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run.py
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import argparse
import os
import numpy as np
import torch
import torch.nn as nn
import torch.utils.data as data
import torchvision.datasets as datasets
from PIL import Image
from utils.data_loader import ImageFromFolderTest
from models.model import STBVMM
def main(args):
# Device choice (auto)
if args.device == 'auto':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
else:
device = args.device
print(f'Using device: {device}')
# Create model
model = STBVMM(img_size=384, patch_size=1, in_chans=3,
embed_dim=192, depths=[6, 6, 6, 6, 6, 6], num_heads=[6, 6, 6, 6, 6, 6],
window_size=8, mlp_ratio=2., qkv_bias=True, qk_scale=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
use_checkpoint=False, img_range=1., resi_connection='1conv',
manipulator_num_resblk=1).to(device)
# 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']
model.load_state_dict(checkpoint['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)
# Data loader
dataset_mag = ImageFromFolderTest(
args.video_path, mag=args.mag, 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=False)
# Generate frames
model.eval()
# Magnification
for i, (xa, xb, mag_factor) in enumerate(data_loader):
if i % args.print_freq == 0:
print('processing sample: %d' % i)
mag_factor = mag_factor.unsqueeze(1).unsqueeze(1).unsqueeze(1)
xa = xa.to(device)
xb = xb.to(device)
mag_factor = mag_factor.to(device)
y_hat, _, _, _ = model(xa, xb, mag_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)
# Save first frame
fn = os.path.join(save_dir, 'STBVMM_%s_%06d.png' % (args.mode, i))
im = Image.fromarray(np.concatenate(tmp, 0))
im.save(fn)
# 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)
# Save frames
fn = os.path.join(save_dir, 'STBVMM_%s_%06d.png' % (args.mode, i+1))
im = Image.fromarray(np.concatenate(y_hat, 0))
im.save(fn)
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Swin Transformer Based Video Motion Magnification')
# Application parameters
parser.add_argument('-i', '--video_path', type=str, metavar='PATH', required=True,
help='path to video input frames')
parser.add_argument('-c', '--load_ckpt', type=str, metavar='PATH', required=True,
help='path to load checkpoint')
parser.add_argument('-o', '--save_dir', default='demo', type=str, metavar='PATH',
help='path to save generated frames (default: demo)')
parser.add_argument('-m', '--mag', metavar='N', default=20.0, type=float,
help='magnification factor (default: 20.0)')
parser.add_argument('--mode', default='static', type=str, choices=['static', 'dynamic'],
help='magnification mode (static, dynamic)')
parser.add_argument('-n', '--num_data', type=int, metavar='N', required=True,
help='number of frames')
# Execute parameters
parser.add_argument('-j', '--workers', default=16, type=int, metavar='N',
help='number of data loading workers (default: 16)')
parser.add_argument('-b', '--batch_size', default=1, type=int,
metavar='N', help='batch size (default: 1)')
parser.add_argument('-p', '--print_freq', default=100, type=int,
metavar='N', help='print frequency (default: 100)')
# Device
parser.add_argument('--device', type=str, default='auto',
choices=['auto', 'cpu', 'cuda'],
help='select device [auto/cpu/cuda] (default: auto)')
args = parser.parse_args()
main(args)