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import numpy as np
import torch
import torch.nn as nn
import copy
import math
import warnings
from mmcv.cnn import build_upsample_layer, Linear, bias_init_with_prob, constant_init, normal_init
import torch.nn.functional as F
from mmcv.cnn import normal_init
from mmpose.core.evaluation import (keypoint_pck_accuracy,
keypoints_from_regression)
from mmpose.core.post_processing import fliplr_regression
from mmpose.models.builder import build_loss, HEADS, build_transformer
from mmpose.core.evaluation import pose_pck_accuracy
from mmpose.models.utils.transformer import inverse_sigmoid
from mmcv.cnn import Conv2d, build_activation_layer
from mmcv.cnn.bricks.transformer import Linear, FFN, build_positional_encoding
from mmcv.cnn import ConvModule
import torch.distributions as distributions
from .rle_regression_head import nets, nett, RealNVP, nets3d, nett3d
from easydict import EasyDict
from mmpose.models.losses.regression_loss import L1Loss
from mmpose.models.losses.rle_loss import RLELoss_poseur, RLEOHKMLoss
from config import cfg
from utils.human_models import smpl_x
from torch.distributions.utils import lazy_property
from torch.distributions import MultivariateNormal
def fliplr_rle_regression(regression,
regression_score,
flip_pairs,
center_mode='static',
center_x=0.5,
center_index=0):
"""Flip human joints horizontally.
Note:
batch_size: N
num_keypoint: K
Args:
regression (np.ndarray([..., K, C])): Coordinates of keypoints, where K
is the joint number and C is the dimension. Example shapes are:
- [N, K, C]: a batch of keypoints where N is the batch size.
- [N, T, K, C]: a batch of pose sequences, where T is the frame
number.
flip_pairs (list[tuple()]): Pairs of keypoints which are mirrored
(for example, left ear -- right ear).
center_mode (str): The mode to set the center location on the x-axis
to flip around. Options are:
- static: use a static x value (see center_x also)
- root: use a root joint (see center_index also)
center_x (float): Set the x-axis location of the flip center. Only used
when center_mode=static.
center_index (int): Set the index of the root joint, whose x location
will be used as the flip center. Only used when center_mode=root.
Returns:
tuple: Flipped human joints.
- regression_flipped (np.ndarray([..., K, C])): Flipped joints.
"""
assert regression.ndim >= 2, f'Invalid pose shape {regression.shape}'
allowed_center_mode = {'static', 'root'}
assert center_mode in allowed_center_mode, 'Get invalid center_mode ' \
f'{center_mode}, allowed choices are {allowed_center_mode}'
if center_mode == 'static':
x_c = center_x
elif center_mode == 'root':
assert regression.shape[-2] > center_index
x_c = regression[..., center_index:center_index + 1, 0]
regression_flipped = regression.copy()
regression_score_flipped = regression_score.copy()
# Swap left-right parts
for left, right in flip_pairs:
regression_flipped[..., left, :] = regression[..., right, :]
regression_flipped[..., right, :] = regression[..., left, :]
regression_score_flipped[..., left, :] = regression_score[..., right, :]
regression_score_flipped[..., right, :] = regression_score[..., left, :]
# Flip horizontally
regression_flipped[..., 0] = x_c * 2 - regression_flipped[..., 0]
return regression_flipped, regression_score_flipped
class Linear_with_norm(nn.Module):
def __init__(self, in_channel, out_channel, bias=True, norm=True):
super(Linear_with_norm, self).__init__()
self.bias = bias
self.norm = norm
self.linear = nn.Linear(in_channel, out_channel, bias)
nn.init.xavier_uniform_(self.linear.weight, gain=0.01)
