeat(inputs, targets, self.loss_funcs))
+
+
+def recursive_sum(inputs):
+    if isinstance(inputs, abc.Sequence):
+        return sum([recursive_sum(x) for x in inputs])
+    return inputs

requirements.txt

@@ -7,4 +7,5 @@ shapely
 scipy
 terminaltables
 pycocotools
+xtcocotools==1.6
 setuptools>=42.0.0