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import torch
from tqdm import tqdm
from StructDiffusion.diffusion.noise_schedule import extract
class Sampler:
def __init__(self, model_class, checkpoint_path, device, debug=False):
self.debug = debug
self.device = device
self.model = model_class.load_from_checkpoint(checkpoint_path)
self.backbone = self.model.model
self.backbone.to(device)
self.backbone.eval()
def sample(self, batch, num_poses):
noise_schedule = self.model.noise_schedule
B = batch["pcs"].shape[0]
x_noisy = torch.randn((B, num_poses, 9), device=self.device)
xs = []
for t_index in tqdm(reversed(range(0, noise_schedule.timesteps)),
desc='sampling loop time step', total=noise_schedule.timesteps):
t = torch.full((B,), t_index, device=self.device, dtype=torch.long)
# noise schedule
betas_t = extract(noise_schedule.betas, t, x_noisy.shape)
sqrt_one_minus_alphas_cumprod_t = extract(noise_schedule.sqrt_one_minus_alphas_cumprod, t, x_noisy.shape)
sqrt_recip_alphas_t = extract(noise_schedule.sqrt_recip_alphas, t, x_noisy.shape)
# predict noise
pcs = batch["pcs"]
sentence = batch["sentence"]
type_index = batch["type_index"]
position_index = batch["position_index"]
pad_mask = batch["pad_mask"]
# calling the backbone instead of the pytorch-lightning model
with torch.no_grad():
predicted_noise = self.backbone.forward(t, pcs, sentence, x_noisy, type_index, position_index, pad_mask)
# compute noisy x at t
model_mean = sqrt_recip_alphas_t * (x_noisy - betas_t * predicted_noise / sqrt_one_minus_alphas_cumprod_t)
if t_index == 0:
x_noisy = model_mean
else:
posterior_variance_t = extract(noise_schedule.posterior_variance, t, x_noisy.shape)
noise = torch.randn_like(x_noisy)
x_noisy = model_mean + torch.sqrt(posterior_variance_t) * noise
xs.append(x_noisy)
xs = list(reversed(xs))