def forward(self, x):
y = x.matmul(self.linear.weight.t())
if self.norm:
x_norm = torch.norm(x, dim=-1, keepdim=True)
y = y / x_norm
if self.bias:
y = y + self.linear.bias
return y
def deepapply(obj, fn):
r"""Applies `fn` to all tensors referenced in `obj`"""
if torch.is_tensor(obj):
obj = fn(obj)
elif isinstance(obj, dict):
for key, value in obj.items():
obj[key] = deepapply(value, fn)
elif isinstance(obj, list):
for i, value in enumerate(obj):
obj[i] = deepapply(value, fn)
elif isinstance(obj, tuple):
obj = tuple(
deepapply(value, fn)
for value in obj
)
elif hasattr(obj, '__dict__'):
deepapply(obj.__dict__, fn)
return obj
__init__ = MultivariateNormal.__init__
def init(self, *args, **kwargs):
__init__(self, *args, **kwargs)
self.__class__ = type(
self.__class__.__name__,
(self.__class__, nn.Module),
{},
)
nn.Module.__init__(self)
MultivariateNormal.__init__ = init
MultivariateNormal._apply = deepapply
@HEADS.register_module()
class Poseur_noise_sample(nn.Module):
"""
rle loss for transformer_utils
"""
def __init__(self,
in_channels,
num_queries=17,
num_reg_fcs=2,
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=128,
normalize=True),
transformer=None,
with_box_refine=False,
as_two_stage=False,
heatmap_size=[64, 48],
num_joints=17,
loss_coord_enc=None,
loss_coord_dec=None,
loss_hp_keypoint=None,
use_heatmap_loss=True,
train_cfg=None,
test_cfg=None,
use_udp=False,
):
super().__init__()
self.use_udp = use_udp
self.num_queries = num_queries
self.num_reg_fcs = num_reg_fcs
self.in_channels = in_channels
self.act_cfg = transformer.get('act_cfg', dict(type='ReLU', inplace=True))
self.activate = build_activation_layer(self.act_cfg)
self.positional_encoding = build_positional_encoding(positional_encoding)
self.with_box_refine = with_box_refine
self.as_two_stage = as_two_stage
if self.as_two_stage:
transformer['as_two_stage'] = self.as_two_stage
self.transformer = build_transformer(transformer)
self.embed_dims = self.transformer.embed_dims
assert 'num_feats' in positional_encoding
num_feats = positional_encoding['num_feats']
assert num_feats * 2 == self.embed_dims, 'embed_dims should' \
f' be exactly 2 times of num_feats. Found {self.embed_dims}' \
f' and {num_feats}.'
self.num_joints = num_joints
# self.num_joints = len(smpl_x.pos_joint_part['rhand'])
self.heatmap_size = heatmap_size
self.loss_coord_enc = build_loss(loss_coord_enc)
self.loss_coord_dec = build_loss(loss_coord_dec)
self.use_dec_rle_loss = isinstance(self.loss_coord_dec, RLELoss_poseur) or isinstance(self.loss_coord_dec,
RLEOHKMLoss)
self.use_heatmap_loss = use_heatmap_loss
if self.use_heatmap_loss:
self.loss_hp = build_loss(loss_hp_keypoint)
self.train_cfg = {} if train_cfg is None else train_cfg
self.test_cfg = {} if test_cfg is None else test_cfg
enc_prior = MultivariateNormal(torch.zeros(2), torch.eye(2))
dec_prior = MultivariateNormal(torch.zeros(2), torch.eye(2))
masks = torch.from_numpy(np.array([[0, 1], [1, 0]] * 3).astype(np.float32))
enc_prior3d = MultivariateNormal(torch.zeros(3), torch.eye(3))
dec_prior3d = MultivariateNormal(torch.zeros(3), torch.eye(3))
masks3d = torch.from_numpy(np.array([[0, 0, 1], [1, 1, 0]] * 3).astype(np.float32))
self.enc_flow2d = RealNVP(nets, nett, masks, enc_prior)
self.enc_flow3d = RealNVP(nets3d, nett3d, masks3d, enc_prior3d)
if self.use_dec_rle_loss:
self.dec_flow2d = RealNVP(nets, nett, masks, dec_prior)
self.dec_flow3d = RealNVP(nets3d, nett3d, masks3d, dec_prior3d)
self._init_layers()
def _init_layers(self):
"""Initialize classification branch and regression branch of head."""
