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test_torch.py
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test_torch.py
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import time
from collections import deque
import numpy as np
import os
from tqdm import tqdm
import torch
import torch.nn.functional as F
from model_torch import ActorCritic
import env as env_valid
import fixed_env as env_test
import load_trace
RANDOM_SEED = 42
S_INFO = 6
S_LEN = 8
A_DIM = 6
VIDEO_BIT_RATE = [300,750,1200,1850,2850,4300] # Kbps
BUFFER_NORM_FACTOR = 10.0
CHUNK_TIL_VIDEO_END_CAP = 48.0
M_IN_K = 1000.0
REBUF_PENALTY = 2.66 # 1 sec rebuffering -> 3 Mbps
SMOOTH_PENALTY = 1
DEFAULT_QUALITY = 1
TEST_TRACES_VALID = './cooked_test_traces/'
# SUMMARY_DIR = './Results/sim/pppo'
# LOG_FILE = './Results/sim/pppo/log'
# TEST_LOG_FOLDER = './Results/sim/pppo/test_results/'
# LOG_FILE_VALID = './Results/sim/pppo/test_results/log_valid_pppo'
# TEST_LOG_FOLDER_VALID = './Results/sim/pppo/test_results/'
# LOG_FILE_TEST = './Results/test/BC/log_hybrid_ppo'
# SUMMARY_DIR = './Results/test/BC/'
# Log_path = './Results/sim/pppo'
def evaluation(model, log_path_ini, net_env, all_file_name, detail_log = True):
# all_file_name = net_env.get_file_name()
state = np.zeros((S_INFO,S_LEN))
state = torch.from_numpy(state)
# reward_sum = 0
done = True
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY
# model.load_state_dict(model.state_dict())
log_path = log_path_ini + '_' + all_file_name[net_env.trace_idx]
log_file = open(log_path, 'w')
time_stamp = 0
for video_count in tqdm(range(len(all_file_name))):
while True:
with torch.no_grad():
prob, _ = model(state.unsqueeze(0).type(torch.FloatTensor))
action = prob.multinomial(num_samples=1).detach()
bit_rate = int(action.squeeze().cpu().numpy())
if detail_log == False:
delay, sleep_time, buffer_size, rebuf, \
video_chunk_size, next_video_chunk_sizes, \
end_of_video, video_chunk_remain = \
net_env.get_video_chunk(bit_rate) ## sample in the environment of virtual player
time_stamp += delay # in ms
time_stamp += sleep_time # in ms
# reward is video quality - rebuffer penalty - smooth penalty
# -- lin scale reward --
# reward = VIDEO_BIT_RATE[bit_rate] / M_IN_K \
# - REBUF_PENALTY * rebuf \
# - SMOOTH_PENALTY * np.abs(VIDEO_BIT_RATE[bit_rate] -
# VIDEO_BIT_RATE[last_bit_rate]) / M_IN_K
# -- log scale reward --
log_bit_rate = np.log(VIDEO_BIT_RATE[bit_rate] / float(VIDEO_BIT_RATE[0]))
log_last_bit_rate = np.log(VIDEO_BIT_RATE[last_bit_rate] / float(VIDEO_BIT_RATE[0]))
reward = log_bit_rate \
- REBUF_PENALTY * rebuf \
- SMOOTH_PENALTY * np.abs(log_bit_rate - log_last_bit_rate)
last_bit_rate = bit_rate
log_file.write(str(time_stamp / M_IN_K) + '\t' +
str(VIDEO_BIT_RATE[bit_rate]) + '\t' +
str(buffer_size) + '\t' +
str(rebuf) + '\t' +
str(video_chunk_size) + '\t' +
str(delay) + '\t' +
str(reward) + '\n')
log_file.flush()
# dequeue history record
state = np.roll(state, -1, axis=1)
# this should be S_INFO number of terms
state[0, -1] = VIDEO_BIT_RATE[bit_rate] / float(np.max(VIDEO_BIT_RATE)) # last quality
state[1, -1] = buffer_size / BUFFER_NORM_FACTOR # 10 sec
state[2, -1] = float(video_chunk_size) / float(delay) / M_IN_K # kilo byte / ms
state[3, -1] = float(delay) / M_IN_K / BUFFER_NORM_FACTOR # 10 sec
state[4, :A_DIM] = np.array(next_video_chunk_sizes) / M_IN_K / M_IN_K # mega byte
