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Commit f5fc733a authored by Byzantine's avatar Byzantine
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Removing research/community models

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research/cognitive_mapping_and_planning/cfgs/config_vision_baseline.py deleted 100644 → 0
View file @ 09bc9f54
# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import pprint
import os
import numpy as np
from tensorflow.python.platform import app
from tensorflow.python.platform import flags
import logging
import src.utils as utils
import cfgs.config_common as cc
import datasets.nav_env_config as nec
import tensorflow as tf
FLAGS = flags.FLAGS
get_solver_vars = cc.get_solver_vars
get_navtask_vars = cc.get_navtask_vars
rgb_resnet_v2_50_path = 'data/init_models/resnet_v2_50/model.ckpt-5136169'
d_resnet_v2_50_path = 'data/init_models/distill_rgb_to_d_resnet_v2_50/model.ckpt-120002'
def get_default_args():
summary_args = utils.Foo(display_interval=1, test_iters=26,
arop_full_summary_iters=14)
control_args = utils.Foo(train=False, test=False,
force_batchnorm_is_training_at_test=False,
reset_rng_seed=False, only_eval_when_done=False,
test_mode=None)
return summary_args, control_args
def get_default_baseline_args():
batch_norm_param = {'center': True, 'scale': True,
'activation_fn':tf.nn.relu}
arch_args = utils.Foo(
pred_neurons=[], goal_embed_neurons=[], img_embed_neurons=[],
batch_norm_param=batch_norm_param, dim_reduce_neurons=64, combine_type='',
encoder='resnet_v2_50', action_sample_type='sample',
action_sample_combine_type='one_or_other',
sample_gt_prob_type='inverse_sigmoid_decay', dagger_sample_bn_false=True,
isd_k=750., use_visit_count=False, lstm_output=False, lstm_ego=False,
lstm_img=False, fc_dropout=0.0, embed_goal_for_state=False,
lstm_output_init_state_from_goal=False)
return arch_args
def get_arch_vars(arch_str):
if arch_str == '': vals = []
else: vals = arch_str.split('_')
ks = ['ver', 'lstm_dim', 'dropout']
# Exp Ver
if len(vals) == 0: vals.append('v0')
# LSTM dimentsions
if len(vals) == 1: vals.append('lstm2048')
# Dropout
if len(vals) == 2: vals.append('noDO')
assert(len(vals) == 3)
vars = utils.Foo()
for k, v in zip(ks, vals):
setattr(vars, k, v)
logging.error('arch_vars: %s', vars)
return vars
def process_arch_str(args, arch_str):
# This function modifies args.
args.arch = get_default_baseline_args()
arch_vars = get_arch_vars(arch_str)
args.navtask.task_params.outputs.rel_goal_loc = True
args.navtask.task_params.input_type = 'vision'
args.navtask.task_params.outputs.images = True
if args.navtask.camera_param.modalities[0] == 'rgb':
args.solver.pretrained_path = rgb_resnet_v2_50_path
elif args.navtask.camera_param.modalities[0] == 'depth':
args.solver.pretrained_path = d_resnet_v2_50_path
else:
logging.fatal('Neither of rgb or d')
if arch_vars.dropout == 'DO':
args.arch.fc_dropout = 0.5
args.tfcode = 'B'
exp_ver = arch_vars.ver
if exp_ver == 'v0':
# Multiplicative interaction between goal loc and image features.
args.arch.combine_type = 'multiply'
args.arch.pred_neurons = [256, 256]
args.arch.goal_embed_neurons = [64, 8]
args.arch.img_embed_neurons = [1024, 512, 256*8]
elif exp_ver == 'v1':
# Additive interaction between goal and image features.
args.arch.combine_type = 'add'
args.arch.pred_neurons = [256, 256]
args.arch.goal_embed_neurons = [64, 256]
args.arch.img_embed_neurons = [1024, 512, 256]
elif exp_ver == 'v2':
# LSTM at the output on top of multiple interactions.
args.arch.combine_type = 'multiply'
args.arch.goal_embed_neurons = [64, 8]
args.arch.img_embed_neurons = [1024, 512, 256*8]
args.arch.lstm_output = True
args.arch.lstm_output_dim = int(arch_vars.lstm_dim[4:])
args.arch.pred_neurons = [256] # The other is inside the LSTM.
elif exp_ver == 'v0blind':
# LSTM only on the goal location.
args.arch.combine_type = 'goalonly'
args.arch.goal_embed_neurons = [64, 256]
args.arch.img_embed_neurons = [2] # I dont know what it will do otherwise.
args.arch.lstm_output = True
args.arch.lstm_output_dim = 256
args.arch.pred_neurons = [256] # The other is inside the LSTM.
else:
logging.fatal('exp_ver: %s undefined', exp_ver)
assert(False)
# Log the arguments
logging.error('%s', args)
return args
def get_args_for_config(config_name):
args = utils.Foo()
args.summary, args.control = get_default_args()
exp_name, mode_str = config_name.split('+')
arch_str, solver_str, navtask_str = exp_name.split('.')
logging.error('config_name: %s', config_name)
logging.error('arch_str: %s', arch_str)
logging.error('navtask_str: %s', navtask_str)
logging.error('solver_str: %s', solver_str)
logging.error('mode_str: %s', mode_str)
args.solver = cc.process_solver_str(solver_str)
args.navtask = cc.process_navtask_str(navtask_str)
args = process_arch_str(args, arch_str)
args.arch.isd_k = args.solver.isd_k
# Train, test, etc.
mode, imset = mode_str.split('_')
args = cc.adjust_args_for_mode(args, mode)
args.navtask.building_names = args.navtask.dataset.get_split(imset)
args.control.test_name = '{:s}_on_{:s}'.format(mode, imset)
# Log the arguments
logging.error('%s', args)
return args
research/cognitive_mapping_and_planning/data/.gitignore deleted 100644 → 0
View file @ 09bc9f54
stanford_building_parser_dataset_raw
stanford_building_parser_dataset
init_models
research/cognitive_mapping_and_planning/data/README.md deleted 100644 → 0
View file @ 09bc9f54
This directory contains the data needed for training and benchmarking various
navigation models.
1. Download the data from the [dataset website]
(http://buildingparser.stanford.edu/dataset.html).
1. [Raw meshes](https://goo.gl/forms/2YSPaO2UKmn5Td5m2). We need the meshes
which are in the noXYZ folder. Download the tar files and place them in
the `stanford_building_parser_dataset_raw` folder. You need to download
`area_1_noXYZ.tar`, `area_3_noXYZ.tar`, `area_5a_noXYZ.tar`,
`area_5b_noXYZ.tar`, `area_6_noXYZ.tar` for training and
`area_4_noXYZ.tar` for evaluation.
2. [Annotations](https://goo.gl/forms/4SoGp4KtH1jfRqEj2) for setting up
tasks. We will need the file called `Stanford3dDataset_v1.2.zip`. Place
the file in the directory `stanford_building_parser_dataset_raw`.
2. Preprocess the data.
1. Extract meshes using `scripts/script_preprocess_meshes_S3DIS.sh`. After
this `ls data/stanford_building_parser_dataset/mesh` should have 6
folders `area1`, `area3`, `area4`, `area5a`, `area5b`, `area6`, with
textures and obj files within each directory.
2. Extract out room information and semantics from zip file using
`scripts/script_preprocess_annoations_S3DIS.sh`. After this there should
be `room-dimension` and `class-maps` folder in
`data/stanford_building_parser_dataset`. (If you find this script to
crash because of an exception in np.loadtxt while processing
`Area_5/office_19/Annotations/ceiling_1.txt`, there is a special
character on line 323474, that should be removed manually.)
3. Download ImageNet Pre-trained models. We used ResNet-v2-50 for representing
images. For RGB images this is pre-trained on ImageNet. For Depth images we
[distill](https://arxiv.org/abs/1507.00448) the RGB model to depth images
using paired RGB-D images. Both there models are available through
`scripts/script_download_init_models.sh`
research/cognitive_mapping_and_planning/datasets/factory.py deleted 100644 → 0
View file @ 09bc9f54
# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Wrapper for selecting the navigation environment that we want to train and
test on.
"""
import numpy as np
import os, glob
import platform
import logging
from tensorflow.python.platform import app
from tensorflow.python.platform import flags
import render.swiftshader_renderer as renderer
import src.file_utils as fu
import src.utils as utils
def get_dataset(dataset_name):
if dataset_name == 'sbpd':
dataset = StanfordBuildingParserDataset(dataset_name)
else:
logging.fatal('Not one of sbpd')
return dataset
class Loader():
def get_data_dir():
pass
def get_meta_data(self, file_name, data_dir=None):
if data_dir is None:
data_dir = self.get_data_dir()
full_file_name = os.path.join(data_dir, 'meta', file_name)
assert(fu.exists(full_file_name)), \
'{:s} does not exist'.format(full_file_name)
ext = os.path.splitext(full_file_name)[1]
if ext == '.txt':
ls = []
with fu.fopen(full_file_name, 'r') as f:
for l in f:
ls.append(l.rstrip())
elif ext == '.pkl':
ls = utils.load_variables(full_file_name)
return ls
def load_building(self, name, data_dir=None):
if data_dir is None:
data_dir = self.get_data_dir()
out = {}
out['name'] = name
out['data_dir'] = data_dir
out['room_dimension_file'] = os.path.join(data_dir, 'room-dimension',
name+'.pkl')
out['class_map_folder'] = os.path.join(data_dir, 'class-maps')
return out
def load_building_meshes(self, building):
dir_name = os.path.join(building['data_dir'], 'mesh', building['name'])
mesh_file_name = glob.glob1(dir_name, '*.obj')[0]
mesh_file_name_full = os.path.join(dir_name, mesh_file_name)
logging.error('Loading building from obj file: %s', mesh_file_name_full)
shape = renderer.Shape(mesh_file_name_full, load_materials=True,
name_prefix=building['name']+'_')
return [shape]
class StanfordBuildingParserDataset(Loader):
def __init__(self, ver):
self.ver = ver
self.data_dir = None
def get_data_dir(self):
if self.data_dir is None:
self.data_dir = 'data/stanford_building_parser_dataset/'
return self.data_dir
def get_benchmark_sets(self):
return self._get_benchmark_sets()
def get_split(self, split_name):
if self.ver == 'sbpd':
return self._get_split(split_name)
else:
logging.fatal('Unknown version.')
def _get_benchmark_sets(self):
sets = ['train1', 'val', 'test']
return sets
def _get_split(self, split_name):
train = ['area1', 'area5a', 'area5b', 'area6']
train1 = ['area1']
val = ['area3']
test = ['area4']
sets = {}
sets['train'] = train
sets['train1'] = train1
sets['val'] = val
sets['test'] = test
sets['all'] = sorted(list(set(train + val + test)))
return sets[split_name]
research/cognitive_mapping_and_planning/datasets/nav_env.py deleted 100644 → 0
View file @ 09bc9f54
# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Navidation Environment. Includes the following classes along with some
helper functions.
Building: Loads buildings, computes traversibility, exposes functionality for
rendering images.
GridWorld: Base class which implements functionality for moving an agent on a
grid world.
NavigationEnv: Base class which generates navigation problems on a grid world.
VisualNavigationEnv: Builds upon NavigationEnv and Building to provide
interface that is used externally to train the agent.
MeshMapper: Class used for distilling the model, testing the mapper.
BuildingMultiplexer: Wrapper class that instantiates a VisualNavigationEnv for
each building and multiplexes between them as needed.