fc_coord_branch = []
for _ in range(self.num_reg_fcs):
fc_coord_branch.append(Linear(self.embed_dims, self.embed_dims))
fc_coord_branch.append(nn.ReLU())
fc_coord_branch.append(Linear(self.embed_dims, 3))
fc_coord_branch = nn.Sequential(*fc_coord_branch)
if self.use_dec_rle_loss:
fc_sigma_branch = []
for _ in range(self.num_reg_fcs):
fc_sigma_branch.append(Linear(self.embed_dims, self.embed_dims))
fc_sigma_branch.append(Linear_with_norm(self.embed_dims, 3, norm=False))
fc_sigma_branch = nn.Sequential(*fc_sigma_branch)
def _get_clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
num_pred = self.transformer.decoder.num_layers
if self.with_box_refine:
self.fc_coord_branches = _get_clones(fc_coord_branch, num_pred)
self.fc_coord_output_branches = _get_clones(fc_coord_branch, num_pred)
if self.use_dec_rle_loss:
self.fc_sigma_branches = _get_clones(fc_sigma_branch, num_pred)
else:
self.fc_coord_branches = nn.ModuleList(
[fc_coord_branch for _ in range(num_pred)])
if isinstance(self.loss_coord_dec, RLELoss) or isinstance(self.loss_coord_dec, RLEOHKMLoss):
self.fc_sigma_branches = nn.ModuleList([fc_sigma_branch for _ in range(1)])
if self.as_two_stage:
self.query_embedding = None
else:
self.query_embedding = nn.Embedding(self.num_queries,
self.embed_dims * 2)
if self.use_heatmap_loss:
from mmcv.cnn import build_upsample_layer
# simplebaseline style
num_layers = 3
num_kernels = [4, 4, 4]
num_filters = [256, 256, 256]
layers = []
for i in range(num_layers):
kernel, padding, output_padding = \
self._get_deconv_cfg(num_kernels[i])
planes = num_filters[i]
if i == 0:
layers.append(
build_upsample_layer(
dict(type='deconv'),
in_channels=self.embed_dims,
out_channels=planes,
kernel_size=kernel,
stride=2,
padding=padding,
output_padding=output_padding,
bias=False))
else:
layers.append(
build_upsample_layer(
dict(type='deconv'),
in_channels=planes,
out_channels=planes,
kernel_size=kernel,
stride=2,
padding=padding,
output_padding=output_padding,
bias=False))
layers.append(nn.BatchNorm2d(planes))
layers.append(nn.ReLU(inplace=True))
self.in_channels = planes
self.deconv_layer = nn.Sequential(*layers)
self.final_layer = nn.Sequential(
ConvModule(
self.embed_dims,
self.num_joints,
kernel_size=1,
stride=1,
padding=0,
norm_cfg=None,
act_cfg=None,
inplace=False)
)
@staticmethod
def _get_deconv_cfg(deconv_kernel):
"""Get configurations for deconv layers."""
if deconv_kernel == 4:
padding = 1
output_padding = 0
elif deconv_kernel == 3:
padding = 1
output_padding = 1
elif deconv_kernel == 2:
padding = 0
output_padding = 0
else:
raise ValueError(f'Not supported num_kernels ({deconv_kernel}).')
return deconv_kernel, padding, output_padding
def init_weights(self):
"""Initialize weights of the DeformDETR head."""
