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test.py
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test.py
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import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data as data
#import torchvision.transforms as transforms
import torchvision.datasets as datasets
from network import MagNet
from data_loader import ImageFromFolderTest
from utils import AverageMeter
import numpy as np
from PIL import Image
from collections import OrderedDict
parser = argparse.ArgumentParser(description='PyTorch Deep Video Magnification')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 0)')
parser.add_argument('-b', '--batch-size', default=4, type=int,
metavar='N', help='mini-batch size (default: 4)')
parser.add_argument('--print-freq', '-p', default=100, type=int,
metavar='N', help='print frequency (default: 100)')
parser.add_argument('--load_ckpt', type=str, metavar='PATH',
help='path to load checkpoint')
parser.add_argument('--save_dir', default='demo', type=str, metavar='PATH',
help='path to save generated frames (default: demo)')
parser.add_argument('--gpu',default=0, type=str, help='cuda_visible_devices')
parser.add_argument('-m', '--amp', default=20.0, type=float,
help='amplification factor (default: 10.0)')
parser.add_argument('--mode', default='static', type=str, choices=['static', 'dynamic','temporal'],
help='amplification mode (static, dynamic, temporal)')
parser.add_argument('--video_path', default='./../demo_video/baby', type=str,
help='path to video frames')
parser.add_argument('--num_data', default=300, type=int,
help='number of frames')
#for temporal filter
parser.add_argument('--fh', default=0.4, type=float)
parser.add_argument('--fl', default=0.04, type=float)
#parser.add_argument('--fs', default=30, type=int)
#parser.add_argument('--ntab', default=2, type=int)
args = parser.parse_args()
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]=str(args.gpu)
def main():
global args
args = parser.parse_args()
print(args)
# create model
model = MagNet().cuda()
#model = torch.nn.DataParallel(model).cuda()
print(model)
# load checkpoint
if os.path.isfile(args.load_ckpt):
print("=> loading checkpoint '{}'".format(args.load_ckpt))
checkpoint = torch.load(args.load_ckpt)
args.start_epoch = checkpoint['epoch']
# to load state_dict trained with DataParallel to model without DataParallel
new_state_dict = OrderedDict()
state_dict = checkpoint['state_dict']
for k, v in state_dict.items():
name = k[7:]
new_state_dict[name]=v
model.load_state_dict(new_state_dict)
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.load_ckpt, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.load_ckpt))
assert(False)
# check saving directory
save_dir = args.save_dir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
print(save_dir)
# cudnn enable
cudnn.benchmark = True
# data loader
dataset_mag = ImageFromFolderTest(args.video_path, mag=args.amp, mode=args.mode, num_data=args.num_data, preprocessing=False)
data_loader = data.DataLoader(dataset_mag,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# generate frames
mag_frames=[]
model.eval()
# static mode or dynamic mode
if args.mode=='static' or args.mode=='dynamic':
for i, (xa, xb, amp_factor) in enumerate(data_loader):
if i%10==0: print('processing sample %d'%i)
amp_factor = amp_factor.unsqueeze(1).unsqueeze(1).unsqueeze(1)
xa=xa.cuda()
xb=xb.cuda()
amp_factor=amp_factor.cuda()
y_hat, _, _, _ = model(xa, xb, xb, amp_factor)
if i==0:
# back to image scale (0-255)
tmp = xa.permute(0,2,3,1).cpu().detach().numpy()
tmp = np.clip(tmp, -1.0, 1.0)
tmp = ((tmp + 1.0) * 127.5).astype(np.uint8)
mag_frames.append(tmp)
# back to image scale (0-255)
y_hat = y_hat.permute(0,2,3,1).cpu().detach().numpy()
y_hat = np.clip(y_hat, -1.0, 1.0)
y_hat = ((y_hat + 1.0) * 127.5).astype(np.uint8)
mag_frames.append(y_hat)
else:
# temporal mode (difference of IIR)
# copy filter coefficients and follow codes from https://github.com/12dmodel/deep_motion_mag
filter_b = [args.fh-args.fl, args.fl-args.fh]
filter_a = [-1.0*(2.0 - args.fh - args.fl), (1.0 - args.fl) * (1.0 - args.fh)]
x_state = []
y_state = []
for i, (xa, xb, amp_factor) in enumerate(data_loader):
if i%10==0: print('processing sample %d'%i)
amp_factor = amp_factor.unsqueeze(1).unsqueeze(1).unsqueeze(1)
xa=xa.cuda()
xb=xb.cuda()
amp_factor=amp_factor.cuda()
vb, mb = model.encoder(xb)
x_state.insert(0,mb.detach())
while len(x_state)<len(filter_b):
x_state.insert(0,mb.detach())
if len(x_state)>len(filter_b):
x_state = x_state[:len(filter_b)]
y = torch.zeros_like(mb)
for i in range(len(x_state)):
y += x_state[i] * filter_b[i]
for i in range(len(y_state)):
y -= y_state[i] * filter_a[i]
y_state.insert(0,y.detach())
if len(y_state) > len(filter_a):
y_state = y_state[:len(filter_a)]
mb_m = model.manipulator(0.0, y, amp_factor)
mb_m += mb - y
y_hat = model.decoder(vb, mb_m)
if i==0:
# back to image scale (0-255)
tmp = xa.permute(0,2,3,1).cpu().detach().numpy()
tmp = np.clip(tmp, -1.0, 1.0)
tmp = ((tmp + 1.0) * 127.5).astype(np.uint8)
mag_frames.append(tmp)
# back to image scale (0-255)
y_hat = y_hat.permute(0,2,3,1).cpu().detach().numpy()
y_hat = np.clip(y_hat, -1.0, 1.0)
y_hat = ((y_hat + 1.0) * 127.5).astype(np.uint8)
mag_frames.append(y_hat)
# save frames
mag_frames = np.concatenate(mag_frames, 0)
for i, frame in enumerate(mag_frames):
fn = os.path.join(save_dir, 'demo_%s_%06d.png'%(args.mode,i))
im = Image.fromarray(frame)
im.save(fn)
if __name__ == '__main__':
main()