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# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import numpy as np
import torch.nn as nn
from mmpose.core.evaluation.top_down_eval import keypoints_from_heatmaps
class TopdownHeatmapBaseHead(nn.Module):
"""Base class for top-down heatmap heads.
All top-down heatmap heads should subclass it.
All subclass should overwrite:
Methods:`get_loss`, supporting to calculate loss.
Methods:`get_accuracy`, supporting to calculate accuracy.
Methods:`forward`, supporting to forward model.
Methods:`inference_model`, supporting to inference model.
"""
__metaclass__ = ABCMeta
@abstractmethod
def get_loss(self, **kwargs):
"""Gets the loss."""
@abstractmethod
def get_accuracy(self, **kwargs):
"""Gets the accuracy."""
@abstractmethod
def forward(self, **kwargs):
"""Forward function."""
@abstractmethod
def inference_model(self, **kwargs):
"""Inference function."""
def decode(self, img_metas, output, **kwargs):
"""Decode keypoints from heatmaps.
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 (np.ndarray[N, K, H, W]): model predicted heatmaps.
"""
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_heatmaps(
output,
c,
s,
unbiased=self.test_cfg.get('unbiased_decoding', False),
post_process=self.test_cfg.get('post_process', 'default'),
kernel=self.test_cfg.get('modulate_kernel', 11),
valid_radius_factor=self.test_cfg.get('valid_radius_factor',
0.0546875),
use_udp=self.test_cfg.get('use_udp', False),
target_type=self.test_cfg.get('target_type', 'GaussianHeatmap'))
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_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
@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
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