self.transformer.init_weights()
# for m in [self.fc_coord_branches, self.fc_sigma_branches]:
for m in [self.fc_coord_branches]:
for mm in m:
if isinstance(mm, nn.Linear):
nn.init.xavier_uniform_(mm.weight, gain=0.01)
for m in [self.fc_coord_output_branches]:
for mm in m:
if isinstance(mm, nn.Linear):
nn.init.xavier_uniform_(mm.weight, gain=0.01)
if self.use_heatmap_loss:
for _, m in self.deconv_layer.named_modules():
if isinstance(m, nn.ConvTranspose2d):
normal_init(m, std=0.001)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
for m in self.final_layer.modules():
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.001, bias=0)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
def forward(self, mlvl_feats, coord_init=None, query_init=None):
batch_size = mlvl_feats[0].size(0)
img_w, img_h = self.train_cfg['image_size']
img_masks = mlvl_feats[0].new_ones(
(batch_size, img_h, img_w))
for img_id in range(batch_size):
img_masks[img_id, :img_h, :img_w] = 0
mlvl_masks = []
mlvl_positional_encodings = []
for feat in mlvl_feats:
mlvl_masks.append(F.interpolate(img_masks[None], size=feat.shape[-2:]).to(torch.bool).squeeze(0))
mlvl_positional_encodings.append(
self.positional_encoding(mlvl_masks[-1]))
query_embeds = None
if not self.as_two_stage:
query_embeds = self.query_embedding.weight
memory, spatial_shapes, level_start_index, hs, init_reference, inter_references, \
enc_outputs = self.transformer(
mlvl_feats,
mlvl_masks,
query_embeds,
mlvl_positional_encodings,
reg_branches=self.fc_coord_branches if self.with_box_refine else None, # noqa:E501
cls_branches=None, # noqa:E501
coord_init=coord_init,
query_init=query_init,
)
hs = hs.permute(0, 2, 1, 3)
outputs_coords = []
dec_outputs = EasyDict(pred_jts=outputs_coords, feat=hs)
return enc_outputs, dec_outputs
def get_loss(self, enc_output, dec_output, coord_target, coord_target_weight, hp_target, hp_target_weight):
losses = dict()
if self.as_two_stage and enc_output is not None:
enc_rle_loss = self.get_enc_rle_loss(enc_output, coord_target, coord_target_weight)
losses.update(enc_rle_loss)
dec_rle_loss = self.get_dec_rle_loss(dec_output, coord_target, coord_target_weight)
losses.update(dec_rle_loss)
return losses
def get_enc_rle_loss(self, output, target, target_weight):
"""Calculate top-down keypoint loss.
Note:
batch_size: N
num_keypoints: K
Args:
output (torch.Tensor[N, K, 2]): Output keypoints.
target (torch.Tensor[N, K, 2]): Target keypoints.
target_weight (torch.Tensor[N, K, 2]):
Weights across different joint types.
"""
losses = dict()
assert not isinstance(self.loss_coord_enc, nn.Sequential)
assert target.dim() == 3 and target_weight.dim() == 3
BATCH_SIZE = output.sigma.size(0)
gt_uvd = target.reshape(output.pred_jts.shape)
gt_uvd_weight = target_weight.reshape(output.pred_jts.shape)
gt_3d_mask = gt_uvd_weight[:, :, 2].reshape(-1)
assert output.pred_jts.shape == output.sigma.shape, (output.pred_jts.shape, output.sigma.shape)
bar_mu = (output.pred_jts - gt_uvd) / output.sigma
bar_mu = bar_mu.reshape(-1, 3)
bar_mu_3d = bar_mu[gt_3d_mask > 0]
bar_mu_2d = bar_mu[gt_3d_mask < 1][:, :2]
# (B, K, 3)
log_phi_3d = self.enc_flow3d.log_prob(bar_mu_3d)
log_phi_2d = self.enc_flow2d.log_prob(bar_mu_2d)
log_phi = torch.zeros_like(bar_mu[:, 0])
# print(gt_3d_mask)
log_phi[gt_3d_mask > 0] = log_phi_3d
log_phi[gt_3d_mask < 1] = log_phi_2d
log_phi = log_phi.reshape(BATCH_SIZE, self.num_joints, 1)
output.nf_loss = torch.log(output.sigma) - log_phi
losses['enc_rle_loss'] = self.loss_coord_enc(output, target, target_weight)
return losses
def get_enc_rle_loss_old(self, output, target, target_weight):
"""Calculate top-down keypoint loss.
