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import yaml
import time
import random
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
import time
from envs.photo_env import PhotoEnhancementEnv
from envs.photo_env import PhotoEnhancementEnvTest
from sac.sac_algorithm import SAC
import multiprocessing as mp
import argparse
import logging
from sac.utils import *
from tqdm.auto import tqdm
from datetime import datetime
import os
from pathlib import Path
import re
def sanitize_filename(name):
return re.sub(r'[^\w\-_\. ]', '_', name)
def getdatetime():
return datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
class Config(object):
def __init__(self, dictionary):
self.__dict__.update(dictionary)
def make_dirs_and_open(file_path, mode):
os.makedirs(os.path.dirname(file_path), exist_ok=True)
return open(file_path, mode)
def main():
current_dir = Path(__file__).parent.absolute()
parser = argparse.ArgumentParser()
parser.add_argument('experiment_tag', help='experiment tag')
parser.add_argument('sac_config', help='YAML sac config file')
parser.add_argument('env_config', help='YAML env config file')
parser.add_argument('outdir', nargs='?', type=str, help='directory to put experiment results',default=os.path.join(current_dir.parent, 'experiments/runs'))
parser.add_argument('save_model', nargs='?',type=bool, default=True)
parser.add_argument('--logger_level', type=int, default=logging.INFO)
args = parser.parse_args()
logger = logging.getLogger(__name__)
# Configure logging to console
console_handler = logging.StreamHandler()
console_handler.setLevel(args.logger_level)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
logger.setLevel(args.logger_level)
with open(args.sac_config) as f:
config_dict =yaml.load(f, Loader=yaml.FullLoader)
with open(args.env_config) as f:
env_config_dict =yaml.load(f, Loader=yaml.FullLoader)
sac_config = Config(config_dict)
env_config = Config(env_config_dict)
exp_name = sanitize_filename(sac_config.exp_name)
exp_tag = sanitize_filename(args.experiment_tag)
run_name = f"{exp_name}__{exp_tag}__{getdatetime()}"
run_name = run_name[:255] # Truncate to 255 characters to avoid potential issues with very long paths
run_dir = os.path.join(args.outdir, run_name)
with make_dirs_and_open(os.path.join(run_dir, 'configs/sac_config.yaml'), 'w') as f:
yaml.dump(config_dict, f, indent=4, default_flow_style=False)
with make_dirs_and_open(os.path.join(run_dir, 'configs/env_config.yaml'), 'w') as f:
yaml.dump(env_config_dict, f, indent=4, default_flow_style=False)
SEED = sac_config.seed
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = sac_config.torch_deterministic
torch.autograd.set_detect_anomaly(True)
print()
env = PhotoEnhancementEnv(
batch_size=env_config.train_batch_size,
imsize=env_config.imsize,
training_mode=True,
done_threshold=env_config.threshold_psnr,
edit_sliders=env_config.sliders_to_use,
features_size=env_config.features_size,
discretize=env_config.discretize,
discretize_step= env_config.discretize_step,
use_txt_features=env_config.use_txt_features,
augment_data=env_config.augment_data,
pre_encoding_device=env_config.pre_encoding_device,
pre_load_images = env_config.pre_load_images,
preprocessor_agent_path=env_config.preprocessor_agent_path,
logger=None
)
test_env = PhotoEnhancementEnvTest(
batch_size=env_config.test_batch_size,
imsize=env_config.imsize,
training_mode=False,
done_threshold=env_config.threshold_psnr,
edit_sliders=env_config.sliders_to_use,
features_size=env_config.features_size,
discretize=env_config.discretize,
discretize_step = env_config.discretize_step,
use_txt_features=env_config.use_txt_features,
augment_data=env_config.augment_data,
pre_encoding_device=env_config.pre_encoding_device,
pre_load_images = env_config.pre_load_images,
preprocessor_agent_path=env_config.preprocessor_agent_path,
logger=None
)
logger.info(f'Sliders used {env.edit_sliders}')
logger.info(f'Number of sliders used { env.num_parameters}')
