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import torch
from tqdm import tqdm
import pytorch3d.transforms as tra3d
from StructDiffusion.diffusion.noise_schedule import extract
from StructDiffusion.diffusion.pose_conversion import get_struct_objs_poses
from StructDiffusion.utils.batch_inference import move_pc_and_create_scene_simple, visualize_batch_pcs, move_pc_and_create_scene_new
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 |