Note:
batch_size: N
num_keypoints: K
Args:
output (torch.Tensor[N, K, 2]): Output keypoints.
target (torch.Tensor[N, K, 2]): Target keypoints.
target_weight (torch.Tensor[N, K, 2]):
Weights across different joint types.
"""
losses = dict()
assert not isinstance(self.loss_coord_enc, nn.Sequential)
assert target.dim() == 3 and target_weight.dim() == 3
BATCH_SIZE = output.sigma.size(0)
gt_uv = target.reshape(output.pred_jts.shape)
bar_mu = (output.pred_jts - gt_uv) / output.sigma
# (B, K, 1)
log_phi = self.enc_flow.log_prob(bar_mu.reshape(-1, 2)).reshape(BATCH_SIZE, self.num_joints, 1)
output.nf_loss = torch.log(output.sigma) - log_phi
losses['enc_rle_loss'] = self.loss_coord_enc(output, target, target_weight)
return losses
def get_dec_rle_loss(self, output, target, target_weight):
"""Calculate top-down keypoint loss.
Note:
batch_size: N
num_keypoints: K
Args:
output (torch.Tensor[N, K, 2]): Output keypoints.
target (torch.Tensor[N, K, 2]): Target keypoints.
target_weight (torch.Tensor[N, K, 2]):
Weights across different joint types.
"""
losses = dict()
assert not isinstance(self.loss_coord_dec, nn.Sequential)
assert target.dim() == 3 and target_weight.dim() == 3
target = target.repeat(1, self.transformer.num_noise_sample + 1, 1)
target_weight = target_weight.repeat(1, self.transformer.num_noise_sample + 1, 1)
if self.with_box_refine:
if self.use_dec_rle_loss:
for i in range(len(output.pred_jts)):
pred_jts, sigma = output.pred_jts[i], output.sigma[i]
output_i = EasyDict(
pred_jts=pred_jts,
sigma=sigma
)
BATCH_SIZE = output_i.sigma.size(0)
gt_uvd = target.reshape(output_i.pred_jts.shape)
gt_uvd_weight = target_weight.reshape(pred_jts.shape)
gt_3d_mask = gt_uvd_weight[:, :, 2].reshape(-1)
assert pred_jts.shape == sigma.shape, (pred_jts.shape, sigma.shape)
bar_mu = (output_i.pred_jts - gt_uvd) / output_i.sigma
bar_mu = bar_mu.reshape(-1, 3)
bar_mu_3d = bar_mu[gt_3d_mask > 0]
bar_mu_2d = bar_mu[gt_3d_mask < 1][:, :2]
# (B, K, 3)
log_phi_3d = self.dec_flow3d.log_prob(bar_mu_3d)
log_phi_2d = self.dec_flow2d.log_prob(bar_mu_2d)
log_phi = torch.zeros_like(bar_mu[:, 0])
log_phi[gt_3d_mask > 0] = log_phi_3d
log_phi[gt_3d_mask < 1] = log_phi_2d
log_phi = log_phi.reshape(BATCH_SIZE, self.num_joints * (self.transformer.num_noise_sample + 1), 1)
output_i.nf_loss = torch.log(output_i.sigma) - log_phi
losses['dec_rle_loss_{}'.format(i)] = self.loss_coord_dec(output_i, target, target_weight)
else:
for i, pred_jts in enumerate(output.pred_jts):
losses['dec_rle_loss_{}'.format(i)] = self.loss_coord_dec(pred_jts, target, target_weight)
else:
if self.use_dec_rle_loss:
BATCH_SIZE = output.sigma.size(0)
gt_uv = target.reshape(output.pred_jts.shape)
bar_mu = (output.pred_jts - gt_uv) / output.sigma
# (B, K, 1)
log_phi = self.dec_flow.log_prob(bar_mu.reshape(-1, 2)).reshape(BATCH_SIZE, self.num_joints, 1)
output.nf_loss = torch.log(output.sigma) - log_phi
losses['dec_rle_loss'] = self.loss_coord_dec(output, target, target_weight) * 0
else:
losses['dec_rle_loss'] = self.loss_coord_dec(output.pred_jts, target + 0.5, target_weight) * 0
return losses
def get_hp_loss(self, output, target, target_weight):
"""Calculate top-down keypoint loss.
