469 lines
16 KiB
Python
469 lines
16 KiB
Python
# Copyright (c) 2021 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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import numpy as np
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import math
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import cv2
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from ppdet.core.workspace import register, create
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from .meta_arch import BaseArch
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from ..keypoint_utils import transform_preds
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from .. import layers as L
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from paddle.nn import functional as F
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__all__ = ['TopDownHRNet', 'TinyPose3DHRNet', 'TinyPose3DHRHeatmapNet']
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@register
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class TopDownHRNet(BaseArch):
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__category__ = 'architecture'
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__inject__ = ['loss']
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def __init__(self,
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width,
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num_joints,
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backbone='HRNet',
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loss='KeyPointMSELoss',
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post_process='HRNetPostProcess',
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flip_perm=None,
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flip=True,
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shift_heatmap=True,
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use_dark=True):
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"""
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HRNet network, see https://arxiv.org/abs/1902.09212
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Args:
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backbone (nn.Layer): backbone instance
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post_process (object): `HRNetPostProcess` instance
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flip_perm (list): The left-right joints exchange order list
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use_dark(bool): Whether to use DARK in post processing
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"""
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super(TopDownHRNet, self).__init__()
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self.backbone = backbone
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self.post_process = HRNetPostProcess(use_dark)
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self.loss = loss
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self.flip_perm = flip_perm
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self.flip = flip
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self.final_conv = L.Conv2d(width, num_joints, 1, 1, 0, bias=True)
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self.shift_heatmap = shift_heatmap
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self.deploy = False
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@classmethod
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def from_config(cls, cfg, *args, **kwargs):
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# backbone
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backbone = create(cfg['backbone'])
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return {'backbone': backbone, }
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def _forward(self):
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feats = self.backbone(self.inputs)
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hrnet_outputs = self.final_conv(feats[0])
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if self.training:
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return self.loss(hrnet_outputs, self.inputs)
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elif self.deploy:
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outshape = hrnet_outputs.shape
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max_idx = paddle.argmax(
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hrnet_outputs.reshape(
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(outshape[0], outshape[1], outshape[2] * outshape[3])),
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axis=-1)
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return hrnet_outputs, max_idx
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else:
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if self.flip:
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self.inputs['image'] = self.inputs['image'].flip([3])
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feats = self.backbone(self.inputs)
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output_flipped = self.final_conv(feats[0])
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output_flipped = self.flip_back(output_flipped.numpy(),
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self.flip_perm)
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output_flipped = paddle.to_tensor(output_flipped.copy())
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if self.shift_heatmap:
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output_flipped[:, :, :, 1:] = output_flipped.clone(
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)[:, :, :, 0:-1]
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hrnet_outputs = (hrnet_outputs + output_flipped) * 0.5
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imshape = (self.inputs['im_shape'].numpy()
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)[:, ::-1] if 'im_shape' in self.inputs else None
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center = self.inputs['center'].numpy(
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) if 'center' in self.inputs else np.round(imshape / 2.)
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scale = self.inputs['scale'].numpy(
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) if 'scale' in self.inputs else imshape / 200.
