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eval_lmdb.py
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eval_lmdb.py
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import torch
import torchvision.utils as vutils
import torch.nn.functional as F
import os
import sys
import datetime
import time
import gc
from pathlib import Path
import numpy as np
import cv2
import math
from sklearn.metrics import mean_absolute_error
from skimage.metrics import structural_similarity
import collections
from utils import *
from data_loader.utils import refine_image_pt, read_frame, load_file_list, norm
from models.utils import warp
from ckpt_manager import CKPT_Manager
# from eval_input import eval_input
# from eval_specific_video import eval_specific_video
from models import create_model
def mae(img1, img2):
mae_0=mean_absolute_error(img1[:,:,0], img2[:,:,0],
multioutput='uniform_average')
mae_1=mean_absolute_error(img1[:,:,1], img2[:,:,1],
multioutput='uniform_average')
mae_2=mean_absolute_error(img1[:,:,2], img2[:,:,2],
multioutput='uniform_average')
return np.mean([mae_0,mae_1,mae_2])
def ssim(img1, img2, PIXEL_MAX = 1.0):
return structural_similarity(img1, img2, data_range=PIXEL_MAX, multichannel=True)
def ssim_masked(img1, img2, mask, PIXEL_MAX = 1.0):
_, s = structural_similarity(img1, img2, data_range=PIXEL_MAX, multichannel=True, full=True)
s = s * mask
mssim = np.sum(s)/np.sum(mask)
return mssim
def psnr(img1, img2, PIXEL_MAX = 1.0):
mse_ = np.mean( (img1 - img2) ** 2 )
return 10 * math.log10(PIXEL_MAX / mse_)
def psnr_masked(img1, img2, mask, PIXEL_MAX = 1.0):
mse_ = np.sum( ( (img1 - img2) ** 2) * mask) / np.sum(mask)
return 10 * math.log10(PIXEL_MAX / mse_)
def init(config, mode = 'deblur'):
date = datetime.datetime.now().strftime('%Y_%m_%d_%H%M')
model = create_model(config)
model.eval()
network = model.get_network().eval()
ckpt_manager = CKPT_Manager(config.LOG_DIR.ckpt, config.mode, config.max_ckpt_num, is_descending = False)
load_state, ckpt_name = ckpt_manager.load_ckpt(network, by_score = config.EVAL.load_ckpt_by_score, name = config.EVAL.ckpt_name, abs_name = config.EVAL.ckpt_abs_name, epoch = config.EVAL.ckpt_epoch)
print('\nLoading checkpoint \'{}\' on model \'{}\': {}'.format(ckpt_name, config.mode, load_state))
save_path_root = config.EVAL.LOG_DIR.save
save_path_root_deblur = os.path.join(save_path_root, mode, ckpt_name.split('.')[0])
save_path_root_deblur_score = save_path_root_deblur
Path(save_path_root_deblur).mkdir(parents=True, exist_ok=True)
torch.save(network.state_dict(), os.path.join(save_path_root_deblur, ckpt_name))
save_path_root_deblur = os.path.join(save_path_root_deblur, config.EVAL.data, date)
# Path(save_path_root_deblur).mkdir(parents=True, exist_ok=True)
return network, model, save_path_root_deblur, save_path_root_deblur_score, ckpt_name
def eval_quan_qual(config):
mode = config.EVAL.eval_mode
network, model, save_path_root_deblur, save_path_root_deblur_score, ckpt_name = init(config, mode)
##
total_norm = 0
total_itr_time = PSNR_mean_total = SSIM_mean_total = 0
total_itr_time_video = PSNR_mean = SSIM_mean = 0
frame_len_prev = 0
for i, inputs in enumerate(model.data_loader_eval):
is_first_frame = inputs['is_first'][0].item()
if is_first_frame:
if i > 0:
PSNR_mean_total = PSNR_mean_total + PSNR_mean
SSIM_mean_total = SSIM_mean_total + SSIM_mean
total_itr_time = total_itr_time + total_itr_time_video
PSNR_mean = PSNR_mean / frame_len_prev
SSIM_mean = SSIM_mean / frame_len_prev
total_itr_time_video = total_itr_time_video / frame_len_prev
print('[MEAN EVAL {}|{}|{}][{}/{}] PSNR: {:.5f} SSIM: {:.5f} ({:.5f}sec)\n\n'.format(config.mode, config.EVAL.data, inputs['video_name'][0], inputs['video_idx'][0], inputs['video_len'][0], PSNR_mean, SSIM_mean, total_itr_time_video))
with open(os.path.join(save_path_root_deblur_score, 'score_{}_{}.txt'.format(config.EVAL.data, config.EVAL.eval_mode)), 'a') as file:
file.write('[MEAN EVAL {}|{}|{}][{}/{}] PSNR: {:.5f} SSIM: {:.5f} ({:.5f}sec)\n\n'.format(config.mode, config.EVAL.data, inputs['video_name'][0], inputs['video_idx'][0], inputs['video_len'][0], PSNR_mean, SSIM_mean, total_itr_time_video))
file.close()
total_itr_time_video = PSNR_mean = SSIM_mean = 0
#########################
init_time = time.time()
with torch.no_grad():
results = model.evaluation(inputs)
# gc.collect()
# torch.cuda.empty_cache()
itr_time = time.time() - init_time
#########################
## evaluation
outs = results
inp = outs['input']
outs['result'] = torch.clamp(outs['result'], 0, 1)
output = outs['result']
PSNR = SSIM = 0
gt = outs['gt']
# quantitative
output_cpu = output.cpu().numpy()[0].transpose(1, 2, 0)
gt_cpu = gt.cpu().numpy()[0].transpose(1, 2, 0)
if config.EVAL.is_quan:
PSNR = psnr(output_cpu, gt_cpu)
SSIM = ssim(output_cpu, gt_cpu)
PSNR_mean = PSNR_mean + PSNR
SSIM_mean = SSIM_mean + SSIM
frame_name = inputs['frame_name'][0]
print('[EVAL {}|{}|{}][{}/{}][{}/{}] {} PSNR: {:.5f} SSIM: {:.5f} ({:.5f}sec)'.format(config.mode, config.EVAL.data, inputs['video_name'][0], inputs['video_idx'][0]+1, inputs['video_len'][0], inputs['frame_idx'][0]+1, inputs['frame_len'][0], frame_name, PSNR, SSIM, itr_time))
with open(os.path.join(save_path_root_deblur_score, 'score_{}_{}.txt'.format(config.EVAL.data, config.EVAL.eval_mode)), 'w' if (i == 0) else 'a') as file:
file.write('[EVAL {}|{}|{}][{}/{}][{}/{}] {} PSNR: {:.5f} SSIM: {:.5f} ({:.5f}sec)\n'.format(config.mode, config.EVAL.data, inputs['video_name'][0], inputs['video_idx'][0]+1, inputs['video_len'][0], inputs['frame_idx'][0]+1, inputs['frame_len'][0], frame_name, PSNR, SSIM, itr_time))
file.close()
# qualitative
## create output dir for a video
for iformat in ['png']:
frame_name_no_ext = frame_name.split('.')[0]
save_path_deblur = os.path.join(save_path_root_deblur, iformat)
Path(save_path_deblur).mkdir(parents=True, exist_ok=True)
Path(os.path.join(save_path_deblur, 'output', inputs['video_name'][0])).mkdir(parents=True, exist_ok=True)
save_file_path_deblur_output = os.path.join(save_path_deblur, 'output', inputs['video_name'][0], '{}.{}'.format(frame_name_no_ext, iformat))
vutils.save_image(output, '{}'.format(save_file_path_deblur_output), nrow=1, padding = 0, normalize = False)
if config.EVAL.save_input_gt:
Path(os.path.join(save_path_deblur, 'input', inputs['video_name'][0])).mkdir(parents=True, exist_ok=True)
save_file_path_deblur_input = os.path.join(save_path_deblur, 'input', inputs['video_name'][0], '{}.{}'.format(frame_name_no_ext, iformat))
vutils.save_image(inp, '{}'.format(save_file_path_deblur_input), nrow=1, padding = 0, normalize = False)
if 'gt' in inputs.keys():
Path(os.path.join(save_path_deblur, 'gt', inputs['video_name'][0])).mkdir(parents=True, exist_ok=True)
save_file_path_deblur_gt = os.path.join(save_path_deblur, 'gt', inputs['video_name'][0], '{}.{}'.format(frame_name_no_ext, iformat))
vutils.save_image(gt, '{}'.format(save_file_path_deblur_gt), nrow=1, padding = 0, normalize = False)
total_itr_time_video = total_itr_time_video + itr_time
total_norm = total_norm + 1
frame_len_prev = inputs['frame_len'][0]
# total average
total_itr_time = (total_itr_time + total_itr_time_video) / total_norm
PSNR_mean_total = (PSNR_mean_total + PSNR_mean) / total_norm
SSIM_mean_total = (SSIM_mean_total + SSIM_mean) / total_norm
sys.stdout.write('\n[TOTAL {}|{}] PSNR: {:.5f} SSIM: {:.5f} ({:.5f}sec)'.format(ckpt_name, config.EVAL.data, PSNR_mean_total, SSIM_mean_total, total_itr_time))
with open(os.path.join(save_path_root_deblur_score, 'score_{}_{}.txt'.format(config.EVAL.data, config.EVAL.eval_mode)), 'a') as file:
file.write('\n[TOTAL {}|{}] PSNR: {:.5f} SSIM: {:.5f} ({:.5f}sec)\n'.format(ckpt_name, config.EVAL.data, PSNR_mean_total, SSIM_mean_total, total_itr_time))
file.close()
def eval(config):
torch.backends.cudnn.benchmark = False
with torch.no_grad():
eval_quan_qual(config)