Note:
batch_size: N
num_keypoints: K
heatmaps height: H
heatmaps weight: W
Args:
output (torch.Tensor[NxKxHxW]): Output heatmaps.
target (torch.Tensor[NxKxHxW]): Target heatmaps.
target_weight (torch.Tensor[NxKx1]):
Weights across different joint types.
"""
losses = dict()
if isinstance(self.loss_hp, nn.Sequential):
if not isinstance(output, dict):
assert len(self.loss_hp) == output.size(0)
assert target.dim() == 5 and target_weight.dim() == 4
num_hp_layers = output.size(0)
for i in range(num_hp_layers):
target_i = target[:, i, :, :, :]
target_weight_i = target_weight[:, i, :, :]
losses['mse_loss_{}'.format(i)] = self.loss_hp[i](output[i], target_i, target_weight_i)
else:
out_hp_backbone = output['backbone']
num_hp_layers = out_hp_backbone.size(0)
for i in range(num_hp_layers):
target_i = target[:, i, :, :, :]
target_weight_i = target_weight[:, i, :, :]
losses['mse_loss_backbone_{}'.format(i)] = self.loss_hp[i](out_hp_backbone[i], target_i,
target_weight_i)
out_hp_enc = output['enc']
for lvl in range(len(out_hp_enc)):
if lvl == 2 or lvl == 5:
# if lvl == 5:
for i in range(3):
target_i = target[:, i + 1, :, :, :]
target_weight_i = target_weight[:, i + 1, :, :]
# losses['reg_loss'] += self.loss(output[i], target, target_weight).sum()
if lvl == 2:
loss_weight = 0.1
elif lvl == 5:
loss_weight = 1.0
losses['mse_loss_enc_layer{}_c{}'.format(lvl, i + 3)] = loss_weight * self.loss_hp[i + 1](
out_hp_enc[lvl][i], target_i, target_weight_i)
else:
assert target.dim() == 4 and target_weight.dim() == 3
losses['mse_loss'] = self.loss_hp(output, target, target_weight)
return losses
def get_accuracy(self, enc_output, dec_output, coord_target, coord_target_weight, hp_target, hp_target_weight):
"""Calculate accuracy for top-down keypoint loss.
Note:
batch_size: N
num_keypoints: K
Args:
output (torch.Tensor[N, K, 2]): Output keypoints.
target (torch.Tensor[N, K, 2]): Target keypoints.
target_weight (torch.Tensor[N, K, 2]):
Weights across different joint types.
"""
accuracy = dict()
# coord_output = output["coord"]
if self.as_two_stage and enc_output is not None:
coord_output = enc_output.pred_jts
N = coord_output.shape[0]
_, avg_acc, cnt = keypoint_pck_accuracy(
coord_output.detach().cpu().numpy(),
coord_target.detach().cpu().numpy(),
coord_target_weight[:, :, 0].detach().cpu().numpy() > 0,
thr=0.05,
normalize=np.ones((N, 2), dtype=np.float32))
accuracy['enc_coord_acc'] = avg_acc
coord_output = dec_output.pred_jts
if coord_output.dim() == 4:
coord_output = coord_output[-1]
N = coord_output.shape[0]
if not self.use_dec_rle_loss:
coord_target += 0.5
# self.num_joints
_, avg_acc, cnt = keypoint_pck_accuracy(
coord_output[:, :self.num_joints].detach().cpu().numpy(),
coord_target.detach().cpu().numpy(),
coord_target_weight[:, :, 0].detach().cpu().numpy() > 0,
thr=0.05,
normalize=np.ones((N, 2), dtype=np.float32))
accuracy['dec_coord_acc'] = avg_acc
# if self.use_heatmap_loss and self.use_multi_stage_memory:
# assert hp_target.dim() == 5 and hp_target_weight.dim() == 4
# _, avg_acc, _ = pose_pck_accuracy(
# hp_output_backbone[0].detach().cpu().numpy(),
# hp_target[:, 0, ...].detach().cpu().numpy(),
# hp_target_weight[:, 0,
# ...].detach().cpu().numpy().squeeze(-1) > 0)
# accuracy['hp_acc_backbone'] = float(avg_acc)
# _, avg_acc, _ = pose_pck_accuracy(
# hp_output_enc[-1][0].detach().cpu().numpy(),
# hp_target[:, 1, ...].detach().cpu().numpy(),
# hp_target_weight[:, 1,
# ...].detach().cpu().numpy().squeeze(-1) > 0)
# accuracy['hp_acc_enc'] = float(avg_acc)
# else:
if self.use_heatmap_loss:
hp_output = dec_output["hp"]
_, avg_acc, _ = pose_pck_accuracy(
hp_output.detach().cpu().numpy(),
hp_target.detach().cpu().numpy(),
hp_target_weight.detach().cpu().numpy().squeeze(-1) > 0)
accuracy['hp_acc'] = float(avg_acc)
return accuracy
def inference_model(self, x, flip_pairs=None):
"""Inference function.