state[5, -1] = np.minimum(video_chunk_remain, CHUNK_TIL_VIDEO_END_CAP) / float(CHUNK_TIL_VIDEO_END_CAP)
state = torch.from_numpy(state)
if end_of_video:
state = np.zeros((S_INFO,S_LEN))
state = torch.from_numpy(state)
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY
log_file.write('\n')
log_file.close()
time_stamp = 0
if video_count >= len(all_file_name):
break
else:
log_path = log_path_ini + '_' + all_file_name[net_env.trace_idx]
log_file = open(log_path, 'w')
break
# else:
# delay, video_chunk_size, video_distortion, reward, next_state, done, rebuf, download_interval = env.step([int(action_1.squeeze()), int(action_2.squeeze()), int(A_DIM-1)]) # Step
# # r_batch.append(reward)
# time_stamp += download_interval
# # log time_stamp, bit_rate, buffer_size, reward
# log_file.write(str(time_stamp) + '\t' +
# str(next_state[0][26]) + '\t' + str(next_state[0][27]) + '\t' + str(next_state[0][28]) + '\t' + # qualities
# str(next_state[0][8]*5.0) + '\t' + ## buffer size
# str(rebuf) + '\t' + ## rebuffering time
# str(reward) + '\t' + # chunk reward
# str(video_distortion) + '\t' +
# str(video_chunk_size) + '\t' +
# str(delay) + '\n')
# log_file.flush()
# a quick hack to prevent the agent from stucking
# actions.append(action[0, 0])
# if actions.count(actions[0]) == actions.maxlen:
# done = True
# if done:
# log_file.write('\n')
# log_file.close()
# state = np.zeros((1,S_LEN))
# time_stamp = 0
# if video_count >= len(all_file_name):
# break
# else:
# log_path = LOG_FILE_VALID + '_' + all_file_name[env.trace_idx]
# log_file = open(log_path, 'w')
# break
# state = torch.from_numpy(state)
def valid(shared_model, epoch, log_file, summary_dir, name_str):
os.system('rm -r ' + summary_dir + '/test_results/')
os.system('mkdir ' + summary_dir + '/test_results/')
model = ActorCritic(A_DIM).type(torch.FloatTensor)
model.eval()
model.load_state_dict(shared_model.state_dict())
log_path_ini = summary_dir + '/test_results/log_valid_pppo'
all_cooked_time, all_cooked_bw, all_file_names = load_trace.load_trace(TEST_TRACES_VALID)
env = env_valid.Environment(all_cooked_time=all_cooked_time,
all_cooked_bw=all_cooked_bw)
evaluation(model, log_path_ini, env, all_file_names, False)
rewards = []
test_log_files = os.listdir(summary_dir + '/test_results/')
for test_log_file in test_log_files:
reward = []
with open(summary_dir + '/test_results/' + test_log_file, 'rb') as f:
for line in f:
parse = line.split()
try:
reward.append(float(parse[-1]))
except IndexError:
break
rewards.append(np.sum(reward[1:]))
rewards = np.array(rewards)
rewards_min = np.min(rewards)
rewards_5per = np.percentile(rewards, 5)
rewards_mean = np.mean(rewards)
rewards_median = np.percentile(rewards, 50)
rewards_95per = np.percentile(rewards, 95)
rewards_max = np.max(rewards)
log_file.write(str(int(epoch)) + '\t' +
str(rewards_min) + '\t' +
str(rewards_5per) + '\t' +
str(rewards_mean) + '\t' +
str(rewards_median) + '\t' +
str(rewards_95per) + '\t' +
str(rewards_max) + '\n')
log_file.flush()
# add_str = 'ppo'
model_save_path = summary_dir + "/%s_%s_%d.model" %(str('abr'), name_str, int(epoch))
torch.save(shared_model.state_dict(), model_save_path)
def test(test_path):
env = env_test.Environment()
model = Actor(A_DIM).type(torch.FloatTensor)
model.eval()
model.load_state_dict(torch.load(test_path))
log_path_ini = LOG_FILE_TEST
env = env_valid.Environment()
evaluation(model, log_path_ini, env, True)