return xs
# class SamplerV2:
#
# def __init__(self, diffusion_model_class, diffusion_checkpoint_path,
# collision_model_class, collision_checkpoint_path,
# device, debug=False):
#
# self.debug = debug
# self.device = device
#
# self.diffusion_model = diffusion_model_class.load_from_checkpoint(diffusion_checkpoint_path)
# self.diffusion_backbone = self.diffusion_model.model
# self.diffusion_backbone.to(device)
# self.diffusion_backbone.eval()
#
# self.collision_model = collision_model_class.load_from_checkpoint(collision_checkpoint_path)
# self.collision_backbone = self.collision_model.model
# self.collision_backbone.to(device)
# self.collision_backbone.eval()
#
# def sample(self, batch, num_poses):
#
# noise_schedule = self.diffusion_model.noise_schedule
#
# B = batch["pcs"].shape[0]
#
# x_noisy = torch.randn((B, num_poses, 9), device=self.device)
#
# xs = []
# for t_index in tqdm(reversed(range(0, noise_schedule.timesteps)),
# desc='sampling loop time step', total=noise_schedule.timesteps):
#
# t = torch.full((B,), t_index, device=self.device, dtype=torch.long)
#
# # noise schedule
# betas_t = extract(noise_schedule.betas, t, x_noisy.shape)
# sqrt_one_minus_alphas_cumprod_t = extract(noise_schedule.sqrt_one_minus_alphas_cumprod, t, x_noisy.shape)
# sqrt_recip_alphas_t = extract(noise_schedule.sqrt_recip_alphas, t, x_noisy.shape)
#
# # predict noise
# pcs = batch["pcs"]
# sentence = batch["sentence"]
# type_index = batch["type_index"]
# position_index = batch["position_index"]
# pad_mask = batch["pad_mask"]
# # calling the backbone instead of the pytorch-lightning model
# with torch.no_grad():
# predicted_noise = self.diffusion_backbone.forward(t, pcs, sentence, x_noisy, type_index, position_index, pad_mask)
#
# # compute noisy x at t
# model_mean = sqrt_recip_alphas_t * (x_noisy - betas_t * predicted_noise / sqrt_one_minus_alphas_cumprod_t)
# if t_index == 0:
# x_noisy = model_mean
# else:
# posterior_variance_t = extract(noise_schedule.posterior_variance, t, x_noisy.shape)
# noise = torch.randn_like(x_noisy)
# x_noisy = model_mean + torch.sqrt(posterior_variance_t) * noise
#
# xs.append(x_noisy)
#
# xs = list(reversed(xs))
#
# visualize = True
#
# struct_pose, pc_poses_in_struct = get_struct_objs_poses(xs[0])
# # struct_pose: B, 1, 4, 4
# # pc_poses_in_struct: B, N, 4, 4
#
# S = B
# num_elite = 10
# ####################################################
# # only keep one copy
#
# # N, P, 3
# obj_xyzs = batch["pcs"][0][:, :, :3]
# print("obj_xyzs shape", obj_xyzs.shape)
#
# # 1, N
# # object_pad_mask: padding location has 1
# num_target_objs = num_poses
# if self.diffusion_backbone.use_virtual_structure_frame:
# num_target_objs -= 1
# object_pad_mask = batch["pad_mask"][0][-num_target_objs:].unsqueeze(0)
# target_object_inds = 1 - object_pad_mask
# print("target_object_inds shape", target_object_inds.shape)
# print("target_object_inds", target_object_inds)
#
# N, P, _ = obj_xyzs.shape
# print("S, N, P: {}, {}, {}".format(S, N, P))
#
# ####################################################
# # S, N, ...
#
# struct_pose = struct_pose.repeat(1, N, 1, 1) # S, N, 4, 4
# struct_pose = struct_pose.reshape(S * N, 4, 4) # S x N, 4, 4
#
# new_obj_xyzs = obj_xyzs.repeat(S, 1, 1, 1) # S, N, P, 3
# current_pc_pose = torch.eye(4).repeat(S, N, 1, 1).to(self.device) # S, N, 4, 4
# current_pc_pose[:, :, :3, 3] = torch.mean(new_obj_xyzs, dim=2) # S, N, 4, 4
# current_pc_pose = current_pc_pose.reshape(S * N, 4, 4) # S x N, 4, 4
#
# # optimize xyzrpy
# obj_params = torch.zeros((S, N, 6)).to(self.device)
# obj_params[:, :, :3] = pc_poses_in_struct[:, :, :3, 3]
# obj_params[:, :, 3:] = tra3d.matrix_to_euler_angles(pc_poses_in_struct[:, :, :3, :3], "XYZ") # S, N, 6
# #
# # new_obj_xyzs_before_cem, goal_pc_pose_before_cem = move_pc(obj_xyzs, obj_params, struct_pose, current_pc_pose, device)
# #
# # if visualize:
# # print("visualizing rearrangements predicted by the generator")
# # visualize_batch_pcs(new_obj_xyzs_before_cem, S, N, P, limit_B=5)
#
# ####################################################
# # rank
#
# # evaluate in batches
# scores = torch.zeros(S).to(self.device)
# no_intersection_scores = torch.zeros(S).to(self.device) # the higher the better
# num_batches = int(S / B)
# if S % B != 0:
# num_batches += 1
# for b in range(num_batches):
# if b + 1 == num_batches:
# cur_batch_idxs_start = b * B
# cur_batch_idxs_end = S
# else:
# cur_batch_idxs_start = b * B
# cur_batch_idxs_end = (b + 1) * B
# cur_batch_size = cur_batch_idxs_end - cur_batch_idxs_start
#
# # print("current batch idxs start", cur_batch_idxs_start)
# # print("current batch idxs end", cur_batch_idxs_end)
# # print("size of the current batch", cur_batch_size)
#
# batch_obj_params = obj_params[cur_batch_idxs_start: cur_batch_idxs_end]
# batch_struct_pose = struct_pose[cur_batch_idxs_start * N: cur_batch_idxs_end * N]
# batch_current_pc_pose = current_pc_pose[cur_batch_idxs_start * N:cur_batch_idxs_end * N]
#