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outputs = self.post_process(hrnet_outputs, center, scale)
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return outputs
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def get_loss(self):
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return self._forward()
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def get_pred(self):
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res_lst = self._forward()
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outputs = {'keypoint': res_lst}
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return outputs
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def flip_back(self, output_flipped, matched_parts):
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assert output_flipped.ndim == 4,\
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'output_flipped should be [batch_size, num_joints, height, width]'
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output_flipped = output_flipped[:, :, :, ::-1]
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for pair in matched_parts:
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tmp = output_flipped[:, pair[0], :, :].copy()
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output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :]
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output_flipped[:, pair[1], :, :] = tmp
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return output_flipped
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class HRNetPostProcess(object):
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def __init__(self, use_dark=True):
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self.use_dark = use_dark
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def get_max_preds(self, heatmaps):
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'''get predictions from score maps
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Args:
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heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
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Returns:
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preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
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maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the keypoints
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'''
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assert isinstance(heatmaps,
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np.ndarray), 'heatmaps should be numpy.ndarray'
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assert heatmaps.ndim == 4, 'batch_images should be 4-ndim'
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batch_size = heatmaps.shape[0]
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num_joints = heatmaps.shape[1]
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width = heatmaps.shape[3]
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heatmaps_reshaped = heatmaps.reshape((batch_size, num_joints, -1))
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idx = np.argmax(heatmaps_reshaped, 2)
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maxvals = np.amax(heatmaps_reshaped, 2)
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maxvals = maxvals.reshape((batch_size, num_joints, 1))
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idx = idx.reshape((batch_size, num_joints, 1))
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preds = np.tile(idx, (1, 1, 2)).astype(np.float32)
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preds[:, :, 0] = (preds[:, :, 0]) % width
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preds[:, :, 1] = np.floor((preds[:, :, 1]) / width)
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pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2))
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pred_mask = pred_mask.astype(np.float32)
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preds *= pred_mask
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return preds, maxvals
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def gaussian_blur(self, heatmap, kernel):
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border = (kernel - 1) // 2
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batch_size = heatmap.shape[0]
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num_joints = heatmap.shape[1]
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height = heatmap.shape[2]
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width = heatmap.shape[3]
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for i in range(batch_size):
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for j in range(num_joints):
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origin_max = np.max(heatmap[i, j])
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dr = np.zeros((height + 2 * border, width + 2 * border))
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dr[border:-border, border:-border] = heatmap[i, j].copy()
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dr = cv2.GaussianBlur(dr, (kernel, kernel), 0)
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heatmap[i, j] = dr[border:-border, border:-border].copy()
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heatmap[i, j] *= origin_max / np.max(heatmap[i, j])
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return heatmap
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def dark_parse(self, hm, coord):
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heatmap_height = hm.shape[0]
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heatmap_width = hm.shape[1]
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px = int(coord[0])
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py = int(coord[1])
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if 1 < px < heatmap_width - 2 and 1 < py < heatmap_height - 2:
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dx = 0.5 * (hm[py][px + 1] - hm[py][px - 1])
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dy = 0.5 * (hm[py + 1][px] - hm[py - 1][px])
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dxx = 0.25 * (hm[py][px + 2] - 2 * hm[py][px] + hm[py][px - 2])
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dxy = 0.25 * (hm[py+1][px+1] - hm[py-1][px+1] - hm[py+1][px-1] \
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+ hm[py-1][px-1])
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dyy = 0.25 * (
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hm[py + 2 * 1][px] - 2 * hm[py][px] + hm[py - 2 * 1][px])
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derivative = np.matrix([[dx], [dy]])
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hessian = np.matrix([[dxx, dxy], [dxy, dyy]])
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if dxx * dyy - dxy**2 != 0:
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hessianinv = hessian.I
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offset = -hessianinv * derivative
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offset = np.squeeze(np.array(offset.T), axis=0)
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coord += offset
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return coord
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def dark_postprocess(self, hm, coords, kernelsize):
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'''DARK postpocessing, Zhang et al. Distribution-Aware Coordinate
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Representation for Human Pose Estimation (CVPR 2020).
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'''
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hm = self.gaussian_blur(hm, kernelsize)
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hm = np.maximum(hm, 1e-10)
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hm = np.log(hm)
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for n in range(coords.shape[0]):
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for p in range(coords.shape[1]):
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coords[n, p] = self.dark_parse(hm[n][p], coords[n][p])
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return coords
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def get_final_preds(self, heatmaps, center, scale, kernelsize=3):
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"""the highest heatvalue location with a quarter offset in the
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direction from the highest response to the second highest response.
