def test(hparams):
model = Autoencoder(hparams)
model.encoder = torch.load("trained_models/train_all/encoder.pt")
model.decoder = torch.load("trained_models/train_all/decoder.pt")
#print(model)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
#from torchsummary import summary
#summary(model, (1, 64, 192))
model.encoder.eval()
model.decoder.eval()
output_dir = "output/{}".format(
os.path.basename(hparams.image_list).split('.')[0])
with open(hparams.image_list) as f:
image_files = f.read().splitlines()
play_thermal(image_files,
hparams,
output_dir,
encoder=model.encoder.to('cpu'),
decoder=model.decoder.to('cpu'),
norm=hparams.norm,
n_channels=hparams.nc,
show=False,
save=False)
if not len(image_files) > 0:
print("did not find any files")
def test(hparams):
model = Autoencoder(hparams)
model.encoder = torch.load("encoder.pt")
model.decoder = torch.load("decoder.pt")
model.encoder.eval()
model.decoder.eval()
folders = sorted(
[y for y in glob(os.path.join(hparams.data_root, '* - *'))])
for folder in folders[2:3]:
#create output folder
output_dir = 'output'
if not os.path.exists(output_dir):
os.mkdir(output_dir)
folder_dir = os.path.join(output_dir, folder.split('/')[-1])
if not os.path.exists(folder_dir):
os.mkdir(folder_dir)
# list images
frame_list = sorted(
[y for y in glob(os.path.join(folder, 'img_*.jpg'))])
if not len(frame_list) > 0:
print("did not find any files")
return
play_thermal(frame_list,
hparams,
folder_dir,
encoder=model.encoder.to('cpu'),
decoder=model.decoder.to('cpu'),
norm=hparams.norm,
n_channels=hparams.nc)