"""
import numpy as np
import os
import re
import matplotlib.pyplot as plt
import graph_tool as gt
import graph_tool.topology
from tensorflow.python.platform import gfile
import logging
import src.file_utils as fu
import src.utils as utils
import src.graph_utils as gu
import src.map_utils as mu
import src.depth_utils as du
import render.swiftshader_renderer as sru
from render.swiftshader_renderer import SwiftshaderRenderer
import cv2
label_nodes_with_class = gu.label_nodes_with_class
label_nodes_with_class_geodesic = gu.label_nodes_with_class_geodesic
get_distance_node_list = gu.get_distance_node_list
convert_to_graph_tool = gu.convert_to_graph_tool
generate_graph = gu.generate_graph
get_hardness_distribution = gu.get_hardness_distribution
rng_next_goal_rejection_sampling = gu.rng_next_goal_rejection_sampling
rng_next_goal = gu.rng_next_goal
rng_room_to_room = gu.rng_room_to_room
rng_target_dist_field = gu.rng_target_dist_field
compute_traversibility = mu.compute_traversibility
make_map = mu.make_map
resize_maps = mu.resize_maps
pick_largest_cc = mu.pick_largest_cc
get_graph_origin_loc = mu.get_graph_origin_loc
generate_egocentric_maps = mu.generate_egocentric_maps
generate_goal_images = mu.generate_goal_images
get_map_to_predict = mu.get_map_to_predict
bin_points = du.bin_points
make_geocentric = du.make_geocentric
get_point_cloud_from_z = du.get_point_cloud_from_z
get_camera_matrix = du.get_camera_matrix
def _get_semantic_maps(folder_name, building_name, map, flip):
# Load file from the cache.
file_name = '{:s}_{:d}_{:d}_{:d}_{:d}_{:d}_{:d}.pkl'
file_name = file_name.format(building_name, map.size[0], map.size[1],
map.origin[0], map.origin[1], map.resolution,
flip)
file_name = os.path.join(folder_name, file_name)
logging.info('Loading semantic maps from %s.', file_name)
if fu.exists(file_name):
a = utils.load_variables(file_name)
maps = a['maps'] #HxWx#C
cats = a['cats']
else:
logging.error('file_name: %s not found.', file_name)
maps = None
cats = None
return maps, cats
def _select_classes(all_maps, all_cats, cats_to_use):
inds = []
for c in cats_to_use:
ind = all_cats.index(c)
inds.append(ind)
out_maps = all_maps[:,:,inds]
return out_maps
def _get_room_dimensions(file_name, resolution, origin, flip=False):
if fu.exists(file_name):
a = utils.load_variables(file_name)['room_dimension']
names = a.keys()
dims = np.concatenate(a.values(), axis=0).reshape((-1,6))
ind = np.argsort(names)
dims = dims[ind,:]
names = [names[x] for x in ind]
if flip:
dims_new = dims*1
dims_new[:,1] = -dims[:,4]
dims_new[:,4] = -dims[:,1]
dims = dims_new*1
dims = dims*100.
dims[:,0] = dims[:,0] - origin[0]
dims[:,1] = dims[:,1] - origin[1]
dims[:,3] = dims[:,3] - origin[0]
dims[:,4] = dims[:,4] - origin[1]
dims = dims / resolution
out = {'names': names, 'dims': dims}
else:
out = None
return out
def _filter_rooms(room_dims, room_regex):
pattern = re.compile(room_regex)
ind = []
for i, name in enumerate(room_dims['names']):
if pattern.match(name):
ind.append(i)
new_room_dims = {}
new_room_dims['names'] = [room_dims['names'][i] for i in ind]
new_room_dims['dims'] = room_dims['dims'][ind,:]*1
return new_room_dims
def _label_nodes_with_room_id(xyt, room_dims):
# Label the room with the ID into things.
node_room_id = -1*np.ones((xyt.shape[0], 1))
dims = room_dims['dims']
for x, name in enumerate(room_dims['names']):
all_ = np.concatenate((xyt[:,[0]] >= dims[x,0],
xyt[:,[0]] <= dims[x,3],
xyt[:,[1]] >= dims[x,1],
xyt[:,[1]] <= dims[x,4]), axis=1)
node_room_id[np.all(all_, axis=1), 0] = x
return node_room_id
def get_path_ids(start_node_id, end_node_id, pred_map):
id = start_node_id
path = [id]
while id != end_node_id:
id = pred_map[id]
path.append(id)
return path
def image_pre(images, modalities):
# Assumes images are ...xHxWxC.
# We always assume images are RGB followed by Depth.
if 'depth' in modalities:
d = images[...,-1][...,np.newaxis]*1.
d[d < 0.01] = np.NaN; isnan = np.isnan(d);
d = 100./d; d[isnan] = 0.;
images = np.concatenate((images[...,:-1], d, isnan), axis=images.ndim-1)
if 'rgb' in modalities:
images[...,:3] = images[...,:3]*1. - 128
return images
def _get_relative_goal_loc(goal_loc, loc, theta):
r = np.sqrt(np.sum(np.square(goal_loc - loc), axis=1))
t = np.arctan2(goal_loc[:,1] - loc[:,1], goal_loc[:,0] - loc[:,0])
t = t-theta[:,0] + np.pi/2
return np.expand_dims(r,axis=1), np.expand_dims(t, axis=1)
def _gen_perturbs(rng, batch_size, num_steps, lr_flip, delta_angle, delta_xy,
structured):
perturbs = []
for i in range(batch_size):
# Doing things one by one for each episode in this batch. This way this
# remains replicatable even when we change the batch size.
p = np.zeros((num_steps+1, 4))
if lr_flip:
# Flip the whole trajectory.
p[:,3] = rng.rand(1)-0.5
if delta_angle > 0:
if structured:
p[:,2] = (rng.rand(1)-0.5)* delta_angle
else:
p[:,2] = (rng.rand(p.shape[0])-0.5)* delta_angle
if delta_xy > 0:
if structured:
p[:,:2] = (rng.rand(1, 2)-0.5)*delta_xy
else:
p[:,:2] = (rng.rand(p.shape[0], 2)-0.5)*delta_xy
perturbs.append(p)
return perturbs
def get_multiplexer_class(args, task_number):
assert(args.task_params.base_class == 'Building')
logging.info('Returning BuildingMultiplexer')
R = BuildingMultiplexer(args, task_number)
return R
class GridWorld():
def __init__(self):
"""Class members that will be assigned by any class that actually uses this
class."""
self.restrict_to_largest_cc = None
self.robot = None
self.env = None
self.category_list = None
self.traversible = None
def get_loc_axis(self, node, delta_theta, perturb=None):
"""Based on the node orientation returns X, and Y axis. Used to sample the
map in egocentric coordinate frame.
"""
if type(node) == tuple:
node = np.array([node])
if perturb is None:
perturb = np.zeros((node.shape[0], 4))
xyt = self.to_actual_xyt_vec(node)
x = xyt[:,[0]] + perturb[:,[0]]
y = xyt[:,[1]] + perturb[:,[1]]
t = xyt[:,[2]] + perturb[:,[2]]
theta = t*delta_theta
loc = np.concatenate((x,y), axis=1)
x_axis = np.concatenate((np.cos(theta), np.sin(theta)), axis=1)
y_axis = np.concatenate((np.cos(theta+np.pi/2.), np.sin(theta+np.pi/2.)),
axis=1)
# Flip the sampled map where need be.
y_axis[np.where(perturb[:,3] > 0)[0], :] *= -1.
return loc, x_axis, y_axis, theta
def to_actual_xyt(self, pqr):
"""Converts from node to location on the map."""
(p, q, r) = pqr
if self.task.n_ori == 6:
out = (p - q * 0.5 + self.task.origin_loc[0],
q * np.sqrt(3.) / 2. + self.task.origin_loc[1], r)
elif self.task.n_ori == 4:
out = (p + self.task.origin_loc[0],
q + self.task.origin_loc[1], r)
return out
def to_actual_xyt_vec(self, pqr):
"""Converts from node array to location array on the map."""
p = pqr[:,0][:, np.newaxis]
q = pqr[:,1][:, np.newaxis]
r = pqr[:,2][:, np.newaxis]
if self.task.n_ori == 6:
out = np.concatenate((p - q * 0.5 + self.task.origin_loc[0],
q * np.sqrt(3.) / 2. + self.task.origin_loc[1],
r), axis=1)
elif self.task.n_ori == 4:
out = np.concatenate((p + self.task.origin_loc[0],
q + self.task.origin_loc[1],
r), axis=1)
return out
def raw_valid_fn_vec(self, xyt):
"""Returns if the given set of nodes is valid or not."""
height = self.traversible.shape[0]
width = self.traversible.shape[1]
x = np.round(xyt[:,[0]]).astype(np.int32)
y = np.round(xyt[:,[1]]).astype(np.int32)
is_inside = np.all(np.concatenate((x >= 0, y >= 0,
x < width, y < height), axis=1), axis=1)
x = np.minimum(np.maximum(x, 0), width-1)
y = np.minimum(np.maximum(y, 0), height-1)
ind = np.ravel_multi_index((y,x), self.traversible.shape)
is_traversible = self.traversible.ravel()[ind]
is_valid = np.all(np.concatenate((is_inside[:,np.newaxis], is_traversible),
axis=1), axis=1)
return is_valid
def valid_fn_vec(self, pqr):
"""Returns if the given set of nodes is valid or not."""
xyt = self.to_actual_xyt_vec(np.array(pqr))
height = self.traversible.shape[0]
width = self.traversible.shape[1]
x = np.round(xyt[:,[0]]).astype(np.int32)
y = np.round(xyt[:,[1]]).astype(np.int32)
is_inside = np.all(np.concatenate((x >= 0, y >= 0,
x < width, y < height), axis=1), axis=1)
x = np.minimum(np.maximum(x, 0), width-1)
y = np.minimum(np.maximum(y, 0), height-1)
ind = np.ravel_multi_index((y,x), self.traversible.shape)
is_traversible = self.traversible.ravel()[ind]
is_valid = np.all(np.concatenate((is_inside[:,np.newaxis], is_traversible),
axis=1), axis=1)
return is_valid
def get_feasible_actions(self, node_ids):
"""Returns the feasible set of actions from the current node."""
a = np.zeros((len(node_ids), self.task_params.num_actions), dtype=np.int32)
gtG = self.task.gtG
next_node = []
for i, c in enumerate(node_ids):
neigh = gtG.vertex(c).out_neighbours()
neigh_edge = gtG.vertex(c).out_edges()
nn = {}
for n, e in zip(neigh, neigh_edge):
_ = gtG.ep['action'][e]
a[i,_] = 1
nn[_] = int(n)
next_node.append(nn)
return a, next_node
def take_action(self, current_node_ids, action):
"""Returns the new node after taking the action action. Stays at the current
node if the action is invalid."""
actions, next_node_ids = self.get_feasible_actions(current_node_ids)
new_node_ids = []
for i, (c,a) in enumerate(zip(current_node_ids, action)):
if actions[i,a] == 1:
new_node_ids.append(next_node_ids[i][a])
else:
new_node_ids.append(c)
return new_node_ids
def set_r_obj(self, r_obj):
"""Sets the SwiftshaderRenderer object used for rendering."""
self.r_obj = r_obj
class Building(GridWorld):
def __init__(self, building_name, robot, env,
category_list=None, small=False, flip=False, logdir=None,
building_loader=None):
self.restrict_to_largest_cc = True
self.robot = robot
self.env = env
self.logdir = logdir
# Load the building meta data.
building = building_loader.load_building(building_name)
if small:
building['mesh_names'] = building['mesh_names'][:5]
# New code.
shapess = building_loader.load_building_meshes(building)
if flip:
for shapes in shapess:
shapes.flip_shape()
vs = []
for shapes in shapess:
vs.append(shapes.get_vertices()[0])
vs = np.concatenate(vs, axis=0)
map = make_map(env.padding, env.resolution, vertex=vs, sc=100.)
map = compute_traversibility(
map, robot.base, robot.height, robot.radius, env.valid_min,
env.valid_max, env.num_point_threshold, shapess=shapess, sc=100.,
n_samples_per_face=env.n_samples_per_face)
room_dims = _get_room_dimensions(building['room_dimension_file'],
env.resolution, map.origin, flip=flip)
class_maps, class_map_names = _get_semantic_maps(
building['class_map_folder'], building_name, map, flip)
self.class_maps = class_maps
self.class_map_names = class_map_names
self.building = building
self.shapess = shapess
self.map = map
self.traversible = map.traversible*1
self.building_name = building_name
self.room_dims = room_dims
self.flipped = flip
self.renderer_entitiy_ids = []
if self.restrict_to_largest_cc:
self.traversible = pick_largest_cc(self.traversible)
def load_building_into_scene(self):
# Loads the scene.
self.renderer_entitiy_ids += self.r_obj.load_shapes(self.shapess)
# Free up memory, we dont need the mesh or the materials anymore.
self.shapess = None
def add_entity_at_nodes(self, nodes, height, shape):
xyt = self.to_actual_xyt_vec(nodes)
nxy = xyt[:,:2]*1.
