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# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import mmcv
import numpy as np
from mmcv.image import imwrite
from mmcv.utils.misc import deprecated_api_warning
from mmcv.visualization.image import imshow
from mmpose.core import imshow_bboxes, imshow_keypoints
from .. import builder
from ..builder import POSENETS
from .base import BasePose
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
@POSENETS.register_module()
class TopDown(BasePose):
"""Top-down pose detectors.
Args:
backbone (dict): Backbone modules to extract feature.
keypoint_head (dict): Keypoint head to process feature.
train_cfg (dict): Config for training. Default: None.
test_cfg (dict): Config for testing. Default: None.
pretrained (str): Path to the pretrained models.
loss_pose (None): Deprecated arguments. Please use
`loss_keypoint` for heads instead.
"""
def __init__(self,
backbone,
neck=None,
keypoint_head=None,
train_cfg=None,
test_cfg=None,
pretrained=None,
loss_pose=None):
super().__init__()
self.fp16_enabled = False
self.backbone = builder.build_backbone(backbone)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
if neck is not None:
self.neck = builder.build_neck(neck)
if keypoint_head is not None:
keypoint_head['train_cfg'] = train_cfg
keypoint_head['test_cfg'] = test_cfg
if 'loss_keypoint' not in keypoint_head and loss_pose is not None:
warnings.warn(
'`loss_pose` for TopDown is deprecated, '
'use `loss_keypoint` for heads instead. See '
'https://github.com/open-mmlab/mmpose/pull/382'
' for more information.', DeprecationWarning)
keypoint_head['loss_keypoint'] = loss_pose
self.keypoint_head = builder.build_head(keypoint_head)
self.pretrained = pretrained
self.init_weights()
@property
def with_neck(self):
"""Check if has neck."""
return hasattr(self, 'neck')
@property
def with_keypoint(self):
"""Check if has keypoint_head."""
return hasattr(self, 'keypoint_head')
def init_weights(self, pretrained=None):
"""Weight initialization for model."""
if pretrained is not None:
self.pretrained = pretrained
self.backbone.init_weights(self.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,
target=None,
target_weight=None,
img_metas=None,
return_loss=True,
return_heatmap=False,
**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 width: 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.
"""
if return_loss:
return self.forward_train(img, target, target_weight, img_metas,
**kwargs)
return self.forward_test(
img, img_metas, return_heatmap=return_heatmap, **kwargs)
def forward_train(self, img, target, target_weight, img_metas, **kwargs):
"""Defines the computation performed at every call when training."""
output = self.backbone(img)
if self.with_neck:
output = self.neck(output)
if self.with_keypoint:
output = self.keypoint_head(output)
# if return loss
losses = dict()
if self.with_keypoint:
keypoint_losses = self.keypoint_head.get_loss(
output, target, target_weight)
losses.update(keypoint_losses)
keypoint_accuracy = self.keypoint_head.get_accuracy(
output, target, target_weight)
losses.update(keypoint_accuracy)
return losses
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_heatmap = self.keypoint_head.inference_model(
features, flip_pairs=None)
if self.test_cfg.get('flip_test', True):
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_flipped_heatmap = self.keypoint_head.inference_model(
features_flipped, img_metas[0]['flip_pairs'])
output_heatmap = (output_heatmap + output_flipped_heatmap)
if self.test_cfg.get('regression_flip_shift', False):
output_heatmap[..., 0] -= 1.0 / img_width
output_heatmap = output_heatmap / 2
if self.with_keypoint:
keypoint_result = self.keypoint_head.decode(
img_metas, output_heatmap, img_size=[img_width, img_height])
result.update(keypoint_result)
if not return_heatmap:
output_heatmap = None
result['output_heatmap'] = output_heatmap
return result
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:
output = self.keypoint_head(output)
return output
@deprecated_api_warning({'pose_limb_color': 'pose_link_color'},
cls_name='TopDown')
def show_result(self,
img,
result,
skeleton=None,
kpt_score_thr=0.3,
bbox_color='green',
pose_kpt_color=None,
pose_link_color=None,
text_color='white',
radius=4,
thickness=1,
font_scale=0.5,
bbox_thickness=1,
win_name='',
show=False,
show_keypoint_weight=False,
wait_time=0,
out_file=None):
"""Draw `result` over `img`.
Args:
img (str or Tensor): The image to be displayed.
result (list[dict]): The results to draw over `img`
(bbox_result, pose_result).
skeleton (list[list]): The connection of keypoints.
skeleton is 0-based indexing.
kpt_score_thr (float, optional): Minimum score of keypoints
to be shown. Default: 0.3.
bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
pose_kpt_color (np.array[Nx3]`): Color of N keypoints.
If None, do not draw keypoints.
pose_link_color (np.array[Mx3]): Color of M links.
If None, do not draw links.
text_color (str or tuple or :obj:`Color`): Color of texts.
radius (int): Radius of circles.
thickness (int): Thickness of lines.
font_scale (float): Font scales of texts.
win_name (str): The window name.
show (bool): Whether to show the image. Default: False.
show_keypoint_weight (bool): Whether to change the transparency
using the predicted confidence scores of keypoints.
wait_time (int): Value of waitKey param.
Default: 0.
out_file (str or None): The filename to write the image.
Default: None.
Returns:
Tensor: Visualized img, only if not `show` or `out_file`.
"""
img = mmcv.imread(img)
img = img.copy()
bbox_result = []
bbox_labels = []
pose_result = []
for res in result:
if 'bbox' in res:
bbox_result.append(res['bbox'])
bbox_labels.append(res.get('label', None))
pose_result.append(res['keypoints'])
if bbox_result:
bboxes = np.vstack(bbox_result)
# draw bounding boxes
imshow_bboxes(
img,
bboxes,
labels=bbox_labels,
colors=bbox_color,
text_color=text_color,
thickness=bbox_thickness,
font_scale=font_scale,
show=False)
if pose_result:
imshow_keypoints(img, pose_result, skeleton, kpt_score_thr,
pose_kpt_color, pose_link_color, radius,
thickness)
if show:
imshow(img, win_name, wait_time)
if out_file is not None:
imwrite(img, out_file)
return img
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