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from torch import nn
import torch
import torch.nn.functional as F
from models.util import Hourglass, make_coordinate_grid, AntiAliasInterpolation2d
class KPDetector(nn.Module):
"""
Detecting a keypoints. Return keypoint position and jacobian near each keypoint.
"""
def __init__(self, block_expansion, num_kp, num_channels, max_features,
num_blocks, temperature, estimate_jacobian=False, scale_factor=1,
single_jacobian_map=False, pad=0):
super(KPDetector, self).__init__()
self.predictor = Hourglass(block_expansion, in_features=num_channels,
max_features=max_features, num_blocks=num_blocks)
self.kp = nn.Conv2d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=(7, 7),
padding=pad)
if estimate_jacobian:
self.num_jacobian_maps = 1 if single_jacobian_map else num_kp
self.jacobian = nn.Conv2d(in_channels=self.predictor.out_filters,
out_channels=4 * self.num_jacobian_maps, kernel_size=(7, 7), padding=pad)
self.jacobian.weight.data.zero_()
self.jacobian.bias.data.copy_(torch.tensor([1, 0, 0, 1] * self.num_jacobian_maps, dtype=torch.float))
else:
self.jacobian = None
self.temperature = temperature
self.scale_factor = scale_factor
if self.scale_factor != 1:
self.down = AntiAliasInterpolation2d(num_channels, self.scale_factor)
def gaussian2kp(self, heatmap):
"""
Extract the mean and from a heatmap
"""
shape = heatmap.shape
heatmap = heatmap.unsqueeze(-1)
grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0)
value = (heatmap * grid).sum(dim=(2, 3))
kp = {'value': value}
return kp
def forward(self, x,with_feature = False):
if self.scale_factor != 1:
x = self.down(x)
feature_map = self.predictor(x)
prediction = self.kp(feature_map)
final_shape = prediction.shape
heatmap = prediction.view(final_shape[0], final_shape[1], -1)
heatmap = F.softmax(heatmap / self.temperature, dim=2)
heatmap = heatmap.view(*final_shape)
out = self.gaussian2kp(heatmap)
if self.jacobian is not None:
jacobian_map = self.jacobian(feature_map)
out["jacobian_map"] = jacobian_map
jacobian_map = jacobian_map.reshape(final_shape[0], self.num_jacobian_maps, 4, final_shape[2],
final_shape[3])
heatmap = heatmap.unsqueeze(2)
jacobian = heatmap * jacobian_map
jacobian = jacobian.view(final_shape[0], final_shape[1], 4, -1)
jacobian = jacobian.sum(dim=-1)
jacobian = jacobian.view(jacobian.shape[0], jacobian.shape[1], 2, 2)
out['jacobian'] = jacobian
out["pred_feature"] = prediction
if with_feature:
out["feature_map"] = feature_map
return out
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