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semantic_mapping.py
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import torch
import torch.nn as nn
from torch.nn import functional as F
import numpy as np
# from utils.distributions import Categorical, DiagGaussian
# from torch.distributions.categorical import Categorical
from utils.model import get_grid, ChannelPool, Flatten, NNBase
import utils.depth_utils as du
class Semantic_Mapping(nn.Module):
"""
Semantic_Mapping
"""
def __init__(self, args):
super(Semantic_Mapping, self).__init__()
self.device = args.device
self.screen_h = args.frame_height
self.screen_w = args.frame_width
self.resolution = args.map_resolution
self.z_resolution = args.map_resolution
self.map_size_cm = args.map_size_cm // args.global_downscaling
self.n_channels = 3
self.vision_range = args.vision_range
self.dropout = 0.5
self.fov = args.hfov
self.du_scale = args.du_scale
self.cat_pred_threshold = args.cat_pred_threshold
self.exp_pred_threshold = args.exp_pred_threshold
self.map_pred_threshold = args.map_pred_threshold
self.num_sem_categories = args.num_sem_categories
self.max_height = int(200 / self.z_resolution)
self.min_height = int(-40 / self.z_resolution)
self.agent_height = args.camera_height * 100.
self.shift_loc = [self.vision_range *
self.resolution // 2, 0, np.pi / 2.0]
self.camera_matrix = du.get_camera_matrix(
self.screen_w, self.screen_h, self.fov)
self.pool = ChannelPool(1)
vr = self.vision_range
self.init_grid = torch.zeros(
1, 1 + self.num_sem_categories, vr, vr,
self.max_height - self.min_height
).float().to(self.device)
self.feat = torch.ones(
1, 1 + self.num_sem_categories,
self.screen_h // self.du_scale * self.screen_w // self.du_scale
).float().to(self.device)
self.max_pool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
self.stair_mask_radius = 30
self.stair_mask = self.get_mask(self.stair_mask_radius).to(self.device)
def forward(self, obs, pose_obs, maps_last, poses_last, eve_angle):
bs, c, h, w = obs.size()
depth = obs[:, 3, :, :]
point_cloud_t = du.get_point_cloud_from_z_t(
depth, self.camera_matrix, self.device, scale=self.du_scale)
agent_view_t = du.transform_camera_view_t(
point_cloud_t, self.agent_height, eve_angle, self.device)
agent_view_centered_t = du.transform_pose_t(
agent_view_t, self.shift_loc, self.device)
max_h = self.max_height
min_h = self.min_height
xy_resolution = self.resolution
z_resolution = self.z_resolution
vision_range = self.vision_range
XYZ_cm_std = agent_view_centered_t.float()
XYZ_cm_std[..., :2] = (XYZ_cm_std[..., :2] / xy_resolution)
XYZ_cm_std[..., :2] = (XYZ_cm_std[..., :2] -
vision_range // 2.) / vision_range * 2.
XYZ_cm_std[..., 2] = XYZ_cm_std[..., 2] / z_resolution
XYZ_cm_std[..., 2] = (XYZ_cm_std[..., 2] -
(max_h + min_h) // 2.) / (max_h - min_h) * 2.
self.feat[:, 1:, :] = nn.AvgPool2d(self.du_scale)(
obs[:, 4:, :, :]
).view(bs, c - 4, h // self.du_scale * w // self.du_scale)
XYZ_cm_std = XYZ_cm_std.permute(0, 3, 1, 2)
XYZ_cm_std = XYZ_cm_std.view(XYZ_cm_std.shape[0],
XYZ_cm_std.shape[1],
XYZ_cm_std.shape[2] * XYZ_cm_std.shape[3])
voxels = du.splat_feat_nd(
self.init_grid * 0., self.feat, XYZ_cm_std).transpose(2, 3)
min_z = int(25 / z_resolution - min_h)
max_z = int((self.agent_height + 50) / z_resolution - min_h)
mid_z = int(self.agent_height / z_resolution - min_h)
agent_height_proj = voxels[..., min_z:max_z].sum(4)
agent_height_stair_proj = voxels[..., mid_z-5:mid_z].sum(4)
all_height_proj = voxels.sum(4)
fp_map_pred = agent_height_proj[:, 0:1, :, :]
fp_exp_pred = all_height_proj[:, 0:1, :, :]
fp_stair_pred = agent_height_stair_proj[:, 0:1, :, :]
fp_map_pred = fp_map_pred / self.map_pred_threshold
fp_stair_pred = fp_stair_pred / self.map_pred_threshold
fp_exp_pred = fp_exp_pred / self.exp_pred_threshold
fp_map_pred = torch.clamp(fp_map_pred, min=0.0, max=1.0)
fp_stair_pred = torch.clamp(fp_stair_pred, min=0.0, max=1.0)
