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test.py
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'''
test Zooming Slow-Mo models on arbitrary datasets
write to txt log file
[kosame] TODO: update the test script to the newest version
'''
import os
import os.path as osp
import glob
import logging
import numpy as np
import cv2
import torch
import utils.util as util
import data.util as data_util
import models.Sakuya_arch as Sakuya_arch
import models.pyflow as pyflow
import time
def main():
scale = 4
N_ot = 7 #3
N_in = 1+ N_ot // 2
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
#### model
#### TODO: change your model path here
model_path = './weights/4x_DESKTOP-MFC5BAN_epoch_48.pth'
model = Sakuya_arch.LunaTokis(64, N_ot, 8, 5, 40)
# model = torch.nn.DataParallel(model)
#### dataset
data_mode = 'SPMC' #'Vid4' #'SPMC'#'Middlebury'#
if data_mode == 'Vid4':
test_dataset_folder = '/data/xiang/SR/Vid4/LR/*'
if data_mode == 'SPMC':
test_dataset_folder = 'D:/Github-package/ZoomingSM-jilin189/jilin_test12/*'
if data_mode == 'Custom':
test_dataset_folder = '../test_example/*' # TODO: put your own data path here
#### evaluation
flip_test = False #True#
crop_border = 0
# temporal padding mode
padding = 'replicate'
save_imgs = True #True#
if 'Custom' in data_mode: save_imgs = True
############################################################################
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
save_folder = './results_ours/{}'.format(data_mode)
util.mkdirs(save_folder)
util.setup_logger('base', save_folder, 'test', level=logging.INFO, screen=True, tofile=True)
logger = logging.getLogger('base')
model_params = util.get_model_total_params(model)
#### log info
logger.info('Data: {} - {}'.format(data_mode, test_dataset_folder))
logger.info('Padding mode: {}'.format(padding))
logger.info('Model path: {}'.format(model_path))
logger.info('Model parameters: {} M'.format(model_params))
logger.info('Save images: {}'.format(save_imgs))
logger.info('Flip Test: {}'.format(flip_test))
def single_forward(model, imgs_in):
with torch.no_grad():
# imgs_in.size(): [1,n,3,h,w] tensor
b, n, c, h, w = imgs_in.size()
# print(imgs_in.size())
h_n = int(4 * np.ceil(h / 4))
w_n = int(4 * np.ceil(w / 4))
# h_n = h
# w_n = w
imgs_temp = imgs_in.new_zeros(b, n, c, h_n, w_n)
imgs_temp[:, :, :, 0:h, 0:w] = imgs_in.cuda(0)
forward_flow = []
backward_flow = []
for i in range(n - 1):
im1 = np.array(imgs_in[:, i, :, :, :].cpu()).astype(float) # (1,3,64,112)
im2 = np.array(imgs_in[:, i + 1, :, :, :].cpu()).astype(float)
im1 = np.squeeze(im1, 0).transpose(1, 2, 0).copy() # (64,112,3)
im2 = np.squeeze(im2, 0).transpose(1, 2, 0).copy()
# Flow Options:
alpha = 0.012
ratio = 0.75
minWidth = 20
nOuterFPIterations = 7
nInnerFPIterations = 1
nSORIterations = 30
colType = 0 # 0 or default:RGB, 1:GRAY (but pass gray image with shape (h,w,1))
u, v, im2W = pyflow.coarse2fine_flow(
im1, im2, alpha, ratio, minWidth, nOuterFPIterations, nInnerFPIterations,
nSORIterations, colType)
u2, v2, im2W2 = pyflow.coarse2fine_flow(
im2, im1, alpha, ratio, minWidth, nOuterFPIterations, nInnerFPIterations,
nSORIterations, colType)
f_flow = np.concatenate((u[..., None], v[..., None]), axis=2)
b_flow = np.concatenate((u2[..., None], v2[..., None]), axis=2)
# h*w*2-->2*h*w
f_flow = torch.from_numpy(f_flow).float() # [2 32 32]
b_flow = torch.from_numpy(b_flow).float() # [2 32 32]
# 2*h*w-->1*2*h*w
f_flow = torch.unsqueeze(f_flow, dim=0).cuda(0) # [1 2 32 32]
b_flow = torch.unsqueeze(b_flow, dim=0).cuda(0) # [1 2 32 32]
forward_flow.append(f_flow)
backward_flow.append(b_flow)
t0 = time.time()
model_output = model(imgs_temp, forward_flow, backward_flow)
t1 = time.time()
print(t1-t0)
# model_output.size(): torch.Size([1, 3, 4h, 4w])
model_output = model_output[:, :, :, 0:4 * h, 0:4 * w]
if isinstance(model_output, list) or isinstance(model_output, tuple):
output = model_output[0]
else:
output = model_output
return output
sub_folder_l = sorted(glob.glob(test_dataset_folder))
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
model = model.to(device)
avg_psnr_l = []
avg_psnr_y_l = []
sub_folder_name_l = []
# total_time = []
# for each sub-folder
for sub_folder in sub_folder_l:
gt_tested_list = []
sub_folder_name = sub_folder.split('/')[-1]