nxy = nxy * self.map.resolution
nxy = nxy + self.map.origin
Ts = np.concatenate((nxy, nxy[:,:1]), axis=1)
Ts[:,2] = height; Ts = Ts / 100.;
# Merge all the shapes into a single shape and add that shape.
shape.replicate_shape(Ts)
entity_ids = self.r_obj.load_shapes([shape])
self.renderer_entitiy_ids += entity_ids
return entity_ids
def add_shapes(self, shapes):
scene = self.r_obj.viz.scene()
for shape in shapes:
scene.AddShape(shape)
def add_materials(self, materials):
scene = self.r_obj.viz.scene()
for material in materials:
scene.AddOrUpdateMaterial(material)
def set_building_visibility(self, visibility):
self.r_obj.set_entity_visible(self.renderer_entitiy_ids, visibility)
def render_nodes(self, nodes, perturb=None, aux_delta_theta=0.):
self.set_building_visibility(True)
if perturb is None:
perturb = np.zeros((len(nodes), 4))
imgs = []
r = 2
elevation_z = r * np.tan(np.deg2rad(self.robot.camera_elevation_degree))
for i in range(len(nodes)):
xyt = self.to_actual_xyt(nodes[i])
lookat_theta = 3.0 * np.pi / 2.0 - (xyt[2]+perturb[i,2]+aux_delta_theta) * (self.task.delta_theta)
nxy = np.array([xyt[0]+perturb[i,0], xyt[1]+perturb[i,1]]).reshape(1, -1)
nxy = nxy * self.map.resolution
nxy = nxy + self.map.origin
camera_xyz = np.zeros((1, 3))
camera_xyz[...] = [nxy[0, 0], nxy[0, 1], self.robot.sensor_height]
camera_xyz = camera_xyz / 100.
lookat_xyz = np.array([-r * np.sin(lookat_theta),
-r * np.cos(lookat_theta), elevation_z])
lookat_xyz = lookat_xyz + camera_xyz[0, :]
self.r_obj.position_camera(camera_xyz[0, :].tolist(),
lookat_xyz.tolist(), [0.0, 0.0, 1.0])
img = self.r_obj.render(take_screenshot=True, output_type=0)
img = [x for x in img if x is not None]
img = np.concatenate(img, axis=2).astype(np.float32)
if perturb[i,3]>0:
img = img[:,::-1,:]
imgs.append(img)
self.set_building_visibility(False)
return imgs
class MeshMapper(Building):
def __init__(self, robot, env, task_params, building_name, category_list,
flip, logdir=None, building_loader=None):
Building.__init__(self, building_name, robot, env, category_list,
small=task_params.toy_problem, flip=flip, logdir=logdir,
building_loader=building_loader)
self.task_params = task_params
self.task = None
self._preprocess_for_task(self.task_params.building_seed)
def _preprocess_for_task(self, seed):
if self.task is None or self.task.seed != seed:
rng = np.random.RandomState(seed)
origin_loc = get_graph_origin_loc(rng, self.traversible)
self.task = utils.Foo(seed=seed, origin_loc=origin_loc,
n_ori=self.task_params.n_ori)
G = generate_graph(self.valid_fn_vec,
self.task_params.step_size, self.task.n_ori,
(0, 0, 0))
gtG, nodes, nodes_to_id = convert_to_graph_tool(G)
self.task.gtG = gtG
self.task.nodes = nodes
self.task.delta_theta = 2.0*np.pi/(self.task.n_ori*1.)
self.task.nodes_to_id = nodes_to_id
logging.info('Building %s, #V=%d, #E=%d', self.building_name,
self.task.nodes.shape[0], self.task.gtG.num_edges())
if self.logdir is not None:
write_traversible = cv2.applyColorMap(self.traversible.astype(np.uint8)*255, cv2.COLORMAP_JET)
img_path = os.path.join(self.logdir,
'{:s}_{:d}_graph.png'.format(self.building_name,
seed))
node_xyt = self.to_actual_xyt_vec(self.task.nodes)
plt.set_cmap('jet');
fig, ax = utils.subplot(plt, (1,1), (12,12))
ax.plot(node_xyt[:,0], node_xyt[:,1], 'm.')
ax.imshow(self.traversible, origin='lower');
ax.set_axis_off(); ax.axis('equal');
ax.set_title('{:s}, {:d}, {:d}'.format(self.building_name,
self.task.nodes.shape[0],
self.task.gtG.num_edges()))
if self.room_dims is not None:
for i, r in enumerate(self.room_dims['dims']*1):
min_ = r[:3]*1
max_ = r[3:]*1
xmin, ymin, zmin = min_
xmax, ymax, zmax = max_
ax.plot([xmin, xmax, xmax, xmin, xmin],
[ymin, ymin, ymax, ymax, ymin], 'g')
with fu.fopen(img_path, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
plt.close(fig)
def _gen_rng(self, rng):
# instances is a list of list of node_ids.
if self.task_params.move_type == 'circle':
_, _, _, _, paths = rng_target_dist_field(self.task_params.batch_size,
self.task.gtG, rng, 0, 1,
compute_path=True)
instances_ = paths
instances = []
for instance_ in instances_:
instance = instance_
for i in range(self.task_params.num_steps):
instance.append(self.take_action([instance[-1]], [1])[0])
instances.append(instance)
elif self.task_params.move_type == 'shortest_path':
_, _, _, _, paths = rng_target_dist_field(self.task_params.batch_size,
self.task.gtG, rng,
self.task_params.num_steps,
self.task_params.num_steps+1,
compute_path=True)
instances = paths
elif self.task_params.move_type == 'circle+forward':
_, _, _, _, paths = rng_target_dist_field(self.task_params.batch_size,
self.task.gtG, rng, 0, 1,
compute_path=True)
instances_ = paths
instances = []
for instance_ in instances_:
instance = instance_
for i in range(self.task_params.n_ori-1):
instance.append(self.take_action([instance[-1]], [1])[0])
while len(instance) <= self.task_params.num_steps:
while self.take_action([instance[-1]], [3])[0] == instance[-1] and len(instance) <= self.task_params.num_steps:
instance.append(self.take_action([instance[-1]], [2])[0])
if len(instance) <= self.task_params.num_steps:
instance.append(self.take_action([instance[-1]], [3])[0])
instances.append(instance)
# Do random perturbation if needed.
perturbs = _gen_perturbs(rng, self.task_params.batch_size,
self.task_params.num_steps,
self.task_params.data_augment.lr_flip,
self.task_params.data_augment.delta_angle,
self.task_params.data_augment.delta_xy,
self.task_params.data_augment.structured)
return instances, perturbs
def worker(self, instances, perturbs):
# Output the images and the free space.
# Make the instances be all the same length.
for i in range(len(instances)):
for j in range(self.task_params.num_steps - len(instances[i]) + 1):
instances[i].append(instances[i][-1])
if perturbs[i].shape[0] < self.task_params.num_steps+1:
p = np.zeros((self.task_params.num_steps+1, 4))
p[:perturbs[i].shape[0], :] = perturbs[i]
p[perturbs[i].shape[0]:, :] = perturbs[i][-1,:]
perturbs[i] = p
instances_ = []
for instance in instances:
instances_ = instances_ + instance
perturbs_ = np.concatenate(perturbs, axis=0)
instances_nodes = self.task.nodes[instances_,:]
instances_nodes = [tuple(x) for x in instances_nodes]
imgs_ = self.render_nodes(instances_nodes, perturbs_)
imgs = []; next = 0;
for instance in instances:
img_i = []
for _ in instance:
img_i.append(imgs_[next])
next = next+1
imgs.append(img_i)
imgs = np.array(imgs)
# Render out the maps in the egocentric view for all nodes and not just the
# last node.
all_nodes = []
for x in instances:
all_nodes = all_nodes + x
all_perturbs = np.concatenate(perturbs, axis=0)
loc, x_axis, y_axis, theta = self.get_loc_axis(
self.task.nodes[all_nodes, :]*1, delta_theta=self.task.delta_theta,
perturb=all_perturbs)
fss = None
valids = None
loc_on_map = None
theta_on_map = None
cum_fs = None
cum_valid = None
incremental_locs = None
incremental_thetas = None
if self.task_params.output_free_space:
fss, valids = get_map_to_predict(loc, x_axis, y_axis,
map=self.traversible*1.,
map_size=self.task_params.map_size)
fss = np.array(fss) > 0.5
fss = np.reshape(fss, [self.task_params.batch_size,
self.task_params.num_steps+1,
self.task_params.map_size,
self.task_params.map_size])
valids = np.reshape(np.array(valids), fss.shape)
if self.task_params.output_transform_to_global_map:
# Output the transform to the global map.
loc_on_map = np.reshape(loc*1, [self.task_params.batch_size,
self.task_params.num_steps+1, -1])
# Converting to location wrt to first location so that warping happens
# properly.
theta_on_map = np.reshape(theta*1, [self.task_params.batch_size,
self.task_params.num_steps+1, -1])
if self.task_params.output_incremental_transform:
# Output the transform to the global map.
incremental_locs_ = np.reshape(loc*1, [self.task_params.batch_size,
self.task_params.num_steps+1, -1])
incremental_locs_[:,1:,:] -= incremental_locs_[:,:-1,:]
t0 = -np.pi/2+np.reshape(theta*1, [self.task_params.batch_size,
self.task_params.num_steps+1, -1])
t = t0*1
incremental_locs = incremental_locs_*1
incremental_locs[:,:,0] = np.sum(incremental_locs_ * np.concatenate((np.cos(t), np.sin(t)), axis=-1), axis=-1)
incremental_locs[:,:,1] = np.sum(incremental_locs_ * np.concatenate((np.cos(t+np.pi/2), np.sin(t+np.pi/2)), axis=-1), axis=-1)
incremental_locs[:,0,:] = incremental_locs_[:,0,:]
# print incremental_locs_[0,:,:], incremental_locs[0,:,:], t0[0,:,:]
incremental_thetas = np.reshape(theta*1, [self.task_params.batch_size,
self.task_params.num_steps+1,
-1])
incremental_thetas[:,1:,:] += -incremental_thetas[:,:-1,:]
if self.task_params.output_canonical_map:
loc_ = loc[0::(self.task_params.num_steps+1), :]
x_axis = np.zeros_like(loc_); x_axis[:,1] = 1
y_axis = np.zeros_like(loc_); y_axis[:,0] = -1
cum_fs, cum_valid = get_map_to_predict(loc_, x_axis, y_axis,
map=self.traversible*1.,
map_size=self.task_params.map_size)
cum_fs = np.array(cum_fs) > 0.5
cum_fs = np.reshape(cum_fs, [self.task_params.batch_size, 1,
self.task_params.map_size,
self.task_params.map_size])
cum_valid = np.reshape(np.array(cum_valid), cum_fs.shape)
inputs = {'fs_maps': fss,
'valid_maps': valids,
'imgs': imgs,
'loc_on_map': loc_on_map,
'theta_on_map': theta_on_map,
'cum_fs_maps': cum_fs,
'cum_valid_maps': cum_valid,
'incremental_thetas': incremental_thetas,
'incremental_locs': incremental_locs}
return inputs
def pre(self, inputs):
inputs['imgs'] = image_pre(inputs['imgs'], self.task_params.modalities)
if inputs['loc_on_map'] is not None:
inputs['loc_on_map'] = inputs['loc_on_map'] - inputs['loc_on_map'][:,[0],:]
if inputs['theta_on_map'] is not None:
inputs['theta_on_map'] = np.pi/2. - inputs['theta_on_map']
return inputs
def _nav_env_reset_helper(type, rng, nodes, batch_size, gtG, max_dist,
num_steps, num_goals, data_augment, **kwargs):
"""Generates and returns a new episode."""
