117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
import torch
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import time
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from torch import nn
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import PytorchBoot.namespace as namespace
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import PytorchBoot.stereotype as stereotype
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from PytorchBoot.factory.component_factory import ComponentFactory
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from PytorchBoot.utils import Log
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@stereotype.pipeline("nbv_reconstruction_pipeline")
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class NBVReconstructionPipeline(nn.Module):
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def __init__(self, config):
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super(NBVReconstructionPipeline, self).__init__()
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self.config = config
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self.module_config = config["modules"]
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self.pts_encoder = ComponentFactory.create(
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namespace.Stereotype.MODULE, self.module_config["pts_encoder"]
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)
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self.pose_encoder = ComponentFactory.create(
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namespace.Stereotype.MODULE, self.module_config["pose_encoder"]
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)
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self.seq_encoder = ComponentFactory.create(
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namespace.Stereotype.MODULE, self.module_config["seq_encoder"]
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)
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self.view_finder = ComponentFactory.create(
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namespace.Stereotype.MODULE, self.module_config["view_finder"]
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)
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self.eps = float(self.config["eps"])
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self.enable_global_scanned_feat = self.config["global_scanned_feat"]
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def forward(self, data):
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mode = data["mode"]
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if mode == namespace.Mode.TRAIN:
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return self.forward_train(data)
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elif mode == namespace.Mode.TEST:
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return self.forward_test(data)
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else:
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Log.error("Unknown mode: {}".format(mode), True)
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def pertube_data(self, gt_delta_9d):
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bs = gt_delta_9d.shape[0]
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random_t = (
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torch.rand(bs, device=gt_delta_9d.device) * (1.0 - self.eps) + self.eps
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)
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random_t = random_t.unsqueeze(-1)
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mu, std = self.view_finder.marginal_prob(gt_delta_9d, random_t)
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std = std.view(-1, 1)
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z = torch.randn_like(gt_delta_9d)
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perturbed_x = mu + z * std
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target_score = -z * std / (std**2)
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return perturbed_x, random_t, target_score, std
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def forward_train(self, data):
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start_time = time.time()
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main_feat = self.get_main_feat(data)
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end_time = time.time()
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print("get_main_feat time: ", end_time - start_time)
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""" get std """
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best_to_world_pose_9d_batch = data["best_to_world_pose_9d"]
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perturbed_x, random_t, target_score, std = self.pertube_data(
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best_to_world_pose_9d_batch
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)
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input_data = {
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"sampled_pose": perturbed_x,
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"t": random_t,
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"main_feat": main_feat,
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}
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estimated_score = self.view_finder(input_data)
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output = {
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"estimated_score": estimated_score,
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"target_score": target_score,
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"std": std,
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}
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return output
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def forward_test(self, data):
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main_feat = self.get_main_feat(data)
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estimated_delta_rot_9d, in_process_sample = self.view_finder.next_best_view(
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main_feat
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)
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result = {
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"pred_pose_9d": estimated_delta_rot_9d,
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"in_process_sample": in_process_sample,
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}
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return result
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def get_main_feat(self, data):
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scanned_n_to_world_pose_9d_batch = data[
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"scanned_n_to_world_pose_9d"
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] # List(B): Tensor(S x 9)
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scanned_pts_batch = data[
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"scanned_pts"
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]
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device = next(self.parameters()).device
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embedding_list_batch = []
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for scanned_n_to_world_pose_9d, scanned_pts in zip(scanned_n_to_world_pose_9d_batch, scanned_pts_batch):
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scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device) # Tensor(S x 9)
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scanned_pts = scanned_pts.to(device) # Tensor(S x N x 3)
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pose_feat_seq = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d) # Tensor(S x Dp)
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pts_feat_seq = self.pts_encoder.encode_points(scanned_pts, require_per_point_feat=False) # Tensor(S x Dl)
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seq_embedding = torch.cat([pose_feat_seq, pts_feat_seq], dim=-1) # Tensor(S x (Dp+Dl))
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embedding_list_batch.append(seq_embedding) # List(B): Tensor(S x (Dp+Dl))
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seq_feat = self.seq_encoder.encode_sequence(embedding_list_batch) # Tensor(B x Ds)
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main_feat = seq_feat # Tensor(B x Ds)
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if torch.isnan(main_feat).any():
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Log.error("nan in main_feat", True)
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return main_feat
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