Returns:
output_regression (np.ndarray): Output regression.
Args:
x (torch.Tensor[N, K, 2]): Input features.
flip_pairs (None | list[tuple()):
Pairs of keypoints which are mirrored.
"""
output_enc, output_dec = self.forward(x)
output_regression, output_regression_score = output_dec.pred_jts.detach().cpu().numpy(), output_dec.maxvals.detach().cpu().numpy()
output_sigma = output_dec.sigma.detach().cpu().numpy()
output_sigma = output_sigma[-1]
output_regression_score = np.concatenate([output_regression_score, output_sigma], axis=2)
if output_regression.ndim == 4:
output_regression = output_regression[-1]
if flip_pairs is not None:
output_regression, output_regression_score = fliplr_rle_regression(
output_regression, output_regression_score, flip_pairs)
return output_regression, output_regression_score
def decode_keypoints(self, img_metas, output_regression, output_regression_score, img_size):
"""Decode keypoints from output regression.
Args:
img_metas (list(dict)): Information about data augmentation
By default this includes:
- "image_file: path to the image file
- "center": center of the bbox
- "scale": scale of the bbox
- "rotation": rotation of the bbox
- "bbox_score": score of bbox
output_regression (np.ndarray[N, K, 2]): model
predicted regression vector.
img_size (tuple(img_width, img_height)): model input image size.
"""
batch_size = len(img_metas)
if 'bbox_id' in img_metas[0]:
bbox_ids = []
else:
bbox_ids = None
c = np.zeros((batch_size, 2), dtype=np.float32)
s = np.zeros((batch_size, 2), dtype=np.float32)
image_paths = []
score = np.ones(batch_size)
for i in range(batch_size):
c[i, :] = img_metas[i]['center']
s[i, :] = img_metas[i]['scale']
image_paths.append(img_metas[i]['image_file'])
if 'bbox_score' in img_metas[i]:
score[i] = np.array(img_metas[i]['bbox_score']).reshape(-1)
if bbox_ids is not None:
bbox_ids.append(img_metas[i]['bbox_id'])
preds, maxvals = keypoints_from_regression(output_regression, c, s,
img_size)
all_preds = np.zeros((batch_size, preds.shape[1], 3), dtype=np.float32)
all_boxes = np.zeros((batch_size, 6), dtype=np.float32)
all_preds[:, :, 0:2] = preds[:, :, 0:2]
# all_preds[:, :, 2:3] = maxvals
all_preds[:, :, 2:3] = output_regression_score
all_boxes[:, 0:2] = c[:, 0:2]
all_boxes[:, 2:4] = s[:, 0:2]
all_boxes[:, 4] = np.prod(s * 200.0, axis=1)
all_boxes[:, 5] = score
result = {}
result['preds'] = all_preds
result['boxes'] = all_boxes
result['image_paths'] = image_paths
result['bbox_ids'] = bbox_ids
return result
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