max_compute = max_dist + 4*num_steps
if type == 'general':
start_node_ids, end_node_ids, dist, pred_map, paths = \
rng_target_dist_field(batch_size, gtG, rng, max_dist, max_compute,
nodes=nodes, compute_path=False)
target_class = None
elif type == 'room_to_room_many':
goal_node_ids = []; dists = [];
node_room_ids = kwargs['node_room_ids']
# Sample the first one
start_node_ids_, end_node_ids_, dist_, _, _ = rng_room_to_room(
batch_size, gtG, rng, max_dist, max_compute,
node_room_ids=node_room_ids, nodes=nodes)
start_node_ids = start_node_ids_
goal_node_ids.append(end_node_ids_)
dists.append(dist_)
for n in range(num_goals-1):
start_node_ids_, end_node_ids_, dist_, _, _ = rng_next_goal(
goal_node_ids[n], batch_size, gtG, rng, max_dist,
max_compute, node_room_ids=node_room_ids, nodes=nodes,
dists_from_start_node=dists[n])
goal_node_ids.append(end_node_ids_)
dists.append(dist_)
target_class = None
elif type == 'rng_rejection_sampling_many':
num_goals = num_goals
goal_node_ids = []; dists = [];
n_ori = kwargs['n_ori']
step_size = kwargs['step_size']
min_dist = kwargs['min_dist']
sampling_distribution = kwargs['sampling_distribution']
target_distribution = kwargs['target_distribution']
rejection_sampling_M = kwargs['rejection_sampling_M']
distribution_bins = kwargs['distribution_bins']
for n in range(num_goals):
if n == 0: input_nodes = None
else: input_nodes = goal_node_ids[n-1]
start_node_ids_, end_node_ids_, dist_, _, _, _, _ = rng_next_goal_rejection_sampling(
input_nodes, batch_size, gtG, rng, max_dist, min_dist,
max_compute, sampling_distribution, target_distribution, nodes,
n_ori, step_size, distribution_bins, rejection_sampling_M)
if n == 0: start_node_ids = start_node_ids_
goal_node_ids.append(end_node_ids_)
dists.append(dist_)
target_class = None
elif type == 'room_to_room_back':
num_goals = num_goals
assert(num_goals == 2), 'num_goals must be 2.'
goal_node_ids = []; dists = [];
node_room_ids = kwargs['node_room_ids']
# Sample the first one.
start_node_ids_, end_node_ids_, dist_, _, _ = rng_room_to_room(
batch_size, gtG, rng, max_dist, max_compute,
node_room_ids=node_room_ids, nodes=nodes)
start_node_ids = start_node_ids_
goal_node_ids.append(end_node_ids_)
dists.append(dist_)
# Set second goal to be starting position, and compute distance to the start node.
goal_node_ids.append(start_node_ids)
dist = []
for i in range(batch_size):
dist_ = gt.topology.shortest_distance(
gt.GraphView(gtG, reversed=True),
source=gtG.vertex(start_node_ids[i]), target=None)
dist_ = np.array(dist_.get_array())
dist.append(dist_)
dists.append(dist)
target_class = None
elif type[:14] == 'to_nearest_obj':
# Generate an episode by sampling one of the target classes (with
# probability proportional to the number of nodes in the world).
# With the sampled class sample a node that is within some distance from
# the sampled class.
class_nodes = kwargs['class_nodes']
sampling = kwargs['sampling']
dist_to_class = kwargs['dist_to_class']
assert(num_goals == 1), 'Only supports a single goal.'
ind = rng.choice(class_nodes.shape[0], size=batch_size)
target_class = class_nodes[ind,1]
start_node_ids = []; dists = []; goal_node_ids = [];
for t in target_class:
if sampling == 'uniform':
max_dist = max_dist
cnts = np.bincount(dist_to_class[t], minlength=max_dist+1)*1.
cnts[max_dist+1:] = 0
p_each = 1./ cnts / (max_dist+1.)
p_each[cnts == 0] = 0
p = p_each[dist_to_class[t]]*1.; p = p/np.sum(p)
start_node_id = rng.choice(p.shape[0], size=1, p=p)[0]
else:
logging.fatal('Sampling not one of uniform.')
start_node_ids.append(start_node_id)
dists.append(dist_to_class[t])
# Dummy goal node, same as the start node, so that vis is better.
goal_node_ids.append(start_node_id)
dists = [dists]
goal_node_ids = [goal_node_ids]
return start_node_ids, goal_node_ids, dists, target_class
class NavigationEnv(GridWorld, Building):
"""Wrapper around GridWorld which sets up navigation tasks.
"""
def _debug_save_hardness(self, seed):
out_path = os.path.join(self.logdir, '{:s}_{:d}_hardness.png'.format(self.building_name, seed))
batch_size = 4000
rng = np.random.RandomState(0)
start_node_ids, end_node_ids, dists, pred_maps, paths, hardnesss, gt_dists = \
rng_next_goal_rejection_sampling(
None, batch_size, self.task.gtG, rng, self.task_params.max_dist,
self.task_params.min_dist, self.task_params.max_dist,
self.task.sampling_distribution, self.task.target_distribution,
self.task.nodes, self.task_params.n_ori, self.task_params.step_size,
self.task.distribution_bins, self.task.rejection_sampling_M)
bins = self.task.distribution_bins
n_bins = self.task.n_bins
with plt.style.context('ggplot'):
fig, axes = utils.subplot(plt, (1,2), (10,10))
ax = axes[0]
_ = ax.hist(hardnesss, bins=bins, weights=np.ones_like(hardnesss)/len(hardnesss))
ax.plot(bins[:-1]+0.5/n_bins, self.task.target_distribution, 'g')
ax.plot(bins[:-1]+0.5/n_bins, self.task.sampling_distribution, 'b')
ax.grid('on')
ax = axes[1]
_ = ax.hist(gt_dists, bins=np.arange(self.task_params.max_dist+1))
ax.grid('on')
ax.set_title('Mean: {:0.2f}, Median: {:0.2f}'.format(np.mean(gt_dists),
np.median(gt_dists)))
with fu.fopen(out_path, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
def _debug_save_map_nodes(self, seed):
"""Saves traversible space along with nodes generated on the graph. Takes
the seed as input."""
img_path = os.path.join(self.logdir, '{:s}_{:d}_graph.png'.format(self.building_name, seed))
node_xyt = self.to_actual_xyt_vec(self.task.nodes)
plt.set_cmap('jet');
fig, ax = utils.subplot(plt, (1,1), (12,12))
ax.plot(node_xyt[:,0], node_xyt[:,1], 'm.')
ax.set_axis_off(); ax.axis('equal');
if self.room_dims is not None:
for i, r in enumerate(self.room_dims['dims']*1):
min_ = r[:3]*1
max_ = r[3:]*1
xmin, ymin, zmin = min_
xmax, ymax, zmax = max_
ax.plot([xmin, xmax, xmax, xmin, xmin],
[ymin, ymin, ymax, ymax, ymin], 'g')
ax.imshow(self.traversible, origin='lower');
with fu.fopen(img_path, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
def _debug_semantic_maps(self, seed):
"""Saves traversible space along with nodes generated on the graph. Takes
the seed as input."""
for i, cls in enumerate(self.task_params.semantic_task.class_map_names):
img_path = os.path.join(self.logdir, '{:s}_flip{:d}_{:s}_graph.png'.format(self.building_name, seed, cls))
maps = self.traversible*1.
maps += 0.5*(self.task.class_maps_dilated[:,:,i])
write_traversible = (maps*1.+1.)/3.0
write_traversible = (write_traversible*255.).astype(np.uint8)[:,:,np.newaxis]
write_traversible = write_traversible + np.zeros((1,1,3), dtype=np.uint8)
fu.write_image(img_path, write_traversible[::-1,:,:])
def _preprocess_for_task(self, seed):
"""Sets up the task field for doing navigation on the grid world."""
if self.task is None or self.task.seed != seed:
rng = np.random.RandomState(seed)
origin_loc = get_graph_origin_loc(rng, self.traversible)
self.task = utils.Foo(seed=seed, origin_loc=origin_loc,
n_ori=self.task_params.n_ori)
G = generate_graph(self.valid_fn_vec, self.task_params.step_size,
self.task.n_ori, (0, 0, 0))
gtG, nodes, nodes_to_id = convert_to_graph_tool(G)
self.task.gtG = gtG
self.task.nodes = nodes
self.task.delta_theta = 2.0*np.pi/(self.task.n_ori*1.)
self.task.nodes_to_id = nodes_to_id
logging.info('Building %s, #V=%d, #E=%d', self.building_name,
self.task.nodes.shape[0], self.task.gtG.num_edges())
type = self.task_params.type
if type == 'general':
# Do nothing
_ = None
elif type == 'room_to_room_many' or type == 'room_to_room_back':
if type == 'room_to_room_back':
assert(self.task_params.num_goals == 2), 'num_goals must be 2.'
self.room_dims = _filter_rooms(self.room_dims, self.task_params.room_regex)
xyt = self.to_actual_xyt_vec(self.task.nodes)
self.task.node_room_ids = _label_nodes_with_room_id(xyt, self.room_dims)
self.task.reset_kwargs = {'node_room_ids': self.task.node_room_ids}
elif type == 'rng_rejection_sampling_many':
n_bins = 20
rejection_sampling_M = self.task_params.rejection_sampling_M
min_dist = self.task_params.min_dist
bins = np.arange(n_bins+1)/(n_bins*1.)
target_d = np.zeros(n_bins); target_d[...] = 1./n_bins;
sampling_d = get_hardness_distribution(
self.task.gtG, self.task_params.max_dist, self.task_params.min_dist,
np.random.RandomState(0), 4000, bins, self.task.nodes,
self.task_params.n_ori, self.task_params.step_size)
self.task.reset_kwargs = {'distribution_bins': bins,
'target_distribution': target_d,
'sampling_distribution': sampling_d,
'rejection_sampling_M': rejection_sampling_M,
'n_bins': n_bins,
'n_ori': self.task_params.n_ori,
'step_size': self.task_params.step_size,
'min_dist': self.task_params.min_dist}
self.task.n_bins = n_bins
self.task.distribution_bins = bins
self.task.target_distribution = target_d
self.task.sampling_distribution = sampling_d
self.task.rejection_sampling_M = rejection_sampling_M
if self.logdir is not None:
self._debug_save_hardness(seed)
elif type[:14] == 'to_nearest_obj':
self.room_dims = _filter_rooms(self.room_dims, self.task_params.room_regex)
xyt = self.to_actual_xyt_vec(self.task.nodes)
self.class_maps = _select_classes(self.class_maps,
self.class_map_names,
self.task_params.semantic_task.class_map_names)*1
self.class_map_names = self.task_params.semantic_task.class_map_names
nodes_xyt = self.to_actual_xyt_vec(np.array(self.task.nodes))
tt = utils.Timer(); tt.tic();
if self.task_params.type == 'to_nearest_obj_acc':
self.task.class_maps_dilated, self.task.node_class_label = label_nodes_with_class_geodesic(
nodes_xyt, self.class_maps,
self.task_params.semantic_task.pix_distance+8, self.map.traversible,
ff_cost=1., fo_cost=1., oo_cost=4., connectivity=8.)
dists = []
for i in range(len(self.class_map_names)):
class_nodes_ = np.where(self.task.node_class_label[:,i])[0]
dists.append(get_distance_node_list(gtG, source_nodes=class_nodes_, direction='to'))
self.task.dist_to_class = dists
a_, b_ = np.where(self.task.node_class_label)
self.task.class_nodes = np.concatenate((a_[:,np.newaxis], b_[:,np.newaxis]), axis=1)
if self.logdir is not None:
self._debug_semantic_maps(seed)
self.task.reset_kwargs = {'sampling': self.task_params.semantic_task.sampling,
'class_nodes': self.task.class_nodes,
'dist_to_class': self.task.dist_to_class}
if self.logdir is not None:
self._debug_save_map_nodes(seed)
def reset(self, rngs):
rng = rngs[0]; rng_perturb = rngs[1];
nodes = self.task.nodes
tp = self.task_params
start_node_ids, goal_node_ids, dists, target_class = \
_nav_env_reset_helper(tp.type, rng, self.task.nodes, tp.batch_size,
self.task.gtG, tp.max_dist, tp.num_steps,
tp.num_goals, tp.data_augment,
**(self.task.reset_kwargs))
start_nodes = [tuple(nodes[_,:]) for _ in start_node_ids]
goal_nodes = [[tuple(nodes[_,:]) for _ in __] for __ in goal_node_ids]
data_augment = tp.data_augment
perturbs = _gen_perturbs(rng_perturb, tp.batch_size,
(tp.num_steps+1)*tp.num_goals,
data_augment.lr_flip, data_augment.delta_angle,
data_augment.delta_xy, data_augment.structured)
perturbs = np.array(perturbs) # batch x steps x 4
end_perturbs = perturbs[:,-(tp.num_goals):,:]*1 # fixed perturb for the goal.
perturbs = perturbs[:,:-(tp.num_goals),:]*1
history = -np.ones((tp.batch_size, tp.num_steps*tp.num_goals), dtype=np.int32)
self.episode = utils.Foo(
start_nodes=start_nodes, start_node_ids=start_node_ids,
goal_nodes=goal_nodes, goal_node_ids=goal_node_ids, dist_to_goal=dists,
perturbs=perturbs, goal_perturbs=end_perturbs, history=history,
target_class=target_class, history_frames=[])
return start_node_ids
def take_action(self, current_node_ids, action, step_number):
"""In addition to returning the action, also returns the reward that the
agent receives."""
goal_number = step_number / self.task_params.num_steps
new_node_ids = GridWorld.take_action(self, current_node_ids, action)
rewards = []
for i, n in enumerate(new_node_ids):
reward = 0
if n == self.episode.goal_node_ids[goal_number][i]:
reward = self.task_params.reward_at_goal
reward = reward - self.task_params.reward_time_penalty
rewards.append(reward)
return new_node_ids, rewards
def get_optimal_action(self, current_node_ids, step_number):
"""Returns the optimal action from the current node."""
goal_number = step_number / self.task_params.num_steps
gtG = self.task.gtG
a = np.zeros((len(current_node_ids), self.task_params.num_actions), dtype=np.int32)
d_dict = self.episode.dist_to_goal[goal_number]
for i, c in enumerate(current_node_ids):
neigh = gtG.vertex(c).out_neighbours()
neigh_edge = gtG.vertex(c).out_edges()
ds = np.array([d_dict[i][int(x)] for x in neigh])
ds_min = np.min(ds)
for i_, e in enumerate(neigh_edge):
if ds[i_] == ds_min:
_ = gtG.ep['action'][e]
a[i, _] = 1
return a
def get_targets(self, current_node_ids, step_number):
"""Returns the target actions from the current node."""
action = self.get_optimal_action(current_node_ids, step_number)
action = np.expand_dims(action, axis=1)
return vars(utils.Foo(action=action))
def get_targets_name(self):
"""Returns the list of names of the targets."""
return ['action']
def cleanup(self):
self.episode = None
class VisualNavigationEnv(NavigationEnv):
"""Class for doing visual navigation in environments. Functions for computing
features on states, etc.
