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188 | from data import *
import random
import scipy.misc as m
import math
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from models import *
from models.nade import *
from torch.autograd import Variable
from PIL import Image
import copy
from model_prior import *
from scene_synth import *
from categoryCounts_dataset import CategoryCountsDataset
class SceneSynthArrangementBaseline(SceneSynth):
def __init__(self, *args, **kwargs):
super(SceneSynthArrangementBaseline, self).__init__(*args, **kwargs)
def synth_room(self, room_id, trial, size, samples, \
save_dir, temperature_cat, temperature_pixel, \
min_p, max_collision, save=True, save_heatmap=False):
room = SynthedRoomArrangementBaseline(room_id, trial, size, samples, self, temperature_cat, temperature_pixel, min_p, max_collision)
while room.should_continue():
room.save_top_down_view(save_dir)
room.save_json(save_dir)
room.add_node()
room.save_top_down_view(save_dir, final=True)
room.save_json(save_dir, final=True)
class SynthedRoomArrangementBaseline(SynthedRoom):
def __init__(self, room_id, trial, size, samples, synthesizer, temperature_cat, temperature_pixel, min_p, max_collision):
self.__dict__.update(locals())
del self.self #Of course I don't care about readability
self.scene = RenderedScene(index = room_id, \
data_dir = synthesizer.data_dir_relative, \
data_root_dir = synthesizer.data_root_dir, \
load_objects = True)
self._parse_original_nodes(self.scene.object_nodes)
self.scene.object_nodes = []
self.composite = self.scene.create_composite()
self.door_window_nodes = self.scene.door_window_nodes
self.object_nodes = []
self.empty_house_json = None
self.failures = 0
self.obj_data = ObjectData()
def _parse_original_nodes(self, nodes):
category_dict = ObjectCategories()
self.category_counts = np.zeros(len(self.synthesizer.categories))
self.remaining_models = [[] for i in range(len(self.synthesizer.categories))]
for node in nodes:
modelId = node["modelId"]
category = node["category"]
self.category_counts[category] += 1
self.remaining_models[category].append(modelId)
#print(self.category_counts)
#print(self.remaining_models)
#print(modelId, category, category2, self.synthesizer.categories[category2])
def should_continue(self):
return self.category_counts.sum() > 0
def add_node(self):
print(self.category_counts)
print(self.remaining_models)
self.location_category_map = None
self.current_room = self.composite.get_composite()
self.existing_collisions = self._get_collisions()
category_counts = self.category_counts
self.count = 0
best_x, best_y, best_p, best_r, best_modelId = None, None, -100, None, None
best_category = None
while True:
self.count += 1
gridx,gridy,category = self._sample_location_category(category_counts)
print(f"Choosing type {self._get_category_name(category)} at grid {gridx}, {gridy}")
x,y = self._sample_exact_location(gridx, gridy, category)
#print(f"Try placing an object at image space coordinate {x}, {y}")
modelId, r, p = self._sample_model_rotation(x, y, category)
print(p)
if p > best_p:
best_p = p
best_x = x
best_y = y
best_modelId = modelId
best_r = r
best_category = category
if p > self.min_p or self.count > 10:
print(f"Choosing model {modelId} rotated by {r} radians")
if self.count > 10:
self.failures += 1000 #bad bad
break
else:
self.failures += 1
print(f"Best probability is {p}, resample location-category")
category_counts[best_category] -= 1
self.remaining_models[best_category].remove(best_modelId)
new_obj = SynthNode(best_modelId, best_category, best_x, best_y, best_r, self)
self.composite.add_height_map(new_obj.get_render(), best_category, math.sin(best_r), math.cos(best_r))
self.object_nodes.append(new_obj)
def _sample_location_category(self, category_counts):
if self.location_category_map is None:
self.location_category_map = self._create_location_category_map(category_counts)
total_p = self.location_category_map.sum()
max_p = np.max(self.location_category_map)
x,y,category = self._sample_location_category_helper(self.location_category_map, total_p)
self.location_category_map[category][x][y] = 0
return x,y,category
def _create_location_category_map(self, category_counts):
location_category_map = super(SynthedRoomArrangementBaseline, self)._create_location_category_map()
for k in range(self.synthesizer.num_categories):
if category_counts[k] == 0:
location_category_map[k] = 0
return location_category_map
def _sample_model_rotation(self, x, y, category, trials=10, num_orientations=16):
num_categories = self.synthesizer.num_categories
synthesizer = self.synthesizer
best_p = -100
best_orientation = 0
best_model = None
models = list(set(self.remaining_models[category]))
#print("Trying different models and orientation...")
for (trial, modelId) in enumerate(models):
inputs = []
for orientation in range(num_orientations):
r = math.pi*orientation/num_orientations*2
new_obj = SynthNode(modelId, category, x, y, r, self).get_render()
new_room = self.composite.add_and_get_composite \
(new_obj, category, math.sin(r), math.cos(r))
new_obj[new_obj>0] = 1
new_room[-1] = new_obj
inputs.append(new_room)
with torch.no_grad():
inputs = torch.stack(inputs)
inputs = inputs.cuda()
outputs = synthesizer.model_rotation(inputs)
outputs = synthesizer.softmax(outputs).cpu().data.numpy()
for orientation in range(num_orientations):
#print(orientation, outputs[orientation][1])
if outputs[orientation][1] > best_p:
p = outputs[orientation][1]
r = math.pi*best_orientation/num_orientations*2
new_node = SynthNode(modelId, category, x, y, r, self)
collisions = self._get_collisions([new_node])
if (len(collisions) - len(self.existing_collisions)) > 0:
p -= 1
if p > best_p:
best_p = p
best_model = modelId
best_orientation = orientation
print(f"Testing model {trial+1} of {len(models)}...", end = "\r")
print()
return best_model, math.pi*best_orientation/num_orientations*2, best_p
|