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main_mcj_sample_GT.py
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import argparse
import traceback
import cv2
import time
import shutil
import logging
import yaml
import sys
import os
import torch
import numpy as np
import torch.utils.tensorboard as tb
import copy
# from runners import *
from runners.ncsn_runner_mcj_GT import *
import os
import matplotlib.pyplot as plt
def plot(img,dpi,figsize):
import matplotlib.pyplot as plt
plt.figure(dpi=dpi, figsize=figsize)
plt.imshow(img, vmin=-1, vmax=1, cmap=plt.cm.seismic)
# plt.title('fake_noisy')
# plt.colorbar()
plt.xticks([])
plt.yticks([])
# plt.axis('off')
plt.tight_layout()
plt.show()
def plot_cmap(img,dpi,figsize,data_range,cmap,cbar=False):
plt.figure(dpi=dpi, figsize=figsize)
plt.imshow(img, vmin=data_range[0], vmax=data_range[1], cmap=cmap)
# plt.title('fake_noisy')
if cbar:
plt.colorbar()
plt.xticks([])
plt.yticks([])
# plt.axis('off')
plt.tight_layout()
plt.show()
def parse_args_and_config():
parser = argparse.ArgumentParser(description=globals()['__doc__'])
parser.add_argument('--config', type=str, default='marmousi.yml', help='Path to the config file') #celeba.yml
parser.add_argument('--seed', type=int, default=1234, help='Random seed')
parser.add_argument('--exp', type=str, default='exp', help='Path for saving running related data.')
parser.add_argument('--doc', type=str, default='marmousi', help='A string for documentation purpose. '
'Will be the name of the log folder.') #celeba
parser.add_argument('--comment', type=str, default='', help='A string for experiment comment')
parser.add_argument('--verbose', type=str, default='info', help='Verbose level: info | debug | warning | critical')
parser.add_argument('-i', '--image_folder', type=str, default='images', help="The folder name of samples") #celeba
#D:\datasets\CelebA\CelebA\Img\img_align_celeba
parser.add_argument('-n', '--num_variations', type=int, default=1, help='Number of variations to produce')
parser.add_argument('-s', '--sigma_0', type=float, default=0.1, help='Noise std to add to observation')
parser.add_argument('--degradation', type=str, default='den', help='Degradation: rec | den')
args = parser.parse_args()
args.log_path = os.path.join(args.exp, 'logs', args.doc)
# parse config file
with open(os.path.join('configs', args.config), 'r') as f:
config = yaml.load(f,Loader = yaml.FullLoader)
new_config = dict2namespace(config)
tb_path = os.path.join(args.exp, 'tensorboard', args.doc)
level = getattr(logging, args.verbose.upper(), None)
if not isinstance(level, int):
raise ValueError('level {} not supported'.format(args.verbose))
handler1 = logging.StreamHandler()
formatter = logging.Formatter('%(levelname)s - %(filename)s - %(asctime)s - %(message)s')
handler1.setFormatter(formatter)
logger = logging.getLogger()
logger.addHandler(handler1)
logger.setLevel(level)
# os.makedirs(os.path.join(args.exp, 'image_samples'), exist_ok=True)
# args.image_folder = os.path.join(args.exp, 'image_samples', args.image_folder)
# args.image_folder ='exp/logs/celeba/results/test'
args.image_folder = 'exp/logs/marmousi/results'
# if not os.path.exists(args.image_folder):
# os.makedirs(args.image_folder)
# else:
# response = input("Image folder already exists. Overwrite? (Y/N)")
# if response.upper() == 'Y':
# overwrite = True
#
# if overwrite:
# shutil.rmtree(args.image_folder)
# os.makedirs(args.image_folder)
# else:
# print("Output image folder exists. Program halted.")
# sys.exit(0)
# add device
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
logging.info("Using device: {}".format(device))
new_config.device = device
# set random seed
torch.manual_seed(args.seed)
np.random.seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
torch.backends.cudnn.benchmark = True
return args, new_config
def dict2namespace(config):
namespace = argparse.Namespace()
for key, value in config.items():
if isinstance(value, dict):
new_value = dict2namespace(value)
else:
new_value = value
setattr(namespace, key, new_value)
return namespace
def main():
import torch
# torch.cuda.empty_cache()
args, config = parse_args_and_config()
os.environ["CUDA_VISIBLE_DEVICES"] = "6"
logging.info("Writing log file to {}".format(args.log_path))
logging.info("Exp instance id = {}".format(os.getpid()))
logging.info("Exp comment = {}".format(args.comment))
logging.info("Config =")
args.doc='marmousi_v2_nm'
args.log_path = os.path.join(args.exp, 'logs', args.doc)
args.log_path_model ='./exp/logs/marmousi_v2_nm'
args.image_folder = 'exp/logs/marmousi_v2_nm/results'
# The ckpt_id of the trained SGMs, the SGMs model is provided. Please retrain it if necessary.
config.sampling.ckpt_id=210000
# 'den' represents the denoising task, and 'rec' represents the reconstruction task (or simultaneous denoising and interpolation)
args.degradation='rec' # rec den
# The number of data processed at the same time, custom, default is 1
config.sampling.batch_size=1
# The number of samples (random solutions) you want to generate, custom
args.num_variations=3
# The parameters of SGMs are fixed and do not need to be set during testing (sampling).
config.data.seis_rescaled = False # False True
config.model.num_classes=500
config.model.sigma_begin=27 #
config.model.sigma_end = 0.01 #
config.model.sigma_dist = 'geometric'
print(">" * 80)
config_dict = copy.copy(vars(config))
print(yaml.dump(config_dict, default_flow_style=False))
print("<" * 80)
#### load data
import scipy.io as sio
original=sio.loadmat('/home/shendi_mcj/datasets/seismic/marmousi/marmousi35/marmousi35.mat')['data'] # shape(2441, 13601)
obs_GT1 = original[750:750+128,7200:7200+128]
plot(obs_GT1, dpi=300, figsize=(3, 3))
obs_GT = torch.from_numpy(obs_GT1).contiguous().view(1, -1, obs_GT1.shape[-2], obs_GT1.shape[-1]).type(
torch.FloatTensor)
# The noise level you set when testing
sigma_preset=0.5*abs(obs_GT).max()
print("sigma_preset:", sigma_preset)
plot_cmap((sigma_preset*abs(obs_GT).max()).cpu().numpy()*np.ones_like(obs_GT1), 300, (3.7, 3), data_range=[0.0, 0.3], cmap=plt.cm.jet, cbar=True)
print("sigma_preset.min:", sigma_preset.min(), "sigma_preset.median:", np.median(sigma_preset), "sigma_preset.max:", sigma_preset.max())
obs=obs_GT+sigma_preset*torch.randn_like(obs_GT)
# Get the shape of the observed data
config.data.image_shape = obs_GT1.shape
# Automatic noise level estimation by VI-non-IID or the user can set it by himself (i.e., according to the interval [sigma_dict['min'],sigma_dict['max']]).
from utils.estimate_sigma_using_VInonIID import estimate_sigma_using_VInonIID
sigma_dict,sigma_map_prd =estimate_sigma_using_VInonIID(obs[0].view(1,-1,obs.shape[2],obs.shape[3]))
# plot_cmap(sigma_map_prd, 300, (3.7, 3), data_range=[0.0, 0.3], cmap=plt.cm.jet, cbar=True)
args.sigma_0 = 1.0*sigma_dict['median']
try:
runner = NCSNRunner(args, config)
runner.sample(obs, obs_GT)
except:
logging.error(traceback.format_exc())
return 0
if __name__ == '__main__':
sys.exit(main())