"""
def __init__(self, robot, env, task_params, category_list=None,
building_name=None, flip=False, logdir=None,
building_loader=None, r_obj=None):
tt = utils.Timer()
tt.tic()
Building.__init__(self, building_name, robot, env, category_list,
small=task_params.toy_problem, flip=flip, logdir=logdir,
building_loader=building_loader)
self.set_r_obj(r_obj)
self.task_params = task_params
self.task = None
self.episode = None
self._preprocess_for_task(self.task_params.building_seed)
if hasattr(self.task_params, 'map_scales'):
self.task.scaled_maps = resize_maps(
self.traversible.astype(np.float32)*1, self.task_params.map_scales,
self.task_params.map_resize_method)
else:
logging.fatal('VisualNavigationEnv does not support scale_f anymore.')
self.task.readout_maps_scaled = resize_maps(
self.traversible.astype(np.float32)*1,
self.task_params.readout_maps_scales,
self.task_params.map_resize_method)
tt.toc(log_at=1, log_str='VisualNavigationEnv __init__: ')
def get_weight(self):
return self.task.nodes.shape[0]
def get_common_data(self):
goal_nodes = self.episode.goal_nodes
start_nodes = self.episode.start_nodes
perturbs = self.episode.perturbs
goal_perturbs = self.episode.goal_perturbs
target_class = self.episode.target_class
goal_locs = []; rel_goal_locs = [];
for i in range(len(goal_nodes)):
end_nodes = goal_nodes[i]
goal_loc, _, _, goal_theta = self.get_loc_axis(
np.array(end_nodes), delta_theta=self.task.delta_theta,
perturb=goal_perturbs[:,i,:])
# Compute the relative location to all goals from the starting location.
loc, _, _, theta = self.get_loc_axis(np.array(start_nodes),
delta_theta=self.task.delta_theta,
perturb=perturbs[:,0,:])
r_goal, t_goal = _get_relative_goal_loc(goal_loc*1., loc, theta)
rel_goal_loc = np.concatenate((r_goal*np.cos(t_goal), r_goal*np.sin(t_goal),
np.cos(goal_theta-theta),
np.sin(goal_theta-theta)), axis=1)
rel_goal_locs.append(np.expand_dims(rel_goal_loc, axis=1))
goal_locs.append(np.expand_dims(goal_loc, axis=1))
map = self.traversible*1.
maps = np.repeat(np.expand_dims(np.expand_dims(map, axis=0), axis=0),
self.task_params.batch_size, axis=0)*1
if self.task_params.type[:14] == 'to_nearest_obj':
for i in range(self.task_params.batch_size):
maps[i,0,:,:] += 0.5*(self.task.class_maps_dilated[:,:,target_class[i]])
rel_goal_locs = np.concatenate(rel_goal_locs, axis=1)
goal_locs = np.concatenate(goal_locs, axis=1)
maps = np.expand_dims(maps, axis=-1)
if self.task_params.type[:14] == 'to_nearest_obj':
rel_goal_locs = np.zeros((self.task_params.batch_size, 1,
len(self.task_params.semantic_task.class_map_names)),
dtype=np.float32)
goal_locs = np.zeros((self.task_params.batch_size, 1, 2),
dtype=np.float32)
for i in range(self.task_params.batch_size):
t = target_class[i]
rel_goal_locs[i,0,t] = 1.
goal_locs[i,0,0] = t
goal_locs[i,0,1] = np.NaN
return vars(utils.Foo(orig_maps=maps, goal_loc=goal_locs,
rel_goal_loc_at_start=rel_goal_locs))
def pre_common_data(self, inputs):
return inputs
def get_features(self, current_node_ids, step_number):
task_params = self.task_params
goal_number = step_number / self.task_params.num_steps
end_nodes = self.task.nodes[self.episode.goal_node_ids[goal_number],:]*1
current_nodes = self.task.nodes[current_node_ids,:]*1
end_perturbs = self.episode.goal_perturbs[:,goal_number,:][:,np.newaxis,:]
perturbs = self.episode.perturbs
target_class = self.episode.target_class
# Append to history.
self.episode.history[:,step_number] = np.array(current_node_ids)
# Render out the images from current node.
outs = {}
if self.task_params.outputs.images:
imgs_all = []
imgs = self.render_nodes([tuple(x) for x in current_nodes],
perturb=perturbs[:,step_number,:])
imgs_all.append(imgs)
aux_delta_thetas = self.task_params.aux_delta_thetas
for i in range(len(aux_delta_thetas)):
imgs = self.render_nodes([tuple(x) for x in current_nodes],
perturb=perturbs[:,step_number,:],
aux_delta_theta=aux_delta_thetas[i])
imgs_all.append(imgs)
imgs_all = np.array(imgs_all) # A x B x H x W x C
imgs_all = np.transpose(imgs_all, axes=[1,0,2,3,4])
imgs_all = np.expand_dims(imgs_all, axis=1) # B x N x A x H x W x C
if task_params.num_history_frames > 0:
if step_number == 0:
# Append the same frame 4 times
for i in range(task_params.num_history_frames+1):
self.episode.history_frames.insert(0, imgs_all*1.)
self.episode.history_frames.insert(0, imgs_all)
self.episode.history_frames.pop()
imgs_all_with_history = np.concatenate(self.episode.history_frames, axis=2)
else:
imgs_all_with_history = imgs_all
outs['imgs'] = imgs_all_with_history # B x N x A x H x W x C
if self.task_params.outputs.node_ids:
outs['node_ids'] = np.array(current_node_ids).reshape((-1,1,1))
outs['perturbs'] = np.expand_dims(perturbs[:,step_number, :]*1., axis=1)
if self.task_params.outputs.analytical_counts:
assert(self.task_params.modalities == ['depth'])
d = image_pre(outs['imgs']*1., self.task_params.modalities)
cm = get_camera_matrix(self.task_params.img_width,
self.task_params.img_height,
self.task_params.img_fov)
XYZ = get_point_cloud_from_z(100./d[...,0], cm)
XYZ = make_geocentric(XYZ*100., self.robot.sensor_height,
self.robot.camera_elevation_degree)
for i in range(len(self.task_params.analytical_counts.map_sizes)):
non_linearity = self.task_params.analytical_counts.non_linearity[i]
count, isvalid = bin_points(XYZ*1.,
map_size=self.task_params.analytical_counts.map_sizes[i],
xy_resolution=self.task_params.analytical_counts.xy_resolution[i],
z_bins=self.task_params.analytical_counts.z_bins[i])
assert(count.shape[2] == 1), 'only works for n_views equal to 1.'
count = count[:,:,0,:,:,:]
isvalid = isvalid[:,:,0,:,:,:]
if non_linearity == 'none':
None
elif non_linearity == 'min10':
count = np.minimum(count, 10.)
elif non_linearity == 'sqrt':
count = np.sqrt(count)
else:
logging.fatal('Undefined non_linearity.')
outs['analytical_counts_{:d}'.format(i)] = count
# Compute the goal location in the cordinate frame of the robot.
if self.task_params.outputs.rel_goal_loc:
if self.task_params.type[:14] != 'to_nearest_obj':
loc, _, _, theta = self.get_loc_axis(current_nodes,
delta_theta=self.task.delta_theta,
perturb=perturbs[:,step_number,:])
goal_loc, _, _, goal_theta = self.get_loc_axis(end_nodes,
delta_theta=self.task.delta_theta,
perturb=end_perturbs[:,0,:])
r_goal, t_goal = _get_relative_goal_loc(goal_loc, loc, theta)
rel_goal_loc = np.concatenate((r_goal*np.cos(t_goal), r_goal*np.sin(t_goal),
np.cos(goal_theta-theta),
np.sin(goal_theta-theta)), axis=1)
outs['rel_goal_loc'] = np.expand_dims(rel_goal_loc, axis=1)
elif self.task_params.type[:14] == 'to_nearest_obj':
rel_goal_loc = np.zeros((self.task_params.batch_size, 1,
len(self.task_params.semantic_task.class_map_names)),
dtype=np.float32)
for i in range(self.task_params.batch_size):
t = target_class[i]
rel_goal_loc[i,0,t] = 1.
outs['rel_goal_loc'] = rel_goal_loc
# Location on map to plot the trajectory during validation.
if self.task_params.outputs.loc_on_map:
loc, x_axis, y_axis, theta = self.get_loc_axis(current_nodes,
delta_theta=self.task.delta_theta,
perturb=perturbs[:,step_number,:])
outs['loc_on_map'] = np.expand_dims(loc, axis=1)
# Compute gt_dist to goal
if self.task_params.outputs.gt_dist_to_goal:
gt_dist_to_goal = np.zeros((len(current_node_ids), 1), dtype=np.float32)
for i, n in enumerate(current_node_ids):
gt_dist_to_goal[i,0] = self.episode.dist_to_goal[goal_number][i][n]
outs['gt_dist_to_goal'] = np.expand_dims(gt_dist_to_goal, axis=1)
# Free space in front of you, map and goal as images.
if self.task_params.outputs.ego_maps:
loc, x_axis, y_axis, theta = self.get_loc_axis(current_nodes,
delta_theta=self.task.delta_theta,
perturb=perturbs[:,step_number,:])
maps = generate_egocentric_maps(self.task.scaled_maps,
self.task_params.map_scales,
self.task_params.map_crop_sizes, loc,
x_axis, y_axis, theta)
for i in range(len(self.task_params.map_scales)):
outs['ego_maps_{:d}'.format(i)] = \
np.expand_dims(np.expand_dims(maps[i], axis=1), axis=-1)
if self.task_params.outputs.readout_maps:
loc, x_axis, y_axis, theta = self.get_loc_axis(current_nodes,
delta_theta=self.task.delta_theta,
perturb=perturbs[:,step_number,:])
maps = generate_egocentric_maps(self.task.readout_maps_scaled,
self.task_params.readout_maps_scales,
self.task_params.readout_maps_crop_sizes,
loc, x_axis, y_axis, theta)
for i in range(len(self.task_params.readout_maps_scales)):
outs['readout_maps_{:d}'.format(i)] = \
np.expand_dims(np.expand_dims(maps[i], axis=1), axis=-1)
# Images for the goal.
if self.task_params.outputs.ego_goal_imgs:
if self.task_params.type[:14] != 'to_nearest_obj':
loc, x_axis, y_axis, theta = self.get_loc_axis(current_nodes,
delta_theta=self.task.delta_theta,
perturb=perturbs[:,step_number,:])
goal_loc, _, _, _ = self.get_loc_axis(end_nodes,
delta_theta=self.task.delta_theta,
perturb=end_perturbs[:,0,:])
rel_goal_orientation = np.mod(
np.int32(current_nodes[:,2:] - end_nodes[:,2:]), self.task_params.n_ori)
goal_dist, goal_theta = _get_relative_goal_loc(goal_loc, loc, theta)
goals = generate_goal_images(self.task_params.map_scales,
self.task_params.map_crop_sizes,
self.task_params.n_ori, goal_dist,
goal_theta, rel_goal_orientation)
for i in range(len(self.task_params.map_scales)):
outs['ego_goal_imgs_{:d}'.format(i)] = np.expand_dims(goals[i], axis=1)
elif self.task_params.type[:14] == 'to_nearest_obj':
for i in range(len(self.task_params.map_scales)):
num_classes = len(self.task_params.semantic_task.class_map_names)
outs['ego_goal_imgs_{:d}'.format(i)] = np.zeros((self.task_params.batch_size, 1,
self.task_params.map_crop_sizes[i],
self.task_params.map_crop_sizes[i],
self.task_params.goal_channels))
for i in range(self.task_params.batch_size):
t = target_class[i]
for j in range(len(self.task_params.map_scales)):
outs['ego_goal_imgs_{:d}'.format(j)][i,:,:,:,t] = 1.
