100 lines
4.2 KiB
Python
100 lines
4.2 KiB
Python
import torch
|
|
from torch import nn
|
|
import PytorchBoot.namespace as namespace
|
|
import PytorchBoot.stereotype as stereotype
|
|
from PytorchBoot.factory.component_factory import ComponentFactory
|
|
from PytorchBoot.utils import Log
|
|
|
|
@stereotype.pipeline("nbv_reconstruction_pipeline_local")
|
|
class NBVReconstructionLocalPointsPipeline(nn.Module):
|
|
def __init__(self, config):
|
|
super(NBVReconstructionLocalPointsPipeline, self).__init__()
|
|
self.config = config
|
|
self.module_config = config["modules"]
|
|
self.pts_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pts_encoder"])
|
|
self.pose_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pose_encoder"])
|
|
self.seq_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["seq_encoder"])
|
|
self.view_finder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["view_finder"])
|
|
self.eps = float(self.config["eps"])
|
|
self.enable_global_scanned_feat = self.config["global_scanned_feat"]
|
|
|
|
def forward(self, data):
|
|
mode = data["mode"]
|
|
|
|
if mode == namespace.Mode.TRAIN:
|
|
return self.forward_train(data)
|
|
elif mode == namespace.Mode.TEST:
|
|
return self.forward_test(data)
|
|
else:
|
|
Log.error("Unknown mode: {}".format(mode), True)
|
|
|
|
def pertube_data(self, gt_delta_9d):
|
|
bs = gt_delta_9d.shape[0]
|
|
random_t = torch.rand(bs, device=gt_delta_9d.device) * (1. - self.eps) + self.eps
|
|
random_t = random_t.unsqueeze(-1)
|
|
mu, std = self.view_finder.marginal_prob(gt_delta_9d, random_t)
|
|
std = std.view(-1, 1)
|
|
z = torch.randn_like(gt_delta_9d)
|
|
perturbed_x = mu + z * std
|
|
target_score = - z * std / (std ** 2)
|
|
return perturbed_x, random_t, target_score, std
|
|
|
|
def forward_train(self, data):
|
|
main_feat = self.get_main_feat(data)
|
|
''' get std '''
|
|
best_to_world_pose_9d_batch = data["best_to_world_pose_9d"]
|
|
perturbed_x, random_t, target_score, std = self.pertube_data(best_to_world_pose_9d_batch)
|
|
input_data = {
|
|
"sampled_pose": perturbed_x,
|
|
"t": random_t,
|
|
"main_feat": main_feat,
|
|
}
|
|
estimated_score = self.view_finder(input_data)
|
|
output = {
|
|
"estimated_score": estimated_score,
|
|
"target_score": target_score,
|
|
"std": std
|
|
}
|
|
return output
|
|
|
|
def forward_test(self,data):
|
|
main_feat = self.get_main_feat(data)
|
|
repeat_num = data.get("repeat_num", 50)
|
|
main_feat = main_feat.repeat(repeat_num, 1)
|
|
estimated_delta_rot_9d, in_process_sample = self.view_finder.next_best_view(
|
|
main_feat
|
|
)
|
|
result = {
|
|
"pred_pose_9d": estimated_delta_rot_9d,
|
|
"in_process_sample": in_process_sample,
|
|
}
|
|
return result
|
|
|
|
|
|
def get_main_feat(self, data):
|
|
scanned_pts_batch = data['scanned_pts']
|
|
scanned_n_to_world_pose_9d_batch = data['scanned_n_to_world_pose_9d']
|
|
device = next(self.parameters()).device
|
|
feat_seq_list = []
|
|
|
|
for scanned_pts,scanned_n_to_world_pose_9d in zip(scanned_pts_batch,scanned_n_to_world_pose_9d_batch):
|
|
scanned_pts = scanned_pts.to(device)
|
|
scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device)
|
|
pts_feat = self.pts_encoder.encode_points(scanned_pts)
|
|
pose_feat = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d)
|
|
seq_feat = torch.cat([pts_feat, pose_feat], dim=-1)
|
|
feat_seq_list.append(seq_feat)
|
|
main_feat = self.seq_encoder.encode_sequence(feat_seq_list)
|
|
|
|
if self.enable_global_scanned_feat:
|
|
combined_scanned_pts_batch = data['combined_scanned_pts']
|
|
global_scanned_feat = self.pts_encoder.encode_points(combined_scanned_pts_batch)
|
|
main_feat = torch.cat([main_feat, global_scanned_feat], dim=-1)
|
|
|
|
|
|
if torch.isnan(main_feat).any():
|
|
Log.error("nan in main_feat", True)
|
|
|
|
return main_feat
|
|
|