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own_sequence_generator.py
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#import tensorflow as tf
import numpy as np
import pdb
import os
import random
import threading
import tensorflow as tf
import time
import json
from networks.free_space_network import FreeSpaceNetwork
#from supervised.sequence_generator import SequenceGenerator
from sequence_generator import SequenceGenerator
from utils import tf_util
from utils import game_util
import constants
#import mcs.cover_floor
import cover_floor
data_buffer = []
data_counts = np.full(constants.REPLAY_BUFFER_SIZE, 9999)
os.environ["CUDA_VISIBLE_DEVICES"] = str(constants.GPU_ID)
def create_scene_numbers(max_scene_number):
scene_numbers = []
for i in range (0,10):
for j in range (0,10):
for k in range(0,10):
for l in range(0,10):
if i == 0 and j == 0 and k == 0 and l == 0:
continue
scene_numbers.append(str(i)+ str(j)+ str(k)+str(l))
if len(scene_numbers) >= max_scene_number:
return scene_numbers
#def explore_scene(scene_type, scene_number):
try:
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
'''
with tf.variable_scope('nav_global_network'):
network = FreeSpaceNetwork(constants.GRU_SIZE, constants.BATCH_SIZE, constants.NUM_UNROLLS)
network.create_net()
training_step = network.training_op
with tf.variable_scope('loss'):
loss_summary_op = tf.summary.merge([
tf.summary.scalar('loss', network.loss),
])
summary_full = tf.summary.merge_all()
conv_var_list = [v for v in tf.trainable_variables() if 'conv' in v.name and 'weight' in v.name and
(v.get_shape().as_list()[0] != 1 or v.get_shape().as_list()[1] != 1)]
for var in conv_var_list:
tf_util.conv_variable_summaries(var, scope=var.name.replace('/', '_')[:-2])
summary_with_images = tf.summary.merge_all()
'''
#sess = tf_util.Session()
sequence_generator = SequenceGenerator(None)
#sess.run(tf.global_variables_initializer())
all_scene_types = ['retrieval_goal-', 'traversal_goal-', 'transferral_goal-']
#scene_types = ['retrieval_goal-', 'traversal_goal-', 'transferral_goal-']
#scene_types = ['retrieval_goal-', 'traversal_goal-']#, 'transferral_goal-']
#scene_types = ['transferral_goal-']
scene_types = ['traversal_goal-']
#scene_types = ['retrieval_goal-']
#scene_numbers = ['0933','0934','0935']
scene_numbers = ['0058']#,'0934','0935']
#scene_numbers = create_scene_numbers(100)
print (scene_numbers)
#exit()
#scene_number = [i]
all_data = {}
training_data = {}
exploration_data = {}
actual_count_of_explored_scenes = {}
total_goal_objects_found = {}
actual_goal_data = {}
for elem in scene_types :
all_data[elem] = {"explored": [], "actual":[], 'explored_total':0, 'actual_total':0}
training_data[elem] = {}
exploration_data[elem] = {}
actual_count_of_explored_scenes[elem] = 0
total_goal_objects_found[elem] = 0
actual_goal_data[elem] = 0
#env = game_util.create_ai2thor_env()
for scene_type in scene_types :
for scene_number in scene_numbers :
current_explored = 0
#new_data, bounds, goal_pose = sequence_generator.explore_scene(str(scene_type)+ scene_number + ".json")
sequence_generator.explore_3d_scene(str(scene_type)+ scene_number + ".json")
#exit()
current_explored_objects = sequence_generator.agent.game_state.discovered_objects
current_explored_uuids = sequence_generator.agent.game_state.discovered_explored
current_explored = len(current_explored_objects)
#sequence_generator.agent.game_state.env.end_scene('', 0.0)
goal = sequence_generator.agent.game_state.goal
goal_objects = []
#if goal['category'] == all_scene_types[-1][:-6]:
# goal_objects.append(goal.metadata['target_1']["id"])
# goal_objects.append(goal.metadata['target_2']["id"])
#print (sequence_generator.agent.game_state.goal.metadata['target']["id"] )
print (type(goal))
#for key,value in sequence_generator.agent.game_state.goal.__dict__.items():
for key,value in goal.metadata.items():
if key == "target" or key == "target_1" or key == "target_2":
goal_objects.append(goal.metadata[key]["id"])
actual_goal_data[scene_type] += 1
#goal_objects.append(goal.metadata['target_2']["id"])
#print (key, type(value))
#sequence_generator.agent.game_state.discovered_objects = []
print ("Total objects discovered = " ,current_explored )
#with open("discovered_data.json","w") as fp:
# print ("number of objects discovered until now : ",len(sequence_generator.agent.game_state.discovered_objects))
# json.dump(sequence_generator.agent.game_state.discovered_objects,fp,indent=1)
for elem in current_explored_uuids:
print (elem)#current_explored_objects
print ("explored objects over, goal next")
for elem in goal_objects:
print (elem)#current_explored_objects
for elem in goal_objects :
if elem in current_explored_uuids:
total_goal_objects_found[scene_type] += 1
'''
Checking for number of objects by using AIthor controller
current_actual = 0
event = game_util.reset_ai2thor_env(env,str(scene_type)+ scene_number + ".json")
current_actual = len(event.metadata['objects'])
'''
#all_data[scene_type]['explored'].append(current_explored)
#all_data[scene_type]['actual'].append(current_actual)
all_data[scene_type]['explored_total'] += current_explored
#all_data[scene_type]['actual_total'] += current_actual
#training_data[scene_type][scene_number] = current_actual
exploration_data[scene_type][scene_number] = current_explored
#for key,items in all_data.items():
#print ("Explored total= ", items['explored_total'])
#print ("Actual", items['actual_total'])
actual_data = json.load(open('training_total_objects_data.json'))
for key,value in exploration_data.items() :
for key2, value2 in value.items() :
actual_count_of_explored_scenes[key] += actual_data[key][key2]
#print ("Total explored = " , all_data.items)
for key,items in all_data.items():
print ("Total explored for scenes in {} is {}".format(key, items['explored_total']))
print ("Total actual for scenes in {} is {}".format( key, actual_count_of_explored_scenes[key]))
print ("Total goals found for scenes in {} is {}".format( key, total_goal_objects_found[key]))
print ("Total goal actual for scenes in {} is {}".format( key, actual_goal_data[key]))
'''
with open("training_total_objects_data.json","w") as fp:
json.dump(training_data,fp,indent=1)
#Actual 2105 - retrieval
#Actual 3670 - traversal
#Actual 3480 - transferal
'''
except:
import traceback
traceback.print_exc()