# Incremental locs and theta (for map warping), always in the original scale
# of the map, the subequent steps in the tf code scale appropriately.
# Scaling is done by just multiplying incremental_locs appropriately.
if self.task_params.outputs.egomotion:
if step_number == 0:
# Zero Ego Motion
incremental_locs = np.zeros((self.task_params.batch_size, 1, 2), dtype=np.float32)
incremental_thetas = np.zeros((self.task_params.batch_size, 1, 1), dtype=np.float32)
else:
previous_nodes = self.task.nodes[self.episode.history[:,step_number-1], :]*1
loc, _, _, theta = self.get_loc_axis(current_nodes,
delta_theta=self.task.delta_theta,
perturb=perturbs[:,step_number,:])
previous_loc, _, _, previous_theta = self.get_loc_axis(
previous_nodes, delta_theta=self.task.delta_theta,
perturb=perturbs[:,step_number-1,:])
incremental_locs_ = np.reshape(loc-previous_loc, [self.task_params.batch_size, 1, -1])
t = -np.pi/2+np.reshape(theta*1, [self.task_params.batch_size, 1, -1])
incremental_locs = incremental_locs_*1
incremental_locs[:,:,0] = np.sum(incremental_locs_ *
np.concatenate((np.cos(t), np.sin(t)),
axis=-1), axis=-1)
incremental_locs[:,:,1] = np.sum(incremental_locs_ *
np.concatenate((np.cos(t+np.pi/2),
np.sin(t+np.pi/2)),
axis=-1), axis=-1)
incremental_thetas = np.reshape(theta-previous_theta,
[self.task_params.batch_size, 1, -1])
outs['incremental_locs'] = incremental_locs
outs['incremental_thetas'] = incremental_thetas
if self.task_params.outputs.visit_count:
# Output the visit count for this state, how many times has the current
# state been visited, and how far in the history was the last visit
# (except this one)
visit_count = np.zeros((self.task_params.batch_size, 1), dtype=np.int32)
last_visit = -np.ones((self.task_params.batch_size, 1), dtype=np.int32)
if step_number >= 1:
h = self.episode.history[:,:(step_number)]
visit_count[:,0] = np.sum(h == np.array(current_node_ids).reshape([-1,1]),
axis=1)
last_visit[:,0] = np.argmax(h[:,::-1] == np.array(current_node_ids).reshape([-1,1]),
axis=1) + 1
last_visit[visit_count == 0] = -1 # -1 if not visited.
outs['visit_count'] = np.expand_dims(visit_count, axis=1)
outs['last_visit'] = np.expand_dims(last_visit, axis=1)
return outs
def get_features_name(self):
f = []
if self.task_params.outputs.images:
f.append('imgs')
if self.task_params.outputs.rel_goal_loc:
f.append('rel_goal_loc')
if self.task_params.outputs.loc_on_map:
f.append('loc_on_map')
if self.task_params.outputs.gt_dist_to_goal:
f.append('gt_dist_to_goal')
if self.task_params.outputs.ego_maps:
for i in range(len(self.task_params.map_scales)):
f.append('ego_maps_{:d}'.format(i))
if self.task_params.outputs.readout_maps:
for i in range(len(self.task_params.readout_maps_scales)):
f.append('readout_maps_{:d}'.format(i))
if self.task_params.outputs.ego_goal_imgs:
for i in range(len(self.task_params.map_scales)):
f.append('ego_goal_imgs_{:d}'.format(i))
if self.task_params.outputs.egomotion:
f.append('incremental_locs')
f.append('incremental_thetas')
if self.task_params.outputs.visit_count:
f.append('visit_count')
f.append('last_visit')
if self.task_params.outputs.analytical_counts:
for i in range(len(self.task_params.analytical_counts.map_sizes)):
f.append('analytical_counts_{:d}'.format(i))
if self.task_params.outputs.node_ids:
f.append('node_ids')
f.append('perturbs')
return f
def pre_features(self, inputs):
if self.task_params.outputs.images:
inputs['imgs'] = image_pre(inputs['imgs'], self.task_params.modalities)
return inputs
class BuildingMultiplexer():
def __init__(self, args, task_number):
params = vars(args)
for k in params.keys():
setattr(self, k, params[k])
self.task_number = task_number
self._pick_data(task_number)
logging.info('Env Class: %s.', self.env_class)
if self.task_params.task == 'planning':
self._setup_planner()
elif self.task_params.task == 'mapping':
self._setup_mapper()
elif self.task_params.task == 'map+plan':
self._setup_mapper()
else:
logging.error('Undefined task: %s'.format(self.task_params.task))
def _pick_data(self, task_number):
logging.error('Input Building Names: %s', self.building_names)
self.flip = [np.mod(task_number / len(self.building_names), 2) == 1]
id = np.mod(task_number, len(self.building_names))
self.building_names = [self.building_names[id]]
self.task_params.building_seed = task_number
logging.error('BuildingMultiplexer: Picked Building Name: %s', self.building_names)
self.building_names = self.building_names[0].split('+')
self.flip = [self.flip[0] for _ in self.building_names]
logging.error('BuildingMultiplexer: Picked Building Name: %s', self.building_names)
logging.error('BuildingMultiplexer: Flipping Buildings: %s', self.flip)
logging.error('BuildingMultiplexer: Set building_seed: %d', self.task_params.building_seed)
self.num_buildings = len(self.building_names)
logging.error('BuildingMultiplexer: Num buildings: %d', self.num_buildings)
def _setup_planner(self):
# Load building env class.
self.buildings = []
for i, building_name in enumerate(self.building_names):
b = self.env_class(robot=self.robot, env=self.env,
task_params=self.task_params,
building_name=building_name, flip=self.flip[i],
logdir=self.logdir, building_loader=self.dataset)
self.buildings.append(b)
def _setup_mapper(self):
# Set up the renderer.
cp = self.camera_param
rgb_shader, d_shader = sru.get_shaders(cp.modalities)
r_obj = SwiftshaderRenderer()
r_obj.init_display(width=cp.width, height=cp.height, fov=cp.fov,
z_near=cp.z_near, z_far=cp.z_far, rgb_shader=rgb_shader,
d_shader=d_shader)
self.r_obj = r_obj
r_obj.clear_scene()
# Load building env class.
self.buildings = []
wt = []
for i, building_name in enumerate(self.building_names):
b = self.env_class(robot=self.robot, env=self.env,
task_params=self.task_params,
building_name=building_name, flip=self.flip[i],
logdir=self.logdir, building_loader=self.dataset,
r_obj=r_obj)
wt.append(b.get_weight())
b.load_building_into_scene()
b.set_building_visibility(False)
self.buildings.append(b)
wt = np.array(wt).astype(np.float32)
wt = wt / np.sum(wt+0.0001)
self.building_sampling_weights = wt
def sample_building(self, rng):
if self.num_buildings == 1:
building_id = rng.choice(range(len(self.building_names)))
else:
building_id = rng.choice(self.num_buildings,
p=self.building_sampling_weights)
b = self.buildings[building_id]
instances = b._gen_rng(rng)
self._building_id = building_id
return self.buildings[building_id], instances
def sample_env(self, rngs):
rng = rngs[0];
if self.num_buildings == 1:
building_id = rng.choice(range(len(self.building_names)))
else:
building_id = rng.choice(self.num_buildings,
p=self.building_sampling_weights)
return self.buildings[building_id]
def pre(self, inputs):
return self.buildings[self._building_id].pre(inputs)
def __del__(self):
self.r_obj.clear_scene()
logging.error('Clearing scene.')
research/cognitive_mapping_and_planning/datasets/nav_env_config.py deleted 100644 → 0
View file @ 09bc9f54
# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Configs for stanford navigation environment.
Base config for stanford navigation enviornment.
"""
import numpy as np
import src.utils as utils
import datasets.nav_env as nav_env
def nav_env_base_config():
"""Returns the base config for stanford navigation environment.
Returns:
Base config for stanford navigation environment.
"""
robot = utils.Foo(radius=15,
base=10,
height=140,
sensor_height=120,
camera_elevation_degree=-15)
env = utils.Foo(padding=10,
resolution=5,
num_point_threshold=2,
valid_min=-10,
valid_max=200,
n_samples_per_face=200)
camera_param = utils.Foo(width=225,
height=225,
z_near=0.05,
z_far=20.0,
fov=60.,
modalities=['rgb'],
img_channels=3)
data_augment = utils.Foo(lr_flip=0,
delta_angle=0.5,
delta_xy=4,
relight=True,
relight_fast=False,
structured=False) # if True, uses the same perturb for the whole episode.
outputs = utils.Foo(images=True,
rel_goal_loc=False,
loc_on_map=True,
gt_dist_to_goal=True,
ego_maps=False,
ego_goal_imgs=False,
egomotion=False,
visit_count=False,
analytical_counts=False,
node_ids=True,
readout_maps=False)
# class_map_names=['board', 'chair', 'door', 'sofa', 'table']
class_map_names = ['chair', 'door', 'table']
semantic_task = utils.Foo(class_map_names=class_map_names, pix_distance=16,
sampling='uniform')
