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grasp_estimator.py
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grasp_estimator.py
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from __future__ import print_function
from models import create_model
import numpy as np
import torch
import time
import trimesh
import trimesh.transformations as tra
#import surface_normal
import copy
import os
from utils import utils
class GraspEstimator:
"""
Includes the code used for running the inference.
"""
def __init__(self, grasp_sampler_opt, grasp_evaluator_opt, opt):
self.grasp_sampler_opt = grasp_sampler_opt
self.grasp_evaluator_opt = grasp_evaluator_opt
self.opt = opt
self.target_pc_size = opt.target_pc_size
self.num_refine_steps = opt.refine_steps
self.refine_method = opt.refinement_method
self.threshold = opt.threshold
self.batch_size = opt.batch_size
self.generate_dense_grasps = opt.generate_dense_grasps
if self.generate_dense_grasps:
self.num_grasps_per_dim = opt.num_grasp_samples
self.num_grasp_samples = opt.num_grasp_samples * opt.num_grasp_samples
else:
self.num_grasp_samples = opt.num_grasp_samples
self.choose_fn = opt.choose_fn
self.choose_fns = {
"all":
None,
"better_than_threshold":
utils.choose_grasps_better_than_threshold,
"better_than_threshold_in_sequence":
utils.choose_grasps_better_than_threshold_in_sequence,
}
self.device = torch.device("cuda:0")
self.grasp_evaluator = create_model(grasp_evaluator_opt)
self.grasp_sampler = create_model(grasp_sampler_opt)
def keep_inliers(self, grasps, confidences, z, pc, inlier_indices_list):
for i, inlier_indices in enumerate(inlier_indices_list):
grasps[i] = grasps[i][inlier_indices]
confidences[i] = confidences[i][inlier_indices]
z[i] = z[i][inlier_indices]
pc[i] = pc[i][inlier_indices]
def generate_and_refine_grasps(
self,
pc,
):
pc_list, pc_mean = self.prepare_pc(pc)
grasps_list, confidence_list, z_list = self.generate_grasps(pc_list)
inlier_indices = utils.get_inlier_grasp_indices(grasps_list,
torch.zeros(1, 3).to(
self.device),
threshold=1.0,
device=self.device)
self.keep_inliers(grasps_list, confidence_list, z_list, pc_list,
inlier_indices)
improved_eulers, improved_ts, improved_success = [], [], []
for pc, grasps in zip(pc_list, grasps_list):
out = self.refine_grasps(pc, grasps, self.refine_method,
self.num_refine_steps)
improved_eulers.append(out[0])
improved_ts.append(out[1])
improved_success.append(out[2])
improved_eulers = np.hstack(improved_eulers)
improved_ts = np.hstack(improved_ts)
improved_success = np.hstack(improved_success)
if self.choose_fn is "all":
selection_mask = np.ones(improved_success.shape, dtype=np.float32)
else:
selection_mask = self.choose_fns[self.choose_fn](improved_eulers,
improved_ts,
improved_success,
self.threshold)
grasps = utils.rot_and_trans_to_grasps(improved_eulers, improved_ts,
selection_mask)
utils.denormalize_grasps(grasps, pc_mean)
refine_indexes, sample_indexes = np.where(selection_mask)
success_prob = improved_success[refine_indexes,
sample_indexes].tolist()
return grasps, success_prob
def prepare_pc(self, pc):
if pc.shape[0] > self.target_pc_size:
pc = utils.regularize_pc_point_count(pc, self.target_pc_size)
pc_mean = np.mean(pc, 0)
pc -= np.expand_dims(pc_mean, 0)
pc = np.tile(pc, (self.num_grasp_samples, 1, 1))
pc = torch.from_numpy(pc).float().to(self.device)
pcs = []
pcs = utils.partition_array_into_subarrays(pc, self.batch_size)
return pcs, pc_mean
def generate_grasps(self, pcs):
all_grasps = []
all_confidence = []
all_z = []
if self.generate_dense_grasps:
latent_samples = self.grasp_sampler.net.module.generate_dense_latents(
self.num_grasps_per_dim)
latent_samples = utils.partition_array_into_subarrays(
latent_samples, self.batch_size)
