from torch import nn import torch import torch.nn.functional as F from modules.util import Hourglass, make_coordinate_grid, AntiAliasInterpolation2d,Hourglass_2branch import pdb 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): if self.scale_factor != 1: x = self.down(x) feature_map = self.predictor(x) #x bz,4,64,64 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) # pdb.set_trace() 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 return out