# time per iteration for cmp is 0.82 seconds per episode with 3.4s overhead per batch.
task_params = utils.Foo(max_dist=32,
step_size=8,
num_steps=40,
num_actions=4,
batch_size=4,
building_seed=0,
num_goals=1,
img_height=None,
img_width=None,
img_channels=None,
modalities=None,
outputs=outputs,
map_scales=[1.],
map_crop_sizes=[64],
rel_goal_loc_dim=4,
base_class='Building',
task='map+plan',
n_ori=4,
type='room_to_room_many',
data_augment=data_augment,
room_regex='^((?!hallway).)*$',
toy_problem=False,
map_channels=1,
gt_coverage=False,
input_type='maps',
full_information=False,
aux_delta_thetas=[],
semantic_task=semantic_task,
num_history_frames=0,
node_ids_dim=1,
perturbs_dim=4,
map_resize_method='linear_noantialiasing',
readout_maps_channels=1,
readout_maps_scales=[],
readout_maps_crop_sizes=[],
n_views=1,
reward_time_penalty=0.1,
reward_at_goal=1.,
discount_factor=0.99,
rejection_sampling_M=100,
min_dist=None)
navtask_args = utils.Foo(
building_names=['area1_gates_wingA_floor1_westpart'],
env_class=nav_env.VisualNavigationEnv,
robot=robot,
task_params=task_params,
env=env,
camera_param=camera_param,
cache_rooms=True)
return navtask_args
research/cognitive_mapping_and_planning/output/README.md deleted 100644 → 0
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### Pre-Trained Models
We provide the following pre-trained models:
Config Name | Checkpoint | Mean Dist. | 50%ile Dist. | 75%ile Dist. | Success %age |
:-: | :-: | :-: | :-: | :-: | :-: |
cmp.lmap_Msc.clip5.sbpd_d_r2r | [ckpt](http://download.tensorflow.org/models/cognitive_mapping_and_planning/2017_04_16/cmp.lmap_Msc.clip5.sbpd_d_r2r.tar) | 4.79 | 0 | 1 | 78.9 |
cmp.lmap_Msc.clip5.sbpd_rgb_r2r | [ckpt](http://download.tensorflow.org/models/cognitive_mapping_and_planning/2017_04_16/cmp.lmap_Msc.clip5.sbpd_rgb_r2r.tar) | 7.74 | 0 | 14 | 62.4 |
cmp.lmap_Msc.clip5.sbpd_d_ST | [ckpt](http://download.tensorflow.org/models/cognitive_mapping_and_planning/2017_04_16/cmp.lmap_Msc.clip5.sbpd_d_ST.tar) | 10.67 | 9 | 19 | 39.7 |
cmp.lmap_Msc.clip5.sbpd_rgb_ST | [ckpt](http://download.tensorflow.org/models/cognitive_mapping_and_planning/2017_04_16/cmp.lmap_Msc.clip5.sbpd_rgb_ST.tar) | 11.27 | 10 | 19 | 35.6 |
cmp.lmap_Msc.clip5.sbpd_d_r2r_h0_64_80 | [ckpt](http:////download.tensorflow.org/models/cognitive_mapping_and_planning/2017_04_16/cmp.lmap_Msc.clip5.sbpd_d_r2r_h0_64_80.tar) | 11.6 | 0 | 19 | 66.9 |
bl.v2.noclip.sbpd_d_r2r | [ckpt](http://download.tensorflow.org/models/cognitive_mapping_and_planning/2017_04_16/bl.v2.noclip.sbpd_d_r2r.tar) | 5.90 | 0 | 6 | 71.2 |
bl.v2.noclip.sbpd_rgb_r2r | [ckpt](http://download.tensorflow.org/models/cognitive_mapping_and_planning/2017_04_16/bl.v2.noclip.sbpd_rgb_r2r.tar) | 10.21 | 1 | 21 | 53.4 |
bl.v2.noclip.sbpd_d_ST | [ckpt](http://download.tensorflow.org/models/cognitive_mapping_and_planning/2017_04_16/bl.v2.noclip.sbpd_d_ST.tar) | 13.29 | 14 | 23 | 28.0 |
bl.v2.noclip.sbpd_rgb_ST | [ckpt](http://download.tensorflow.org/models/cognitive_mapping_and_planning/2017_04_16/bl.v2.noclip.sbpd_rgb_ST.tar) | 13.37 | 13 | 20 | 24.2 |
bl.v2.noclip.sbpd_d_r2r_h0_64_80 | [ckpt](http:////download.tensorflow.org/models/cognitive_mapping_and_planning/2017_04_16/bl.v2.noclip.sbpd_d_r2r_h0_64_80.tar) | 15.30 | 0 | 29 | 57.9 |
research/cognitive_mapping_and_planning/patches/GLES2_2_0.py.patch deleted 100644 → 0
View file @ 09bc9f54
10c10
< from OpenGL import platform, constant, arrays
---
> from OpenGL import platform, constant, arrays, contextdata
249a250
> from OpenGL._bytes import _NULL_8_BYTE
399c400
< array = ArrayDatatype.asArray( pointer, type )
---
> array = arrays.ArrayDatatype.asArray( pointer, type )
405c406
< ArrayDatatype.voidDataPointer( array )
---
> arrays.ArrayDatatype.voidDataPointer( array )
research/cognitive_mapping_and_planning/patches/apply_patches.sh deleted 100644 → 0
View file @ 09bc9f54
# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
echo $VIRTUAL_ENV
patch $VIRTUAL_ENV/local/lib/python2.7/site-packages/OpenGL/GLES2/VERSION/GLES2_2_0.py patches/GLES2_2_0.py.patch
patch $VIRTUAL_ENV/local/lib/python2.7/site-packages/OpenGL/platform/ctypesloader.py patches/ctypesloader.py.patch
research/cognitive_mapping_and_planning/patches/ctypesloader.py.patch deleted 100644 → 0
View file @ 09bc9f54
45c45,46
< return dllType( name, mode )
---
> print './' + name
> return dllType( './' + name, mode )
47,48c48,53
< err.args += (name,fullName)
< raise
---
> try:
> print name
> return dllType( name, mode )
> except:
> err.args += (name,fullName)
> raise
research/cognitive_mapping_and_planning/render/depth_rgb_encoded.fp deleted 100644 → 0
View file @ 09bc9f54
// This shader computes per-pixel depth (-z coordinate in the camera space, or
// orthogonal distance to the camera plane). The result is multiplied by the
// `kFixedPointFraction` constant and is encoded to RGB channels as an integer
// (R being the least significant byte).
#ifdef GL_ES
#ifdef GL_FRAGMENT_PRECISION_HIGH
precision highp float;
#else
precision mediump float;
#endif
#endif
const float kFixedPointFraction = 1000.0;
varying float vDepth;
void main(void) {
float d = vDepth;
// Encode the depth to RGB.
d *= (kFixedPointFraction / 255.0);
gl_FragColor.r = mod(d, 1.0);
d = (d - gl_FragColor.r) / 255.0;
gl_FragColor.g = mod(d, 1.0);
d = (d - gl_FragColor.g) / 255.0;
gl_FragColor.b = mod(d, 1.0);
gl_FragColor.a = 1.0;
}
research/cognitive_mapping_and_planning/render/depth_rgb_encoded.vp deleted 100644 → 0
View file @ 09bc9f54
uniform mat4 uViewMatrix;
uniform mat4 uProjectionMatrix;
attribute vec3 aPosition;
varying float vDepth;
void main(void) {
vec4 worldPosition = vec4(aPosition, 1.0);
vec4 viewPosition = uViewMatrix * worldPosition;
gl_Position = uProjectionMatrix * viewPosition;
// Orthogonal depth is simply -z in the camera space.
vDepth = -viewPosition.z;
}
research/cognitive_mapping_and_planning/render/rgb_flat_color.fp deleted 100644 → 0
View file @ 09bc9f54
precision highp float;
varying vec4 vColor;
varying vec2 vTextureCoord;
uniform sampler2D uTexture;
void main(void) {
vec4 color = vColor;
color = texture2D(uTexture, vTextureCoord);
gl_FragColor = color;
}
research/cognitive_mapping_and_planning/render/rgb_flat_color.vp deleted 100644 → 0
View file @ 09bc9f54
uniform mat4 uViewMatrix;
uniform mat4 uProjectionMatrix;
uniform vec4 uColor;
attribute vec4 aColor;
attribute vec3 aPosition;
attribute vec2 aTextureCoord;
varying vec4 vColor;
varying vec2 vTextureCoord;
void main(void) {
vec4 worldPosition = vec4(aPosition, 1.0);
gl_Position = uProjectionMatrix * (uViewMatrix * worldPosition);
vColor = aColor * uColor;
vTextureCoord = aTextureCoord;
}
research/cognitive_mapping_and_planning/render/swiftshader_renderer.py deleted 100644 → 0
View file @ 09bc9f54
# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Implements loading and rendering of meshes. Contains 2 classes:
Shape: Class that exposes high level functions for loading and manipulating
shapes. This currently is bound to assimp
(https://github.com/assimp/assimp). If you want to interface to a different
library, reimplement this class with bindings to your mesh loading library.
SwiftshaderRenderer: Class that renders Shapes. Currently this uses python
bindings to OpenGL (EGL), bindings to an alternate renderer may be implemented
here.
"""
import numpy as np, os
import cv2, ctypes, logging, os, numpy as np
import pyassimp as assimp
from OpenGL.GLES2 import *
from OpenGL.EGL import *
import src.rotation_utils as ru
__version__ = 'swiftshader_renderer'
def get_shaders(modalities):
rgb_shader = 'rgb_flat_color' if 'rgb' in modalities else None
d_shader = 'depth_rgb_encoded' if 'depth' in modalities else None
return rgb_shader, d_shader
def sample_points_on_faces(vs, fs, rng, n_samples_per_face):
idx = np.repeat(np.arange(fs.shape[0]), n_samples_per_face)
r = rng.rand(idx.size, 2)
r1 = r[:,:1]; r2 = r[:,1:]; sqrt_r1 = np.sqrt(r1);
v1 = vs[fs[idx, 0], :]; v2 = vs[fs[idx, 1], :]; v3 = vs[fs[idx, 2], :];
pts = (1-sqrt_r1)*v1 + sqrt_r1*(1-r2)*v2 + sqrt_r1*r2*v3
v1 = vs[fs[:,0], :]; v2 = vs[fs[:, 1], :]; v3 = vs[fs[:, 2], :];
ar = 0.5*np.sqrt(np.sum(np.cross(v1-v3, v2-v3)**2, 1))
return pts, ar, idx
class Shape():
def get_pyassimp_load_options(self):
load_flags = assimp.postprocess.aiProcess_Triangulate;
load_flags = load_flags | assimp.postprocess.aiProcess_SortByPType;
load_flags = load_flags | assimp.postprocess.aiProcess_OptimizeMeshes;
load_flags = load_flags | assimp.postprocess.aiProcess_RemoveRedundantMaterials;
load_flags = load_flags | assimp.postprocess.aiProcess_FindDegenerates;
load_flags = load_flags | assimp.postprocess.aiProcess_GenSmoothNormals;
load_flags = load_flags | assimp.postprocess.aiProcess_JoinIdenticalVertices;
load_flags = load_flags | assimp.postprocess.aiProcess_ImproveCacheLocality;
load_flags = load_flags | assimp.postprocess.aiProcess_GenUVCoords;
load_flags = load_flags | assimp.postprocess.aiProcess_FindInvalidData;
return load_flags
def __init__(self, obj_file, material_file=None, load_materials=True,
name_prefix='', name_suffix=''):
if material_file is not None:
logging.error('Ignoring material file input, reading them off obj file.')
load_flags = self.get_pyassimp_load_options()
scene = assimp.load(obj_file, processing=load_flags)
filter_ind = self._filter_triangles(scene.meshes)
self.meshes = [scene.meshes[i] for i in filter_ind]
for m in self.meshes:
m.name = name_prefix + m.name + name_suffix
dir_name = os.path.dirname(obj_file)
# Load materials
materials = None
if load_materials:
materials = []
for m in self.meshes:
file_name = os.path.join(dir_name, m.material.properties[('file', 1)])
assert(os.path.exists(file_name)), \
'Texture file {:s} foes not exist.'.format(file_name)
img_rgb = cv2.imread(file_name)[::-1,:,::-1]
if img_rgb.shape[0] != img_rgb.shape[1]:
logging.warn('Texture image not square.')
sz = np.maximum(img_rgb.shape[0], img_rgb.shape[1])
sz = int(np.power(2., np.ceil(np.log2(sz))))
img_rgb = cv2.resize(img_rgb, (sz,sz), interpolation=cv2.INTER_LINEAR)
else:
sz = img_rgb.shape[0]
sz_ = int(np.power(2., np.ceil(np.log2(sz))))
if sz != sz_:
logging.warn('Texture image not square of power of 2 size. ' +
'Changing size from %d to %d.', sz, sz_)
sz = sz_
img_rgb = cv2.resize(img_rgb, (sz,sz), interpolation=cv2.INTER_LINEAR)
materials.append(img_rgb)
self.scene = scene
self.materials = materials
def _filter_triangles(self, meshes):
select = []
for i in range(len(meshes)):
if meshes[i].primitivetypes == 4:
select.append(i)
return select
def flip_shape(self):
for m in self.meshes:
m.vertices[:,1] = -m.vertices[:,1]
bb = m.faces*1
bb[:,1] = m.faces[:,2]
bb[:,2] = m.faces[:,1]
m.faces = bb
# m.vertices[:,[0,1]] = m.vertices[:,[1,0]]
def get_vertices(self):
vs = []
for m in self.meshes:
vs.append(m.vertices)
vss = np.concatenate(vs, axis=0)
return vss, vs
def get_faces(self):
vs = []
for m in self.meshes:
v = m.faces
vs.append(v)
return vs
def get_number_of_meshes(self):
return len(self.meshes)
def scale(self, sx=1., sy=1., sz=1.):
pass
def sample_points_on_face_of_shape(self, i, n_samples_per_face, sc):
v = self.meshes[i].vertices*sc
f = self.meshes[i].faces
p, face_areas, face_idx = sample_points_on_faces(
v, f, np.random.RandomState(0), n_samples_per_face)
return p, face_areas, face_idx
def __del__(self):
scene = self.scene
assimp.release(scene)
class SwiftshaderRenderer():
def __init__(self):
self.entities = {}
def init_display(self, width, height, fov, z_near, z_far, rgb_shader,
d_shader):
self.init_renderer_egl(width, height)
dir_path = os.path.dirname(os.path.realpath(__file__))
if d_shader is not None and rgb_shader is not None:
logging.fatal('Does not support setting both rgb_shader and d_shader.')