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Args:
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heatmaps (numpy.ndarray): The predicted heatmaps
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center (numpy.ndarray): The boxes center
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scale (numpy.ndarray): The scale factor
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Returns:
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preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
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maxvals: numpy.ndarray([batch_size, num_joints, 1]), the maximum confidence of the keypoints
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"""
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coords, maxvals = self.get_max_preds(heatmaps)
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heatmap_height = heatmaps.shape[2]
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heatmap_width = heatmaps.shape[3]
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if self.use_dark:
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coords = self.dark_postprocess(heatmaps, coords, kernelsize)
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else:
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for n in range(coords.shape[0]):
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for p in range(coords.shape[1]):
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hm = heatmaps[n][p]
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px = int(math.floor(coords[n][p][0] + 0.5))
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py = int(math.floor(coords[n][p][1] + 0.5))
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if 1 < px < heatmap_width - 1 and 1 < py < heatmap_height - 1:
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diff = np.array([
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hm[py][px + 1] - hm[py][px - 1],
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hm[py + 1][px] - hm[py - 1][px]
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])
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coords[n][p] += np.sign(diff) * .25
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preds = coords.copy()
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# Transform back
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for i in range(coords.shape[0]):
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preds[i] = transform_preds(coords[i], center[i], scale[i],
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[heatmap_width, heatmap_height])
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return preds, maxvals
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def __call__(self, output, center, scale):
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preds, maxvals = self.get_final_preds(output.numpy(), center, scale)
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outputs = [[
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np.concatenate(
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(preds, maxvals), axis=-1), np.mean(
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maxvals, axis=1)
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]]
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return outputs
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class TinyPose3DPostProcess(object):
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def __init__(self):
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pass
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def __call__(self, output, center, scale):
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"""
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Args:
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output (numpy.ndarray): numpy.ndarray([batch_size, num_joints, 3]), keypoints coords
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scale (numpy.ndarray): The scale factor
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Returns:
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preds: numpy.ndarray([batch_size, num_joints, 3]), keypoints coords
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"""
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preds = output.numpy().copy()
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# Transform back
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for i in range(output.shape[0]): # batch_size
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preds[i][:, 0] = preds[i][:, 0] * scale[i][0]
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preds[i][:, 1] = preds[i][:, 1] * scale[i][1]
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return preds
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def soft_argmax(heatmaps, joint_num):
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dims = heatmaps.shape
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depth_dim = (int)(dims[1] / joint_num)
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heatmaps = heatmaps.reshape((-1, joint_num, depth_dim * dims[2] * dims[3]))
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heatmaps = F.softmax(heatmaps, 2)
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heatmaps = heatmaps.reshape((-1, joint_num, depth_dim, dims[2], dims[3]))
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accu_x = heatmaps.sum(axis=(2, 3))
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accu_y = heatmaps.sum(axis=(2, 4))
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accu_z = heatmaps.sum(axis=(3, 4))
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accu_x = accu_x * paddle.arange(1, 33)
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accu_y = accu_y * paddle.arange(1, 33)
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accu_z = accu_z * paddle.arange(1, 33)
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accu_x = accu_x.sum(axis=2, keepdim=True) - 1
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accu_y = accu_y.sum(axis=2, keepdim=True) - 1
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accu_z = accu_z.sum(axis=2, keepdim=True) - 1
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coord_out = paddle.concat(
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(accu_x, accu_y, accu_z), axis=2) # [batch_size, joint_num, 3]
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return coord_out
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@register
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class TinyPose3DHRHeatmapNet(BaseArch):
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__category__ = 'architecture'
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__inject__ = ['loss']
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def __init__(
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self,
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width, # 40, backbone输出的channel数目
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num_joints,
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backbone='HRNet',
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loss='KeyPointRegressionMSELoss',
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post_process=TinyPose3DPostProcess):
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"""
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Args:
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backbone (nn.Layer): backbone instance
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post_process (object): post process instance
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"""
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super(TinyPose3DHRHeatmapNet, self).__init__()
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self.backbone = backbone
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self.post_process = TinyPose3DPostProcess()
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self.loss = loss
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self.deploy = False
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self.num_joints = num_joints
