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import warnings
import mmcv
import numpy as np
from mmcv.image import imwrite
from mmcv.visualization.image import imshow
from mmpose.core import imshow_keypoints
from .. import builder
from ..builder import POSENETS
from .base import BasePose
import torch
from config import cfg
try:
from mmcv.runner import auto_fp16
except ImportError:
warnings.warn('auto_fp16 from mmpose will be deprecated from v0.15.0'
'Please install mmcv>=1.1.4')
from mmpose.core import auto_fp16
from .top_down import TopDown
@POSENETS.register_module()
class Poseur(TopDown):
def __init__(self, *args, **kwargs):
if 'filp_fuse_type' in kwargs:
self.filp_fuse_type = kwargs.pop('filp_fuse_type')
else:
self.filp_fuse_type = 'default'
super().__init__(*args, **kwargs)
def init_weights(self, pretrained=None):
"""Weight initialization for model."""
self.backbone.init_weights(pretrained)
if self.with_neck:
self.neck.init_weights()
if self.with_keypoint:
self.keypoint_head.init_weights()
@auto_fp16(apply_to=('img',))
def forward(self,
img,
coord_target=None,
coord_target_weight=None,
bbox_target=None,
bbox_target_weight=None,
hp_target=None,
hp_target_weight=None,
img_metas=None,
return_loss=True,
return_heatmap=False,
coord_init=None,
query_init=None,
**kwargs):
"""Calls either forward_train or forward_test depending on whether
return_loss=True. Note this setting will change the expected inputs.
When `return_loss=True`, img and img_meta are single-nested (i.e.
Tensor and List[dict]), and when `resturn_loss=False`, img and img_meta
should be double nested (i.e. List[Tensor], List[List[dict]]), with
the outer list indicating test time augmentations.
Note:
batch_size: N
num_keypoints: K
num_img_channel: C (Default: 3)
img height: imgH
img weight: imgW
heatmaps height: H
heatmaps weight: W
Args:
img (torch.Tensor[NxCximgHximgW]): Input images.
target (torch.Tensor[NxKxHxW]): Target heatmaps.
target_weight (torch.Tensor[NxKx1]): Weights across
different joint types.
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
return_loss (bool): Option to `return loss`. `return loss=True`
for training, `return loss=False` for validation & test.
return_heatmap (bool) : Option to return heatmap.
Returns:
dict|tuple: if `return loss` is true, then return losses.
Otherwise, return predicted poses, boxes, image paths
and heatmaps.
"""
return self.forward_mesh_recovery(img, coord_init=coord_init, query_init=query_init,
**kwargs)
# if return_loss:
# return self.forward_train(img,
# coord_target, coord_target_weight,
# hp_target, hp_target_weight, img_metas,
# **kwargs)
# return self.forward_test(
# img, img_metas, return_heatmap=return_heatmap, **kwargs)
def forward_train(self, img, coord_target, coord_target_weight,
hp_target, hp_target_weight, img_metas, **kwargs):
"""
:param img:
:param coord_target: [2, 17, 2]
:param coord_target_weight: [2, 17, 2]
:param hp_target: [2, 4, 17, 64, 48]
:param hp_target_weight: [2, 4, 17, 1]
:param img_metas:
:param kwargs:
:return:
"""
"""Defines the computation performed at every call when training."""
output = self.backbone(img)
img_feat = output[-1]
if self.with_neck:
output = self.neck(output)
if self.with_keypoint:
# output = self.keypoint_head(output, img_metas)
enc_output, dec_output = self.keypoint_head(output)
return img_feat, enc_output, dec_output, None
def seperate_sigma_from_score(self, score):
if score.shape[2] == 3:
sigma = score[:, :, [1, 2]]
score = score[:, :, [0]]
return score, sigma
elif score.shape[2] == 1:
return score, None
else:
raise
def forward_mesh_recovery(self, output, coord_init=None, query_init=None, **kwargs):
"""
:param img:
:param coord_target: [2, 17, 2]
:param coord_target_weight: [2, 17, 2]
:param hp_target: [2, 4, 17, 64, 48]
:param hp_target_weight: [2, 4, 17, 1]
:param img_metas:
:param kwargs:
:return:
"""
"""Defines the computation performed at every call when training."""