# new_obj_xyzs, _, subsampled_scene_xyz, _, obj_pair_xyzs = \
# move_pc_and_create_scene_new(obj_xyzs, batch_obj_params, batch_struct_pose, batch_current_pc_pose,
# target_object_inds, self.device,
# return_scene_pts=False,
# return_scene_pts_and_pc_idxs=False,
# num_scene_pts=False,
# normalize_pc=False,
# return_pair_pc=True,
# num_pair_pc_pts=self.collision_model.data_cfg.num_scene_pts,
# normalize_pair_pc=self.collision_model.data_cfg.normalize_pc)
#
# #######################################
# # predict whether there are pairwise collisions
# # if collision_score_weight > 0:
# with torch.no_grad():
# _, num_comb, num_pair_pc_pts, _ = obj_pair_xyzs.shape
# # obj_pair_xyzs = obj_pair_xyzs.reshape(cur_batch_size * num_comb, num_pair_pc_pts, -1)
# collision_logits = self.collision_backbone.forward(obj_pair_xyzs.reshape(cur_batch_size * num_comb, num_pair_pc_pts, -1))
# collision_scores = self.collision_backbone.convert_logits(collision_logits).reshape(cur_batch_size, num_comb) # cur_batch_size, num_comb
#
# # debug
# # for bi, this_obj_pair_xyzs in enumerate(obj_pair_xyzs):
# # print("batch id", bi)
# # for pi, obj_pair_xyz in enumerate(this_obj_pair_xyzs):
# # print("pair", pi)
# # # obj_pair_xyzs: 2 * P, 5
# # print("collision score", collision_scores[bi, pi])
# # trimesh.PointCloud(obj_pair_xyz[:, :3].cpu()).show()
#
# # 1 - mean() since the collision model predicts 1 if there is a collision
# no_intersection_scores[cur_batch_idxs_start:cur_batch_idxs_end] = 1 - torch.mean(collision_scores, dim=1)
# if visualize:
# print("no intersection scores", no_intersection_scores)
# # #######################################
# # if discriminator_score_weight > 0:
# # # # debug:
# # # print(subsampled_scene_xyz.shape)
# # # print(subsampled_scene_xyz[0])
# # # trimesh.PointCloud(subsampled_scene_xyz[0, :, :3].cpu().numpy()).show()
# # #
# # with torch.no_grad():
# #
# # # Important: since this discriminator only uses local structure param, takes sentence from the first and last position
# # # local_sentence = sentence[:, [0, 4]]
# # # local_sentence_pad_mask = sentence_pad_mask[:, [0, 4]]
# # # sentence_disc, sentence_pad_mask_disc, position_index_dic = discriminator_inference.dataset.tensorfy_sentence(raw_sentence_discriminator, raw_sentence_pad_mask_discriminator, raw_position_index_discriminator)
# #
# # sentence_disc = torch.LongTensor(
# # [discriminator_tokenizer.tokenize(*i) for i in raw_sentence_discriminator])
# # sentence_pad_mask_disc = torch.LongTensor(raw_sentence_pad_mask_discriminator)
# # position_index_dic = torch.LongTensor(raw_position_index_discriminator)
# #
# # preds = discriminator_model.forward(subsampled_scene_xyz,
# # sentence_disc.unsqueeze(0).repeat(cur_batch_size, 1).to(device),
# # sentence_pad_mask_disc.unsqueeze(0).repeat(cur_batch_size,
# # 1).to(device),
# # position_index_dic.unsqueeze(0).repeat(cur_batch_size, 1).to(
# # device))
# # # preds = discriminator_model.forward(subsampled_scene_xyz)
# # preds = discriminator_model.convert_logits(preds)
# # preds = preds["is_circle"] # cur_batch_size,
# # scores[cur_batch_idxs_start:cur_batch_idxs_end] = preds
# # if visualize:
# # print("discriminator scores", scores)
#
# # scores = scores * discriminator_score_weight + no_intersection_scores * collision_score_weight
# scores = no_intersection_scores
# sort_idx = torch.argsort(scores).flip(dims=[0])[:num_elite]
# elite_obj_params = obj_params[sort_idx] # num_elite, N, 6
# elite_struct_poses = struct_pose.reshape(S, N, 4, 4)[sort_idx] # num_elite, N, 4, 4
# elite_struct_poses = elite_struct_poses.reshape(num_elite * N, 4, 4) # num_elite x N, 4, 4
# elite_scores = scores[sort_idx]
# print("elite scores:", elite_scores)
#
# ####################################################
# # # visualize best samples
# # num_scene_pts = 4096 # if discriminator_num_scene_pts is None else discriminator_num_scene_pts
# # batch_current_pc_pose = current_pc_pose[0: num_elite * N]
# # best_new_obj_xyzs, best_goal_pc_pose, best_subsampled_scene_xyz, _, _ = \
# # move_pc_and_create_scene_new(obj_xyzs, elite_obj_params, elite_struct_poses, batch_current_pc_pose,
# # target_object_inds, self.device,
# # return_scene_pts=True, num_scene_pts=num_scene_pts, normalize_pc=True)
# # if visualize:
# # print("visualizing elite rearrangements ranked by collision model/discriminator")
# # visualize_batch_pcs(best_new_obj_xyzs, num_elite, limit_B=num_elite)
#
# # num_elite, N, 6
# elite_obj_params = elite_obj_params.reshape(num_elite * N, -1)
# pc_poses_in_struct = torch.eye(4).repeat(num_elite * N, 1, 1).to(self.device)
# pc_poses_in_struct[:, :3, :3] = tra3d.euler_angles_to_matrix(elite_obj_params[:, 3:], "XYZ")
# pc_poses_in_struct[:, :3, 3] = elite_obj_params[:, :3]
# pc_poses_in_struct = pc_poses_in_struct.reshape(num_elite, N, 4, 4) # num_elite, N, 4, 4
#
# struct_pose = elite_struct_poses.reshape(num_elite, N, 4, 4)[:, 0,].unsqueeze(1) # num_elite, 1, 4, 4
#
# return struct_pose, pc_poses_in_struct |