if d_shader is not None:
assert rgb_shader is None
shader = d_shader
self.modality = 'depth'
if rgb_shader is not None:
assert d_shader is None
shader = rgb_shader
self.modality = 'rgb'
self.create_shaders(os.path.join(dir_path, shader+'.vp'),
os.path.join(dir_path, shader + '.fp'))
aspect = width*1./(height*1.)
self.set_camera(fov, z_near, z_far, aspect)
def init_renderer_egl(self, width, height):
major,minor = ctypes.c_long(),ctypes.c_long()
logging.info('init_renderer_egl: EGL_DEFAULT_DISPLAY: %s', EGL_DEFAULT_DISPLAY)
egl_display = eglGetDisplay(EGL_DEFAULT_DISPLAY)
logging.info('init_renderer_egl: egl_display: %s', egl_display)
eglInitialize(egl_display, major, minor)
logging.info('init_renderer_egl: EGL_OPENGL_API, EGL_OPENGL_ES_API: %s, %s',
EGL_OPENGL_API, EGL_OPENGL_ES_API)
eglBindAPI(EGL_OPENGL_ES_API)
num_configs = ctypes.c_long()
configs = (EGLConfig*1)()
local_attributes = [EGL_RED_SIZE, 8, EGL_GREEN_SIZE, 8, EGL_BLUE_SIZE, 8,
EGL_DEPTH_SIZE, 16, EGL_SURFACE_TYPE, EGL_PBUFFER_BIT,
EGL_RENDERABLE_TYPE, EGL_OPENGL_ES2_BIT, EGL_NONE,]
logging.error('init_renderer_egl: local attributes: %s', local_attributes)
local_attributes = arrays.GLintArray.asArray(local_attributes)
success = eglChooseConfig(egl_display, local_attributes, configs, 1, num_configs)
logging.error('init_renderer_egl: eglChooseConfig success, num_configs: %d, %d', success, num_configs.value)
egl_config = configs[0]
context_attributes = [EGL_CONTEXT_CLIENT_VERSION, 2, EGL_NONE]
context_attributes = arrays.GLintArray.asArray(context_attributes)
egl_context = eglCreateContext(egl_display, egl_config, EGL_NO_CONTEXT, context_attributes)
buffer_attributes = [EGL_WIDTH, width, EGL_HEIGHT, height, EGL_NONE]
buffer_attributes = arrays.GLintArray.asArray(buffer_attributes)
egl_surface = eglCreatePbufferSurface(egl_display, egl_config, buffer_attributes)
eglMakeCurrent(egl_display, egl_surface, egl_surface, egl_context)
logging.error("init_renderer_egl: egl_display: %s egl_surface: %s, egl_config: %s", egl_display, egl_surface, egl_context)
glViewport(0, 0, width, height);
self.egl_display = egl_display
self.egl_surface = egl_surface
self.egl_config = egl_config
self.egl_mapping = {}
self.render_timer = None
self.load_timer = None
self.height = height
self.width = width
def create_shaders(self, v_shader_file, f_shader_file):
v_shader = glCreateShader(GL_VERTEX_SHADER)
with open(v_shader_file, 'r') as f:
ls = ''
for l in f:
ls = ls + l
glShaderSource(v_shader, ls)
glCompileShader(v_shader);
assert(glGetShaderiv(v_shader, GL_COMPILE_STATUS) == 1)
f_shader = glCreateShader(GL_FRAGMENT_SHADER)
with open(f_shader_file, 'r') as f:
ls = ''
for l in f:
ls = ls + l
glShaderSource(f_shader, ls)
glCompileShader(f_shader);
assert(glGetShaderiv(f_shader, GL_COMPILE_STATUS) == 1)
egl_program = glCreateProgram();
assert(egl_program)
glAttachShader(egl_program, v_shader)
glAttachShader(egl_program, f_shader)
glLinkProgram(egl_program);
assert(glGetProgramiv(egl_program, GL_LINK_STATUS) == 1)
glUseProgram(egl_program)
glBindAttribLocation(egl_program, 0, "aPosition")
glBindAttribLocation(egl_program, 1, "aColor")
glBindAttribLocation(egl_program, 2, "aTextureCoord")
self.egl_program = egl_program
self.egl_mapping['vertexs'] = 0
self.egl_mapping['vertexs_color'] = 1
self.egl_mapping['vertexs_tc'] = 2
glClearColor(0.0, 0.0, 0.0, 1.0);
# glEnable(GL_CULL_FACE); glCullFace(GL_BACK);
glEnable(GL_DEPTH_TEST);
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
def set_camera(self, fov_vertical, z_near, z_far, aspect):
width = 2*np.tan(np.deg2rad(fov_vertical)/2.0)*z_near*aspect;
height = 2*np.tan(np.deg2rad(fov_vertical)/2.0)*z_near;
egl_program = self.egl_program
c = np.eye(4, dtype=np.float32)
c[3,3] = 0
c[3,2] = -1
c[2,2] = -(z_near+z_far)/(z_far-z_near)
c[2,3] = -2.0*(z_near*z_far)/(z_far-z_near)
c[0,0] = 2.0*z_near/width
c[1,1] = 2.0*z_near/height
c = c.T
projection_matrix_o = glGetUniformLocation(egl_program, 'uProjectionMatrix')
projection_matrix = np.eye(4, dtype=np.float32)
projection_matrix[...] = c
projection_matrix = np.reshape(projection_matrix, (-1))
glUniformMatrix4fv(projection_matrix_o, 1, GL_FALSE, projection_matrix)
def load_default_object(self):
v = np.array([[0.0, 0.5, 0.0, 1.0, 1.0, 0.0, 1.0],
[-0.5, -0.5, 0.0, 1.0, 0.0, 1.0, 1.0],
[0.5, -0.5, 0.0, 1.0, 1.0, 1.0, 1.0]], dtype=np.float32)
v = np.concatenate((v,v+0.1), axis=0)
v = np.ascontiguousarray(v, dtype=np.float32)
vbo = glGenBuffers(1)
glBindBuffer (GL_ARRAY_BUFFER, vbo)
glBufferData (GL_ARRAY_BUFFER, v.dtype.itemsize*v.size, v, GL_STATIC_DRAW)
glVertexAttribPointer(0, 3, GL_FLOAT, GL_FALSE, 28, ctypes.c_void_p(0))
glVertexAttribPointer(1, 4, GL_FLOAT, GL_FALSE, 28, ctypes.c_void_p(12))
glEnableVertexAttribArray(0);
glEnableVertexAttribArray(1);
self.num_to_render = 6;
def _actual_render(self):
for entity_id, entity in self.entities.iteritems():
if entity['visible']:
vbo = entity['vbo']
tbo = entity['tbo']
num = entity['num']
glBindBuffer(GL_ARRAY_BUFFER, vbo)
glVertexAttribPointer(self.egl_mapping['vertexs'], 3, GL_FLOAT, GL_FALSE,
20, ctypes.c_void_p(0))
glVertexAttribPointer(self.egl_mapping['vertexs_tc'], 2, GL_FLOAT,
GL_FALSE, 20, ctypes.c_void_p(12))
glEnableVertexAttribArray(self.egl_mapping['vertexs']);
glEnableVertexAttribArray(self.egl_mapping['vertexs_tc']);
glBindTexture(GL_TEXTURE_2D, tbo)
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR);
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR);
glDrawArrays(GL_TRIANGLES, 0, num)
def render(self, take_screenshot=False, output_type=0):
# self.render_timer.tic()
self._actual_render()
# self.render_timer.toc(log_at=1000, log_str='render timer', type='time')
np_rgb_img = None
np_d_img = None
c = 1000.
if take_screenshot:
if self.modality == 'rgb':
screenshot_rgba = np.zeros((self.height, self.width, 4), dtype=np.uint8)
glReadPixels(0, 0, self.width, self.height, GL_RGBA, GL_UNSIGNED_BYTE, screenshot_rgba)
np_rgb_img = screenshot_rgba[::-1,:,:3];
if self.modality == 'depth':
screenshot_d = np.zeros((self.height, self.width, 4), dtype=np.uint8)
glReadPixels(0, 0, self.width, self.height, GL_RGBA, GL_UNSIGNED_BYTE, screenshot_d)
np_d_img = screenshot_d[::-1,:,:3];
np_d_img = np_d_img[:,:,2]*(255.*255./c) + np_d_img[:,:,1]*(255./c) + np_d_img[:,:,0]*(1./c)
np_d_img = np_d_img.astype(np.float32)
np_d_img[np_d_img == 0] = np.NaN
np_d_img = np_d_img[:,:,np.newaxis]
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
return np_rgb_img, np_d_img
def _load_mesh_into_gl(self, mesh, material):
vvt = np.concatenate((mesh.vertices, mesh.texturecoords[0,:,:2]), axis=1)
vvt = np.ascontiguousarray(vvt[mesh.faces.reshape((-1)),:], dtype=np.float32)
num = vvt.shape[0]
vvt = np.reshape(vvt, (-1))
vbo = glGenBuffers(1)
glBindBuffer(GL_ARRAY_BUFFER, vbo)
glBufferData(GL_ARRAY_BUFFER, vvt.dtype.itemsize*vvt.size, vvt, GL_STATIC_DRAW)
tbo = glGenTextures(1)
glBindTexture(GL_TEXTURE_2D, tbo)
glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB, material.shape[1],
material.shape[0], 0, GL_RGB, GL_UNSIGNED_BYTE,
np.reshape(material, (-1)))
return num, vbo, tbo
def load_shapes(self, shapes):
entities = self.entities
entity_ids = []
for i, shape in enumerate(shapes):
for j in range(len(shape.meshes)):
name = shape.meshes[j].name
assert name not in entities, '{:s} entity already exists.'.format(name)
num, vbo, tbo = self._load_mesh_into_gl(shape.meshes[j], shape.materials[j])
entities[name] = {'num': num, 'vbo': vbo, 'tbo': tbo, 'visible': False}
entity_ids.append(name)
return entity_ids
def set_entity_visible(self, entity_ids, visibility):
for entity_id in entity_ids:
self.entities[entity_id]['visible'] = visibility
def position_camera(self, camera_xyz, lookat_xyz, up):
camera_xyz = np.array(camera_xyz)
lookat_xyz = np.array(lookat_xyz)
up = np.array(up)
lookat_to = lookat_xyz - camera_xyz
lookat_from = np.array([0, 1., 0.])
up_from = np.array([0, 0., 1.])
up_to = up * 1.
# np.set_printoptions(precision=2, suppress=True)
# print up_from, lookat_from, up_to, lookat_to
r = ru.rotate_camera_to_point_at(up_from, lookat_from, up_to, lookat_to)
R = np.eye(4, dtype=np.float32)
R[:3,:3] = r
t = np.eye(4, dtype=np.float32)
t[:3,3] = -camera_xyz
view_matrix = np.dot(R.T, t)
flip_yz = np.eye(4, dtype=np.float32)
flip_yz[1,1] = 0; flip_yz[2,2] = 0; flip_yz[1,2] = 1; flip_yz[2,1] = -1;
view_matrix = np.dot(flip_yz, view_matrix)
view_matrix = view_matrix.T
# print np.concatenate((R, t, view_matrix), axis=1)
view_matrix = np.reshape(view_matrix, (-1))
view_matrix_o = glGetUniformLocation(self.egl_program, 'uViewMatrix')
glUniformMatrix4fv(view_matrix_o, 1, GL_FALSE, view_matrix)
return None, None #camera_xyz, q
def clear_scene(self):
keys = self.entities.keys()
for entity_id in keys:
entity = self.entities.pop(entity_id, None)
vbo = entity['vbo']
tbo = entity['tbo']
num = entity['num']
glDeleteBuffers(1, [vbo])
glDeleteTextures(1, [tbo])
def __del__(self):
self.clear_scene()
eglMakeCurrent(self.egl_display, EGL_NO_SURFACE, EGL_NO_SURFACE, EGL_NO_CONTEXT)
eglDestroySurface(self.egl_display, self.egl_surface)
eglTerminate(self.egl_display)
research/cognitive_mapping_and_planning/requirements.txt deleted 100644 → 0
View file @ 09bc9f54
numpy
pillow
PyOpenGL
PyOpenGL-accelerate
six
networkx
scikit-image
scipy
opencv-python
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