logger.info(f'Sliders used {test_env .edit_sliders}')
logger.info(f'Number of sliders used {test_env .num_parameters}')
writer = SummaryWriter(run_dir)
writer.add_text(
"SAC_hyperparameters",
"|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(sac_config).items()])),
)
writer.add_text(
"env_parameters",
"|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(env_config).items()])),
)
try:
agent = SAC(env,sac_config,writer)
if env_config.preprocessor_agent_path!=None: #Double agent mode
test_env.preprocessor_agent = env.preprocessor_agent # share the same preprocessor agent
agent.backbone.model.load_state_dict(env.preprocessor_agent.backbone.model.state_dict())
agent.backbone.eval().requires_grad_(False)
agent.start_time = time.time()
logger.info(f'Start Training at {getdatetime()}')
for i in tqdm(range(sac_config.total_timesteps), position=0, leave=True):
episode_count = 0
agent.reset_env()
envs_mean_rewards =[]
if agent.global_step>env_config.backbone_warmup:
agent.backbone.train().requires_grad_(True)
while True:
episode_count+=1
agent.global_step+=1
rewards,batch_dones = agent.train()
envs_mean_rewards.append(rewards.mean().item())
if(batch_dones==True).any():
num_env_done = int(batch_dones.sum().item())
agent.writer.add_scalar("charts/num_env_done", num_env_done , agent.global_step)
if agent.global_step % 100 == 0:
ens_mean_episodic_return = sum(envs_mean_rewards)
agent.writer.add_scalar("charts/mean_episodic_return", ens_mean_episodic_return, agent.global_step)
if (batch_dones==True).all()==True or episode_count==sac_config.max_episode_timesteps:
episode_count=0
break
if agent.global_step%200==0:
agent.backbone.eval().requires_grad_(False)
agent.actor.eval().requires_grad_(False)
agent.qf1.eval().requires_grad_(False)
agent.qf2.eval().requires_grad_(False)
with torch.no_grad():
n_images = 5
obs = test_env.reset()
actions = agent.actor.get_action(**obs.to(sac_config.device))
_,rewards,dones = test_env.step(actions[0])
agent.writer.add_scalar("charts/test_mean_episodic_return", rewards.mean().item(), agent.global_step)
if env_config.preprocessor_agent_path!=None:
agent.writer.add_images("test_images",test_env.original_image[:n_images],0)
agent.writer.add_images("test_images",test_env.state['source_image'][:n_images],1)
agent.writer.add_images("test_images",test_env.state['enhanced_image'][:n_images],2)
agent.writer.add_images("test_images",test_env.state['target_image'][:n_images],3)
else:
agent.writer.add_images("test_images",test_env.state['source_image'][:n_images],0)
agent.writer.add_images("test_images",test_env.state['enhanced_image'][:n_images],1)
agent.writer.add_images("test_images",test_env.state['target_image'][:n_images],2)
agent.backbone.train().requires_grad_(True)
agent.actor.train().requires_grad_(True)
agent.qf1.train().requires_grad_(True)
agent.qf2.train().requires_grad_(True)
logger.info(f'Ended training at {getdatetime()}')
if args.save_model:
models_dir = os.path.join(run_dir, 'models')
os.makedirs(models_dir, exist_ok=True)
logger.info(f"Saving models in {models_dir}")
torch.save(agent.backbone.state_dict(), run_dir+'/models/backbone.pth')
save_actor_head(agent.actor, run_dir+'/models/actor_head.pth')
save_critic_head(agent.qf1, run_dir+'/models/qf1_head.pth')
save_critic_head(agent.qf2, run_dir+'/models/qf2_head.pth')
writer.close()
except Exception as e:
logger.exception("An error occurred during training")
if agent.global_step>1000:
if args.save_model:
models_dir = os.path.join(run_dir, 'models')
os.makedirs(models_dir, exist_ok=True)
logger.info(f"Saving models after exception in {models_dir}")
torch.save(agent.backbone.state_dict(), run_dir+'/models/backbone.pth')
save_actor_head(agent.actor, run_dir+'/models/actor_head.pth')
save_critic_head(agent.qf1, run_dir+'/models/qf1_head.pth')
save_critic_head(agent.qf2, run_dir+'/models/qf2_head.pth')
writer.close()
if __name__=="__main__":
main() |