/
script_testing.py
149 lines (135 loc) · 5.49 KB
/
script_testing.py
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from __future__ import absolute_import, print_function
import os
import utils
import torch
from torchvision import transforms
from torch.utils.data import DataLoader
import numpy as np
import data
import scipy.io as sio
from options.testing_options import TestOptions
import utils
import time
from models import AutoEncoderCov3D, AutoEncoderCov3DMem
###
opt_parser = TestOptions()
opt = opt_parser.parse(is_print=True)
use_cuda = opt.UseCUDA
device = torch.device("cuda" if use_cuda else "cpu")
###
batch_size_in = opt.BatchSize #1
chnum_in_ = opt.ImgChnNum # channel number of the input images
framenum_in_ = opt.FrameNum # frame number of the input images in a video clip
mem_dim_in = opt.MemDim
sparse_shrink_thres = opt.ShrinkThres
img_crop_size = 0
######
model_setting = utils.get_model_setting(opt)
## data path
data_root = opt.DataRoot + opt.Dataset + '/'
data_frame_dir = data_root + 'Test/'
data_idx_dir = data_root + 'Test_idx/'
############ model path
model_root = opt.ModelRoot
if(opt.ModelFilePath):
model_path = opt.ModelFilePath
else:
model_path = os.path.join(model_root, model_setting + '.pt')
### test result path
te_res_root = opt.OutRoot
te_res_path = te_res_root + '/' + 'res_' + model_setting
utils.mkdir(te_res_path)
###### loading trained model
if (opt.ModelName == 'AE'):
model = AutoEncoderCov3D(chnum_in_)
elif(opt.ModelName=='MemAE'):
model = AutoEncoderCov3DMem(chnum_in_, mem_dim_in, shrink_thres=sparse_shrink_thres)
else:
model = []
print('Wrong Name.')
##
model_para = torch.load(model_path)
model.load_state_dict(model_para)
model.to(device)
model.eval()
##
if(chnum_in_==1):
norm_mean = [0.5]
norm_std = [0.5]
elif(chnum_in_==3):
norm_mean = (0.5, 0.5, 0.5)
norm_std = (0.5, 0.5, 0.5)
frame_trans = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std)
])
unorm_trans = utils.UnNormalize(mean=norm_mean, std=norm_std)
# ##
video_list = utils.get_subdir_list(data_idx_dir)
video_num = len(video_list)
##
with torch.no_grad():
for ite_vid in range(video_num):
video_name = video_list[ite_vid]
video_idx_path = os.path.join(data_idx_dir, video_name) # idx path of the current sub dir
video_frame_path = os.path.join(data_frame_dir, video_name) # frame path of the current sub dir
# info for current video
idx_name_list = [name for name in os.listdir(video_idx_path) \
if os.path.isfile(os.path.join(video_idx_path, name))]
idx_name_list.sort()
# load data (frame clips) for single video
video_dataset = data.VideoDatasetOneDir(video_idx_path, video_frame_path, transform=frame_trans)
video_data_loader = DataLoader(video_dataset,
batch_size=batch_size_in,
shuffle=False
)
# testing results on a single video sequence
print('[vidx %02d/%d] [vname %s]' % (ite_vid+1, video_num, video_name))
recon_error_list = []
#
for batch_idx, (item, frames) in enumerate(video_data_loader):
idx_name = idx_name_list[item[0]]
idx_data = sio.loadmat(os.path.join(video_idx_path, idx_name))
v_name = idx_data['v_name'][0] # video name
frame_idx = idx_data['idx'][0, :] # frame index list for a video clip
######
frames = frames.to(device)
##
if (opt.ModelName == 'AE'):
recon_frames = model(frames)
###### calculate reconstruction error (MSE)
recon_np = utils.vframes2imgs(unorm_trans(recon_frames.data), step=1, batch_idx=0)
input_np = utils.vframes2imgs(unorm_trans(frames.data), step=1, batch_idx=0)
r = utils.crop_image(recon_np, img_crop_size) - utils.crop_image(input_np, img_crop_size)
# recon_error = np.mean(sum(r**2)**0.5)
recon_error = np.mean(r ** 2) # **0.5
recon_error_list += [recon_error]
elif (opt.ModelName == 'MemAE'):
recon_res = model(frames)
recon_frames = recon_res['output']
r = recon_frames - frames
r = utils.crop_image(r, img_crop_size)
sp_error_map = torch.sum(r**2, dim=1)**0.5
s = sp_error_map.size()
sp_error_vec = sp_error_map.view(s[0], -1)
recon_error = torch.mean(sp_error_vec, dim=-1)
recon_error_list += recon_error.cpu().tolist()
######
# elif (opt.ModelName == 'MemAE'):
# recon_res = model(frames)
# recon_frames = recon_res['output']
# recon_np = utils.btv2btf(unorm_trans(recon_frames.data))
# input_np = utils.btv2btf(unorm_trans(frames.data))
# r = utils.crop_image(recon_np, img_crop_size) - utils.crop_image(input_np, img_crop_size)
# sp_error_map = np.sum(r**2, axis=1)**0.5
# tmp_s = sp_error_map.shape
# sp_error_vec = np.reshape(sp_error_map, (tmp_s[0], -1))
# recon_error = np.mean(sp_error_vec, axis=-1)
# recon_error_list += recon_error.tolist()
#######
else:
recon_error = -1
print('Wrong ModelName.')
np.save(os.path.join(te_res_path, video_name + '.npy'), recon_error_list)
## evaluation
utils.eval_video(data_root, te_res_path, is_show=False)