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self.final_conv = L.Conv2d(width, num_joints * 32, 1, 1, 0, bias=True)
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@classmethod
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def from_config(cls, cfg, *args, **kwargs):
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# backbone
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backbone = create(cfg['backbone'])
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return {'backbone': backbone, }
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def _forward(self):
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feats = self.backbone(self.inputs) # feats:[[batch_size, 40, 32, 24]]
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hrnet_outputs = self.final_conv(feats[0])
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res = soft_argmax(hrnet_outputs, self.num_joints)
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return res
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def get_loss(self):
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pose3d = self._forward()
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loss = self.loss(pose3d, None, self.inputs)
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outputs = {'loss': loss}
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return outputs
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def get_pred(self):
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res_lst = self._forward()
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outputs = {'pose3d': res_lst}
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return outputs
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def flip_back(self, output_flipped, matched_parts):
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assert output_flipped.ndim == 4,\
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'output_flipped should be [batch_size, num_joints, height, width]'
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output_flipped = output_flipped[:, :, :, ::-1]
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for pair in matched_parts:
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tmp = output_flipped[:, pair[0], :, :].copy()
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output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :]
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output_flipped[:, pair[1], :, :] = tmp
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return output_flipped
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@register
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class TinyPose3DHRNet(BaseArch):
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__category__ = 'architecture'
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__inject__ = ['loss']
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def __init__(self,
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width,
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num_joints,
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fc_channel=768,
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backbone='HRNet',
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loss='KeyPointRegressionMSELoss',
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post_process=TinyPose3DPostProcess):
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"""
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Args:
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backbone (nn.Layer): backbone instance
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post_process (object): post process instance
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"""
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super(TinyPose3DHRNet, self).__init__()
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self.backbone = backbone
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self.post_process = TinyPose3DPostProcess()
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self.loss = loss
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self.deploy = False
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self.num_joints = num_joints
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self.final_conv = L.Conv2d(width, num_joints, 1, 1, 0, bias=True)
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self.flatten = paddle.nn.Flatten(start_axis=2, stop_axis=3)
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self.fc1 = paddle.nn.Linear(fc_channel, 256)
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self.act1 = paddle.nn.ReLU()
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self.fc2 = paddle.nn.Linear(256, 64)
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self.act2 = paddle.nn.ReLU()
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self.fc3 = paddle.nn.Linear(64, 3)
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@classmethod
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def from_config(cls, cfg, *args, **kwargs):
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# backbone
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backbone = create(cfg['backbone'])
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return {'backbone': backbone, }
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def _forward(self):
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'''
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self.inputs is a dict
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'''
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feats = self.backbone(
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self.inputs) # feats:[[batch_size, 40, width/4, height/4]]
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hrnet_outputs = self.final_conv(
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feats[0]) # hrnet_outputs: [batch_size, num_joints*32,32,32]
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flatten_res = self.flatten(
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hrnet_outputs) # [batch_size,num_joints*32,32*32]
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res = self.fc1(flatten_res)
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res = self.act1(res)
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res = self.fc2(res)
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res = self.act2(res)
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res = self.fc3(res)
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if self.training:
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return self.loss(res, self.inputs)
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else: # export model need
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return res
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def get_loss(self):
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return self._forward()
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def get_pred(self):
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res_lst = self._forward()
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outputs = {'pose3d': res_lst}
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return outputs
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def flip_back(self, output_flipped, matched_parts):
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assert output_flipped.ndim == 4,\
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'output_flipped should be [batch_size, num_joints, height, width]'
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output_flipped = output_flipped[:, :, :, ::-1]
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for pair in matched_parts:
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tmp = output_flipped[:, pair[0], :, :].copy()
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output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :]
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output_flipped[:, pair[1], :, :] = tmp
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return output_flipped
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