fp_exp_pred = torch.clamp(fp_exp_pred, min=0.0, max=1.0)
pose_pred = poses_last
agent_view = torch.zeros(bs, c,
self.map_size_cm // self.resolution,
self.map_size_cm // self.resolution
).to(self.device).float()
x1 = self.map_size_cm // (self.resolution * 2) - self.vision_range // 2
x2 = x1 + self.vision_range
y1 = self.map_size_cm // (self.resolution * 2)
y2 = y1 + self.vision_range
agent_view[:, 0:1, y1:y2, x1:x2] = fp_map_pred
agent_view[:, 1:2, y1:y2, x1:x2] = fp_exp_pred
agent_view[:, 4:, y1:y2, x1:x2] = torch.clamp(
agent_height_proj[:, 1:, :, :] / self.cat_pred_threshold,
min=0.0, max=1.0)
agent_view_stair = agent_view.clone().detach()
agent_view_stair[:, 0:1, y1:y2, x1:x2] = fp_stair_pred
corrected_pose = pose_obs
def get_new_pose_batch(pose, rel_pose_change):
pose[:, 1] += rel_pose_change[:, 0] * \
torch.sin(pose[:, 2] / 57.29577951308232) \
+ rel_pose_change[:, 1] * \
torch.cos(pose[:, 2] / 57.29577951308232)
pose[:, 0] += rel_pose_change[:, 0] * \
torch.cos(pose[:, 2] / 57.29577951308232) \
- rel_pose_change[:, 1] * \
torch.sin(pose[:, 2] / 57.29577951308232)
pose[:, 2] += rel_pose_change[:, 2] * 57.29577951308232
pose[:, 2] = torch.fmod(pose[:, 2] - 180.0, 360.0) + 180.0
pose[:, 2] = torch.fmod(pose[:, 2] + 180.0, 360.0) - 180.0
return pose
current_poses = get_new_pose_batch(poses_last, corrected_pose)
st_pose = current_poses.clone().detach()
st_pose[:, :2] = - (st_pose[:, :2]
* 100.0 / self.resolution
- self.map_size_cm // (self.resolution * 2)) /\
(self.map_size_cm // (self.resolution * 2))
st_pose[:, 2] = 90. - (st_pose[:, 2])
rot_mat, trans_mat = get_grid(st_pose, agent_view.size(),
self.device)
rotated = F.grid_sample(agent_view, rot_mat, align_corners=True)
translated = F.grid_sample(rotated, trans_mat, align_corners=True)
# translated[:, 18:19, :, :] = -self.max_pool(-translated[:, 18:19, :, :])
diff_ob_ex = translated[:, 1:2, :, :] - self.max_pool(translated[:, 0:1, :, :])
diff_ob_ex[diff_ob_ex>0.8] = 1.0
diff_ob_ex[diff_ob_ex!=1.0] = 0.0
maps2 = torch.cat((maps_last.unsqueeze(1), translated.unsqueeze(1)), 1)
map_pred, _ = torch.max(maps2, 1)
if eve_angle == 0:
map_pred[:, 0:1, :, :][diff_ob_ex == 1.0] = 0.0
# stairs view
rot_mat_stair, trans_mat_stair = get_grid(st_pose, agent_view_stair.size(),
self.device)
rotated_stair = F.grid_sample(agent_view_stair, rot_mat_stair, align_corners=True)
translated_stair = F.grid_sample(rotated_stair, trans_mat_stair, align_corners=True)
stair_mask = torch.zeros(self.map_size_cm // self.resolution, self.map_size_cm // self.resolution).to(self.device)
s_y = int(current_poses[0][1]*100/5)
s_x = int(current_poses[0][0]*100/5)
limit_up = self.map_size_cm // self.resolution - self.stair_mask_radius - 1
limit_be = self.stair_mask_radius
if s_y > limit_up:
s_y = limit_up
if s_y < self.stair_mask_radius:
s_y = self.stair_mask_radius
if s_x > limit_up:
s_x = limit_up
if s_x < self.stair_mask_radius:
s_x = self.stair_mask_radius
stair_mask[int(s_y-self.stair_mask_radius):int(s_y+self.stair_mask_radius), int(s_x-self.stair_mask_radius):int(s_x+self.stair_mask_radius)] = self.stair_mask
translated_stair[0, 0:1, :, :] *= stair_mask
translated_stair[0, 1:2, :, :] *= stair_mask
# translated_stair[:, 13:14, :, :] = -self.max_pool(-translated_stair[:, 13:14, :, :])
diff_ob_ex = translated_stair[:, 1:2, :, :] - translated_stair[:, 0:1, :, :]
diff_ob_ex[diff_ob_ex>0.8] = 1.0
diff_ob_ex[diff_ob_ex!=1.0] = 0.0
maps3 = torch.cat((maps_last.unsqueeze(1), translated_stair.unsqueeze(1)), 1)
map_pred_stair, _ = torch.max(maps3, 1)
if eve_angle == 0:
map_pred_stair[:, 0:1, :, :][diff_ob_ex == 1.0] = 0.0
return translated.squeeze(0), map_pred.squeeze(0), map_pred_stair.squeeze(0), current_poses.squeeze(0)
def get_mask(self, step_size):
size = int(step_size) * 2
mask = torch.zeros(size, size)
for i in range(size):
for j in range(size):
if ((i + 0.5) - (size // 2)) ** 2 + \
((j + 0.5) - (size // 2)) ** 2 <= \
step_size ** 2:
mask[i, j] = 1
return mask