# output = self.backbone(img)
img_feat = output[-1]
# print(len(output))
if self.with_neck:
output = self.neck(output)
if self.with_keypoint:
# output = self.keypoint_head(output, img_metas)
enc_output, dec_output = self.keypoint_head(output, coord_init=coord_init, query_init=query_init)
return dec_output.feat[-1]
def forward_test(self, img, img_metas, return_heatmap=False, **kwargs):
"""Defines the computation performed at every call when testing."""
assert img.size(0) == len(img_metas)
batch_size, _, img_height, img_width = img.shape
if batch_size > 1:
assert 'bbox_id' in img_metas[0]
result = {}
features = self.backbone(img)
if self.with_neck:
features = self.neck(features)
if self.with_keypoint:
output_regression, output_regression_score = self.keypoint_head.inference_model(
features, flip_pairs=None)
output_regression_score, output_regression_sigma = self.seperate_sigma_from_score(output_regression_score)
if self.test_cfg['flip_test']:
img_flipped = img.flip(3)
features_flipped = self.backbone(img_flipped)
if self.with_neck:
features_flipped = self.neck(features_flipped)
if self.with_keypoint:
output_regression_flipped, output_regression_score_flipped = self.keypoint_head.inference_model(
features_flipped, img_metas[0]['flip_pairs'])
output_regression_score_flipped, output_regression_sigma_flipped = \
self.seperate_sigma_from_score(output_regression_score_flipped)
if self.filp_fuse_type == 'default':
output_regression = (output_regression +
output_regression_flipped) * 0.5
output_regression_score = (output_regression_score +
output_regression_score_flipped) * 0.5
elif self.filp_fuse_type == 'type1':
# output_regression = (output_regression * output_regression_score + output_regression_flipped * output_regression_score_flipped)\
# /(output_regression_score + output_regression_score_flipped+1e-9)
output_regression, output_regression_flipped = \
torch.from_numpy(output_regression), torch.from_numpy(output_regression_flipped)
output_regression_score, output_regression_score_flipped = \
torch.from_numpy(output_regression_score), torch.from_numpy(output_regression_score_flipped)
output_regression = (
output_regression * output_regression_score + output_regression_flipped * output_regression_score_flipped) \
/ (output_regression_score + output_regression_score_flipped + 1e-9)
diff = 1 - (output_regression_score - output_regression_score_flipped).abs()
output_regression_score = (output_regression_score * output_regression_score_flipped * diff) ** 2
output_regression = output_regression.numpy()
output_regression_score = output_regression_score.numpy()
elif self.filp_fuse_type == 'type2':
# output_regression = (output_regression * output_regression_score + output_regression_flipped * output_regression_score_flipped)\
# /(output_regression_score + output_regression_score_flipped+1e-9)
output_regression, output_regression_flipped = \
torch.from_numpy(output_regression), torch.from_numpy(output_regression_flipped)
output_regression_sigma, output_regression_sigma_flipped = \
torch.from_numpy(output_regression_sigma), torch.from_numpy(output_regression_sigma_flipped)
output_regression_p, output_regression_p_flipped = \
self.get_p(output_regression_sigma), self.get_p(output_regression_sigma_flipped)
p_to_coord_index = 5
output_regression = (
output_regression * output_regression_p ** p_to_coord_index + output_regression_flipped * output_regression_p_flipped ** p_to_coord_index) \
/ (
output_regression_p ** p_to_coord_index + output_regression_p_flipped ** p_to_coord_index + 1e-10)
output_regression_score = (output_regression_p + output_regression_p_flipped) * 0.5
output_regression = output_regression.numpy()
output_regression_score = output_regression_score.numpy()
else:
NotImplementedError
if self.with_keypoint:
keypoint_result = self.keypoint_head.decode_keypoints(
img_metas, output_regression, output_regression_score, [img_width, img_height])
result.update(keypoint_result)
if not return_heatmap:
output_heatmap = None
result['output_heatmap'] = output_heatmap
return result
def get_p(self, output_regression_sigma, p_x=0.2):
output_regression_p = (1 - np.exp(-(p_x / output_regression_sigma)))
output_regression_p = output_regression_p[:, :, 0] * output_regression_p[:, :, 1]
output_regression_p = output_regression_p[:, :, None]
return output_regression_p * 0.7
# 0.2 0.7 7421
# 0.2 0.7 7610
# 0.17 0.7
def forward_dummy(self, img):
"""Used for computing network FLOPs.
See ``tools/get_flops.py``.
Args:
img (torch.Tensor): Input image.
Returns:
Tensor: Output heatmaps.
"""
output = self.backbone(img)
if self.with_neck:
output = self.neck(output)
if self.with_keypoint:
img_h, img_w = 256, 192
img_metas = [{}]
img_metas[0]['batch_input_shape'] = (img_h, img_w)
img_metas[0]['img_shape'] = (img_h, img_w, 3)
# output = self.keypoint_head(output, img_metas)
output = self.keypoint_head(output)
return output
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