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infer_h5.py
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infer_h5.py
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'''
------------------------------------------------------
RUN INFERENCE ON SAMPLE TEST SEQUENCES - ATTENTION NET
------------------------------------------------------
'''
import os
import numpy as np
import torch
import torch.nn.functional as F
import logging
import glob
import argparse
from skimage.metrics import peak_signal_noise_ratio as compare_psnr
from skimage.metrics import structural_similarity as compare_ssim
import h5py
from unet import UNet
import utils
from inverse import ShiftVarConv2D, StandardConv2D
## parse arguments
parser = argparse.ArgumentParser()
parser.add_argument('--savedir', type=str, default='results' ,help='export dir name to dump results')
parser.add_argument('--ckpt', type=str, required=True, help='checkpoint full name')
parser.add_argument('--blocksize', type=int, default=8, help='tile size for code default 3x3')
parser.add_argument('--subframes', type=int, default=16, help='num sub frames')
parser.add_argument('--gpu', type=str, required=True, help='GPU ID')
parser.add_argument('--two_bucket', action='store_true', help='1 bucket or 2 buckets')
parser.add_argument('--save_gif', action='store_true', help='saves gifs')
parser.add_argument('--log_interval', type=int, default=1, help='save gifs interval')
parser.add_argument('--intermediate', action='store_true', help="intermediate reconstruction")
parser.add_argument('--flutter', action='store_true', help="for flutter shutter")
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
save_path = os.path.join(args.savedir,args.ckpt.split('.')[0])
if not os.path.exists(save_path):
os.mkdir(save_path)
os.mkdir(os.path.join(save_path, 'frames'))
## configure runtime logging
logging.basicConfig(level=logging.INFO,
filename=os.path.join(save_path, 'logfile.log'),
format='%(asctime)s - %(message)s',
filemode='w')
console = logging.StreamHandler()
console.setLevel(logging.INFO)
console.setFormatter(logging.Formatter('%(asctime)s - %(message)s'))
logging.getLogger().addHandler(console)
logging.info(args)
## load test sequences
data_path = 'data/eccv_test_ours.h5'
with h5py.File(data_path,'r') as f:
image_paths = f['test']
input_params = {'height': image_paths.shape[-2],
'width': image_paths.shape[-1]}
## load net
if args.intermediate:
num_features = args.subframes
else:
num_features = 64
if args.flutter:
assert not args.two_bucket
invNet = StandardConv2D(out_channels=num_features).cuda()
else:
invNet = ShiftVarConv2D(out_channels=num_features, block_size=args.blocksize, two_bucket=args.two_bucket).cuda()
uNet = UNet(in_channel=num_features, out_channel=args.subframes, instance_norm=False).cuda()
## load checkpoint
ckpt = torch.load(os.path.join('models', args.ckpt))
invNet.load_state_dict(ckpt['invnet_state_dict'])
uNet.load_state_dict(ckpt['unet_state_dict'])
invNet.eval()
uNet.eval()
## load checkpoint
ckpt = torch.load(os.path.join('models', args.ckpt))
uNet.load_state_dict(ckpt['unet_state_dict'])
uNet.eval()
c2b_code = ckpt['c2b_state_dict']['code']
code_repeat = c2b_code.repeat(1, 1, input_params['height']//args.blocksize, input_params['width']//args.blocksize)
logging.info('Starting inference')
psnr_sum = 0.
ssim_sum = 0.
full_gt = []
full_pred = []
with torch.no_grad():
with h5py.File(data_path,'r') as f:
image_paths = f['test']
print(f'testing on data of size {image_paths.shape}')
for seq in range(len(image_paths)):
vid = torch.cuda.FloatTensor(image_paths[seq:seq+1,...])
b1 = torch.sum(code_repeat*vid, dim=1, keepdim=True) / torch.sum(code_repeat, dim=1, keepdim=True)
if not args.two_bucket:
interm_vid = invNet(b1)
else:
b0 = torch.mean(vid, dim=1, keepdim=True)
b_stack = torch.cat([b1,b0], dim=1)
interm_vid = invNet(b_stack)
highres_vid = uNet(interm_vid) # (1,16,H,W)
assert highres_vid.shape == vid.shape
highres_vid = torch.clamp(highres_vid, min=0, max=1)
## converting tensors to numpy arrays
b1_np = b1.squeeze().data.cpu().numpy() # (H,W)
if args.two_bucket:
b0_np = b0.squeeze().data.cpu().numpy()
vid_np = vid.squeeze().data.cpu().numpy() # (9,H,W)
highres_np = highres_vid.squeeze().data.cpu().numpy() # (9,H,W)
full_pred.append(highres_np)
full_gt.append(vid_np)
## psnr
psnr = compare_psnr(highres_np, vid_np)
psnr_sum += psnr
## ssim
ssim = 0.
for sf in range(vid_np.shape[0]):
ssim += compare_ssim(highres_np[sf], vid_np[sf],
gaussian_weights=True, sigma=1.5, use_sample_covariance=False, data_range=1.0)
ssim = ssim / vid_np.shape[0]
ssim_sum += ssim
if seq%args.log_interval == 0:
logging.info('Seq %.2d PSNR: %.2f SSIM: %.3f'%(seq+1, psnr, ssim))
## saving images and gifs
if args.save_gif:
utils.save_image(b1_np, os.path.join(save_path, 'seq_%.2d_coded.png'%(seq+1)))
if args.two_bucket:
utils.save_image(b0_np, os.path.join(save_path, 'seq_%.2d_complement.png'%(seq+1)))
utils.save_gif(vid_np, os.path.join(save_path, 'seq_%.2d_gt.gif'%(seq+1)))
utils.save_gif(highres_np, os.path.join(save_path, 'seq_%.2d_recon.gif'%(seq+1)))
for sub_frame in range(vid_np.shape[0]):
utils.save_image(highres_np[sub_frame], os.path.join(save_path, 'frames', 'seq_%.2d_recon_%.2d.png'%(seq+1, sub_frame+1)))
logging.info('Average PSNR: %.2f'%(psnr_sum/(len(image_paths))))
logging.info('Average SSIM: %.3f'%(ssim_sum/(len(image_paths))))
logging.info('Saved images and gifs for all sequences')
with h5py.File('predict.h5','w') as write:
full_gt = np.stack(full_gt,0)
full_pred = np.stack(full_pred,0)
print(f'shape of full_gt:{full_gt.shape} and full_pred:{full_pred.shape}')
write.create_dataset('gt',data=full_gt,compression='gzip')
write.create_dataset('pred',data=full_pred,compression='gzip')
logging.info('Finished inference')