sub_folder_name_l.append(sub_folder_name)
save_sub_folder = osp.join(save_folder, sub_folder_name)
if data_mode == 'SPMC':
sub_folder = sub_folder + '/input4/'
img_LR_l = sorted(glob.glob(sub_folder + '/*'))
print(save_sub_folder)
if save_imgs:
util.mkdirs(save_sub_folder)
#### read LR images
imgs = util.read_seq_imgs(sub_folder)
#### read GT images
img_GT_l = []
if data_mode == 'SPMC':
sub_folder_GT = osp.join(sub_folder.replace('/input4/', '/truth/'))
else:
sub_folder_GT = osp.join(sub_folder.replace('/LR/', '/HR/'))
if 'Custom' not in data_mode:
for img_GT_path in sorted(glob.glob(osp.join(sub_folder_GT,'*'))):
img_GT_l.append(util.read_image(img_GT_path))
avg_psnr, avg_psnr_sum, cal_n = 0,0,0
avg_psnr_y, avg_psnr_sum_y = 0,0
if len(img_LR_l) == len(img_GT_l):
skip = True
else:
skip = False
if 'Custom' in data_mode:
select_idx_list = util.test_index_generation(False, N_ot, len(img_LR_l))
else:
select_idx_list = util.test_index_generation(skip, N_ot, len(img_LR_l))
# process each image
for select_idxs in select_idx_list:
# get input images
select_idx = select_idxs[0]
gt_idx = select_idxs[1]
imgs_in = imgs.index_select(0, torch.LongTensor(select_idx)).unsqueeze(0).to(device)
t1 = time.time()
output = single_forward(model, imgs_in)
t0 = time.time()
# print(t0-t1)
outputs = output.data.float().cpu().squeeze(0)
if flip_test:
# flip W
output = single_forward(model, torch.flip(imgs_in, (-1, )))
output = torch.flip(output, (-1, ))
output = output.data.float().cpu().squeeze(0)
outputs = outputs + output
# flip H
output = single_forward(model, torch.flip(imgs_in, (-2, )))
output = torch.flip(output, (-2, ))
output = output.data.float().cpu().squeeze(0)
outputs = outputs + output
# flip both H and W
output = single_forward(model, torch.flip(imgs_in, (-2, -1)))
output = torch.flip(output, (-2, -1))
output = output.data.float().cpu().squeeze(0)
outputs = outputs + output
outputs = outputs / 4
# save imgs
for idx, name_idx in enumerate(gt_idx):
if name_idx in gt_tested_list:
continue
gt_tested_list.append(name_idx)
output_f = outputs[idx,:,:,:].squeeze(0)
output = util.tensor2img(output_f)
if save_imgs:
cv2.imwrite(osp.join(save_sub_folder, '{:08d}.png'.format(name_idx+1)), output)
if 'Custom' not in data_mode:
#### calculate PSNR
output = output / 255.
GT = np.copy(img_GT_l[name_idx])
if crop_border == 0:
cropped_output = output
cropped_GT = GT
else:
cropped_output = output[crop_border:-crop_border, crop_border:-crop_border, :]
cropped_GT = GT[crop_border:-crop_border, crop_border:-crop_border, :]
crt_psnr = util.calculate_psnr(cropped_output * 255, cropped_GT * 255)
cropped_GT_y = data_util.bgr2ycbcr(cropped_GT, only_y=True)
cropped_output_y = data_util.bgr2ycbcr(cropped_output, only_y=True)
crt_psnr_y = util.calculate_psnr(cropped_output_y * 255, cropped_GT_y * 255)
logger.info('{:3d} - {:25}.png \tPSNR: {:.6f} dB PSNR-Y: {:.6f} dB'.format(name_idx + 1, name_idx+1, crt_psnr, crt_psnr_y))
avg_psnr_sum += crt_psnr
avg_psnr_sum_y += crt_psnr_y
cal_n += 1
if 'Custom' not in data_mode:
avg_psnr = avg_psnr_sum / cal_n
avg_psnr_y = avg_psnr_sum_y / cal_n
logger.info('Folder {} - Average PSNR: {:.6f} dB PSNR-Y: {:.6f} dB for {} frames; '.format(sub_folder_name, avg_psnr, avg_psnr_y, cal_n))
avg_psnr_l.append(avg_psnr)
avg_psnr_y_l.append(avg_psnr_y)
if 'Custom' not in data_mode:
logger.info('################ Tidy Outputs ################')
for name, psnr, psnr_y in zip(sub_folder_name_l, avg_psnr_l, avg_psnr_y_l):
logger.info('Folder {} - Average PSNR: {:.6f} dB PSNR-Y: {:.6f} dB. '
.format(name, psnr, psnr_y))
logger.info('################ Final Results ################')
logger.info('Data: {} - {}'.format(data_mode, test_dataset_folder))
logger.info('Padding mode: {}'.format(padding))
logger.info('Model path: {}'.format(model_path))
logger.info('Save images: {}'.format(save_imgs))
logger.info('Flip Test: {}'.format(flip_test))
logger.info('Total Average PSNR: {:.6f} dB PSNR-Y: {:.6f} dB for {} clips. '
.format(
sum(avg_psnr_l) / len(avg_psnr_l), sum(avg_psnr_y_l) / len(avg_psnr_y_l), len(sub_folder_l)))
# logger.info('Total Runtime: {:.6f} s Average Runtime: {:.6f} for {} images.'
# .format(sum(total_time), sum(total_time)/171, 171))
if __name__ == '__main__':
main()