for latent_sample, pc in zip(latent_samples, pcs):
grasps, confidence, z = self.grasp_sampler.generate_grasps(
pc, latent_sample)
all_grasps.append(grasps)
all_confidence.append(confidence)
all_z.append(z)
else:
for pc in pcs:
grasps, confidence, z = self.grasp_sampler.generate_grasps(pc)
all_grasps.append(grasps)
all_confidence.append(confidence)
all_z.append(z)
return all_grasps, all_confidence, all_z
def refine_grasps(self, pc, grasps, refine_method, num_refine_steps=10):
grasp_eulers, grasp_translations = utils.convert_qt_to_rt(grasps)
if refine_method == "gradient":
improve_fun = self.improve_grasps_gradient_based
grasp_eulers = torch.autograd.Variable(grasp_eulers.to(
self.device),
requires_grad=True)
grasp_translations = torch.autograd.Variable(grasp_translations.to(
self.device),
requires_grad=True)
else:
improve_fun = self.improve_grasps_sampling_based
improved_success = []
improved_eulers = []
improved_ts = []
improved_eulers.append(grasp_eulers.cpu().data.numpy())
improved_ts.append(grasp_translations.cpu().data.numpy())
last_success = None
for i in range(num_refine_steps):
success_prob, last_success = improve_fun(pc, grasp_eulers,
grasp_translations,
last_success)
improved_success.append(success_prob.cpu().data.numpy())
improved_eulers.append(grasp_eulers.cpu().data.numpy())
improved_ts.append(grasp_translations.cpu().data.numpy())
# we need to run the success on the final improved grasps
grasp_pcs = utils.control_points_from_rot_and_trans(
grasp_eulers, grasp_translations, self.device)
improved_success.append(
self.grasp_evaluator.evaluate_grasps(
pc, grasp_pcs).squeeze().cpu().data.numpy())
return np.asarray(improved_eulers), np.asarray(
improved_ts), np.asarray(improved_success)
def improve_grasps_gradient_based(
self, pcs, grasp_eulers, grasp_trans, last_success
): #euler_angles, translation, eval_and_improve, metadata):
grasp_pcs = utils.control_points_from_rot_and_trans(
grasp_eulers, grasp_trans, self.device)
success = self.grasp_evaluator.evaluate_grasps(pcs, grasp_pcs)
success.squeeze().backward(
torch.ones(success.shape[0]).to(self.device))
delta_t = grasp_trans.grad
norm_t = torch.norm(delta_t, p=2, dim=-1).to(self.device)
# Adjust the alpha so that it won't update more than 1 cm. Gradient is only valid
# in small neighborhood.
alpha = torch.min(0.01 / norm_t, torch.tensor(1.0).to(self.device))
grasp_trans.data += grasp_trans.grad * alpha[:, None]
temp = grasp_eulers.clone()
grasp_eulers.data += grasp_eulers.grad * alpha[:, None]
return success.squeeze(), None
def improve_grasps_sampling_based(self,
pcs,
grasp_eulers,
grasp_trans,
last_success=None):
with torch.no_grad():
if last_success is None:
grasp_pcs = utils.control_points_from_rot_and_trans(
grasp_eulers, grasp_trans, self.device)
last_success = self.grasp_evaluator.evaluate_grasps(
pcs, grasp_pcs)
delta_t = 2 * (torch.rand(grasp_trans.shape).to(self.device) - 0.5)
delta_t *= 0.02
delta_euler_angles = (
torch.rand(grasp_eulers.shape).to(self.device) - 0.5) * 2
perturbed_translation = grasp_trans + delta_t
perturbed_euler_angles = grasp_eulers + delta_euler_angles
grasp_pcs = utils.control_points_from_rot_and_trans(
perturbed_euler_angles, perturbed_translation, self.device)
perturbed_success = self.grasp_evaluator.evaluate_grasps(
pcs, grasp_pcs)
ratio = perturbed_success / torch.max(
last_success,
torch.tensor(0.0001).to(self.device))
mask = torch.rand(ratio.shape).to(self.device) <= ratio
next_success = last_success
ind = torch.where(mask)[0]
next_success[ind] = perturbed_success[ind]
grasp_trans[ind].data = perturbed_translation.data[ind]
grasp_eulers[ind].data = perturbed_euler_angles.data[ind]
return last_success.squeeze(), next_success